Sample records for accurately predict metabolic

  1. XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

    PubMed

    Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

    2013-12-23

    Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

  2. Integrating metabolic performance, thermal tolerance, and plasticity enables for more accurate predictions on species vulnerability to acute and chronic effects of global warming.

    PubMed

    Magozzi, Sarah; Calosi, Piero

    2015-01-01

    Predicting species vulnerability to global warming requires a comprehensive, mechanistic understanding of sublethal and lethal thermal tolerances. To date, however, most studies investigating species physiological responses to increasing temperature have focused on the underlying physiological traits of either acute or chronic tolerance in isolation. Here we propose an integrative, synthetic approach including the investigation of multiple physiological traits (metabolic performance and thermal tolerance), and their plasticity, to provide more accurate and balanced predictions on species and assemblage vulnerability to both acute and chronic effects of global warming. We applied this approach to more accurately elucidate relative species vulnerability to warming within an assemblage of six caridean prawns occurring in the same geographic, hence macroclimatic, region, but living in different thermal habitats. Prawns were exposed to four incubation temperatures (10, 15, 20 and 25 °C) for 7 days, their metabolic rates and upper thermal limits were measured, and plasticity was calculated according to the concept of Reaction Norms, as well as Q10 for metabolism. Compared to species occupying narrower/more stable thermal niches, species inhabiting broader/more variable thermal environments (including the invasive Palaemon macrodactylus) are likely to be less vulnerable to extreme acute thermal events as a result of their higher upper thermal limits. Nevertheless, they may be at greater risk from chronic exposure to warming due to the greater metabolic costs they incur. Indeed, a trade-off between acute and chronic tolerance was apparent in the assemblage investigated. However, the invasive species P. macrodactylus represents an exception to this pattern, showing elevated thermal limits and plasticity of these limits, as well as a high metabolic control. In general, integrating multiple proxies for species physiological acute and chronic responses to increasing

  3. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Zuniga, Cristal; Li, Chien -Ting; Huelsman, Tyler

    The green microalgae Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organismmore » to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Moreover, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.« less

  4. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

    DOE PAGES

    Zuniga, Cristal; Li, Chien -Ting; Huelsman, Tyler; ...

    2016-07-02

    The green microalgae Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organismmore » to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Moreover, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.« less

  5. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

    PubMed

    Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler; Levering, Jennifer; Zielinski, Daniel C; McConnell, Brian O; Long, Christopher P; Knoshaug, Eric P; Guarnieri, Michael T; Antoniewicz, Maciek R; Betenbaugh, Michael J; Zengler, Karsten

    2016-09-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. © 2016 American Society of Plant Biologists. All rights reserved.

  6. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions1

    PubMed Central

    Zuñiga, Cristal; Li, Chien-Ting; Zielinski, Daniel C.; Guarnieri, Michael T.; Antoniewicz, Maciek R.; Zengler, Karsten

    2016-01-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. PMID:27372244

  7. Predicting gestational age using neonatal metabolic markers

    PubMed Central

    Ryckman, Kelli K.; Berberich, Stanton L.; Dagle, John M.

    2016-01-01

    Background Accurate gestational age estimation is extremely important for clinical care decisions of the newborn as well as for perinatal health research. Although prenatal ultrasound dating is one of the most accurate methods for estimating gestational age, it is not feasible in all settings. Identifying novel and accurate methods for gestational age estimation at birth is important, particularly for surveillance of preterm birth rates in areas without routine ultrasound dating. Objective We hypothesized that metabolic and endocrine markers captured by routine newborn screening could improve gestational age estimation in the absence of prenatal ultrasound technology. Study Design This is a retrospective analysis of 230,013 newborn metabolic screening records collected by the Iowa Newborn Screening Program between 2004 and 2009. The data were randomly split into a model-building dataset (n = 153,342) and a model-testing dataset (n = 76,671). We performed multiple linear regression modeling with gestational age, in weeks, as the outcome measure. We examined 44 metabolites, including biomarkers of amino acid and fatty acid metabolism, thyroid-stimulating hormone, and 17-hydroxyprogesterone. The coefficient of determination (R2) and the root-mean-square error were used to evaluate models in the model-building dataset that were then tested in the model-testing dataset. Results The newborn metabolic regression model consisted of 88 parameters, including the intercept, 37 metabolite measures, 29 squared metabolite measures, and 21 cubed metabolite measures. This model explained 52.8% of the variation in gestational age in the model-testing dataset. Gestational age was predicted within 1 week for 78% of the individuals and within 2 weeks of gestation for 95% of the individuals. This model yielded an area under the curve of 0.899 (95% confidence interval 0.895−0.903) in differentiating those born preterm (<37 weeks) from those born term (≥37 weeks). In the subset of

  8. Predicting gestational age using neonatal metabolic markers.

    PubMed

    Ryckman, Kelli K; Berberich, Stanton L; Dagle, John M

    2016-04-01

    Accurate gestational age estimation is extremely important for clinical care decisions of the newborn as well as for perinatal health research. Although prenatal ultrasound dating is one of the most accurate methods for estimating gestational age, it is not feasible in all settings. Identifying novel and accurate methods for gestational age estimation at birth is important, particularly for surveillance of preterm birth rates in areas without routine ultrasound dating. We hypothesized that metabolic and endocrine markers captured by routine newborn screening could improve gestational age estimation in the absence of prenatal ultrasound technology. This is a retrospective analysis of 230,013 newborn metabolic screening records collected by the Iowa Newborn Screening Program between 2004 and 2009. The data were randomly split into a model-building dataset (n = 153,342) and a model-testing dataset (n = 76,671). We performed multiple linear regression modeling with gestational age, in weeks, as the outcome measure. We examined 44 metabolites, including biomarkers of amino acid and fatty acid metabolism, thyroid-stimulating hormone, and 17-hydroxyprogesterone. The coefficient of determination (R(2)) and the root-mean-square error were used to evaluate models in the model-building dataset that were then tested in the model-testing dataset. The newborn metabolic regression model consisted of 88 parameters, including the intercept, 37 metabolite measures, 29 squared metabolite measures, and 21 cubed metabolite measures. This model explained 52.8% of the variation in gestational age in the model-testing dataset. Gestational age was predicted within 1 week for 78% of the individuals and within 2 weeks of gestation for 95% of the individuals. This model yielded an area under the curve of 0.899 (95% confidence interval 0.895-0.903) in differentiating those born preterm (<37 weeks) from those born term (≥37 weeks). In the subset of infants born small-for-gestational age

  9. Alignment-Based Prediction of Sites of Metabolism.

    PubMed

    de Bruyn Kops, Christina; Friedrich, Nils-Ole; Kirchmair, Johannes

    2017-06-26

    Prediction of metabolically labile atom positions in a molecule (sites of metabolism) is a key component of the simulation of xenobiotic metabolism as a whole, providing crucial information for the development of safe and effective drugs. In 2008, an exploratory study was published in which sites of metabolism were derived based on molecular shape- and chemical feature-based alignment to a molecule whose site of metabolism (SoM) had been determined by experiments. We present a detailed analysis of the breadth of applicability of alignment-based SoM prediction, including transfer of the approach from a structure- to ligand-based method and extension of the applicability of the models from cytochrome P450 2C9 to all cytochrome P450 isozymes involved in drug metabolism. We evaluate the effect of molecular similarity of the query and reference molecules on the ability of this approach to accurately predict SoMs. In addition, we combine the alignment-based method with a leading chemical reactivity model to take reactivity into account. The combined model yielded superior performance in comparison to the alignment-based approach and the reactivity models with an average area under the receiver operating characteristic curve of 0.85 in cross-validation experiments. In particular, early enrichment was improved, as evidenced by higher BEDROC scores (mean BEDROC = 0.59 for α = 20.0, mean BEDROC = 0.73 for α = 80.5).

  10. Validated Predictions of Metabolic Energy Consumption for Submaximal Effort Movement

    PubMed Central

    Tsianos, George A.; MacFadden, Lisa N.

    2016-01-01

    Physical performance emerges from complex interactions among many physiological systems that are largely driven by the metabolic energy demanded. Quantifying metabolic demand is an essential step for revealing the many mechanisms of physical performance decrement, but accurate predictive models do not exist. The goal of this study was to investigate if a recently developed model of muscle energetics and force could be extended to reproduce the kinematics, kinetics, and metabolic demand of submaximal effort movement. Upright dynamic knee extension against various levels of ergometer load was simulated. Task energetics were estimated by combining the model of muscle contraction with validated models of lower limb musculotendon paths and segment dynamics. A genetic algorithm was used to compute the muscle excitations that reproduced the movement with the lowest energetic cost, which was determined to be an appropriate criterion for this task. Model predictions of oxygen uptake rate (VO2) were well within experimental variability for the range over which the model parameters were confidently known. The model's accurate estimates of metabolic demand make it useful for assessing the likelihood and severity of physical performance decrement for a given task as well as investigating underlying physiologic mechanisms. PMID:27248429

  11. 3D gut-liver chip with a PK model for prediction of first-pass metabolism.

    PubMed

    Lee, Dong Wook; Ha, Sang Keun; Choi, Inwook; Sung, Jong Hwan

    2017-11-07

    Accurate prediction of first-pass metabolism is essential for improving the time and cost efficiency of drug development process. Here, we have developed a microfluidic gut-liver co-culture chip that aims to reproduce the first-pass metabolism of oral drugs. This chip consists of two separate layers for gut (Caco-2) and liver (HepG2) cell lines, where cells can be co-cultured in both 2D and 3D forms. Both cell lines were maintained well in the chip, verified by confocal microscopy and measurement of hepatic enzyme activity. We investigated the PK profile of paracetamol in the chip, and corresponding PK model was constructed, which was used to predict PK profiles for different chip design parameters. Simulation results implied that a larger absorption surface area and a higher metabolic capacity are required to reproduce the in vivo PK profile of paracetamol more accurately. Our study suggests the possibility of reproducing the human PK profile on a chip, contributing to accurate prediction of pharmacological effect of drugs.

  12. Mental models accurately predict emotion transitions.

    PubMed

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

    Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

  13. Mental models accurately predict emotion transitions

    PubMed Central

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

    Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

  14. The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools.

    PubMed

    De Buck, Stefan S; Sinha, Vikash K; Fenu, Luca A; Gilissen, Ron A; Mackie, Claire E; Nijsen, Marjoleen J

    2007-04-01

    The aim of this study was to assess a physiologically based modeling approach for predicting drug metabolism, tissue distribution, and bioavailability in rat for a structurally diverse set of neutral and moderate-to-strong basic compounds (n = 50). Hepatic blood clearance (CL(h)) was projected using microsomal data and shown to be well predicted, irrespective of the type of hepatic extraction model (80% within 2-fold). Best predictions of CL(h) were obtained disregarding both plasma and microsomal protein binding, whereas strong bias was seen using either blood binding only or both plasma and microsomal protein binding. Two mechanistic tissue composition-based equations were evaluated for predicting volume of distribution (V(dss)) and tissue-to-plasma partitioning (P(tp)). A first approach, which accounted for ionic interactions with acidic phospholipids, resulted in accurate predictions of V(dss) (80% within 2-fold). In contrast, a second approach, which disregarded ionic interactions, was a poor predictor of V(dss) (60% within 2-fold). The first approach also yielded accurate predictions of P(tp) in muscle, heart, and kidney (80% within 3-fold), whereas in lung, liver, and brain, predictions ranged from 47% to 62% within 3-fold. Using the second approach, P(tp) prediction accuracy in muscle, heart, and kidney was on average 70% within 3-fold, and ranged from 24% to 54% in all other tissues. Combining all methods for predicting V(dss) and CL(h) resulted in accurate predictions of the in vivo half-life (70% within 2-fold). Oral bioavailability was well predicted using CL(h) data and Gastroplus Software (80% within 2-fold). These results illustrate that physiologically based prediction tools can provide accurate predictions of rat pharmacokinetics.

  15. Heart rate during basketball game play and volleyball drills accurately predicts oxygen uptake and energy expenditure.

    PubMed

    Scribbans, T D; Berg, K; Narazaki, K; Janssen, I; Gurd, B J

    2015-09-01

    There is currently little information regarding the ability of metabolic prediction equations to accurately predict oxygen uptake and exercise intensity from heart rate (HR) during intermittent sport. The purpose of the present study was to develop and, cross-validate equations appropriate for accurately predicting oxygen cost (VO2) and energy expenditure from HR during intermittent sport participation. Eleven healthy adult males (19.9±1.1yrs) were recruited to establish the relationship between %VO2peak and %HRmax during low-intensity steady state endurance (END), moderate-intensity interval (MOD) and high intensity-interval exercise (HI), as performed on a cycle ergometer. Three equations (END, MOD, and HI) for predicting %VO2peak based on %HRmax were developed. HR and VO2 were directly measured during basketball games (6 male, 20.8±1.0 yrs; 6 female, 20.0±1.3yrs) and volleyball drills (12 female; 20.8±1.0yrs). Comparisons were made between measured and predicted VO2 and energy expenditure using the 3 equations developed and 2 previously published equations. The END and MOD equations accurately predicted VO2 and energy expenditure, while the HI equation underestimated, and the previously published equations systematically overestimated VO2 and energy expenditure. Intermittent sport VO2 and energy expenditure can be accurately predicted from heart rate data using either the END (%VO2peak=%HRmax x 1.008-17.17) or MOD (%VO2peak=%HRmax x 1.2-32) equations. These 2 simple equations provide an accessible and cost-effective method for accurate estimation of exercise intensity and energy expenditure during intermittent sport.

  16. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

    PubMed

    Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

    2017-05-01

    Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

  17. NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.

    PubMed

    Schmidt, Matthew C; Rocha, Andrea M; Padmanabhan, Kanchana; Shpanskaya, Yekaterina; Banfield, Jill; Scott, Kathleen; Mihelcic, James R; Samatova, Nagiza F

    2012-01-01

    Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to

  18. NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

    PubMed Central

    Padmanabhan, Kanchana; Shpanskaya, Yekaterina; Banfield, Jill; Scott, Kathleen; Mihelcic, James R.; Samatova, Nagiza F.

    2012-01-01

    Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to

  19. Waist-to-height: cutoff matters in predicting metabolic syndrome in Mexican children.

    PubMed

    Elizondo-Montemayor, Leticia; Serrano-González, Mónica; Ugalde-Casas, Patricia A; Bustamante-Careaga, Humberto; Cuello-García, Carlos

    2011-06-01

    Body-mass index (BMI), waist circumference (WC), and, recently, waist-to-height ratio (WHtR) have been proposed as clinical indexes to identify children at cardiometabolic risk. The aim was to identify the usefulness of WHtR cutoffs, WC, and BMI as predictors of metabolic syndrome in Mexican children, according to BMI z-scores, and the severity of obesity to cardiometabolic risk factors and metabolic syndrome. This was a cross-sectional study of 214 overweight/obese and 47 normal-weight Mexican children 6-12 years old. Children were divided in groups according to BMI z-scores. Anthropometric and biochemical measurements were determined. Receiver-operating characteristic (ROC) curves and areas under the curves were calculated to compare the abilities of the anthropometric measurements to predict metabolic syndrome. The overall prevalence of metabolic syndrome was 23.3%, ranging from 11.0% in the overweight group to 73.9% in the severely obese one. Children with metabolic syndrome had significantly higher WHtR, WC, BMI, percentage of body fat, triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), systolic and diastolic blood pressure, and lower high-density lipoprotein cholesterol (HDL-C). A WHtR cutoff point of 0.59 from the ROC curve was identified as strong predictor of metabolic syndrome in our population, whereas a cutoff of 0.5 showed very poor specificity (22.7%). WC predicted metabolic syndrome as well. Cutoff values for WHtR make a difference in predicting metabolic syndrome. A cutoff of 0.59 for WHtR strongly predicted metabolic syndrome; it might be a simpler to use screening tools and counters for short people. Further studies are required to determine the cutoff points for an accurate prediction, because there are few in children and none in Mexico.

  20. Prediction of in vivo developmental toxicity by combination of Hand1-Luc embryonic stem cell test and metabolic stability test with clarification of metabolically inapplicable candidates.

    PubMed

    Nagahori, Hirohisa; Suzuki, Noriyuki; Le Coz, Florian; Omori, Takashi; Saito, Koichi

    2016-09-30

    Hand1-Luc Embryonic Stem Cell Test (Hand1-Luc EST) is a promising alternative method for evaluation of developmental toxicity. However, the problems of predictivity have remained due to appropriateness of the solubility, metabolic system, and prediction model. Therefore, we assessed the usefulness of rat liver S9 metabolic stability test using LC-MS/MS to develop new prediction model. A total of 71 chemicals were analyzed by measuring cytotoxicity and differentiation toxicity, and highly reproducible (CV=20%) results were obtained. The first prediction model was developed by discriminant analysis performed on a full dataset using Hand1-Luc EST, and 66.2% of the chemicals were correctly classified by the cross-validated classification. A second model was developed with additional descriptors obtained from the metabolic stability test to calculate hepatic availability, and an accuracy of 83.3% was obtained with applicability domain of 50.7% (=36/71) after exclusion of 22 metabolically inapplicable candidates, which potentially have a metabolic activation property. A step-wise prediction scheme with combination of Hand1-Luc EST and metabolic stability test was therefore proposed. The current results provide a promising in vitro test method for accurately predicting in vivo developmental toxicity. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  1. Anthropometry and physical activity level in the prediction of metabolic syndrome in children.

    PubMed

    Andaki, Alynne Christian Ribeiro; Tinôco, Adelson Luiz Araújo; Mendes, Edmar Lacerda; Andaki Júnior, Roberto; Hills, Andrew P; Amorim, Paulo Roberto S

    2014-10-01

    To evaluate the effectiveness of anthropometric measures and physical activity level in the prediction of metabolic syndrome (MetS) in children. Cross-sectional study with children from public and private schools. Children underwent an anthropometric assessment, blood pressure measurement and biochemical evaluation of serum for determination of TAG, HDL-cholesterol and glucose. Physical activity level was calculated and number of steps per day obtained using a pedometer for seven consecutive days. Viçosa, south-eastern Brazil. Boys and girls (n 187), mean age 9·90 (SD 0·7) years. Conicity index, sum of four skinfolds, physical activity level and number of steps per day were accurate in predicting MetS in boys. Anthropometric indicators were accurate in predicting MetS for girls, specifically BMI, waist circumference measured at the narrowest point and at the level of the umbilicus, four skinfold thickness measures evaluated separately, the sum of subscapular and triceps skinfold thickness, the sum of four skinfolds and body fat percentage. The sum of four skinfolds was the most accurate method in predicting MetS in both genders.

  2. Predicting growth of the healthy infant using a genome scale metabolic model.

    PubMed

    Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

    2017-01-01

    An estimated 165 million children globally have stunted growth, and extensive growth data are available. Genome scale metabolic models allow the simulation of molecular flux over each metabolic enzyme, and are well adapted to analyze biological systems. We used a human genome scale metabolic model to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body composition. The model corroborates the finding that essential amino and fatty acids do not limit growth, but that energy is the main growth limiting factor. Disruptions to the supply and demand of energy markedly affected the predicted growth, indicating that elevated energy expenditure may be detrimental. The model was used to simulate the metabolic effect of mineral deficiencies, and showed the greatest growth reduction for deficiencies in copper, iron, and magnesium ions which affect energy production through oxidative phosphorylation. The model and simulation method were integrated to a platform and shared with the research community. The growth model constitutes another step towards the complete representation of human metabolism, and may further help improve the understanding of the mechanisms underlying stunting.

  3. Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software.

    PubMed

    Peach, Megan L; Zakharov, Alexey V; Liu, Ruifeng; Pugliese, Angelo; Tawa, Gregory; Wallqvist, Anders; Nicklaus, Marc C

    2012-10-01

    Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

  4. Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software

    PubMed Central

    Peach, Megan L; Zakharov, Alexey V; Liu, Ruifeng; Pugliese, Angelo; Tawa, Gregory; Wallqvist, Anders; Nicklaus, Marc C

    2014-01-01

    Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely. PMID:23088273

  5. Continuous Metabolic Monitoring Based on Multi-Analyte Biomarkers to Predict Exhaustion

    PubMed Central

    Kastellorizios, Michail; Burgess, Diane J.

    2015-01-01

    This work introduces the concept of multi-analyte biomarkers for continuous metabolic monitoring. The importance of using more than one marker lies in the ability to obtain a holistic understanding of the metabolism. This is showcased for the detection and prediction of exhaustion during intense physical exercise. The findings presented here indicate that when glucose and lactate changes over time are combined into multi-analyte biomarkers, their monitoring trends are more sensitive in the subcutaneous tissue, an implantation-friendly peripheral tissue, compared to the blood. This unexpected observation was confirmed in normal as well as type 1 diabetic rats. This study was designed to be of direct value to continuous monitoring biosensor research, where single analytes are typically monitored. These findings can be implemented in new multi-analyte continuous monitoring technologies for more accurate insulin dosing, as well as for exhaustion prediction studies based on objective data rather than the subject’s perception. PMID:26028477

  6. Continuous metabolic monitoring based on multi-analyte biomarkers to predict exhaustion.

    PubMed

    Kastellorizios, Michail; Burgess, Diane J

    2015-06-01

    This work introduces the concept of multi-analyte biomarkers for continuous metabolic monitoring. The importance of using more than one marker lies in the ability to obtain a holistic understanding of the metabolism. This is showcased for the detection and prediction of exhaustion during intense physical exercise. The findings presented here indicate that when glucose and lactate changes over time are combined into multi-analyte biomarkers, their monitoring trends are more sensitive in the subcutaneous tissue, an implantation-friendly peripheral tissue, compared to the blood. This unexpected observation was confirmed in normal as well as type 1 diabetic rats. This study was designed to be of direct value to continuous monitoring biosensor research, where single analytes are typically monitored. These findings can be implemented in new multi-analyte continuous monitoring technologies for more accurate insulin dosing, as well as for exhaustion prediction studies based on objective data rather than the subject's perception.

  7. Metabolic theory predicts animal self-thinning.

    PubMed

    Jonsson, Tomas

    2017-05-01

    The metabolic theory of ecology (MTE) predicts observed patterns in ecology based on metabolic rates of individuals. The theory is influential but also criticized for a lack of firm empirical evidence confirming MTE's quantitative predictions of processes, e.g. outcome of competition, at population or community level. Self-thinning is a well-known population level phenomenon among plants, but a much less studied phenomenon in animal populations and no consensus exists on what a universal thinning slope for animal populations might be, or if it exists. The goal of this study was to use animal self-thinning as a tool to test population-level predictions from MTE, by analysing (i) if self-thinning can be induced in populations of house crickets (Acheta domesticus) and (ii) if the resulting thinning trajectories can be predicted from metabolic theory, using estimates of the species-specific metabolic rate of A. domesticus. I performed a laboratory study where the growth of A. domesticus was followed, from hatching until emergence as adults, in 71 cohorts of five different starting densities. Ninety-six per cent of all cohorts in the three highest starting densities showed evidence of self-thinning, with estimated thinning slopes in general being remarkably close to that expected under metabolic constraints: A cross-sectional analysis of all data showing evidence of self-thinning produced an ordinary least square (OLS) slope of -1·11, exactly that predicted from specific metabolic allometry of A. domesticus. This result is furthermore supported by longitudinal analyses, allowing for independent responses within cohorts, producing a mean OLS slope across cohorts of -1·13 and a fixed effect linear mixed effects models slope of -1·09. Sensitivity analysis showed that these results are robust to how the criterion for on-going self-thinning was defined. Finally, also as predicted by metabolic theory, temperature had a negative effect on the thinning intercept, producing

  8. Skinfold reference curves and their use in predicting metabolic syndrome risk in children.

    PubMed

    Andaki, Alynne C R; Quadros, Teresa M B de; Gordia, Alex P; Mota, Jorge; Tinôco, Adelson L A; Mendes, Edmar L

    To draw skinfold (SF) reference curves (subscapular, suprailiac, biceps, triceps) and to determine SF cutoff points for predicting the risk of metabolic syndrome (MetS) in children aged 6-10 years old. This was a cross-sectional study with a random sample of 1480 children aged 6-10 years old, 52.2% females, from public and private schools located in the urban and rural areas of the municipality of Uberaba (MG). Anthropometry, blood pressure, and fasting blood samples were taken at school, following specific protocols. The LMS method was used to draw the reference curves and ROC curve analysis to determine the accuracy and cutoff points for the evaluated skinfolds. The four SF evaluated (subscapular, suprailiac, biceps, and triceps) and their sum (∑4SF) were accurate in predicting MetS for both girls and boys. Additionally, cutoffs have been proposed and percentile curves (p5, p10, p25, p50, p75, p90, and p95) were outlined for the four SF and ∑4SF, for both genders. SF measurements were accurate in predicting metabolic syndrome in children aged 6-10 years old. Age- and gender-specific smoothed percentiles curves of SF provide a reference for the detection of risk for MetS in children. Copyright © 2017. Published by Elsevier Editora Ltda.

  9. FindPrimaryPairs: An efficient algorithm for predicting element-transferring reactant/product pairs in metabolic networks.

    PubMed

    Steffensen, Jon Lund; Dufault-Thompson, Keith; Zhang, Ying

    2018-01-01

    The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/).

  10. Biomarker Surrogates Do Not Accurately Predict Sputum Eosinophils and Neutrophils in Asthma

    PubMed Central

    Hastie, Annette T.; Moore, Wendy C.; Li, Huashi; Rector, Brian M.; Ortega, Victor E.; Pascual, Rodolfo M.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

    2013-01-01

    Background Sputum eosinophils (Eos) are a strong predictor of airway inflammation, exacerbations, and aid asthma management, whereas sputum neutrophils (Neu) indicate a different severe asthma phenotype, potentially less responsive to TH2-targeted therapy. Variables such as blood Eos, total IgE, fractional exhaled nitric oxide (FeNO) or FEV1% predicted, may predict airway Eos, while age, FEV1%predicted, or blood Neu may predict sputum Neu. Availability and ease of measurement are useful characteristics, but accuracy in predicting airway Eos and Neu, individually or combined, is not established. Objectives To determine whether blood Eos, FeNO, and IgE accurately predict sputum eosinophils, and age, FEV1% predicted, and blood Neu accurately predict sputum neutrophils (Neu). Methods Subjects in the Wake Forest Severe Asthma Research Program (N=328) were characterized by blood and sputum cells, healthcare utilization, lung function, FeNO, and IgE. Multiple analytical techniques were utilized. Results Despite significant association with sputum Eos, blood Eos, FeNO and total IgE did not accurately predict sputum Eos, and combinations of these variables failed to improve prediction. Age, FEV1%predicted and blood Neu were similarly unsatisfactory for prediction of sputum Neu. Factor analysis and stepwise selection found FeNO, IgE and FEV1% predicted, but not blood Eos, correctly predicted 69% of sputum Eospredicted 64% of sputum Neupredict both sputum Eos and Neu accurately assigned only 41% of samples. Conclusion Despite statistically significant associations FeNO, IgE, blood Eos and Neu, FEV1%predicted, and age are poor surrogates, separately and combined, for accurately predicting sputum eosinophils and neutrophils. PMID:23706399

  11. Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways.

    PubMed

    Saidi, Rabie; Boudellioua, Imane; Martin, Maria J; Solovyev, Victor

    2017-01-01

    It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

  12. Accurate Identification of Fear Facial Expressions Predicts Prosocial Behavior

    PubMed Central

    Marsh, Abigail A.; Kozak, Megan N.; Ambady, Nalini

    2009-01-01

    The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants’ ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale. PMID:17516803

  13. Accurate identification of fear facial expressions predicts prosocial behavior.

    PubMed

    Marsh, Abigail A; Kozak, Megan N; Ambady, Nalini

    2007-05-01

    The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants' ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale.

  14. Novel predictive models for metabolic syndrome risk: a "big data" analytic approach.

    PubMed

    Steinberg, Gregory B; Church, Bruce W; McCall, Carol J; Scott, Adam B; Kalis, Brian P

    2014-06-01

    We applied a proprietary "big data" analytic platform--Reverse Engineering and Forward Simulation (REFS)--to dimensions of metabolic syndrome extracted from a large data set compiled from Aetna's databases for 1 large national customer. Our goals were to accurately predict subsequent risk of metabolic syndrome and its various factors on both a population and individual level. The study data set included demographic, medical claim, pharmacy claim, laboratory test, and biometric screening results for 36,944 individuals. The platform reverse-engineered functional models of systems from diverse and large data sources and provided a simulation framework for insight generation. The platform interrogated data sets from the results of 2 Comprehensive Metabolic Syndrome Screenings (CMSSs) as well as complete coverage records; complete data from medical claims, pharmacy claims, and lab results for 2010 and 2011; and responses to health risk assessment questions. The platform predicted subsequent risk of metabolic syndrome, both overall and by risk factor, on population and individual levels, with ROC/AUC varying from 0.80 to 0.88. We demonstrated that improving waist circumference and blood glucose yielded the largest benefits on subsequent risk and medical costs. We also showed that adherence to prescribed medications and, particularly, adherence to routine scheduled outpatient doctor visits, reduced subsequent risk. The platform generated individualized insights using available heterogeneous data within 3 months. The accuracy and short speed to insight with this type of analytic platform allowed Aetna to develop targeted cost-effective care management programs for individuals with or at risk for metabolic syndrome.

  15. Development of gold-immobilized P450 platform for exploring the effect of oligomer formation on P450-mediated metabolism for in vitro to in vivo drug metabolism predictions

    NASA Astrophysics Data System (ADS)

    Kabulski, Jarod L.

    The cytochrome P450 (P450) enzyme family is responsible for the biotransformation of a wide range of endogenous and xenobiotic compounds, as well as being the major metabolic enzyme in first pass drug metabolism. In vivo drug metabolism for P450 enzymes is predicted using in vitro data obtained from a reconstituted expressed P450 system, but these systems have not always been proven to accurately represent in vivo enzyme kinetics, due to interactions caused by oligomer formation. These in vitro systems use soluble P450 enzymes prone to oligomer formation and studies have shown that increased states of protein aggregation directly affect the P450 enzyme kinetics. We have developed an immobilized enzyme system that isolates the enzyme and can be used to elucidate the effect of P450 aggregation on metabolism kinetics. The long term goal of my research is to develop a tool that will help improve the assessment of pharmaceuticals by better predicting in vivo kinetics in an in vitro system. The central hypothesis of this research is that P450-mediated kinetics measured in vitro is dependent on oligomer formation and that the accurate prediction of in vivo P450-mediated kinetics requires elucidation of the effect of oligomer formation. The rationale is that the development of a P450 bound to a Au platform can be used to control the aggregation of enzymes and bonding to Au may also permit replacement of the natural redox partners with an electrode capable of supplying a constant flow of electrons. This dissertation explains the details of the enzyme attachment, monitoring substrate binding, and metabolism using physiological and electrochemical methods, determination of enzyme kinetics, and the development of an immobilized-P450 enzyme bioreactor. This work provides alternative approaches to studying P450-mediated kinetics, a platform for controlling enzyme aggregation, electrochemically-driven P450 metabolism, and for investigating the effect of protein

  16. Computer-aided prediction of xenobiotic metabolism in the human body

    NASA Astrophysics Data System (ADS)

    Bezhentsev, V. M.; Tarasova, O. A.; Dmitriev, A. V.; Rudik, A. V.; Lagunin, A. A.; Filimonov, D. A.; Poroikov, V. V.

    2016-08-01

    The review describes the major databases containing information about the metabolism of xenobiotics, including data on drug metabolism, metabolic enzymes, schemes of biotransformation and the structures of some substrates and metabolites. Computational approaches used to predict the interaction of xenobiotics with metabolic enzymes, prediction of metabolic sites in the molecule, generation of structures of potential metabolites for subsequent evaluation of their properties are considered. The advantages and limitations of various computational methods for metabolism prediction and the prospects for their applications to improve the safety and efficacy of new drugs are discussed. Bibliography — 165 references.

  17. Accurate Binding Free Energy Predictions in Fragment Optimization.

    PubMed

    Steinbrecher, Thomas B; Dahlgren, Markus; Cappel, Daniel; Lin, Teng; Wang, Lingle; Krilov, Goran; Abel, Robert; Friesner, Richard; Sherman, Woody

    2015-11-23

    Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

  18. Predicting the extent of metabolism using in vitro permeability rate measurements and in silico permeability rate predictions

    PubMed Central

    Hosey, Chelsea M; Benet, Leslie Z

    2015-01-01

    The Biopharmaceutics Drug Disposition Classification System (BDDCS) can be utilized to predict drug disposition, including interactions with other drugs and transporter or metabolizing enzyme effects based on the extent of metabolism and solubility of a drug. However, defining the extent of metabolism relies upon clinical data. Drugs exhibiting high passive intestinal permeability rates are extensively metabolized. Therefore, we aimed to determine if in vitro measures of permeability rate or in silico permeability rate predictions could predict the extent of metabolism, to determine a reference compound representing the permeability rate above which compounds would be expected to be extensively metabolized, and to predict the major route of elimination of compounds in a two-tier approach utilizing permeability rate and a previously published model predicting the major route of elimination of parent drug. Twenty-two in vitro permeability rate measurement data sets in Caco-2 and MDCK cell lines and PAMPA were collected from the literature, while in silico permeability rate predictions were calculated using ADMET Predictor™ or VolSurf+. The potential for permeability rate to differentiate between extensively and poorly metabolized compounds was analyzed with receiver operating characteristic curves. Compounds that yielded the highest sensitivity-specificity average were selected as permeability rate reference standards. The major route of elimination of poorly permeable drugs was predicted by our previously published model and the accuracies and predictive values were calculated. The areas under the receiver operating curves were >0.90 for in vitro measures of permeability rate and >0.80 for the VolSurf+ model of permeability rate, indicating they were able to predict the extent of metabolism of compounds. Labetalol and zidovudine predicted greater than 80% of extensively metabolized drugs correctly and greater than 80% of poorly metabolized drugs correctly in Caco

  19. Can phenological models predict tree phenology accurately under climate change conditions?

    NASA Astrophysics Data System (ADS)

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2014-05-01

    The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

  20. Do hand-held calorimeters provide reliable and accurate estimates of resting metabolic rate?

    PubMed

    Van Loan, Marta D

    2007-12-01

    This paper provides an overview of a new technique for indirect calorimetry and the assessment of resting metabolic rate. Information from the research literature includes findings on the reliability and validity of a new hand-held indirect calorimeter as well as use in clinical and field settings. Research findings to date are of mixed results. The MedGem instrument has provided more consistent results when compared to the Douglas bag method of measuring metabolic rate. The BodyGem instrument has been shown to be less accurate when compared to standard metabolic carts. Furthermore, when the Body Gem has been used with clinical patients or with under nourished individuals the results have not been acceptable. Overall, there is not a large enough body of evidence to definitively support the use of these hand-held devices for assessment of metabolic rate in a wide variety of clinical or research environments.

  1. Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors

    PubMed Central

    Fu, Chien-wei; Lin, Thy-Hou

    2017-01-01

    As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D) are computed and classified using the support vector machine (SVM) for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA− representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes. PMID:28072829

  2. ASTRAL, DRAGON and SEDAN scores predict stroke outcome more accurately than physicians.

    PubMed

    Ntaios, G; Gioulekas, F; Papavasileiou, V; Strbian, D; Michel, P

    2016-11-01

    ASTRAL, SEDAN and DRAGON scores are three well-validated scores for stroke outcome prediction. Whether these scores predict stroke outcome more accurately compared with physicians interested in stroke was investigated. Physicians interested in stroke were invited to an online anonymous survey to provide outcome estimates in randomly allocated structured scenarios of recent real-life stroke patients. Their estimates were compared to scores' predictions in the same scenarios. An estimate was considered accurate if it was within 95% confidence intervals of actual outcome. In all, 244 participants from 32 different countries responded assessing 720 real scenarios and 2636 outcomes. The majority of physicians' estimates were inaccurate (1422/2636, 53.9%). 400 (56.8%) of physicians' estimates about the percentage probability of 3-month modified Rankin score (mRS) > 2 were accurate compared with 609 (86.5%) of ASTRAL score estimates (P < 0.0001). 394 (61.2%) of physicians' estimates about the percentage probability of post-thrombolysis symptomatic intracranial haemorrhage were accurate compared with 583 (90.5%) of SEDAN score estimates (P < 0.0001). 160 (24.8%) of physicians' estimates about post-thrombolysis 3-month percentage probability of mRS 0-2 were accurate compared with 240 (37.3%) DRAGON score estimates (P < 0.0001). 260 (40.4%) of physicians' estimates about the percentage probability of post-thrombolysis mRS 5-6 were accurate compared with 518 (80.4%) DRAGON score estimates (P < 0.0001). ASTRAL, DRAGON and SEDAN scores predict outcome of acute ischaemic stroke patients with higher accuracy compared to physicians interested in stroke. © 2016 EAN.

  3. Humanized mouse lines and their application for prediction of human drug metabolism and toxicological risk assessment

    PubMed Central

    Cheung, Connie; Gonzalez, Frank J

    2008-01-01

    Cytochrome P450s (P450s) are important enzymes involved in the metabolism of xenobiotics, particularly clinically used drugs, and are also responsible for metabolic activation of chemical carcinogens and toxins. Many xenobiotics can activate nuclear receptors that in turn induce the expression of genes encoding xenobiotic metabolizing enzymes and drug transporters. Marked species differences in the expression and regulation of cytochromes P450 and xenobiotic nuclear receptors exist. Thus obtaining reliable rodent models to accurately reflect human drug and carcinogen metabolism is severely limited. Humanized transgenic mice were developed in an effort to create more reliable in vivo systems to study and predict human responses to xenobiotics. Human P450s or human xenobiotic-activated nuclear receptors were introduced directly or replaced the corresponding mouse gene, thus creating “humanized” transgenic mice. Mice expressing human CYP1A1/CYP1A2, CYP2E1, CYP2D6, CYP3A4, CY3A7, PXR, PPARα were generated and characterized. These humanized mouse models offers a broad utility in the evaluation and prediction of toxicological risk that may aid in the development of safer drugs. PMID:18682571

  4. Predicting metabolic adaptation, body weight change, and energy intake in humans

    PubMed Central

    2010-01-01

    Complex interactions between carbohydrate, fat, and protein metabolism underlie the body's remarkable ability to adapt to a variety of diets. But any imbalances between the intake and utilization rates of these macronutrients will result in changes in body weight and composition. Here, I present the first computational model that simulates how diet perturbations result in adaptations of fuel selection and energy expenditure that predict body weight and composition changes in both obese and nonobese men and women. No model parameters were adjusted to fit these data other than the initial conditions for each subject group (e.g., initial body weight and body fat mass). The model provides the first realistic simulations of how diet perturbations result in adaptations of whole body energy expenditure, fuel selection, and various metabolic fluxes that ultimately give rise to body weight change. The validated model was used to estimate free-living energy intake during a long-term weight loss intervention, a variable that has never previously been measured accurately. PMID:19934407

  5. Distinguishing between the Permeability Relationships with Absorption and Metabolism To Improve BCS and BDDCS Predictions in Early Drug Discovery

    PubMed Central

    2015-01-01

    The biopharmaceutics classification system (BCS) and biopharmaceutics drug distribution classification system (BDDCS) are complementary classification systems that can improve, simplify, and accelerate drug discovery, development, and regulatory processes. Drug permeability has been widely accepted as a screening tool for determining intestinal absorption via the BCS during the drug development and regulatory approval processes. Currently, predicting clinically significant drug interactions during drug development is a known challenge for industry and regulatory agencies. The BDDCS, a modification of BCS that utilizes drug metabolism instead of intestinal permeability, predicts drug disposition and potential drug–drug interactions in the intestine, the liver, and most recently the brain. Although correlations between BCS and BDDCS have been observed with drug permeability rates, discrepancies have been noted in drug classifications between the two systems utilizing different permeability models, which are accepted as surrogate models for demonstrating human intestinal permeability by the FDA. Here, we recommend the most applicable permeability models for improving the prediction of BCS and BDDCS classifications. We demonstrate that the passive transcellular permeability rate, characterized by means of permeability models that are deficient in transporter expression and paracellular junctions (e.g., PAMPA and Caco-2), will most accurately predict BDDCS metabolism. These systems will inaccurately predict BCS classifications for drugs that particularly are substrates of highly expressed intestinal transporters. Moreover, in this latter case, a system more representative of complete human intestinal permeability is needed to accurately predict BCS absorption. PMID:24628254

  6. Distinguishing between the permeability relationships with absorption and metabolism to improve BCS and BDDCS predictions in early drug discovery.

    PubMed

    Larregieu, Caroline A; Benet, Leslie Z

    2014-04-07

    The biopharmaceutics classification system (BCS) and biopharmaceutics drug distribution classification system (BDDCS) are complementary classification systems that can improve, simplify, and accelerate drug discovery, development, and regulatory processes. Drug permeability has been widely accepted as a screening tool for determining intestinal absorption via the BCS during the drug development and regulatory approval processes. Currently, predicting clinically significant drug interactions during drug development is a known challenge for industry and regulatory agencies. The BDDCS, a modification of BCS that utilizes drug metabolism instead of intestinal permeability, predicts drug disposition and potential drug-drug interactions in the intestine, the liver, and most recently the brain. Although correlations between BCS and BDDCS have been observed with drug permeability rates, discrepancies have been noted in drug classifications between the two systems utilizing different permeability models, which are accepted as surrogate models for demonstrating human intestinal permeability by the FDA. Here, we recommend the most applicable permeability models for improving the prediction of BCS and BDDCS classifications. We demonstrate that the passive transcellular permeability rate, characterized by means of permeability models that are deficient in transporter expression and paracellular junctions (e.g., PAMPA and Caco-2), will most accurately predict BDDCS metabolism. These systems will inaccurately predict BCS classifications for drugs that particularly are substrates of highly expressed intestinal transporters. Moreover, in this latter case, a system more representative of complete human intestinal permeability is needed to accurately predict BCS absorption.

  7. Discovery of metabolic signatures for predicting whole organism toxicology.

    PubMed

    Hines, Adam; Staff, Fred J; Widdows, John; Compton, Russell M; Falciani, Francesco; Viant, Mark R

    2010-06-01

    Toxicological studies in sentinel organisms frequently use biomarkers to assess biological effect. Development of "omic" technologies has enhanced biomarker discovery at the molecular level, providing signatures unique to toxicant mode-of-action (MOA). However, these signatures often lack relevance to organismal responses, such as growth or reproduction, limiting their value for environmental monitoring. Our primary objective was to discover metabolic signatures in chemically exposed organisms that can predict physiological toxicity. Marine mussels (Mytilus edulis) were exposed for 7 days to 12 and 50 microg/l copper and 50 and 350 microg/l pentachlorophenol (PCP), toxicants with unique MOAs. Physiological responses comprised an established measure of organism energetic fitness, scope for growth (SFG). Metabolic fingerprints were measured in the same individuals using nuclear magnetic resonance-based metabolomics. Metabolic signatures predictive of SFG were sought using optimal variable selection strategies and multivariate regression and then tested upon independently field-sampled mussels from rural and industrialized sites. Copper and PCP induced rational metabolic and physiological changes. Measured and predicted SFG were highly correlated for copper (r(2) = 0.55, P = 2.82 x 10(-7)) and PCP (r(2) = 0.66, P = 3.20 x 10(-6)). Predictive metabolites included methionine and arginine/phosphoarginine for copper and allantoin, valine, and methionine for PCP. When tested on field-sampled animals, metabolic signatures predicted considerably reduced fitness of mussels from the contaminated (SFG = 6.0 J/h/g) versus rural (SFG = 15.2 J/h/g) site. We report the first successful discovery of metabolic signatures in chemically exposed environmental organisms that inform on molecular MOA and that can predict physiological toxicity. This could have far-reaching implications for monitoring impacts on environmental health.

  8. Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached?

    PubMed

    Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

    2016-01-01

    Information about drug metabolism is an essential component of drug development. Modeling the drug metabolism requires identification of the involved enzymes, rate and extent of metabolism, the sites of metabolism etc. There has been continuous attempts in the prediction of metabolism of drugs using artificial intelligence in effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are number of predictive models available for metabolism using Support vector machines, Artificial neural networks, Bayesian classifiers etc. There is an urgent need to review their progress so far and address the existing challenges in prediction of metabolism. In this attempt, we are presenting the currently available literature models and some of the critical issues regarding prediction of drug metabolism.

  9. Refining metabolic models and accounting for regulatory effects.

    PubMed

    Kim, Joonhoon; Reed, Jennifer L

    2014-10-01

    Advances in genome-scale metabolic modeling allow us to investigate and engineer metabolism at a systems level. Metabolic network reconstructions have been made for many organisms and computational approaches have been developed to convert these reconstructions into predictive models. However, due to incomplete knowledge these reconstructions often have missing or extraneous components and interactions, which can be identified by reconciling model predictions with experimental data. Recent studies have provided methods to further improve metabolic model predictions by incorporating transcriptional regulatory interactions and high-throughput omics data to yield context-specific metabolic models. Here we discuss recent approaches for resolving model-data discrepancies and building context-specific metabolic models. Once developed highly accurate metabolic models can be used in a variety of biotechnology applications. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms

    PubMed Central

    2012-01-01

    Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for

  11. Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

    NASA Astrophysics Data System (ADS)

    Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

    2018-02-01

    Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

  12. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

    DOE PAGES

    Bordbar, Aarash; Yurkovich, James T.; Paglia, Giuseppe; ...

    2017-04-07

    In this study, the increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBAmore » predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.« less

  13. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Bordbar, Aarash; Yurkovich, James T.; Paglia, Giuseppe

    In this study, the increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBAmore » predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.« less

  14. Inference and Prediction of Metabolic Network Fluxes

    PubMed Central

    Nikoloski, Zoran; Perez-Storey, Richard; Sweetlove, Lee J.

    2015-01-01

    In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping. PMID:26392262

  15. Radiomics biomarkers for accurate tumor progression prediction of oropharyngeal cancer

    NASA Astrophysics Data System (ADS)

    Hadjiiski, Lubomir; Chan, Heang-Ping; Cha, Kenny H.; Srinivasan, Ashok; Wei, Jun; Zhou, Chuan; Prince, Mark; Papagerakis, Silvana

    2017-03-01

    Accurate tumor progression prediction for oropharyngeal cancers is crucial for identifying patients who would best be treated with optimized treatment and therefore minimize the risk of under- or over-treatment. An objective decision support system that can merge the available radiomics, histopathologic and molecular biomarkers in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate assessment of oropharyngeal tumor progression. In this study, we evaluated the feasibility of developing individual and combined predictive models based on quantitative image analysis from radiomics, histopathology and molecular biomarkers for oropharyngeal tumor progression prediction. With IRB approval, 31, 84, and 127 patients with head and neck CT (CT-HN), tumor tissue microarrays (TMAs) and molecular biomarker expressions, respectively, were collected. For 8 of the patients all 3 types of biomarkers were available and they were sequestered in a test set. The CT-HN lesions were automatically segmented using our level sets based method. Morphological, texture and molecular based features were extracted from CT-HN and TMA images, and selected features were merged by a neural network. The classification accuracy was quantified using the area under the ROC curve (AUC). Test AUCs of 0.87, 0.74, and 0.71 were obtained with the individual predictive models based on radiomics, histopathologic, and molecular features, respectively. Combining the radiomics and molecular models increased the test AUC to 0.90. Combining all 3 models increased the test AUC further to 0.94. This preliminary study demonstrates that the individual domains of biomarkers are useful and the integrated multi-domain approach is most promising for tumor progression prediction.

  16. SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

    PubMed

    Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

    2008-05-01

    Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are

  17. SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

    PubMed Central

    Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

    2008-01-01

    Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of

  18. Integrating Transcriptomics with Metabolic Modeling Predicts Biomarkers and Drug Targets for Alzheimer's Disease

    PubMed Central

    Stempler, Shiri; Yizhak, Keren; Ruppin, Eytan

    2014-01-01

    Accumulating evidence links numerous abnormalities in cerebral metabolism with the progression of Alzheimer's disease (AD), beginning in its early stages. Here, we integrate transcriptomic data from AD patients with a genome-scale computational human metabolic model to characterize the altered metabolism in AD, and employ state-of-the-art metabolic modelling methods to predict metabolic biomarkers and drug targets in AD. The metabolic descriptions derived are first tested and validated on a large scale versus existing AD proteomics and metabolomics data. Our analysis shows a significant decrease in the activity of several key metabolic pathways, including the carnitine shuttle, folate metabolism and mitochondrial transport. We predict several metabolic biomarkers of AD progression in the blood and the CSF, including succinate and prostaglandin D2. Vitamin D and steroid metabolism pathways are enriched with predicted drug targets that could mitigate the metabolic alterations observed. Taken together, this study provides the first network wide view of the metabolic alterations associated with AD progression. Most importantly, it offers a cohort of new metabolic leads for the diagnosis of AD and its treatment. PMID:25127241

  19. PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

    PubMed Central

    Bendl, Jaroslav; Stourac, Jan; Salanda, Ondrej; Pavelka, Antonin; Wieben, Eric D.; Zendulka, Jaroslav; Brezovsky, Jan; Damborsky, Jiri

    2014-01-01

    Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. PMID:24453961

  20. Predicting metabolic syndrome using decision tree and support vector machine methods.

    PubMed

    Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

    2016-05-01

    Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According

  1. Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Zuniga, Cristal; Levering, Jennifer; Antoniewicz, Maciek R.

    Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. We used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealedmore » an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted fluxes trends and gene expression trends was found for 65% of multi-subunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acids metabolism. Moreover, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.« less

  2. Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris

    DOE PAGES

    Zuniga, Cristal; Levering, Jennifer; Antoniewicz, Maciek R.; ...

    2017-09-26

    Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. We used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealedmore » an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted fluxes trends and gene expression trends was found for 65% of multi-subunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acids metabolism. Moreover, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.« less

  3. Altered drug metabolism during pregnancy: hormonal regulation of drug-metabolizing enzymes.

    PubMed

    Jeong, Hyunyoung

    2010-06-01

    Medication use during pregnancy is prevalent, but pharmacokinetic information of most drugs used during pregnancy is lacking in spite of known effects of pregnancy on drug disposition. Accurate pharmacokinetic information is essential for optimal drug therapy in mother and fetus. Thus, understanding how pregnancy influences drug disposition is important for better prediction of pharmacokinetic changes of drugs in pregnant women. Pregnancy is known to affect hepatic drug metabolism, but the underlying mechanisms remain unknown. Physiological changes accompanying pregnancy are probably responsible for the reported alteration in drug metabolism during pregnancy. These include elevated concentrations of various hormones such as estrogen, progesterone, placental growth hormones and prolactin. This review covers how these hormones influence expression of drug-metabolizing enzymes (DMEs), thus potentially responsible for altered drug metabolism during pregnancy. The reader will gain a greater understanding of the altered drug metabolism in pregnant women and the regulatory effects of pregnancy hormones on expression of DMEs. In-depth studies in hormonal regulatory mechanisms as well as confirmatory studies in pregnant women are warranted for systematic understanding and prediction of the changes in hepatic drug metabolism during pregnancy.

  4. Metabolic theory predicts whole-ecosystem properties.

    PubMed

    Schramski, John R; Dell, Anthony I; Grady, John M; Sibly, Richard M; Brown, James H

    2015-02-24

    Understanding the effects of individual organisms on material cycles and energy fluxes within ecosystems is central to predicting the impacts of human-caused changes on climate, land use, and biodiversity. Here we present a theory that integrates metabolic (organism-based bottom-up) and systems (ecosystem-based top-down) approaches to characterize how the metabolism of individuals affects the flows and stores of materials and energy in ecosystems. The theory predicts how the average residence time of carbon molecules, total system throughflow (TST), and amount of recycling vary with the body size and temperature of the organisms and with trophic organization. We evaluate the theory by comparing theoretical predictions with outputs of numerical models designed to simulate diverse ecosystem types and with empirical data for real ecosystems. Although residence times within different ecosystems vary by orders of magnitude-from weeks in warm pelagic oceans with minute phytoplankton producers to centuries in cold forests with large tree producers-as predicted, all ecosystems fall along a single line: residence time increases linearly with slope = 1.0 with the ratio of whole-ecosystem biomass to primary productivity (B/P). TST was affected predominantly by primary productivity and recycling by the transfer of energy from microbial decomposers to animal consumers. The theory provides a robust basis for estimating the flux and storage of energy, carbon, and other materials in terrestrial, marine, and freshwater ecosystems and for quantifying the roles of different kinds of organisms and environments at scales from local ecosystems to the biosphere.

  5. Altered drug metabolism during pregnancy: Hormonal regulation of drug-metabolizing enzymes

    PubMed Central

    Jeong, Hyunyoung

    2013-01-01

    Importance of the field Medication use during pregnancy is prevalent, but pharmacokinetic information of most drugs used during pregnancy is lacking in spite of known effects of pregnancy on drug disposition. Accurate pharmacokinetic information is essential for optimal drug therapy in mother and fetus. Thus, understanding how pregnancy influences drug disposition is important for better prediction of pharmacokinetic changes of drugs in pregnant women. Areas covered in this review Pregnancy is known to affect hepatic drug metabolism, but the underlying mechanisms remain unknown. Physiological changes accompanying pregnancy are likely responsible for the reported alteration in drug metabolism during pregnancy. These include elevated concentrations of various hormones such as estrogen, progesterone, placental growth hormones and prolactin. This review covers how these hormones influence expression of drug-metabolizing enzymes, thus potentially responsible for altered drug metabolism during pregnancy. What the reader will gain The reader will gain a greater understanding of the altered drug metabolism in pregnant women and the regulatory effects of pregnancy hormones on expression of drug-metabolizing enzymes. Take home message In-depth studies in hormonal regulatory mechanisms as well as confirmatory studies in pregnant women are warranted for systematic understanding and prediction of the changes in hepatic drug metabolism during pregnancy. PMID:20367533

  6. Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

    PubMed Central

    Burger, Lukas; van Nimwegen, Erik

    2008-01-01

    Accurate and large-scale prediction of protein–protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners. PMID:18277381

  7. In silico prediction of cytochrome P450-mediated drug metabolism.

    PubMed

    Zhang, Tao; Chen, Qi; Li, Li; Liu, Limin Angela; Wei, Dong-Qing

    2011-06-01

    The application of combinatorial chemistry and high-throughput screening technique enables the large number of chemicals to be generated and tested simultaneously, which will facilitate the drug development and discovery. At the same time, it brings about a challenge of how to efficiently identify the potential drug candidates from thousands of compounds. A way used to deal with the challenge is to consider the drug pharmacokinetic properties, such as absorption, distribution, metabolism and excretion (ADME), in the early stage of drug development. Among ADME properties, metabolism is of importance due to the strong association with efficacy and safety of drug. The review will focus on in silico approaches for prediction of Cytochrome P450-mediated drug metabolism. We will describe these predictive methods from two aspects, structure-based and data-based. Moreover, the applications and limitations of various methods will be discussed. Finally, we provide further direction toward improving the predictive accuracy of these in silico methods.

  8. A Novel Method for Accurate Operon Predictions in All SequencedProkaryotes

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Price, Morgan N.; Huang, Katherine H.; Alm, Eric J.

    2004-12-01

    We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacterpylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, andmore » its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from sixphylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC6803 has many operons even though it has unusually wide spacings between conserved adjacent genes.« less

  9. Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

    PubMed

    Yu, Haoyu S; Deng, Yuqing; Wu, Yujie; Sindhikara, Dan; Rask, Amy R; Kimura, Takayuki; Abel, Robert; Wang, Lingle

    2017-12-12

    Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.

  10. Thermodynamic Constraints Improve Metabolic Networks.

    PubMed

    Krumholz, Elias W; Libourel, Igor G L

    2017-08-08

    In pursuit of establishing a realistic metabolic phenotypic space, the reversibility of reactions is thermodynamically constrained in modern metabolic networks. The reversibility constraints follow from heuristic thermodynamic poise approximations that take anticipated cellular metabolite concentration ranges into account. Because constraints reduce the feasible space, draft metabolic network reconstructions may need more extensive reconciliation, and a larger number of genes may become essential. Notwithstanding ubiquitous application, the effect of reversibility constraints on the predictive capabilities of metabolic networks has not been investigated in detail. Instead, work has focused on the implementation and validation of the thermodynamic poise calculation itself. With the advance of fast linear programming-based network reconciliation, the effects of reversibility constraints on network reconciliation and gene essentiality predictions have become feasible and are the subject of this study. Networks with thermodynamically informed reversibility constraints outperformed gene essentiality predictions compared to networks that were constrained with randomly shuffled constraints. Unconstrained networks predicted gene essentiality as accurately as thermodynamically constrained networks, but predicted substantially fewer essential genes. Networks that were reconciled with sequence similarity data and strongly enforced reversibility constraints outperformed all other networks. We conclude that metabolic network analysis confirmed the validity of the thermodynamic constraints, and that thermodynamic poise information is actionable during network reconciliation. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  11. Accurate prediction of energy expenditure using a shoe-based activity monitor.

    PubMed

    Sazonova, Nadezhda; Browning, Raymond C; Sazonov, Edward

    2011-07-01

    The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors. We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals. Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors. These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

  12. ColoLipidGene: signature of lipid metabolism-related genes to predict prognosis in stage-II colon cancer patients

    PubMed Central

    Vargas, Teodoro; Moreno-Rubio, Juan; Herranz, Jesús; Cejas, Paloma; Molina, Susana; González-Vallinas, Margarita; Mendiola, Marta; Burgos, Emilio; Aguayo, Cristina; Custodio, Ana B.; Machado, Isidro; Ramos, David; Gironella, Meritxell; Espinosa-Salinas, Isabel; Ramos, Ricardo; Martín-Hernández, Roberto; Risueño, Alberto; De Las Rivas, Javier; Reglero, Guillermo; Yaya, Ricardo; Fernández-Martos, Carlos; Aparicio, Jorge; Maurel, Joan; Feliu, Jaime; de Molina, Ana Ramírez

    2015-01-01

    Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolism-related genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk group. PMID:25749516

  13. Are EMS call volume predictions based on demand pattern analysis accurate?

    PubMed

    Brown, Lawrence H; Lerner, E Brooke; Larmon, Baxter; LeGassick, Todd; Taigman, Michael

    2007-01-01

    Most EMS systems determine the number of crews they will deploy in their communities and when those crews will be scheduled based on anticipated call volumes. Many systems use historical data to calculate their anticipated call volumes, a method of prediction known as demand pattern analysis. To evaluate the accuracy of call volume predictions calculated using demand pattern analysis. Seven EMS systems provided 73 consecutive weeks of hourly call volume data. The first 20 weeks of data were used to calculate three common demand pattern analysis constructs for call volume prediction: average peak demand (AP), smoothed average peak demand (SAP), and 90th percentile rank (90%R). The 21st week served as a buffer. Actual call volumes in the last 52 weeks were then compared to the predicted call volumes by using descriptive statistics. There were 61,152 hourly observations in the test period. All three constructs accurately predicted peaks and troughs in call volume but not exact call volume. Predictions were accurate (+/-1 call) 13% of the time using AP, 10% using SAP, and 19% using 90%R. Call volumes were overestimated 83% of the time using AP, 86% using SAP, and 74% using 90%R. When call volumes were overestimated, predictions exceeded actual call volume by a median (Interquartile range) of 4 (2-6) calls for AP, 4 (2-6) for SAP, and 3 (2-5) for 90%R. Call volumes were underestimated 4% of time using AP, 4% using SAP, and 7% using 90%R predictions. When call volumes were underestimated, call volumes exceeded predictions by a median (Interquartile range; maximum under estimation) of 1 (1-2; 18) call for AP, 1 (1-2; 18) for SAP, and 2 (1-3; 20) for 90%R. Results did not vary between systems. Generally, demand pattern analysis estimated or overestimated call volume, making it a reasonable predictor for ambulance staffing patterns. However, it did underestimate call volume between 4% and 7% of the time. Communities need to determine if these rates of over

  14. Rapid and accurate prediction and scoring of water molecules in protein binding sites.

    PubMed

    Ross, Gregory A; Morris, Garrett M; Biggin, Philip C

    2012-01-01

    Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

  15. Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

    PubMed Central

    Blais, Edik M.; Rawls, Kristopher D.; Dougherty, Bonnie V.; Li, Zhuo I.; Kolling, Glynis L.; Ye, Ping; Wallqvist, Anders; Papin, Jason A.

    2017-01-01

    The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications. PMID:28176778

  16. Cross-validation of resting metabolic rate prediction equations

    USDA-ARS?s Scientific Manuscript database

    Background: Knowledge of the resting metabolic rate (RMR) is necessary for determining individual total energy requirements. Measurement of RMR is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, the accuracy of these equations...

  17. Multi-fidelity machine learning models for accurate bandgap predictions of solids

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

    Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

  18. Multi-fidelity machine learning models for accurate bandgap predictions of solids

    DOE PAGES

    Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

    2016-12-28

    Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

  19. Dynamic substrate preferences predict metabolic properties of a simple microbial consortium

    DOE PAGES

    Erbilgin, Onur; Bowen, Benjamin P.; Kosina, Suzanne M.; ...

    2017-01-23

    Mixed cultures of different microbial species are increasingly being used to carry out a specific biochemical function in lieu of engineering a single microbe to do the same t ask. However, knowing how different species' metabolisms will integrate to reach a desired outcome is a difficult problem that has been studied in great detail using steady-state models. However, many biotechnological processes, as well as natural habitats, represent a more dynamic system. Examining how individual species use resources in their growth medium or environment (exometabolomics) over time in batch culture conditions can provide rich phenotypic data that encompasses regulation and transporters,more » creating an opportunity to integrate the data into a predictive model of resource use by a mixed community. Here we use exometabolomic profiling to examine the time-varying substrate depletion from a mixture of 19 amino acids and glucose by two Pseudomonas and one Bacillus species isolated from ground water. Contrary to studies in model organisms, we found surprisingly few correlations between resource preferences and maximal growth rate or biomass composition. We then modeled patterns of substrate depletion, and used these models to examine if substrate usage preferences and substrate depletion kinetics of individual isolates can be used to predict the metabolism of a co-culture of the isolates. We found that most of the substrates fit the model predictions, except for glucose and histidine, which were depleted more slowly than predicted, and proline, glycine, glutamate, lysine and arginine, which were all consumed significantly faster. Our results indicate that a significant portion of a model community's overall metabolism can be predicted based on the metabolism of the individuals. Based on the nature of our model, the resources that significantly deviate from the prediction highlight potential metabolic pathways affected by species-species interactions, which when further

  20. Measuring the value of accurate link prediction for network seeding.

    PubMed

    Wei, Yijin; Spencer, Gwen

    2017-01-01

    The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight? We introduce optimized-against-a-sample ([Formula: see text]) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates [Formula: see text] under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.

  1. Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

    PubMed

    Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

    2013-01-02

    Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

  2. Simple prediction scores predict good and devastating outcomes after stroke more accurately than physicians.

    PubMed

    Reid, John Michael; Dai, Dingwei; Delmonte, Susanna; Counsell, Carl; Phillips, Stephen J; MacLeod, Mary Joan

    2017-05-01

    physicians are often asked to prognosticate soon after a patient presents with stroke. This study aimed to compare two outcome prediction scores (Five Simple Variables [FSV] score and the PLAN [Preadmission comorbidities, Level of consciousness, Age, and focal Neurologic deficit]) with informal prediction by physicians. demographic and clinical variables were prospectively collected from consecutive patients hospitalised with acute ischaemic or haemorrhagic stroke (2012-13). In-person or telephone follow-up at 6 months established vital and functional status (modified Rankin score [mRS]). Area under the receiver operating curves (AUC) was used to establish prediction score performance. five hundred and seventy-five patients were included; 46% female, median age 76 years, 88% ischaemic stroke. Six months after stroke, 47% of patients had a good outcome (alive and independent, mRS 0-2) and 26% a devastating outcome (dead or severely dependent, mRS 5-6). The FSV and PLAN scores were superior to physician prediction (AUCs of 0.823-0.863 versus 0.773-0.805, P < 0.0001) for good and devastating outcomes. The FSV score was superior to the PLAN score for predicting good outcomes and vice versa for devastating outcomes (P < 0.001). Outcome prediction was more accurate for those with later presentations (>24 hours from onset). the FSV and PLAN scores are validated in this population for outcome prediction after both ischaemic and haemorrhagic stroke. The FSV score is the least complex of all developed scores and can assist outcome prediction by physicians. © The Author 2016. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com

  3. PROXIMAL: a method for Prediction of Xenobiotic Metabolism.

    PubMed

    Yousofshahi, Mona; Manteiga, Sara; Wu, Charmian; Lee, Kyongbum; Hassoun, Soha

    2015-12-22

    Contamination of the environment with bioactive chemicals has emerged as a potential public health risk. These substances that may cause distress or disease in humans can be found in air, water and food supplies. An open question is whether these chemicals transform into potentially more active or toxic derivatives via xenobiotic metabolizing enzymes expressed in the body. We present a new prediction tool, which we call PROXIMAL (Prediction of Xenobiotic Metabolism) for identifying possible transformation products of xenobiotic chemicals in the liver. Using reaction data from DrugBank and KEGG, PROXIMAL builds look-up tables that catalog the sites and types of structural modifications performed by Phase I and Phase II enzymes. Given a compound of interest, PROXIMAL searches for substructures that match the sites cataloged in the look-up tables, applies the corresponding modifications to generate a panel of possible transformation products, and ranks the products based on the activity and abundance of the enzymes involved. PROXIMAL generates transformations that are specific for the chemical of interest by analyzing the chemical's substructures. We evaluate the accuracy of PROXIMAL's predictions through case studies on two environmental chemicals with suspected endocrine disrupting activity, bisphenol A (BPA) and 4-chlorobiphenyl (PCB3). Comparisons with published reports confirm 5 out of 7 and 17 out of 26 of the predicted derivatives for BPA and PCB3, respectively. We also compare biotransformation predictions generated by PROXIMAL with those generated by METEOR and Metaprint2D-react, two other prediction tools. PROXIMAL can predict transformations of chemicals that contain substructures recognizable by human liver enzymes. It also has the ability to rank the predicted metabolites based on the activity and abundance of enzymes involved in xenobiotic transformation.

  4. Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

    PubMed Central

    Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong; Weiner, Brian E.; Fischer, Axel W.; Meiler, Jens

    2017-01-01

    Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein–membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein–membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein–protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org. PMID:26804342

  5. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    DOE PAGES

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; ...

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

  6. XenoSite server: a web-available site of metabolism prediction tool.

    PubMed

    Matlock, Matthew K; Hughes, Tyler B; Swamidass, S Joshua

    2015-04-01

    Cytochrome P450 enzymes (P450s) are metabolic enzymes that process the majority of FDA-approved, small-molecule drugs. Understanding how these enzymes modify molecule structure is key to the development of safe, effective drugs. XenoSite server is an online implementation of the XenoSite, a recently published computational model for P450 metabolism. XenoSite predicts which atomic sites of a molecule--sites of metabolism (SOMs)--are modified by P450s. XenoSite server accepts input in common chemical file formats including SDF and SMILES and provides tools for visualizing the likelihood that each atomic site is a site of metabolism for a variety of important P450s, as well as a flat file download of SOM predictions. XenoSite server is available at http://swami.wustl.edu/xenosite. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  7. Metabolic network prediction through pairwise rational kernels.

    PubMed

    Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

    2014-09-26

    Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy

  8. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA

    PubMed Central

    2017-01-01

    Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository. PMID:28263984

  9. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    PubMed

    Biggs, Matthew B; Papin, Jason A

    2017-03-01

    Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  10. Predicting selective drug targets in cancer through metabolic networks

    PubMed Central

    Folger, Ori; Jerby, Livnat; Frezza, Christian; Gottlieb, Eyal; Ruppin, Eytan; Shlomi, Tomer

    2011-01-01

    The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled. PMID:21694718

  11. Estimation of basal metabolic rate in Chinese: are the current prediction equations applicable?

    PubMed

    Camps, Stefan G; Wang, Nan Xin; Tan, Wei Shuan Kimberly; Henry, C Jeyakumar

    2016-08-31

    Measurement of basal metabolic rate (BMR) is suggested as a tool to estimate energy requirements. Therefore, BMR prediction equations have been developed in multiple populations because indirect calorimetry is not always feasible. However, there is a paucity of data on BMR measured in overweight and obese adults living in Asia and equations developed for this group of interest. The aim of this study was to develop a new BMR prediction equation for Chinese adults applicable for a large BMI range and compare it with commonly used prediction equations. Subjects were 121 men and 111 women (age: 21-67 years, BMI: 16-41 kg/m(2)). Height, weight, and BMR were measured. Continuous open-circuit indirect calorimetry using a ventilated hood system for 30 min was used to measure BMR. A regression equation was derived using stepwise regression and accuracy was compared to 6 existing equations (Harris-Benedict, Henry, Liu, Yang, Owen and Mifflin). Additionally, the newly derived equation was cross-validated in a separate group of 70 Chinese subjects (26 men and 44 women, age: 21-69 years, BMI: 17-39 kg/m(2)). The equation developed from our data was: BMR (kJ/d) = 52.6 x weight (kg) + 828 x gender + 1960 (women = 0, men = 1; R(2) = 0.81). The accuracy rate (within 10 % accurate) was 78 % which compared well to Owen (70 %), Henry (67 %), Mifflin (67 %), Liu (58 %), Harris-Benedict (45 %) and Yang (37 %) for the whole range of BMI. For a BMI greater than 23, the Singapore equation reached an accuracy rate of 76 %. Cross-validation proved an accuracy rate of 80 %. To date, the newly developed Singapore equation is the most accurate BMR prediction equation in Chinese and is applicable for use in a large BMI range including those overweight and obese.

  12. An accurate model for predicting high frequency noise of nanoscale NMOS SOI transistors

    NASA Astrophysics Data System (ADS)

    Shen, Yanfei; Cui, Jie; Mohammadi, Saeed

    2017-05-01

    A nonlinear and scalable model suitable for predicting high frequency noise of N-type Metal Oxide Semiconductor (NMOS) transistors is presented. The model is developed for a commercial 45 nm CMOS SOI technology and its accuracy is validated through comparison with measured performance of a microwave low noise amplifier. The model employs the virtual source nonlinear core and adds parasitic elements to accurately simulate the RF behavior of multi-finger NMOS transistors up to 40 GHz. For the first time, the traditional long-channel thermal noise model is supplemented with an injection noise model to accurately represent the noise behavior of these short-channel transistors up to 26 GHz. The developed model is simple and easy to extract, yet very accurate.

  13. Modeling central metabolism and energy biosynthesis across microbial life.

    PubMed

    Edirisinghe, Janaka N; Weisenhorn, Pamela; Conrad, Neal; Xia, Fangfang; Overbeek, Ross; Stevens, Rick L; Henry, Christopher S

    2016-08-08

    Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to

  14. Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

    NASA Astrophysics Data System (ADS)

    Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

    2017-04-01

    Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

  15. Competitive Abilities in Experimental Microcosms Are Accurately Predicted by a Demographic Index for R*

    PubMed Central

    Murrell, Ebony G.; Juliano, Steven A.

    2012-01-01

    Resource competition theory predicts that R*, the equilibrium resource amount yielding zero growth of a consumer population, should predict species' competitive abilities for that resource. This concept has been supported for unicellular organisms, but has not been well-tested for metazoans, probably due to the difficulty of raising experimental populations to equilibrium and measuring population growth rates for species with long or complex life cycles. We developed an index (Rindex) of R* based on demography of one insect cohort, growing from egg to adult in a non-equilibrium setting, and tested whether Rindex yielded accurate predictions of competitive abilities using mosquitoes as a model system. We estimated finite rate of increase (λ′) from demographic data for cohorts of three mosquito species raised with different detritus amounts, and estimated each species' Rindex using nonlinear regressions of λ′ vs. initial detritus amount. All three species' Rindex differed significantly, and accurately predicted competitive hierarchy of the species determined in simultaneous pairwise competition experiments. Our Rindex could provide estimates and rigorous statistical comparisons of competitive ability for organisms for which typical chemostat methods and equilibrium population conditions are impractical. PMID:22970128

  16. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

    PubMed Central

    Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

    2017-01-01

    Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

  17. Cross-validation of recent and longstanding resting metabolic rate prediction equations

    USDA-ARS?s Scientific Manuscript database

    Resting metabolic rate (RMR) measurement is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, their accuracy likely varies across individuals. Understanding the factors that influence predicted RMR accuracy at the individual lev...

  18. SIFTER search: a web server for accurate phylogeny-based protein function prediction

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

  19. SIFTER search: a web server for accurate phylogeny-based protein function prediction

    DOE PAGES

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    2015-05-15

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

  20. Prediction of work metabolism from heart rate measurements in forest work: some practical methodological issues.

    PubMed

    Dubé, Philippe-Antoine; Imbeau, Daniel; Dubeau, Denise; Auger, Isabelle; Leone, Mario

    2015-01-01

    Individual heart rate (HR) to workload relationships were determined using 93 submaximal step-tests administered to 26 healthy participants attending physical activities in a university training centre (laboratory study) and 41 experienced forest workers (field study). Predicted maximum aerobic capacity (MAC) was compared to measured MAC from a maximal treadmill test (laboratory study) to test the effect of two age-predicted maximum HR Equations (220-age and 207-0.7 × age) and two clothing insulation levels (0.4 and 0.91 clo) during the step-test. Work metabolism (WM) estimated from forest work HR was compared against concurrent work V̇O2 measurements while taking into account the HR thermal component. Results show that MAC and WM can be accurately predicted from work HR measurements and simple regression models developed in this study (1% group mean prediction bias and up to 25% expected prediction bias for a single individual). Clothing insulation had no impact on predicted MAC nor age-predicted maximum HR equations. Practitioner summary: This study sheds light on four practical methodological issues faced by practitioners regarding the use of HR methodology to assess WM in actual work environments. More specifically, the effect of wearing work clothes and the use of two different maximum HR prediction equations on the ability of a submaximal step-test to assess MAC are examined, as well as the accuracy of using an individual's step-test HR to workload relationship to predict WM from HR data collected during actual work in the presence of thermal stress.

  1. On the effects of alternative optima in context-specific metabolic model predictions

    PubMed Central

    Nikoloski, Zoran

    2017-01-01

    The integration of experimental data into genome-scale metabolic models can greatly improve flux predictions. This is achieved by restricting predictions to a more realistic context-specific domain, like a particular cell or tissue type. Several computational approaches to integrate data have been proposed—generally obtaining context-specific (sub)models or flux distributions. However, these approaches may lead to a multitude of equally valid but potentially different models or flux distributions, due to possible alternative optima in the underlying optimization problems. Although this issue introduces ambiguity in context-specific predictions, it has not been generally recognized, especially in the case of model reconstructions. In this study, we analyze the impact of alternative optima in four state-of-the-art context-specific data integration approaches, providing both flux distributions and/or metabolic models. To this end, we present three computational methods and apply them to two particular case studies: leaf-specific predictions from the integration of gene expression data in a metabolic model of Arabidopsis thaliana, and liver-specific reconstructions derived from a human model with various experimental data sources. The application of these methods allows us to obtain the following results: (i) we sample the space of alternative flux distributions in the leaf- and the liver-specific case and quantify the ambiguity of the predictions. In addition, we show how the inclusion of ℓ1-regularization during data integration reduces the ambiguity in both cases. (ii) We generate sets of alternative leaf- and liver-specific models that are optimal to each one of the evaluated model reconstruction approaches. We demonstrate that alternative models of the same context contain a marked fraction of disparate reactions. Further, we show that a careful balance between model sparsity and metabolic functionality helps in reducing the discrepancies between alternative

  2. On the effects of alternative optima in context-specific metabolic model predictions.

    PubMed

    Robaina-Estévez, Semidán; Nikoloski, Zoran

    2017-05-01

    The integration of experimental data into genome-scale metabolic models can greatly improve flux predictions. This is achieved by restricting predictions to a more realistic context-specific domain, like a particular cell or tissue type. Several computational approaches to integrate data have been proposed-generally obtaining context-specific (sub)models or flux distributions. However, these approaches may lead to a multitude of equally valid but potentially different models or flux distributions, due to possible alternative optima in the underlying optimization problems. Although this issue introduces ambiguity in context-specific predictions, it has not been generally recognized, especially in the case of model reconstructions. In this study, we analyze the impact of alternative optima in four state-of-the-art context-specific data integration approaches, providing both flux distributions and/or metabolic models. To this end, we present three computational methods and apply them to two particular case studies: leaf-specific predictions from the integration of gene expression data in a metabolic model of Arabidopsis thaliana, and liver-specific reconstructions derived from a human model with various experimental data sources. The application of these methods allows us to obtain the following results: (i) we sample the space of alternative flux distributions in the leaf- and the liver-specific case and quantify the ambiguity of the predictions. In addition, we show how the inclusion of ℓ1-regularization during data integration reduces the ambiguity in both cases. (ii) We generate sets of alternative leaf- and liver-specific models that are optimal to each one of the evaluated model reconstruction approaches. We demonstrate that alternative models of the same context contain a marked fraction of disparate reactions. Further, we show that a careful balance between model sparsity and metabolic functionality helps in reducing the discrepancies between alternative

  3. 13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production

    PubMed Central

    Guo, Weihua; Sheng, Jiayuan; Feng, Xueyang

    2015-01-01

    Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, 13C Metabolic Flux Analysis (13C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many 13C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of 13C-MFA and illustrate how 13C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms PMID:28952565

  4. Citric Acid Metabolism in Resistant Hypertension: Underlying Mechanisms and Metabolic Prediction of Treatment Response.

    PubMed

    Martin-Lorenzo, Marta; Martinez, Paula J; Baldan-Martin, Montserrat; Ruiz-Hurtado, Gema; Prado, Jose Carlos; Segura, Julian; de la Cuesta, Fernando; Barderas, Maria G; Vivanco, Fernando; Ruilope, Luis Miguel; Alvarez-Llamas, Gloria

    2017-11-01

    Resistant hypertension (RH) affects 9% to 12% of hypertensive adults. Prolonged exposure to suboptimal blood pressure control results in end-organ damage and cardiovascular risk. Spironolactone is the most effective drug for treatment, but not all patients respond and side effects are not negligible. Little is known on the mechanisms responsible for RH. We aimed to identify metabolic alterations in urine. In addition, a potential capacity of metabolites to predict response to spironolactone was investigated. Urine was collected from 29 patients with RH and from a group of 13 subjects with pseudo-RH. For patients, samples were collected before and after spironolactone administration and were classified in responders (n=19) and nonresponders (n=10). Nuclear magnetic resonance was applied to identify altered metabolites and pathways. Metabolites were confirmed by liquid chromatography-mass spectrometry. Citric acid cycle was the pathway most significantly altered ( P <0.0001). Metabolic concentrations were quantified and ranged from ng/mL malate to μg/mL citrate. Citrate and oxaloacetate increased in RH versus pseudoresistant. Together with α-ketoglutarate and malate, they were able to discriminate between responders and nonresponders, being the 4 metabolites increased in nonresponders. Combined as a prediction panel, they showed receiver operating characteristiccurve with area under the curve of 0.96. We show that citric acid cycle and deregulation of reactive oxygen species homeostasis control continue its activation after hypertension was developed. A metabolic panel showing alteration before spironolactone treatment and predicting future response of patients is shown. These molecular indicators will contribute optimizing the rate of control of RH patients with spironolactone. © 2017 American Heart Association, Inc.

  5. FDG metabolism associated with tau-amyloid interaction predicts memory decline

    PubMed Central

    Hanseeuw, Bernard J.; Betensky, Rebecca A.; Schultz, Aaron P.; Papp, Kate V.; Mormino, Elizabeth C.; Sepulcre, Jorge; Bark, John S.; Cosio, Danielle M.; LaPoint, Molly; Chhatwal, Jasmeer P.; Rentz, Dorene M.; Sperling, Reisa A.; Johnson, Keith

    2017-01-01

    Objective To evaluate in normal older adults and preclinical Alzheimer’s disease (AD) the impact of amyloid and regional tauopathy on cerebral glucose metabolism and subsequent memory decline. Methods We acquired positron emission tomography using F18 Flortaucipir (tau), C11 Pittsburgh Compound B (amyloid) and F18 Fluorodeoxyglucose in 90 clinically normal elderly of the Harvard Aging Brain Study. Results Posterior cingulate metabolism decreased when both amyloid and neocortical tau were high and predicted subsequent memory decline in a larger sample of normal elderly. In contrast, frontal hypometabolism related to the common age-related entorhinal tauopathy, but this dysfunction was independent of amyloid, and did not predict significant memory decline. Neocortical tauopathy was positively associated with metabolism in individuals with sub-threshold amyloid, suggesting that glucose metabolism increases before decreasing in the course of preclinical AD. Interpretation Our study identified a synergistic effect of amyloid and tau deposits and demonstrated for the first time in normal elderly its link to AD-like hypometabolism and to AD-like memory decline. The amyloid effect was seen with tau in neocortex, but not with tau in entorhinal cortex, which is the common site of age-related tauopathy. Entorhinal tau was associated with frontal hypometabolism, but this dysfunction was not associated with memory loss. PMID:28253546

  6. Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features.

    PubMed

    Li, Hongyang; Panwar, Bharat; Omenn, Gilbert S; Guan, Yuanfang

    2018-02-01

    The olfactory stimulus-percept problem has been studied for more than a century, yet it is still hard to precisely predict the odor given the large-scale chemoinformatic features of an odorant molecule. A major challenge is that the perceived qualities vary greatly among individuals due to different genetic and cultural backgrounds. Moreover, the combinatorial interactions between multiple odorant receptors and diverse molecules significantly complicate the olfaction prediction. Many attempts have been made to establish structure-odor relationships for intensity and pleasantness, but no models are available to predict the personalized multi-odor attributes of molecules. In this study, we describe our winning algorithm for predicting individual and population perceptual responses to various odorants in the DREAM Olfaction Prediction Challenge. We find that random forest model consisting of multiple decision trees is well suited to this prediction problem, given the large feature spaces and high variability of perceptual ratings among individuals. Integrating both population and individual perceptions into our model effectively reduces the influence of noise and outliers. By analyzing the importance of each chemical feature, we find that a small set of low- and nondegenerative features is sufficient for accurate prediction. Our random forest model successfully predicts personalized odor attributes of structurally diverse molecules. This model together with the top discriminative features has the potential to extend our understanding of olfactory perception mechanisms and provide an alternative for rational odorant design.

  7. Incremental Predictive Value of Serum AST-to-ALT Ratio for Incident Metabolic Syndrome: The ARIRANG Study

    PubMed Central

    Ahn, Song Vogue; Baik, Soon Koo; Cho, Youn zoo; Koh, Sang Baek; Huh, Ji Hye; Chang, Yoosoo; Sung, Ki-Chul; Kim, Jang Young

    2016-01-01

    Aims The ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT) is of great interest as a possible novel marker of metabolic syndrome. However, longitudinal studies emphasizing the incremental predictive value of the AST-to-ALT ratio in diagnosing individuals at higher risk of developing metabolic syndrome are very scarce. Therefore, our study aimed to evaluate the AST-to-ALT ratio as an incremental predictor of new onset metabolic syndrome in a population-based cohort study. Material and Methods The population-based cohort study included 2276 adults (903 men and 1373 women) aged 40–70 years, who participated from 2005–2008 (baseline) without metabolic syndrome and were followed up from 2008–2011. Metabolic syndrome was defined according to the harmonized definition of metabolic syndrome. Serum concentrations of AST and ALT were determined by enzymatic methods. Results During an average follow-up period of 2.6-years, 395 individuals (17.4%) developed metabolic syndrome. In a multivariable adjusted model, the odds ratio (95% confidence interval) for new onset of metabolic syndrome, comparing the fourth quartile to the first quartile of the AST-to-ALT ratio, was 0.598 (0.422–0.853). The AST-to-ALT ratio also improved the area under the receiver operating characteristic curve (AUC) for predicting new cases of metabolic syndrome (0.715 vs. 0.732, P = 0.004). The net reclassification improvement of prediction models including the AST-to-ALT ratio was 0.23 (95% CI: 0.124–0.337, P<0.001), and the integrated discrimination improvement was 0.0094 (95% CI: 0.0046–0.0143, P<0.001). Conclusions The AST-to-ALT ratio independently predicted the future development of metabolic syndrome and had incremental predictive value for incident metabolic syndrome. PMID:27560931

  8. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

    PubMed

    Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

    2017-04-01

    Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

  9. Anthropometric measurements as predictive indicators of metabolic risk in a Mexican population

    PubMed

    Domínguez-Reyes, Teresa; Quiroz-Vargas, Irma; Salgado-Bernabé, Aralia Berenice; Salgado-Goytia, Lorenzo; Muñoz-Valle, José Francisco; Parra-Rojas, Isela

    2017-02-01

    Introduction: Currently, it is considered that the body fat accumulation at central level is associated with the presence of hypertriglyceridemia, hypertension and diabetes. The body mass index (BMI) has been used to identify obesity in the general population, but can not detect the distribution of body fat, so that can be used other anthropometric measures to assess adiposity and determine their relationship with the presence of metabolic disorders that present people with excess weight. Objective: To evaluate anthropometric measurements such as waist-hip ratio (WHR), BMI and waist circumference (WC) as predictive indicators of metabolic risk factors in Mexican adults. Methods:A descriptive cross-sectional study was conducted in a total of 490 subjects (27-46 years), grouped by gender. All participants were determined anthropometric measurements and biochemical parameters. ROC curves of anthropometric parameters were set to identify the best predictive indicator of metabolic risk. Results: The metabolic risk factor most prevalent after abdominal obesity in women was hypertriglyceridemia, followed by hyperglycemia, hypercholesterolemia and high blood pressure, which are found most often in men than in women, although the presence of abdominal obesity was found most frequently in women (73.9% vs.37.3%). WC was the best predictive indicator to have one or more metabolic risk factors [area under the curve AUC = 0.85 (95% CI, 0.78 to 0.92)], followed by the BMI [AUC = 0.79 (95% CI, 0.72 to 0.88)], and finally the WHC [AUC = 0.63 (95% CI, 0.52 to 0.74)]. Also shows that abdominal obesity duplicate the risk of metabolic syndrome. Conclusion: Waist circumference is a better indicator of metabolic risk in both genders compared with BMI and the WHC.

  10. Predictive utility of blood pressure, waist circumference and body mass index for metabolic syndrome in patients with schizophrenia in Singapore.

    PubMed

    Nurjono, Milawaty; Lee, Jimmy

    2013-05-01

    This study aims to examine and compare the predictive utility of blood pressure (BP), waist circumference (WC) and body mass index (BMI), and to determine optimal cut-off values in prediction of metabolic syndrome (MetS) in patients with chronic schizophrenia. About 100 patients with chronic schizophrenia were recruited. BMI and BP were measured and laboratory tests to evaluate patients' high-density lipoprotein cholesterol, triglycerides and glucose levels were performed. Presence of MetS was examined according to AHA/NHLBI guidelines. Predictive utility of BP, WC and BMI was examined using receiver operating curve and discriminant indices were determined accordingly. Forty-six (46%) patients were identified to have MetS. BMI of ≥23 kg m(-2) was most accurate (AUC = 0.83, P < 0.001), with sensitivity of 93.5%, specificity of 48.1%, positive predictive value of 60.6% and negative predictive value of 92.9% in identifying MetS. This finding has immediate and significant clinical implications in the local population with schizophrenia. © 2012 Wiley Publishing Asia Pty Ltd.

  11. Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants

    DOE PAGES

    Schläpfer, Pascal; Zhang, Peifen; Wang, Chuan; ...

    2017-04-01

    Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we will need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can bemore » used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.« less

  12. Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Schläpfer, Pascal; Zhang, Peifen; Wang, Chuan

    Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we will need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can bemore » used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.« less

  13. Ensemble predictive model for more accurate soil organic carbon spectroscopic estimation

    NASA Astrophysics Data System (ADS)

    Vašát, Radim; Kodešová, Radka; Borůvka, Luboš

    2017-07-01

    A myriad of signal pre-processing strategies and multivariate calibration techniques has been explored in attempt to improve the spectroscopic prediction of soil organic carbon (SOC) over the last few decades. Therefore, to come up with a novel, more powerful, and accurate predictive approach to beat the rank becomes a challenging task. However, there may be a way, so that combine several individual predictions into a single final one (according to ensemble learning theory). As this approach performs best when combining in nature different predictive algorithms that are calibrated with structurally different predictor variables, we tested predictors of two different kinds: 1) reflectance values (or transforms) at each wavelength and 2) absorption feature parameters. Consequently we applied four different calibration techniques, two per each type of predictors: a) partial least squares regression and support vector machines for type 1, and b) multiple linear regression and random forest for type 2. The weights to be assigned to individual predictions within the ensemble model (constructed as a weighted average) were determined by an automated procedure that ensured the best solution among all possible was selected. The approach was tested at soil samples taken from surface horizon of four sites differing in the prevailing soil units. By employing the ensemble predictive model the prediction accuracy of SOC improved at all four sites. The coefficient of determination in cross-validation (R2cv) increased from 0.849, 0.611, 0.811 and 0.644 (the best individual predictions) to 0.864, 0.650, 0.824 and 0.698 for Site 1, 2, 3 and 4, respectively. Generally, the ensemble model affected the final prediction so that the maximal deviations of predicted vs. observed values of the individual predictions were reduced, and thus the correlation cloud became thinner as desired.

  14. Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

    DOE PAGES

    Broddrick, Jared T.; Rubin, Benjamin E.; Welkie, David G.; ...

    2016-12-20

    The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. In this paper, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting inmore » the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Finally, coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.« less

  15. Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

    PubMed Central

    Broddrick, Jared T.; Rubin, Benjamin E.; Welkie, David G.; Du, Niu; Mih, Nathan; Diamond, Spencer; Lee, Jenny J.; Golden, Susan S.; Palsson, Bernhard O.

    2016-01-01

    The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology. PMID:27911809

  16. Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Broddrick, Jared T.; Rubin, Benjamin E.; Welkie, David G.

    The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. In this paper, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting inmore » the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Finally, coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.« less

  17. Towards First Principles-Based Prediction of Highly Accurate Electrochemical Pourbaix Diagrams

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Zeng, Zhenhua; Chan, Maria K. Y.; Zhao, Zhi-Jian

    2015-08-13

    Electrochemical potential/pH (Pourbaix) diagrams underpin many aqueous electrochemical processes and are central to the identification of stable phases of metals for processes ranging from electrocatalysis to corrosion. Even though standard DFT calculations are potentially powerful tools for the prediction of such diagrams, inherent errors in the description of transition metal (hydroxy)oxides, together with neglect of van der Waals interactions, have limited the reliability of such predictions for even the simplest pure metal bulk compounds, and corresponding predictions for more complex alloy or surface structures are even more challenging. In the present work, through synergistic use of a Hubbard U correction,more » a state-of-the-art dispersion correction, and a water-based bulk reference state for the calculations, these errors are systematically corrected. The approach describes the weak binding that occurs between hydroxyl-containing functional groups in certain compounds in Pourbaix diagrams, corrects for self-interaction errors in transition metal compounds, and reduces residual errors on oxygen atoms by preserving a consistent oxidation state between the reference state, water, and the relevant bulk phases. The strong performance is illustrated on a series of bulk transition metal (Mn, Fe, Co and Ni) hydroxides, oxyhydroxides, binary, and ternary oxides, where the corresponding thermodynamics of redox and (de)hydration are described with standard errors of 0.04 eV per (reaction) formula unit. The approach further preserves accurate descriptions of the overall thermodynamics of electrochemically-relevant bulk reactions, such as water formation, which is an essential condition for facilitating accurate analysis of reaction energies for electrochemical processes on surfaces. The overall generality and transferability of the scheme suggests that it may find useful application in the construction of a broad array of electrochemical phase diagrams, including

  18. Modeling central metabolism and energy biosynthesis across microbial life

    DOE PAGES

    Edirisinghe, Janaka N.; Weisenhorn, Pamela; Conrad, Neal; ...

    2016-08-08

    Here, automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. As a result, to overcome this challenge, we developed methods and tools to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of modelmore » organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. In conclusion, we predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential

  19. Modeling central metabolism and energy biosynthesis across microbial life

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Edirisinghe, Janaka N.; Weisenhorn, Pamela; Conrad, Neal

    Here, automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. As a result, to overcome this challenge, we developed methods and tools to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of modelmore » organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. In conclusion, we predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential

  20. Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals.

    PubMed

    Doré, Bruce P; Meksin, Robert; Mather, Mara; Hirst, William; Ochsner, Kevin N

    2016-06-01

    In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting (a) the overall intensity of their future negative emotion, and (b) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  1. Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals

    PubMed Central

    Doré, B.P.; Meksin, R.; Mather, M.; Hirst, W.; Ochsner, K.N

    2016-01-01

    In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting 1) the overall intensity of their future negative emotion, and 2) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. PMID:27100309

  2. Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum.

    PubMed

    Wiback, Sharon J; Mahadevan, Radhakrishnan; Palsson, Bernhard Ø

    2004-05-05

    Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the alpha-spectrum. The alpha-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The alpha-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the alpha-spectrum extreme pathway matrix that incorporates measured uptake and byproduct secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques. Copyright 2004 Wiley Periodicals, Inc.

  3. How accurate is automated gap filling of metabolic models?

    PubMed

    Karp, Peter D; Weaver, Daniel; Latendresse, Mario

    2018-06-19

    Reaction gap filling is a computational technique for proposing the addition of reactions to genome-scale metabolic models to permit those models to run correctly. Gap filling completes what are otherwise incomplete models that lack fully connected metabolic networks. The models are incomplete because they are derived from annotated genomes in which not all enzymes have been identified. Here we compare the results of applying an automated likelihood-based gap filler within the Pathway Tools software with the results of manually gap filling the same metabolic model. Both gap-filling exercises were applied to the same genome-derived qualitative metabolic reconstruction for Bifidobacterium longum subsp. longum JCM 1217, and to the same modeling conditions - anaerobic growth under four nutrients producing 53 biomass metabolites. The solution computed by the gap-filling program GenDev contained 12 reactions, but closer examination showed that solution was not minimal; two of the twelve reactions can be removed to yield a set of ten reactions that enable model growth. The manually curated solution contained 13 reactions, eight of which were shared with the 12-reaction computed solution. Thus, GenDev achieved recall of 61.5% and precision of 66.6%. These results suggest that although computational gap fillers are populating metabolic models with significant numbers of correct reactions, automatically gap-filled metabolic models also contain significant numbers of incorrect reactions. Our conclusion is that manual curation of gap-filler results is needed to obtain high-accuracy models. Many of the differences between the manual and automatic solutions resulted from using expert biological knowledge to direct the choice of reactions within the curated solution, such as reactions specific to the anaerobic lifestyle of B. longum.

  4. Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants.

    PubMed

    Schläpfer, Pascal; Zhang, Peifen; Wang, Chuan; Kim, Taehyong; Banf, Michael; Chae, Lee; Dreher, Kate; Chavali, Arvind K; Nilo-Poyanco, Ricardo; Bernard, Thomas; Kahn, Daniel; Rhee, Seung Y

    2017-04-01

    Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters. © 2017 American Society of Plant Biologists. All Rights Reserved.

  5. High Order Schemes in Bats-R-US for Faster and More Accurate Predictions

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Toth, G.; Gombosi, T. I.

    2014-12-01

    BATS-R-US is a widely used global magnetohydrodynamics model that originally employed second order accurate TVD schemes combined with block based Adaptive Mesh Refinement (AMR) to achieve high resolution in the regions of interest. In the last years we have implemented fifth order accurate finite difference schemes CWENO5 and MP5 for uniform Cartesian grids. Now the high order schemes have been extended to generalized coordinates, including spherical grids and also to the non-uniform AMR grids including dynamic regridding. We present numerical tests that verify the preservation of free-stream solution and high-order accuracy as well as robust oscillation-free behavior near discontinuities. We apply the new high order accurate schemes to both heliospheric and magnetospheric simulations and show that it is robust and can achieve the same accuracy as the second order scheme with much less computational resources. This is especially important for space weather prediction that requires faster than real time code execution.

  6. Plant interactions alter the predictions of metabolic scaling theory.

    PubMed

    Lin, Yue; Berger, Uta; Grimm, Volker; Huth, Franka; Weiner, Jacob

    2013-01-01

    Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of -4/3 between mean individual biomass and density during density-dependent mortality (self-thinning). Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms' internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than -4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive.

  7. Role of androgen ratios in the prediction of the metabolic phenotype in polycystic ovary syndrome.

    PubMed

    Minooee, Sonia; Ramezani Tehrani, Fahimeh; Tohidi, Maryam; Azizi, Fereidoun

    2017-05-01

    To identify the androgen ratio that best predicts insulin resistance and metabolic syndrome among women with polycystic ovary syndrome (PCOS). Data for 180 women with PCOS and 180 healthy controls were extracted from two previous studies in Iran (conducted during 2008-2010 and 2011-2013). The diagnosis of PCOS was based on the Rotterdam criteria. The serum concentration of different androgens was measured. Receiver operating characteristic curve analysis was used to assess the ability of various androgen ratios to predict insulin resistance and metabolic syndrome. Among women with PCOS, the testosterone-to-androstenedione ratio was the best predictor of insulin resistance (sensitivity 0.83, specificity 0.42) and metabolic syndrome (sensitivity 0.85, specificity 0.70). Among healthy controls, the ratio of free androgen index to testosterone was the best predictor of insulin resistance (sensitivity 0.84, specificity 0.33) and metabolic syndrome (sensitivity 0.91, specificity 0.17). The prediction of insulin resistance and metabolic syndrome among women with PCOS was best accomplished with the testosterone-to-androstenedione ratio. © 2017 International Federation of Gynecology and Obstetrics.

  8. Shape shifting predicts ontogenetic changes in metabolic scaling in diverse aquatic invertebrates

    PubMed Central

    Glazier, Douglas S.; Hirst, Andrew G.; Atkinson, David

    2015-01-01

    Metabolism fuels all biological activities, and thus understanding its variation is fundamentally important. Much of this variation is related to body size, which is commonly believed to follow a 3/4-power scaling law. However, during ontogeny, many kinds of animals and plants show marked shifts in metabolic scaling that deviate from 3/4-power scaling predicted by general models. Here, we show that in diverse aquatic invertebrates, ontogenetic shifts in the scaling of routine metabolic rate from near isometry (bR = scaling exponent approx. 1) to negative allometry (bR < 1), or the reverse, are associated with significant changes in body shape (indexed by bL = the scaling exponent of the relationship between body mass and body length). The observed inverse correlations between bR and bL are predicted by metabolic scaling theory that emphasizes resource/waste fluxes across external body surfaces, but contradict theory that emphasizes resource transport through internal networks. Geometric estimates of the scaling of surface area (SA) with body mass (bA) further show that ontogenetic shifts in bR and bA are positively correlated. These results support new metabolic scaling theory based on SA influences that may be applied to ontogenetic shifts in bR shown by many kinds of animals and plants. PMID:25652833

  9. Accurate high-throughput structure mapping and prediction with transition metal ion FRET

    PubMed Central

    Yu, Xiaozhen; Wu, Xiongwu; Bermejo, Guillermo A.; Brooks, Bernard R.; Taraska, Justin W.

    2013-01-01

    Mapping the landscape of a protein’s conformational space is essential to understanding its functions and regulation. The limitations of many structural methods have made this process challenging for most proteins. Here, we report that transition metal ion FRET (tmFRET) can be used in a rapid, highly parallel screen, to determine distances from multiple locations within a protein at extremely low concentrations. The distances generated through this screen for the protein Maltose Binding Protein (MBP) match distances from the crystal structure to within a few angstroms. Furthermore, energy transfer accurately detects structural changes during ligand binding. Finally, fluorescence-derived distances can be used to guide molecular simulations to find low energy states. Our results open the door to rapid, accurate mapping and prediction of protein structures at low concentrations, in large complex systems, and in living cells. PMID:23273426

  10. Investigation of metabolic objectives in cultured hepatocytes.

    PubMed

    Uygun, Korkut; Matthew, Howard W T; Huang, Yinlun

    2007-06-15

    Using optimization based methods to predict fluxes in metabolic flux balance models has been a successful approach for some microorganisms, enabling construction of in silico models and even inference of some regulatory motifs. However, this success has not been translated to mammalian cells. The lack of knowledge about metabolic objectives in mammalian cells is a major obstacle that prevents utilization of various metabolic engineering tools and methods for tissue engineering and biomedical purposes. In this work, we investigate and identify possible metabolic objectives for hepatocytes cultured in vitro. To achieve this goal, we present a special data-mining procedure for identifying metabolic objective functions in mammalian cells. This multi-level optimization based algorithm enables identifying the major fluxes in the metabolic objective from MFA data in the absence of information about critical active constraints of the system. Further, once the objective is determined, active flux constraints can also be identified and analyzed. This information can be potentially used in a predictive manner to improve cell culture results or clinical metabolic outcomes. As a result of the application of this method, it was found that in vitro cultured hepatocytes maximize oxygen uptake, coupling of urea and TCA cycles, and synthesis of serine and urea. Selection of these fluxes as the metabolic objective enables accurate prediction of the flux distribution in the system given a limited amount of flux data; thus presenting a workable in silico model for cultured hepatocytes. It is observed that an overall homeostasis picture is also emergent in the findings.

  11. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers.

    PubMed

    Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2008-06-01

    Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate binding affinity prediction of peptides of length 8, 10 and 11. The method gives the opportunity to predict peptides with a different length than nine for MHC alleles where no such peptides have been measured. As validation, the performance of this approach is compared to predictors trained on peptides of the peptide length in question. In this validation, the approximation method has an accuracy that is comparable to or better than methods trained on a peptide length identical to the predicted peptides. The algorithm has been implemented in the web-accessible servers NetMHC-3.0: http://www.cbs.dtu.dk/services/NetMHC-3.0, and NetMHCpan-1.1: http://www.cbs.dtu.dk/services/NetMHCpan-1.1

  12. Fatty Liver Index and Lipid Accumulation Product Can Predict Metabolic Syndrome in Subjects without Fatty Liver Disease

    PubMed Central

    Cheng, Yuan-Lung; Wang, Yuan-Jen; Lan, Keng-Hsin; Huo, Teh-Ia; Hsieh, Wei-Yao; Hou, Ming-Chih; Lee, Fa-Yauh; Wu, Jaw-Ching; Lee, Shou-Dong

    2017-01-01

    Background. Fatty liver index (FLI) and lipid accumulation product (LAP) are indexes originally designed to assess the risk of fatty liver and cardiovascular disease, respectively. Both indexes have been proven to be reliable markers of subsequent metabolic syndrome; however, their ability to predict metabolic syndrome in subjects without fatty liver disease has not been clarified. Methods. We enrolled consecutive subjects who received health check-up services at Taipei Veterans General Hospital from 2002 to 2009. Fatty liver disease was diagnosed by abdominal ultrasonography. The ability of the FLI and LAP to predict metabolic syndrome was assessed by analyzing the area under the receiver operating characteristic (AUROC) curve. Results. Male sex was strongly associated with metabolic syndrome, and the LAP and FLI were better than other variables to predict metabolic syndrome among the 29,797 subjects. Both indexes were also better than other variables to detect metabolic syndrome in subjects without fatty liver disease (AUROC: 0.871 and 0.879, resp.), and the predictive power was greater among women. Conclusion. Metabolic syndrome increases the cardiovascular disease risk. The FLI and LAP could be used to recognize the syndrome in both subjects with and without fatty liver disease who require lifestyle modifications and counseling. PMID:28194177

  13. Best Fitting Prediction Equations for Basal Metabolic Rate: Informing Obesity Interventions in Diverse Populations

    PubMed Central

    Sabounchi, Nasim S.; Rahmandad, Hazhir; Ammerman, Alice

    2014-01-01

    Basal Metabolic Rate (BMR) represents the largest component of total energy expenditure and is a major contributor to energy balance. Therefore, accurately estimating BMR is critical for developing rigorous obesity prevention and control strategies. Over the past several decades, numerous BMR formulas have been developed targeted to different population groups. A comprehensive literature search revealed 248 BMR estimation equations developed using diverse ranges of age, gender, race, fat free mass, fat mass, height, waist-to-hip ratio, body mass index, and weight. A subset of 47 studies included enough detail to allow for development of meta-regression equations. Utilizing these studies, meta-equations were developed targeted to twenty specific population groups. This review provides a comprehensive summary of available BMR equations and an estimate of their accuracy. An accompanying online BMR prediction tool (available at http://www.sdl.ise.vt.edu/tutorials.html) was developed to automatically estimate BMR based on the most appropriate equation after user-entry of individual age, race, gender, and weight. PMID:23318720

  14. Evaluation of a genome-scale in silico metabolic model for Geobacter metallireducens by using proteomic data from a field biostimulation experiment.

    PubMed

    Fang, Yilin; Wilkins, Michael J; Yabusaki, Steven B; Lipton, Mary S; Long, Philip E

    2012-12-01

    Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens-specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

  15. Fluxes through plant metabolic networks: measurements, predictions, insights and challenges.

    PubMed

    Kruger, Nicholas J; Ratcliffe, R George

    2015-01-01

    Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

  16. A Mechanism-Based Model for the Prediction of the Metabolic Sites of Steroids Mediated by Cytochrome P450 3A4.

    PubMed

    Dai, Zi-Ru; Ai, Chun-Zhi; Ge, Guang-Bo; He, Yu-Qi; Wu, Jing-Jing; Wang, Jia-Yue; Man, Hui-Zi; Jia, Yan; Yang, Ling

    2015-06-30

    Early prediction of xenobiotic metabolism is essential for drug discovery and development. As the most important human drug-metabolizing enzyme, cytochrome P450 3A4 has a large active cavity and metabolizes a broad spectrum of substrates. The poor substrate specificity of CYP3A4 makes it a huge challenge to predict the metabolic site(s) on its substrates. This study aimed to develop a mechanism-based prediction model based on two key parameters, including the binding conformation and the reaction activity of ligands, which could reveal the process of real metabolic reaction(s) and the site(s) of modification. The newly established model was applied to predict the metabolic site(s) of steroids; a class of CYP3A4-preferred substrates. 38 steroids and 12 non-steroids were randomly divided into training and test sets. Two major metabolic reactions, including aliphatic hydroxylation and N-dealkylation, were involved in this study. At least one of the top three predicted metabolic sites was validated by the experimental data. The overall accuracy for the training and test were 82.14% and 86.36%, respectively. In summary, a mechanism-based prediction model was established for the first time, which could be used to predict the metabolic site(s) of CYP3A4 on steroids with high predictive accuracy.

  17. Comparison of predictive equations for resting metabolic rate in obese psychiatric patients taking olanzapine.

    PubMed

    Skouroliakou, Maria; Giannopoulou, Ifigenia; Kostara, Christina; Vasilopoulou, Melanie

    2009-02-01

    The prediction of resting metabolic rate (RMR) is important to determine the energy expenditure of obese patients with severe mental illnesses (SMIs). However, there is lack of research concerning the most accurate RMR predictive equations. The purpose of this study was to compare the validity of four RMR equations on patients with SMIs taking olanzapine. One hundred twenty-eight obese (body mass index >30 kg/m(2)) patients with SMIs (41 men and 87 women) treated with olanzapine were tested from 2005 to 2008. Measurements of anthropometric parameters (height, weight, body mass index, waist circumference) and body composition (using the BodPod) were performed at the beginning of the study. RMR was measured using indirect calorimetry. Comparisons between measured and estimated RMRs from four equations (Harris-Benedict adjusted and current body weights, Schofield, and Mifflin-St. Jeor) were performed using Pearson's correlation coefficient and Bland-Altman analysis. Significant correlations were found between the measured and predicted RMRs with all four equations (P < 0.001), with the Mifflin-St. Jeor equation demonstrating the strongest correlation in men and women (r = 0.712, P < 0.001). In men and women, the Bland-Altman analysis revealed no significant bias in the RMR prediction using the Harris-Benedict adjusted body weight and the Mifflin equations (P > 0.05). However, in men and women, the Harris-Benedict current body weight and the Schofield equations showed significant overestimation error in the RMR prediction (P < 0.001). When estimating RMR in men and women with SMIs taking olanzapine, the Mifflin-St. Jeor and Harris-Benedict adjusted body weight equations appear to be the most appropriate for clinical use.

  18. Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break.

    PubMed

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean-Michel; García de Cortázar-Atauri, Iñaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2016-10-01

    The onset of the growing season of trees has been earlier by 2.3 days per decade during the last 40 years in temperate Europe because of global warming. The effect of temperature on plant phenology is, however, not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud endodormancy, and, on the other hand, higher temperatures are necessary to promote bud cell growth afterward. Different process-based models have been developed in the last decades to predict the date of budbreak of woody species. They predict that global warming should delay or compromise endodormancy break at the species equatorward range limits leading to a delay or even impossibility to flower or set new leaves. These models are classically parameterized with flowering or budbreak dates only, with no information on the endodormancy break date because this information is very scarce. Here, we evaluated the efficiency of a set of phenological models to accurately predict the endodormancy break dates of three fruit trees. Our results show that models calibrated solely with budbreak dates usually do not accurately predict the endodormancy break date. Providing endodormancy break date for the model parameterization results in much more accurate prediction of this latter, with, however, a higher error than that on budbreak dates. Most importantly, we show that models not calibrated with endodormancy break dates can generate large discrepancies in forecasted budbreak dates when using climate scenarios as compared to models calibrated with endodormancy break dates. This discrepancy increases with mean annual temperature and is therefore the strongest after 2050 in the southernmost regions. Our results claim for the urgent need of massive measurements of endodormancy break dates in forest and fruit trees to yield more robust projections of phenological changes in a near future. © 2016 John Wiley & Sons Ltd.

  19. How accurate is our clinical prediction of "minimal prostate cancer"?

    PubMed

    Leibovici, Dan; Shikanov, Sergey; Gofrit, Ofer N; Zagaja, Gregory P; Shilo, Yaniv; Shalhav, Arieh L

    2013-07-01

    Recommendations for active surveillance versus immediate treatment for low risk prostate cancer are based on biopsy and clinical data, assuming that a low volume of well-differentiated carcinoma will be associated with a low progression risk. However, the accuracy of clinical prediction of minimal prostate cancer (MPC) is unclear. To define preoperative predictors for MPC in prostatectomy specimens and to examine the accuracy of such prediction. Data collected on 1526 consecutive radical prostatectomy patients operated in a single center between 2003 and 2008 included: age, body mass index, preoperative prostate-specific antigen level, biopsy Gleason score, clinical stage, percentage of positive biopsy cores, and maximal core length (MCL) involvement. MPC was defined as < 5% of prostate volume involvement with organ-confined Gleason score < or = 6. Univariate and multivariate logistic regression analyses were used to define independent predictors of minimal disease. Classification and Regression Tree (CART) analysis was used to define cutoff values for the predictors and measure the accuracy of prediction. MPC was found in 241 patients (15.8%). Clinical stage, biopsy Gleason's score, percent of positive biopsy cores, and maximal involved core length were associated with minimal disease (OR 0.42, 0.1, 0.92, and 0.9, respectively). Independent predictors of MPC included: biopsy Gleason score, percent of positive cores and MCL (OR 0.21, 095 and 0.95, respectively). CART showed that when the MCL exceeded 11.5%, the likelihood of MPC was 3.8%. Conversely, when applying the most favorable preoperative conditions (Gleason < or = 6, < 20% positive cores, MCL < or = 11.5%) the chance of minimal disease was 41%. Biopsy Gleason score, the percent of positive cores and MCL are independently associated with MPC. While preoperative prediction of significant prostate cancer was accurate, clinical prediction of MPC was incorrect 59% of the time. Caution is necessary when

  20. Predicting effects of structural stress in a genome-reduced model bacterial metabolism

    NASA Astrophysics Data System (ADS)

    Güell, Oriol; Sagués, Francesc; Serrano, M. Ángeles

    2012-08-01

    Mycoplasma pneumoniae is a human pathogen recently proposed as a genome-reduced model for bacterial systems biology. Here, we study the response of its metabolic network to different forms of structural stress, including removal of individual and pairs of reactions and knockout of genes and clusters of co-expressed genes. Our results reveal a network architecture as robust as that of other model bacteria regarding multiple failures, although less robust against individual reaction inactivation. Interestingly, metabolite motifs associated to reactions can predict the propagation of inactivation cascades and damage amplification effects arising in double knockouts. We also detect a significant correlation between gene essentiality and damages produced by single gene knockouts, and find that genes controlling high-damage reactions tend to be expressed independently of each other, a functional switch mechanism that, simultaneously, acts as a genetic firewall to protect metabolism. Prediction of failure propagation is crucial for metabolic engineering or disease treatment.

  1. Shape shifting predicts ontogenetic changes in metabolic scaling in diverse aquatic invertebrates.

    PubMed

    Glazier, Douglas S; Hirst, Andrew G; Atkinson, David

    2015-03-07

    Metabolism fuels all biological activities, and thus understanding its variation is fundamentally important. Much of this variation is related to body size, which is commonly believed to follow a 3/4-power scaling law. However, during ontogeny, many kinds of animals and plants show marked shifts in metabolic scaling that deviate from 3/4-power scaling predicted by general models. Here, we show that in diverse aquatic invertebrates, ontogenetic shifts in the scaling of routine metabolic rate from near isometry (bR = scaling exponent approx. 1) to negative allometry (bR < 1), or the reverse, are associated with significant changes in body shape (indexed by bL = the scaling exponent of the relationship between body mass and body length). The observed inverse correlations between bR and bL are predicted by metabolic scaling theory that emphasizes resource/waste fluxes across external body surfaces, but contradict theory that emphasizes resource transport through internal networks. Geometric estimates of the scaling of surface area (SA) with body mass (bA) further show that ontogenetic shifts in bR and bA are positively correlated. These results support new metabolic scaling theory based on SA influences that may be applied to ontogenetic shifts in bR shown by many kinds of animals and plants. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  2. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

    DOE PAGES

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; ...

    2015-06-04

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstratemore » prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.« less

  3. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

    PubMed Central

    2015-01-01

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. PMID:26113956

  4. Psychosocial stress predicts abnormal glucose metabolism: the Australian Diabetes, Obesity and Lifestyle (AusDiab) study.

    PubMed

    Williams, Emily D; Magliano, Dianna J; Tapp, Robyn J; Oldenburg, Brian F; Shaw, Jonathan E

    2013-08-01

    The evidence supporting a relationship between stress and diabetes has been inconsistent. This study examined the effects of stress on abnormal glucose metabolism, using a population-based sample of 3,759, with normoglycemia at baseline, from the Australian Diabetes, Obesity and Lifestyle study. Perceived stress and stressful life events were measured at baseline, with health behavior and anthropometric information also collected. Oral glucose tolerance tests were undertaken at baseline and 5-year follow-up. The primary outcome was the development of abnormal glucose metabolism (impaired fasting glucose, impaired glucose tolerance, and type 2 diabetes), according to WHO 1999 criteria. Perceived stress predicted incident abnormal glucose metabolism in women but not men, after multivariate adjustment. Life events showed an inconsistent relationship with abnormal glucose metabolism. Perceived stress predicted abnormal glucose metabolism in women. Healthcare professionals should consider psychosocial adversity when assessing risk factor profiles for the development of diabetes.

  5. Predicting when biliary excretion of parent drug is a major route of elimination in humans.

    PubMed

    Hosey, Chelsea M; Broccatelli, Fabio; Benet, Leslie Z

    2014-09-01

    Biliary excretion is an important route of elimination for many drugs, yet measuring the extent of biliary elimination is difficult, invasive, and variable. Biliary elimination has been quantified for few drugs with a limited number of subjects, who are often diseased patients. An accurate prediction of which drugs or new molecular entities are significantly eliminated in the bile may predict potential drug-drug interactions, pharmacokinetics, and toxicities. The Biopharmaceutics Drug Disposition Classification System (BDDCS) characterizes significant routes of drug elimination, identifies potential transporter effects, and is useful in understanding drug-drug interactions. Class 1 and 2 drugs are primarily eliminated in humans via metabolism and will not exhibit significant biliary excretion of parent compound. In contrast, class 3 and 4 drugs are primarily excreted unchanged in the urine or bile. Here, we characterize the significant elimination route of 105 orally administered class 3 and 4 drugs. We introduce and validate a novel model, predicting significant biliary elimination using a simple classification scheme. The model is accurate for 83% of 30 drugs collected after model development. The model corroborates the observation that biliarily eliminated drugs have high molecular weights, while demonstrating the necessity of considering route of administration and extent of metabolism when predicting biliary excretion. Interestingly, a predictor of potential metabolism significantly improves predictions of major elimination routes of poorly metabolized drugs. This model successfully predicts the major elimination route for poorly permeable/poorly metabolized drugs and may be applied prior to human dosing.

  6. How accurate are resting energy expenditure prediction equations in obese trauma and burn patients?

    PubMed

    Stucky, Chee-Chee H; Moncure, Michael; Hise, Mary; Gossage, Clint M; Northrop, David

    2008-01-01

    While the prevalence of obesity continues to increase in our society, outdated resting energy expenditure (REE) prediction equations may overpredict energy requirements in obese patients. Accurate feeding is essential since overfeeding has been demonstrated to adversely affect outcomes. The first objective was to compare REE calculated by prediction equations to the measured REE in obese trauma and burn patients. Our hypothesis was that an equation using fat-free mass would give a more accurate prediction. The second objective was to consider the effect of a commonly used injury factor on the predicted REE. A retrospective chart review was performed on 28 patients. REE was measured using indirect calorimetry and compared with the Harris-Benedict and Cunningham equations, and an equation using type II diabetes as a factor. Statistical analyses used were paired t test, +/-95% confidence interval, and the Bland-Altman method. Measured average REE in trauma and burn patients was 21.37 +/- 5.26 and 21.81 +/- 3.35 kcal/kg/d, respectively. Harris-Benedict underpredicted REE in trauma and burn patients to the least extent, while the Cunningham equation underpredicted REE in both populations to the greatest extent. Using an injury factor of 1.2, Cunningham continued to underestimate REE in both populations, while the Harris-Benedict and Diabetic equations overpredicted REE in both populations. The measured average REE is significantly less than current guidelines. This finding suggests that a hypocaloric regimen is worth considering for ICU patients. Also, if an injury factor of 1.2 is incorporated in certain equations, patients may be given too many calories.

  7. Predicting Metabolic Cost of Running with and without Backpack Loads

    DTIC Science & Technology

    1987-01-01

    would higher than generated by our prediction model. he more demanding for the cardiorespiratory sys - These differences, however, could be accounted...197) nabes te pedicionof rg cost ill women walking and running in %hoes and metabol cot aor walki) nande runin arditio af wieoots. I rgiinomics 29:439

  8. Accessing Autonomic Function Can Early Screen Metabolic Syndrome

    PubMed Central

    Dai, Meng; Li, Mian; Yang, Zhi; Xu, Min; Xu, Yu; Lu, Jieli; Chen, Yuhong; Liu, Jianmin; Ning, Guang; Bi, Yufang

    2012-01-01

    Background Clinical diagnosis of the metabolic syndrome is time-consuming and invasive. Convenient instruments that do not require laboratory or physical investigation would be useful in early screening individuals at high risk of metabolic syndrome. Examination of the autonomic function can be taken as a directly reference and screening indicator for predicting metabolic syndrome. Methodology and Principal Findings The EZSCAN test, as an efficient and noninvasive technology, can access autonomic function through measuring electrochemical skin conductance. In this study, we used EZSCAN value to evaluate autonomic function and to detect metabolic syndrome in 5,887 participants aged 40 years or older. The EZSCAN test diagnostic accuracy was analyzed by receiver operating characteristic curves. Among the 5,815 participants in the final analysis, 2,541 were diagnosed as metabolic syndrome and the overall prevalence was 43.7%. Prevalence of the metabolic syndrome increased with the elevated EZSCAN risk level (p for trend <0.0001). Moreover, EZSCAN value was associated with an increase in the number of metabolic syndrome components (p for trend <0.0001). Compared with the no risk group (EZSCAN value 0–24), participants at the high risk group (EZSCAN value: 50–100) had a 2.35 fold increased risk of prevalent metabolic syndrome after the multiple adjustments. The area under the curve of the EZSCAN test was 0.62 (95% confidence interval [CI], 0.61–0.64) for predicting metabolic syndrome. The optimal operating point for the EZSCAN value to detect a high risk of prevalent metabolic syndrome was 30 in this study, while the sensitivity and specificity were 71.2% and 46.7%, respectively. Conclusions and Significance In conclusion, although less sensitive and accurate when compared with the clinical definition of metabolic syndrome, we found that the EZSCAN test is a good and simple screening technique for early predicting metabolic syndrome. PMID:22916265

  9. ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Visel, Axel; Blow, Matthew J.; Li, Zirong

    2009-02-01

    A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. Wemore » tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.« less

  10. Prediction of metabolism-induced hepatotoxicity on three-dimensional hepatic cell culture and enzyme microarrays.

    PubMed

    Yu, Kyeong-Nam; Nadanaciva, Sashi; Rana, Payal; Lee, Dong Woo; Ku, Bosung; Roth, Alexander D; Dordick, Jonathan S; Will, Yvonne; Lee, Moo-Yeal

    2018-03-01

    Human liver contains various oxidative and conjugative enzymes that can convert nontoxic parent compounds to toxic metabolites or, conversely, toxic parent compounds to nontoxic metabolites. Unlike primary hepatocytes, which contain myriad drug-metabolizing enzymes (DMEs), but are difficult to culture and maintain physiological levels of DMEs, immortalized hepatic cell lines used in predictive toxicity assays are easy to culture, but lack the ability to metabolize compounds. To address this limitation and predict metabolism-induced hepatotoxicity in high-throughput, we developed an advanced miniaturized three-dimensional (3D) cell culture array (DataChip 2.0) and an advanced metabolizing enzyme microarray (MetaChip 2.0). The DataChip is a functionalized micropillar chip that supports the Hep3B human hepatoma cell line in a 3D microarray format. The MetaChip is a microwell chip containing immobilized DMEs found in the human liver. As a proof of concept for generating compound metabolites in situ on the chip and rapidly assessing their toxicity, 22 model compounds were dispensed into the MetaChip and sandwiched with the DataChip. The IC 50 values obtained from the chip platform were correlated with rat LD 50 values, human C max values, and drug-induced liver injury categories to predict adverse drug reactions in vivo. As a result, the platform had 100% sensitivity, 86% specificity, and 93% overall predictivity at optimum cutoffs of IC 50 and C max values. Therefore, the DataChip/MetaChip platform could be used as a high-throughput, early stage, microscale alternative to conventional in vitro multi-well plate platforms and provide a rapid and inexpensive assessment of metabolism-induced toxicity at early phases of drug development.

  11. Genes associated with metabolic syndrome predict disease-free survival in stage II colorectal cancer patients. A novel link between metabolic dysregulation and colorectal cancer.

    PubMed

    Vargas, Teodoro; Moreno-Rubio, Juan; Herranz, Jesús; Cejas, Paloma; Molina, Susana; González-Vallinas, Margarita; Ramos, Ricardo; Burgos, Emilio; Aguayo, Cristina; Custodio, Ana B; Reglero, Guillermo; Feliu, Jaime; Ramírez de Molina, Ana

    2014-12-01

    Studies have recently suggested that metabolic syndrome and its components increase the risk of colorectal cancer. Both diseases are increasing in most countries, and the genetic association between them has not been fully elucidated. The objective of this study was to assess the association between genetic risk factors of metabolic syndrome or related conditions (obesity, hyperlipidaemia, diabetes mellitus type 2) and clinical outcome in stage II colorectal cancer patients. Expression levels of several genes related to metabolic syndrome and associated alterations were analysed by real-time qPCR in two equivalent but independent sets of stage II colorectal cancer patients. Using logistic regression models and cross-validation analysis with all tumour samples, we developed a metabolic syndrome-related gene expression profile to predict clinical outcome in stage II colorectal cancer patients. The results showed that a gene expression profile constituted by genes previously related to metabolic syndrome was significantly associated with clinical outcome of stage II colorectal cancer patients. This metabolic profile was able to identify patients with a low risk and high risk of relapse. Its predictive value was validated using an independent set of stage II colorectal cancer patients. The identification of a set of genes related to metabolic syndrome that predict survival in intermediate-stage colorectal cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy and avoid the toxic and unnecessary chemotherapy in patients classified as low risk. Our results also confirm the linkage between metabolic disorder and colorectal cancer and suggest the potential for cancer prevention and/or treatment by targeting these genes. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  12. Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

    PubMed Central

    Noecker, Cecilia; Schaefer, Krista; Zaccheo, Kelly; Yang, Yiding; Day, Judy; Ganusov, Vitaly V.

    2015-01-01

    Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV). First, we found that the mode of virus production by infected cells (budding vs. bursting) has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral dose. These results

  13. PASMet: a web-based platform for prediction, modelling and analyses of metabolic systems

    PubMed Central

    Sriyudthsak, Kansuporn; Mejia, Ramon Francisco; Arita, Masanori; Hirai, Masami Yokota

    2016-01-01

    PASMet (Prediction, Analysis and Simulation of Metabolic networks) is a web-based platform for proposing and verifying mathematical models to understand the dynamics of metabolism. The advantages of PASMet include user-friendliness and accessibility, which enable biologists and biochemists to easily perform mathematical modelling. PASMet offers a series of user-functions to handle the time-series data of metabolite concentrations. The functions are organised into four steps: (i) Prediction of a probable metabolic pathway and its regulation; (ii) Construction of mathematical models; (iii) Simulation of metabolic behaviours; and (iv) Analysis of metabolic system characteristics. Each function contains various statistical and mathematical methods that can be used independently. Users who may not have enough knowledge of computing or programming can easily and quickly analyse their local data without software downloads, updates or installations. Users only need to upload their files in comma-separated values (CSV) format or enter their model equations directly into the website. Once the time-series data or mathematical equations are uploaded, PASMet automatically performs computation on server-side. Then, users can interactively view their results and directly download them to their local computers. PASMet is freely available with no login requirement at http://pasmet.riken.jp/ from major web browsers on Windows, Mac and Linux operating systems. PMID:27174940

  14. Improving medical decisions for incapacitated persons: does focusing on "accurate predictions" lead to an inaccurate picture?

    PubMed

    Kim, Scott Y H

    2014-04-01

    The Patient Preference Predictor (PPP) proposal places a high priority on the accuracy of predicting patients' preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate, individualized statistical model has significant obstacles. First, it will be impossible to validate the PPP beyond the limit imposed by 60%-80% reliability of people's preferences for future medical decisions--a figure no better than the known average accuracy of surrogates. Second, evidence supports the view that a sizable minority of persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate, and authoritative than is commonly assumed.

  15. Metabolic Network Modeling of Microbial Communities

    PubMed Central

    Biggs, Matthew B.; Medlock, Gregory L.; Kolling, Glynis L.

    2015-01-01

    Genome-scale metabolic network reconstructions and constraint-based analysis are powerful methods that have the potential to make functional predictions about microbial communities. Current use of genome-scale metabolic networks to characterize the metabolic functions of microbial communities includes species compartmentalization, separating species-level and community-level objectives, dynamic analysis, the “enzyme-soup” approach, multi-scale modeling, and others. There are many challenges inherent to the field, including a need for tools that accurately assign high-level omics signals to individual community members, new automated reconstruction methods that rival manual curation, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be proportional advances in the fields of ecology, health science, and microbial community engineering. PMID:26109480

  16. Rapid and accurate prediction of degradant formation rates in pharmaceutical formulations using high-performance liquid chromatography-mass spectrometry.

    PubMed

    Darrington, Richard T; Jiao, Jim

    2004-04-01

    Rapid and accurate stability prediction is essential to pharmaceutical formulation development. Commonly used stability prediction methods include monitoring parent drug loss at intended storage conditions or initial rate determination of degradants under accelerated conditions. Monitoring parent drug loss at the intended storage condition does not provide a rapid and accurate stability assessment because often <0.5% drug loss is all that can be observed in a realistic time frame, while the accelerated initial rate method in conjunction with extrapolation of rate constants using the Arrhenius or Eyring equations often introduces large errors in shelf-life prediction. In this study, the shelf life prediction of a model pharmaceutical preparation utilizing sensitive high-performance liquid chromatography-mass spectrometry (LC/MS) to directly quantitate degradant formation rates at the intended storage condition is proposed. This method was compared to traditional shelf life prediction approaches in terms of time required to predict shelf life and associated error in shelf life estimation. Results demonstrated that the proposed LC/MS method using initial rates analysis provided significantly improved confidence intervals for the predicted shelf life and required less overall time and effort to obtain the stability estimation compared to the other methods evaluated. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

  17. Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction

    PubMed Central

    Tabei, Yasuo; Yamanishi, Yoshihiro; Kotera, Masaaki

    2016-01-01

    Motivation: Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes. Results: In this study we propose a novel method to predict the enzyme orthologs that catalyze the putative reactions to facilitate the de novo reconstruction of metabolic pathways from metabolome-scale compound sets. The algorithm detects the chemical transformation patterns of substrate–product pairs using chemical graph alignments, and constructs a set of enzyme-specific classifiers to simultaneously predict all the enzyme orthologs that could catalyze the putative reactions of the substrate–product pairs in the joint learning framework. The originality of the method lies in its ability to make predictions for thousands of enzyme orthologs simultaneously, as well as its extraction of enzyme-specific chemical transformation patterns of substrate–product pairs. We demonstrate the usefulness of the proposed method by applying it to some ten thousands of metabolic compounds, and analyze the extracted chemical transformation patterns that provide insights into the characteristics and specificities of enzymes. The proposed method will open the door to both primary (central) and secondary metabolism in genomics research, increasing research productivity to tackle a wide variety of environmental and public health matters. Availability and Implementation: Contact: maskot@bio.titech.ac.jp PMID:27307627

  18. Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants1[OPEN

    PubMed Central

    Zhang, Peifen; Kim, Taehyong; Banf, Michael; Chavali, Arvind K.; Nilo-Poyanco, Ricardo; Bernard, Thomas

    2017-01-01

    Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters. PMID:28228535

  19. A comparative evaluation of models to predict human intestinal metabolism from nonclinical data

    PubMed Central

    Yau, Estelle; Petersson, Carl; Dolgos, Hugues

    2017-01-01

    Abstract Extensive gut metabolism is often associated with the risk of low and variable bioavailability. The prediction of the fraction of drug escaping gut wall metabolism as well as transporter‐mediated secretion (F g) has been challenged by the lack of appropriate preclinical models. The purpose of this study is to compare the performance of models that are widely employed in the pharmaceutical industry today to estimate F g and, based on the outcome, to provide recommendations for the prediction of human F g during drug discovery and early drug development. The use of in vitro intrinsic clearance from human liver microsomes (HLM) in three mechanistic models – the ADAM, Q gut and Competing Rates – was evaluated for drugs whose metabolism is dominated by CYP450s, assuming that the effect of transporters is negligible. The utility of rat as a model for human F g was also explored. The ADAM, Q gut and Competing Rates models had comparable prediction success (70%, 74%, 69%, respectively) and bias (AFE = 1.26, 0.74 and 0.81, respectively). However, the ADAM model showed better accuracy compared with the Q gut and Competing Rates models (RMSE =0.20 vs 0.30 and 0.25, respectively). Rat is not a good model (prediction success =32%, RMSE =0.48 and AFE = 0.44) as it seems systematically to under‐predict human F g. Hence, we would recommend the use of rat to identify the need for F g assessment, followed by the use of HLM in simple models to predict human F g. © 2017 Merck KGaA. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd. PMID:28152562

  20. Kinetic approach to degradation mechanisms in polymer solar cells and their accurate lifetime predictions

    NASA Astrophysics Data System (ADS)

    Arshad, Muhammad Azeem; Maaroufi, AbdelKrim

    2018-07-01

    A beginning has been made in the present study regarding the accurate lifetime predictions of polymer solar cells. Certain reservations about the conventionally employed temperature accelerated lifetime measurements test for its unworthiness of predicting reliable lifetimes of polymer solar cells are brought into light. Critical issues concerning the accelerated lifetime testing include, assuming reaction mechanism instead of determining it, and relying solely on the temperature acceleration of a single property of material. An advanced approach comprising a set of theoretical models to estimate the accurate lifetimes of polymer solar cells is therefore suggested in order to suitably alternate the accelerated lifetime testing. This approach takes into account systematic kinetic modeling of various possible polymer degradation mechanisms under natural weathering conditions. The proposed kinetic approach is substantiated by its applications on experimental aging data-sets of polymer solar materials/solar cells including, P3HT polymer film, bulk heterojunction (MDMO-PPV:PCBM) and dye-sensitized solar cells. Based on the suggested approach, an efficacious lifetime determination formula for polymer solar cells is derived and tested on dye-sensitized solar cells. Some important merits of the proposed method are also pointed out and its prospective applications are discussed.

  1. Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

    PubMed

    Pagán, Josué; Risco-Martín, José L; Moya, José M; Ayala, José L

    2016-08-01

    Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling.

    PubMed

    Bhaskara, Ramachandra M; Padhi, Amrita; Srinivasan, Narayanaswamy

    2014-07-01

    With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions. © 2013 Wiley Periodicals, Inc.

  3. Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants

    PubMed Central

    Hill, Camilla Beate; Czauderna, Tobias; Klapperstück, Matthias; Roessner, Ute; Schreiber, Falk

    2015-01-01

    Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions. PMID:26557642

  4. Do dual-route models accurately predict reading and spelling performance in individuals with acquired alexia and agraphia?

    PubMed

    Rapcsak, Steven Z; Henry, Maya L; Teague, Sommer L; Carnahan, Susan D; Beeson, Pélagie M

    2007-06-18

    Coltheart and co-workers [Castles, A., Bates, T. C., & Coltheart, M. (2006). John Marshall and the developmental dyslexias. Aphasiology, 20, 871-892; Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204-256] have demonstrated that an equation derived from dual-route theory accurately predicts reading performance in young normal readers and in children with reading impairment due to developmental dyslexia or stroke. In this paper, we present evidence that the dual-route equation and a related multiple regression model also accurately predict both reading and spelling performance in adult neurological patients with acquired alexia and agraphia. These findings provide empirical support for dual-route theories of written language processing.

  5. Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

    DOE PAGES

    Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.; ...

    2017-03-06

    Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

  6. Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.

    Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

  7. Reconciled Rat and Human Metabolic Networks for Comparative Toxicogenomics and Biomarker Predictions

    DTIC Science & Technology

    2017-02-08

    compared with the original human GPR rules (Supplementary Fig. 3). The consensus-based approach for filtering orthology annotations was designed to...ARTICLE Received 29 Jan 2016 | Accepted 13 Dec 2016 | Published 8 Feb 2017 Reconciled rat and human metabolic networks for comparative toxicogenomics...predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature- based evidence

  8. Metabolic syndrome and atypical antipsychotics: Possibility of prediction and control.

    PubMed

    Franch Pato, Clara M; Molina Rodríguez, Vicente; Franch Valverde, Juan I

    Schizophrenia and other psychotic disorders are associated with high morbidity and mortality, due to inherent health factors, genetic factors, and factors related to psychopharmacological treatment. Antipsychotics, like other drugs, have side-effects that can substantially affect the physical health of patients, with substantive differences in the side-effect profile and in the patients in which these side-effects occur. To understand and identify these risk groups could help to prevent the occurrence of the undesired effects. A prospective study, with 24 months follow-up, was conducted in order to analyse the physical health of severe mental patients under maintenance treatment with atypical antipsychotics, as well as to determine any predictive parameters at anthropometric and/or analytical level for good/bad outcome of metabolic syndrome in these patients. There were no significant changes in the physical and biochemical parameters individually analysed throughout the different visits. The baseline abdominal circumference (lambda Wilks P=.013) and baseline HDL-cholesterol levels (lambda Wilks P=.000) were the parameters that seem to be more relevant above the rest of the metabolic syndrome constituents diagnosis criteria as predictors in the long-term. In the search for predictive factors of metabolic syndrome, HDL-cholesterol and abdominal circumference at the time of inclusion were selected, as such that the worst the baseline results were, the higher probability of long-term improvement. Copyright © 2016 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.

  9. Intermolecular potentials and the accurate prediction of the thermodynamic properties of water

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Shvab, I.; Sadus, Richard J., E-mail: rsadus@swin.edu.au

    2013-11-21

    The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm{sup 3} for a wide range of temperatures (298–650 K) and pressures (0.1–700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC/E and TIP4P/2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys.more » 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC/E and TIP4P/2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K.« less

  10. ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer

    NASA Astrophysics Data System (ADS)

    Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

    2016-05-01

    At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts

  11. The Pandolf equation under-predicts the metabolic rate of contemporary military load carriage.

    PubMed

    Drain, Jace R; Aisbett, Brad; Lewis, Michael; Billing, Daniel C

    2017-11-01

    This investigation assessed the accuracy of error of the Pandolf load carriage energy expenditure equation when simulating contemporary military conditions (load distribution, external load and walking speed). Within-participant design. Sixteen male participants completed 10 trials comprised of five walking speeds (2.5, 3.5, 4.5, 5.5 and 6.5km·h -1 ) and two external loads (22.7 and 38.4kg). The Pandolf equation demonstrated poor predictive precision, with a mean bias of 124.9W and -48.7 to 298.5W 95% limits of agreement. Furthermore, the Pandolf equation systematically under-predicted metabolic rate (p<0.05) across the 10 speed-load combinations. Predicted metabolic rate error ranged from 12-33% across all conditions with the 'moderate' walking speeds (i.e. 4.5-5.5km·h -1 ) yielding less prediction error (12-17%) when compared to the slower and faster walking speeds (21-33%). Factors such as mechanical efficiency and load distribution contribute to the impaired predictive accuracy. The authors suggest the Pandolf equation should be applied to military load carriage with caution. Copyright © 2017 Sports Medicine Australia. All rights reserved.

  12. A comparative evaluation of models to predict human intestinal metabolism from nonclinical data.

    PubMed

    Yau, Estelle; Petersson, Carl; Dolgos, Hugues; Peters, Sheila Annie

    2017-04-01

    Extensive gut metabolism is often associated with the risk of low and variable bioavailability. The prediction of the fraction of drug escaping gut wall metabolism as well as transporter-mediated secretion (F g ) has been challenged by the lack of appropriate preclinical models. The purpose of this study is to compare the performance of models that are widely employed in the pharmaceutical industry today to estimate F g and, based on the outcome, to provide recommendations for the prediction of human F g during drug discovery and early drug development. The use of in vitro intrinsic clearance from human liver microsomes (HLM) in three mechanistic models - the ADAM, Q gut and Competing Rates - was evaluated for drugs whose metabolism is dominated by CYP450s, assuming that the effect of transporters is negligible. The utility of rat as a model for human F g was also explored. The ADAM, Q gut and Competing Rates models had comparable prediction success (70%, 74%, 69%, respectively) and bias (AFE = 1.26, 0.74 and 0.81, respectively). However, the ADAM model showed better accuracy compared with the Q gut and Competing Rates models (RMSE =0.20 vs 0.30 and 0.25, respectively). Rat is not a good model (prediction success =32%, RMSE =0.48 and AFE = 0.44) as it seems systematically to under-predict human F g . Hence, we would recommend the use of rat to identify the need for F g assessment, followed by the use of HLM in simple models to predict human F g . © 2017 Merck KGaA. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd. © 2017 Merck KGaA. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd.

  13. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

    PubMed

    Wang, Sheng; Sun, Siqi; Li, Zhen; Zhang, Renyu; Xu, Jinbo

    2017-01-01

    Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have

  14. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

    PubMed Central

    Li, Zhen; Zhang, Renyu

    2017-01-01

    Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact

  15. Obtaining Accurate Probabilities Using Classifier Calibration

    ERIC Educational Resources Information Center

    Pakdaman Naeini, Mahdi

    2016-01-01

    Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

  16. A specific prediction equation is necessary to estimate peak oxygen uptake in obese patients with metabolic syndrome.

    PubMed

    Debeaumont, D; Tardif, C; Folope, V; Castres, I; Lemaitre, F; Tourny, C; Dechelotte, P; Thill, C; Darmon, A; Coquart, J B

    2016-06-01

    The aims were to: (1) compare peak oxygen uptake ([Formula: see text]peak) predicted from four standard equations to actual [Formula: see text]peak measured from a cardiopulmonary exercise test (CPET) in obese patients with metabolic syndrome (MetS), and (2) develop a new equation to accurately estimate [Formula: see text]peak in obese women with MetS. Seventy-five obese patients with MetS performed a CPET. Anthropometric data were also collected for each participant. [Formula: see text]peak was predicted from four prediction equations (from Riddle et al., Hansen et al., Wasserman et al. or Gläser et al.) and then compared with the actual [Formula: see text]peak measured during the CPET. The accuracy of the predictions was determined with the Bland-Altman method. When accuracy was low, a new prediction equation including anthropometric variables was proposed. [Formula: see text]peak predicted from the equation of Wasserman et al. was not significantly different from actual [Formula: see text]peak in women. Moreover, a significant correlation was found between the predicted and actual values (p < 0.001, r = 0.69). In men, no significant difference was noted between actual [Formula: see text]peak and [Formula: see text]peak predicted from the prediction equation of Gläser et al., and these two values were also correlated (p = 0.03, r = 0.44). However, the LoA95% was wide, whatever the prediction equation or gender. Regression analysis suggested a new prediction equation derived from age and height for obese women with MetS. The methods of Wasserman et al. and Gläser et al. are valid to predict [Formula: see text]peak in obese women and men with MetS, respectively. However, the accuracy of the predictions was low for both methods. Consequently, a new prediction equation including age and height was developed for obese women with MetS. However, new prediction equation remains to develop in obese men with MetS.

  17. Predictive Genomic Analyses Inform the Basis for Vitamin Metabolism and Provisioning in Bacteria-Arthropod Endosymbioses

    PubMed Central

    Serbus, Laura R.; Rodriguez, Brian Garcia; Sharmin, Zinat; Momtaz, A. J. M. Zehadee; Christensen, Steen

    2017-01-01

    The requirement of vitamins for core metabolic processes creates a unique set of pressures for arthropods subsisting on nutrient-limited diets. While endosymbiotic bacteria carried by arthropods have been widely implicated in vitamin provisioning, the underlying molecular mechanisms are not well understood. To address this issue, standardized predictive assessment of vitamin metabolism was performed in 50 endosymbionts of insects and arachnids. The results predicted that arthropod endosymbionts overall have little capacity for complete de novo biosynthesis of conventional or active vitamin forms. Partial biosynthesis pathways were commonly predicted, suggesting a substantial role in vitamin provisioning. Neither taxonomic relationships between host and symbiont, nor the mode of host-symbiont interaction were clear predictors of endosymbiont vitamin pathway capacity. Endosymbiont genome size and the synthetic capacity of nonsymbiont taxonomic relatives were more reliable predictors. We developed a new software application that also predicted that last-step conversion of intermediates into active vitamin forms may contribute further to vitamin biosynthesis by endosymbionts. Most instances of predicted vitamin conversion were paralleled by predictions of vitamin use. This is consistent with achievement of provisioning in some cases through upregulation of pathways that were retained for endosymbiont benefit. The predicted absence of other enzyme classes further suggests a baseline of vitamin requirement by the majority of endosymbionts, as well as some instances of putative mutualism. Adaptation of this workflow to analysis of other organisms and metabolic pathways will provide new routes for considering the molecular basis for symbiosis on a comprehensive scale. PMID:28455417

  18. Predictive Genomic Analyses Inform the Basis for Vitamin Metabolism and Provisioning in Bacteria-Arthropod Endosymbioses.

    PubMed

    Serbus, Laura R; Rodriguez, Brian Garcia; Sharmin, Zinat; Momtaz, A J M Zehadee; Christensen, Steen

    2017-06-07

    The requirement of vitamins for core metabolic processes creates a unique set of pressures for arthropods subsisting on nutrient-limited diets. While endosymbiotic bacteria carried by arthropods have been widely implicated in vitamin provisioning, the underlying molecular mechanisms are not well understood. To address this issue, standardized predictive assessment of vitamin metabolism was performed in 50 endosymbionts of insects and arachnids. The results predicted that arthropod endosymbionts overall have little capacity for complete de novo biosynthesis of conventional or active vitamin forms. Partial biosynthesis pathways were commonly predicted, suggesting a substantial role in vitamin provisioning. Neither taxonomic relationships between host and symbiont, nor the mode of host-symbiont interaction were clear predictors of endosymbiont vitamin pathway capacity. Endosymbiont genome size and the synthetic capacity of nonsymbiont taxonomic relatives were more reliable predictors. We developed a new software application that also predicted that last-step conversion of intermediates into active vitamin forms may contribute further to vitamin biosynthesis by endosymbionts. Most instances of predicted vitamin conversion were paralleled by predictions of vitamin use. This is consistent with achievement of provisioning in some cases through upregulation of pathways that were retained for endosymbiont benefit. The predicted absence of other enzyme classes further suggests a baseline of vitamin requirement by the majority of endosymbionts, as well as some instances of putative mutualism. Adaptation of this workflow to analysis of other organisms and metabolic pathways will provide new routes for considering the molecular basis for symbiosis on a comprehensive scale. Copyright © 2017 Serbus et al.

  19. Sasang constitutional types for the risk prediction of metabolic syndrome: a 14-year longitudinal prospective cohort study.

    PubMed

    Lee, Sunghee; Lee, Seung Ku; Kim, Jong Yeol; Cho, Namhan; Shin, Chol

    2017-09-02

    To examine whether the use of Sasang constitutional (SC) types, such as Tae-yang (TY), Tae-eum (TE), So-yang (SY), and So-eum (SE) types, increases the accuracy of risk prediction for metabolic syndrome. From 2001 to 2014, 3529 individuals aged 40 to 69 years participated in a longitudinal prospective cohort. The Cox proportional hazard model was utilized to predict the risk of developing metabolic syndrome. During the 14 year follow-up, 1591 incident events of metabolic syndrome were observed. Individuals with TE type had higher body mass indexes and waist circumferences than individuals with SY and SE types. The risk of developing metabolic syndrome was the highest among individuals with the TE type, followed by the SY type and the SE type. When the prediction risk models for incident metabolic syndrome were compared, the area under the curve for the model using SC types was significantly increased to 0.8173. Significant predictors for incident metabolic syndrome were different according to the SC types. For individuals with the TE type, the significant predictors were age, sex, body mass index (BMI), education, smoking, drinking, fasting glucose level, high-density lipoprotein (HDL) cholesterol level, systolic and diastolic blood pressure, and triglyceride level. For Individuals with the SE type, the predictors were sex, smoking, fasting glucose, HDL cholesterol level, systolic and diastolic blood pressure, and triglyceride level, while the predictors in individuals with the SY type were age, sex, BMI, smoking, drinking, total cholesterol level, fasting glucose level, HDL cholesterol level, systolic and diastolic blood pressure, and triglyceride level. In this prospective cohort study among 3529 individuals, we observed that utilizing the SC types significantly increased the accuracy of the risk prediction for the development of metabolic syndrome.

  20. Limb-Enhancer Genie: An accessible resource of accurate enhancer predictions in the developing limb

    DOE PAGES

    Monti, Remo; Barozzi, Iros; Osterwalder, Marco; ...

    2017-08-21

    Epigenomic mapping of enhancer-associated chromatin modifications facilitates the genome-wide discovery of tissue-specific enhancers in vivo. However, reliance on single chromatin marks leads to high rates of false-positive predictions. More sophisticated, integrative methods have been described, but commonly suffer from limited accessibility to the resulting predictions and reduced biological interpretability. Here we present the Limb-Enhancer Genie (LEG), a collection of highly accurate, genome-wide predictions of enhancers in the developing limb, available through a user-friendly online interface. We predict limb enhancers using a combination of > 50 published limb-specific datasets and clusters of evolutionarily conserved transcription factor binding sites, taking advantage ofmore » the patterns observed at previously in vivo validated elements. By combining different statistical models, our approach outperforms current state-of-the-art methods and provides interpretable measures of feature importance. Our results indicate that including a previously unappreciated score that quantifies tissue-specific nuclease accessibility significantly improves prediction performance. We demonstrate the utility of our approach through in vivo validation of newly predicted elements. Moreover, we describe general features that can guide the type of datasets to include when predicting tissue-specific enhancers genome-wide, while providing an accessible resource to the general biological community and facilitating the functional interpretation of genetic studies of limb malformations.« less

  1. A global approach to analysis and interpretation of metabolic data for plant natural product discovery.

    PubMed

    Hur, Manhoi; Campbell, Alexis Ann; Almeida-de-Macedo, Marcia; Li, Ling; Ransom, Nick; Jose, Adarsh; Crispin, Matt; Nikolau, Basil J; Wurtele, Eve Syrkin

    2013-04-01

    Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publicly available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis

  2. A global approach to analysis and interpretation of metabolic data for plant natural product discovery†

    PubMed Central

    Hur, Manhoi; Campbell, Alexis Ann; Almeida-de-Macedo, Marcia; Li, Ling; Ransom, Nick; Jose, Adarsh; Crispin, Matt; Nikolau, Basil J.

    2013-01-01

    Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publically available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical

  3. The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization.

    PubMed

    Noor, Elad; Flamholz, Avi; Bar-Even, Arren; Davidi, Dan; Milo, Ron; Liebermeister, Wolfram

    2016-11-01

    Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell's capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified.

  4. The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization

    PubMed Central

    Noor, Elad; Flamholz, Avi; Bar-Even, Arren; Davidi, Dan; Milo, Ron; Liebermeister, Wolfram

    2016-01-01

    Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified

  5. DRUM: A New Framework for Metabolic Modeling under Non-Balanced Growth. Application to the Carbon Metabolism of Unicellular Microalgae

    PubMed Central

    Baroukh, Caroline; Muñoz-Tamayo, Rafael; Steyer, Jean-Philippe; Bernard, Olivier

    2014-01-01

    Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates. PMID:25105494

  6. Ensemble-based docking: From hit discovery to metabolism and toxicity predictions

    DOE PAGES

    Evangelista, Wilfredo; Weir, Rebecca; Ellingson, Sally; ...

    2016-07-29

    The use of ensemble-based docking for the exploration of biochemical pathways and toxicity prediction of drug candidates is described. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials.

  7. Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

    PubMed Central

    Garcia Lopez, Sebastian; Kim, Philip M.

    2014-01-01

    Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

  8. Sensitivity, specificity, and predictive values of pediatric metabolic syndrome components in relation to adult metabolic syndrome: the Princeton LRC follow-up study.

    PubMed

    Huang, Terry T-K; Nansel, Tonja R; Belsheim, Allen R; Morrison, John A

    2008-02-01

    To estimate the sensitivity, specificity, and predictive values of pediatric metabolic syndrome (MetS) components (obesity, fasting glucose, triglycerides, high-density lipoprotein, and blood pressure) at various cutoff points in relation to adult MetS. Data from the National Heart, Lung, and Blood Institute Lipid Research Clinics Princeton Prevalence Study (1973-1976) and the Princeton Follow-up Study (2000-2004) were used to calculate sensitivity, specificity, and positive and negative predictive values for each component at a given cutoff point and for aggregates of components. Individual pediatric components alone showed low to moderate sensitivity, high specificity, and moderate predictive values in relation to adult MetS. When all 5 pediatric MetS components were considered, the presence of at least 1 abnormality had higher sensitivity for adult MetS than individual components alone. When multiple abnormalities were mandatory for MetS, positive predictive value was high and sensitivity was low. Childhood body mass alone showed neither high sensitivity nor high positive predictive value for adult MetS. Considering multiple metabolic variables in childhood can improve the predictive usefulness for adult MetS, compared with each component or body mass alone. MetS variables may be useful for identifying some children who are at risk for prevention interventions.

  9. Sensitivity, Specificity, and Predictive Values of Pediatric Metabolic Syndrome Components in Relation to Adult Metabolic Syndrome: The Princeton LRC Follow-up Study

    PubMed Central

    Huang, Terry T-K; Nansel, Tonja R.; Belsheim, Allen R.; Morrison, John A.

    2008-01-01

    Objective To estimate the sensitivity, specificity, and predictive values of pediatric metabolic syndrome (MetS) components (obesity, fasting glucose, triglycerides, high-density lipoprotein, and blood pressure) at various cutoffs in relation to adult MetS. Study design Data from the NHLBI Lipid Research Clinics (LRC) Princeton Prevalence Study (1973–76) and the Princeton Follow-up Study (PFS, 2000-4) were used to calculate sensitivity, specificity, and positive and negative predictive values for each component at a given cutoff, as well as for aggregates of components. Results Individual pediatric components alone showed low to moderate sensitivity, high specificity, and moderate predictive values in relation to adult MetS. When all five pediatric MetS components were considered, the presence of at least one abnormality had higher sensitivity for adult MetS than individual components alone. When multiple abnormalities were mandatory for MetS, positive predictive value was high and sensitivity was low. Childhood body mass alone showed neither high sensitivity nor high positive predictive value for adult MetS. Conclusions Considering multiple metabolic variables in childhood can improve the predictive utility for adult MetS, compared to each component or body mass alone. MetS variables may be useful for identifying some at risk children for prevention interventions. PMID:18206687

  10. Toward accurate prediction of pKa values for internal protein residues: the importance of conformational relaxation and desolvation energy.

    PubMed

    Wallace, Jason A; Wang, Yuhang; Shi, Chuanyin; Pastoor, Kevin J; Nguyen, Bao-Linh; Xia, Kai; Shen, Jana K

    2011-12-01

    Proton uptake or release controls many important biological processes, such as energy transduction, virus replication, and catalysis. Accurate pK(a) prediction informs about proton pathways, thereby revealing detailed acid-base mechanisms. Physics-based methods in the framework of molecular dynamics simulations not only offer pK(a) predictions but also inform about the physical origins of pK(a) shifts and provide details of ionization-induced conformational relaxation and large-scale transitions. One such method is the recently developed continuous constant pH molecular dynamics (CPHMD) method, which has been shown to be an accurate and robust pK(a) prediction tool for naturally occurring titratable residues. To further examine the accuracy and limitations of CPHMD, we blindly predicted the pK(a) values for 87 titratable residues introduced in various hydrophobic regions of staphylococcal nuclease and variants. The predictions gave a root-mean-square deviation of 1.69 pK units from experiment, and there were only two pK(a)'s with errors greater than 3.5 pK units. Analysis of the conformational fluctuation of titrating side-chains in the context of the errors of calculated pK(a) values indicate that explicit treatment of conformational flexibility and the associated dielectric relaxation gives CPHMD a distinct advantage. Analysis of the sources of errors suggests that more accurate pK(a) predictions can be obtained for the most deeply buried residues by improving the accuracy in calculating desolvation energies. Furthermore, it is found that the generalized Born implicit-solvent model underlying the current CPHMD implementation slightly distorts the local conformational environment such that the inclusion of an explicit-solvent representation may offer improvement of accuracy. Copyright © 2011 Wiley-Liss, Inc.

  11. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasetsmore » having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds

  12. Predicting basal metabolic rates in Malaysian adult elite athletes.

    PubMed

    Wong, Jyh Eiin; Poh, Bee Koon; Nik Shanita, Safii; Izham, Mohd Mohamad; Chan, Kai Quin; Tai, Meng De; Ng, Wei Wei; Ismail, Mohd Noor

    2012-11-01

    This study aimed to measure the basal metabolic rate (BMR) of elite athletes and develop a gender specific predictive equation to estimate their energy requirements. 92 men and 33 women (aged 18-31 years) from 15 sports, who had been training six hours daily for at least one year, were included in the study. Body composition was measured using the bioimpedance technique, and BMR by indirect calorimetry. The differences between measured and estimated BMR using various predictive equations were calculated. The novel equation derived from stepwise multiple regression was evaluated using Bland and Altman analysis. The predictive equations of Cunningham and the Food and Agriculture Organization/World Health Organization/United Nations University either over- or underestimated the measured BMR by up to ± 6%, while the equations of Ismail et al, developed from the local non-athletic population, underestimated the measured BMR by 14%. The novel predictive equation for the BMR of athletes was BMR (kcal/day) = 669 + 13 (weight in kg) + 192 (gender: 1 for men and 0 for women) (R2 0.548; standard error of estimates 163 kcal). Predicted BMRs of elite athletes by this equation were within 1.2% ± 9.5% of the measured BMR values. The novel predictive equation presented in this study can be used to calculate BMR for adult Malaysian elite athletes. Further studies may be required to validate its predictive capabilities for other sports, nationalities and age groups.

  13. RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.

    PubMed

    Zaretzki, Jed; Bergeron, Charles; Rydberg, Patrik; Huang, Tao-wei; Bennett, Kristin P; Breneman, Curt M

    2011-07-25

    This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.

  14. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins.

    PubMed

    Jones, David T; Singh, Tanya; Kosciolek, Tomasz; Tetchner, Stuart

    2015-04-01

    Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts-around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

  15. Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation.

    PubMed

    Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

    2017-09-01

    Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.

  16. WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.

    PubMed

    Chi, Sang-Mun; Nam, Dougu

    2012-04-01

    We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method. WegoLoc and User's guide are freely available at the website http://www.btool.org/WegoLoc smchiks@ks.ac.kr; dougnam@unist.ac.kr Supplementary data is available at http://www.btool.org/WegoLoc.

  17. Sex-specific lean body mass predictive equations are accurate in the obese paediatric population

    PubMed Central

    Jackson, Lanier B.; Henshaw, Melissa H.; Carter, Janet; Chowdhury, Shahryar M.

    2015-01-01

    Background The clinical assessment of lean body mass (LBM) is challenging in obese children. A sex-specific predictive equation for LBM derived from anthropometric data was recently validated in children. Aim The purpose of this study was to independently validate these predictive equations in the obese paediatric population. Subjects and methods Obese subjects aged 4–21 were analysed retrospectively. Predicted LBM (LBMp) was calculated using equations previously developed in children. Measured LBM (LBMm) was derived from dual-energy x-ray absorptiometry. Agreement was expressed as [(LBMm-LBMp)/LBMm] with 95% limits of agreement. Results Of 310 enrolled patients, 195 (63%) were females. The mean age was 11.8 ± 3.4 years and mean BMI Z-score was 2.3 ± 0.4. The average difference between LBMm and LBMp was −0.6% (−17.0%, 15.8%). Pearson’s correlation revealed a strong linear relationship between LBMm and LBMp (r=0.97, p<0.01). Conclusion This study validates the use of these clinically-derived sex-specific LBM predictive equations in the obese paediatric population. Future studies should use these equations to improve the ability to accurately classify LBM in obese children. PMID:26287383

  18. Accurate prediction of cation-π interaction energy using substituent effects.

    PubMed

    Sayyed, Fareed Bhasha; Suresh, Cherumuttathu H

    2012-06-14

    (M(+))' and ΔV(min). All the Φ-X···M(+) systems showed good agreement between the calculated and predicted E(M(+))() values, suggesting that the ΔV(min) approach to substituent effect is accurate and useful for predicting the interactive behavior of substituted π-systems with cations.

  19. Uric Acid Levels Can Predict Metabolic Syndrome and Hypertension in Adolescents: A 10-Year Longitudinal Study.

    PubMed

    Sun, Hai-Lun; Pei, Dee; Lue, Ko-Huang; Chen, Yen-Lin

    2015-01-01

    The relationships between uric acid and chronic disease risk factors such as metabolic syndrome, type 2 diabetes mellitus, and hypertension have been studied in adults. However, whether these relationships exist in adolescents is unknown. We randomly selected 8,005 subjects who were between 10 to 15 years old at baseline. Measurements of uric acid were used to predict the future occurrence of metabolic syndrome, hypertension, and type 2 diabetes. In total, 5,748 adolescents were enrolled and followed for a median of 7.2 years. Using cutoff points of uric acid for males and females (7.3 and 6.2 mg/dl, respectively), a high level of uric acid was either the second or third best predictor for hypertension in both genders (hazard ratio: 2.920 for males, 5.222 for females; p<0.05). However, uric acid levels failed to predict type 2 diabetes mellitus, and only predicted metabolic syndrome in males (hazard ratio: 1.658; p<0.05). The same results were found in multivariate adjusted analysis. In conclusion, a high level of uric acid indicated a higher likelihood of developing hypertension in both genders and metabolic syndrome in males after 10 years of follow-up. However, uric acid levels did not affect the occurrence of type 2 diabetes in both genders.

  20. Regional variations in the diversity and predicted metabolic potential of benthic prokaryotes in coastal northern Zhejiang, East China Sea

    PubMed Central

    Wang, Kai; Ye, Xiansen; Zhang, Huajun; Chen, Heping; Zhang, Demin; Liu, Lian

    2016-01-01

    Knowledge about the drivers of benthic prokaryotic diversity and metabolic potential in interconnected coastal sediments at regional scales is limited. We collected surface sediments across six zones covering ~200 km in coastal northern Zhejiang, East China Sea and combined 16 S rRNA gene sequencing, community-level metabolic prediction, and sediment physicochemical measurements to investigate variations in prokaryotic diversity and metabolic gene composition with geographic distance and under local environmental conditions. Geographic distance was the most influential factor in prokaryotic β-diversity compared with major environmental drivers, including temperature, sediment texture, acid-volatile sulfide, and water depth, but a large unexplained variation in community composition suggested the potential effects of unmeasured abiotic/biotic factors and stochastic processes. Moreover, prokaryotic assemblages showed a biogeographic provincialism across the zones. The predicted metabolic gene composition similarly shifted as taxonomic composition did. Acid-volatile sulfide was strongly correlated with variation in metabolic gene composition. The enrichments in the relative abundance of sulfate-reducing bacteria and genes relevant with dissimilatory sulfate reduction were observed and predicted, respectively, in the Yushan area. These results provide insights into the relative importance of geographic distance and environmental condition in driving benthic prokaryotic diversity in coastal areas and predict specific biogeochemically-relevant genes for future studies. PMID:27917954

  1. Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dyar, M. Darby; McCanta, Molly; Breves, Elly

    2016-03-01

    Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yield accurate resultsmore » from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

  2. Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dyar, M. Darby; McCanta, Molly; Breves, Elly

    2016-03-01

    Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe 3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe 3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yieldmore » accurate results from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

  3. Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods

    PubMed Central

    2014-01-01

    Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods. PMID:25374614

  4. Computational modeling to predict nitrogen balance during acute metabolic decompensation in patients with urea cycle disorders

    PubMed Central

    MacLeod, Erin L.; Hall, Kevin D.; McGuire, Peter J.

    2015-01-01

    SUMMARY Nutritional management of acute metabolic decompensation in amino acid inborn errors of metabolism (AA IEM) aims to restore nitrogen balance. While nutritional recommendations have been published, they have never been rigorously evaluated. Furthermore, despite these recommendations, there is a wide variation in the nutritional strategies employed amongst providers, particularly regarding the inclusion of parenteral lipids for protein-free caloric support. Since randomized clinical trials during acute metabolic decompensation are difficult and potentially dangerous, mathematical modeling of metabolism can serve as a surrogate for the preclinical evaluation of nutritional interventions aimed at restoring nitrogen balance during acute decompensation in AA IEM. A validated computational model of human macronutrient metabolism was adapted to predict nitrogen balance in response to various nutritional interventions in a simulated patient with a urea cycle disorder (UCD) during acute metabolic decompensation due to dietary non-adherence or infection. The nutritional interventions were constructed from published recommendations as well as clinical anecdotes. Overall, dextrose alone (DEX) was predicted to be better at restoring nitrogen balance and limiting nitrogen excretion during dietary non-adherence and infection scenarios, suggesting that the published recommended nutritional strategy involving dextrose and parenteral lipids (ISO) may be suboptimal. The implications for patients with AA IEM are that the medical course during acute metabolic decompensation may be influenced by the choice of protein-free caloric support. These results are also applicable to intensive care patients undergoing catabolism (postoperative phase or sepsis), where parenteral nutritional support aimed at restoring nitrogen balance may be more tailored regarding metabolic fuel selection. PMID:26260782

  5. Computational modeling to predict nitrogen balance during acute metabolic decompensation in patients with urea cycle disorders.

    PubMed

    MacLeod, Erin L; Hall, Kevin D; McGuire, Peter J

    2016-01-01

    Nutritional management of acute metabolic decompensation in amino acid inborn errors of metabolism (AA IEM) aims to restore nitrogen balance. While nutritional recommendations have been published, they have never been rigorously evaluated. Furthermore, despite these recommendations, there is a wide variation in the nutritional strategies employed amongst providers, particularly regarding the inclusion of parenteral lipids for protein-free caloric support. Since randomized clinical trials during acute metabolic decompensation are difficult and potentially dangerous, mathematical modeling of metabolism can serve as a surrogate for the preclinical evaluation of nutritional interventions aimed at restoring nitrogen balance during acute decompensation in AA IEM. A validated computational model of human macronutrient metabolism was adapted to predict nitrogen balance in response to various nutritional interventions in a simulated patient with a urea cycle disorder (UCD) during acute metabolic decompensation due to dietary non-adherence or infection. The nutritional interventions were constructed from published recommendations as well as clinical anecdotes. Overall, dextrose alone (DEX) was predicted to be better at restoring nitrogen balance and limiting nitrogen excretion during dietary non-adherence and infection scenarios, suggesting that the published recommended nutritional strategy involving dextrose and parenteral lipids (ISO) may be suboptimal. The implications for patients with AA IEM are that the medical course during acute metabolic decompensation may be influenced by the choice of protein-free caloric support. These results are also applicable to intensive care patients undergoing catabolism (postoperative phase or sepsis), where parenteral nutritional support aimed at restoring nitrogen balance may be more tailored regarding metabolic fuel selection.

  6. Accurate indel prediction using paired-end short reads

    PubMed Central

    2013-01-01

    Background One of the major open challenges in next generation sequencing (NGS) is the accurate identification of structural variants such as insertions and deletions (indels). Current methods for indel calling assign scores to different types of evidence or counter-evidence for the presence of an indel, such as the number of split read alignments spanning the boundaries of a deletion candidate or reads that map within a putative deletion. Candidates with a score above a manually defined threshold are then predicted to be true indels. As a consequence, structural variants detected in this manner contain many false positives. Results Here, we present a machine learning based method which is able to discover and distinguish true from false indel candidates in order to reduce the false positive rate. Our method identifies indel candidates using a discriminative classifier based on features of split read alignment profiles and trained on true and false indel candidates that were validated by Sanger sequencing. We demonstrate the usefulness of our method with paired-end Illumina reads from 80 genomes of the first phase of the 1001 Genomes Project ( http://www.1001genomes.org) in Arabidopsis thaliana. Conclusion In this work we show that indel classification is a necessary step to reduce the number of false positive candidates. We demonstrate that missing classification may lead to spurious biological interpretations. The software is available at: http://agkb.is.tuebingen.mpg.de/Forschung/SV-M/. PMID:23442375

  7. The use of in vitro technologies coupled with high resolution accurate mass LC-MS for studying drug metabolism in equine drug surveillance.

    PubMed

    Scarth, James P; Spencer, Holly A; Timbers, Sarah E; Hudson, Simon C; Hillyer, Lynn L

    2010-01-01

    The detection of drug abuse in horseracing often requires knowledge of drug metabolism, especially if urine is the matrix of choice. In this study, equine liver/lung microsomes/S9 tissue fractions were used to study the phase I metabolism of eight drugs of relevance to equine drug surveillance (acepromazine, azaperone, celecoxib, fentanyl, fluphenazine, mepivacaine, methylphenidate and tripelennamine). In vitro samples were analyzed qualitatively alongside samples originating from in vivo administrations using LC-MS on a high resolution accurate mass Thermo Orbitrap Discovery instrument and by LC-MS/MS on an Applied Biosystems Sciex 5500 Q Trap.Using high resolution accurate mass full-scan analysis on the Orbitrap, the in vitro systems were found to generate at least the two most abundant phase I metabolites observed in vitro for all eight drugs studied. In the majority of cases, in vitro experiments were also able to generate the minor in vivo metabolites and sometimes metabolites that were only observed in vitro. More detailed analyses of fentanyl incubates using LC-MS/MS showed that it was possible to generate good quality spectra from the metabolites generated in vitro. These data support the suggestion of using in vitro incubates as metabolite reference material in place of in vivo post-administration samples in accordance with new qualitative identification guidelines in the 2009 International Laboratory Accreditation Cooperation-G7 (ILAC-G7) document.In summary, the in vitro and in vivo phase I metabolism results reported herein compare well and demonstrate the potential of in vitro studies to compliment, refine and reduce the existing equine in vivo paradigm. © 2010 John Wiley & Sons, Ltd.

  8. pKa prediction of monoprotic small molecules the SMARTS way.

    PubMed

    Lee, Adam C; Yu, Jing-Yu; Crippen, Gordon M

    2008-10-01

    Realizing favorable absorption, distribution, metabolism, elimination, and toxicity profiles is a necessity due to the high attrition rate of lead compounds in drug development today. The ability to accurately predict bioavailability can help save time and money during the screening and optimization processes. As several robust programs already exist for predicting logP, we have turned our attention to the fast and robust prediction of pK(a) for small molecules. Using curated data from the Beilstein Database and Lange's Handbook of Chemistry, we have created a decision tree based on a novel set of SMARTS strings that can accurately predict the pK(a) for monoprotic compounds with R(2) of 0.94 and root mean squared error of 0.68. Leave-some-out (10%) cross-validation achieved Q(2) of 0.91 and root mean squared error of 0.80.

  9. Reconstruction of 24 Penicillium genome-scale metabolic models shows diversity based on their secondary metabolism.

    PubMed

    Prigent, Sylvain; Nielsen, Jens Christian; Frisvad, Jens Christian; Nielsen, Jens

    2018-06-05

    Modelling of metabolism at the genome-scale have proved to be an efficient method for explaining observed phenotypic traits in living organisms. Further, it can be used as a means of predicting the effect of genetic modifications e.g. for development of microbial cell factories. With the increasing amount of genome sequencing data available, a need exists to accurately and efficiently generate such genome-scale metabolic models (GEMs) of non-model organisms, for which data is sparse. In this study, we present an automatic reconstruction approach applied to 24 Penicillium species, which have potential for production of pharmaceutical secondary metabolites or used in the manufacturing of food products such as cheeses. The models were based on the MetaCyc database and a previously published Penicillium GEM, and gave rise to comprehensive genome-scale metabolic descriptions. The models proved that while central carbon metabolism is highly conserved, secondary metabolic pathways represent the main diversity among the species. The automatic reconstruction approach presented in this study can be applied to generate GEMs of other understudied organisms, and the developed GEMs are a useful resource for the study of Penicillium metabolism, for example with the scope of developing novel cell factories. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  10. Anthropometric Indicators Predict Metabolic Syndrome Diagnosis in Maintenance Hemodialysis Patients.

    PubMed

    Vogt, Barbara Perez; Ponce, Daniela; Caramori, Jacqueline Costa Teixeira

    2016-06-01

    Obesity has been considered the key in metabolic syndrome (MetS) development, and fat accumulation may be responsible for the occurrence of metabolic abnormalities in hemodialysis patients. The use of gold-standard methods to evaluate obesity is limited, and anthropometric measures may be the simplest methods. However, no study has investigated the association between anthropometric indexes and MetS in these patients. Therefore, the aim was to determine which anthropometric indexes had the best association and prediction for MetS in patients undergoing hemodialysis. Cross-sectional study that included patients older than 18 years, undergoing hemodialysis for at least 3 months. Patients with liver disease and cancer or those receiving corticosteroids or antiretroviral therapy were excluded. Diagnostic criteria from Harmonizing Metabolic Syndrome were used for the diagnosis of MetS. Anthropometric indexes evaluated were body mass index (BMI); percent standard of triceps skinfold thickness and of middle arm muscle circumference; waist circumference (WC); sagittal abdominal diameter; neck circumference; waist-to-hip, waist-to-thigh, and waist-to-height ratios; sagittal index; conicity index; and body fat percentage. Ninety-eight patients were included, 54.1% male, and mean age was 57.8 ± 12.9 years. The prevalence of MetS was 74.5%. Individuals with MetS had increased accumulation of abdominal fat and general obesity. Waist-to-height ratio was the variable independently associated with MetS diagnosis (odds ratio, 1.21; 95% confidence interval, 1.09-1.34; P < .01) and that better predicts MetS, followed by WC and BMI (area under the curve of 0.840, 0.836, and 0.798, respectively, P < .01). Waist-to-height ratio was the best anthropometric predictor of MetS in maintenance hemodialysis patients. © 2015 American Society for Parenteral and Enteral Nutrition.

  11. An update on the potential role of intestinal first-pass metabolism for the prediction of drug-drug interactions: the role of PBPK modeling.

    PubMed

    Alqahtani, Saeed; Bukhari, Ishfaq; Albassam, Ahmed; Alenazi, Maha

    2018-05-28

    The intestinal absorption process is a combination of several events that are governed by various factors. Several transport mechanisms are involved in drug absorption through enterocytes via active and/or passive processes. The transported molecules then undergo intestinal metabolism, which together with intestinal transport may affect the systemic availability of drugs. Many studies have provided clear evidence on the significant role of intestinal first-pass metabolism on drug bioavailability and degree of drug-drug interactions (DDIs). Areas covered: This review provides an update on the role of intestinal first-pass metabolism in the oral bioavailability of drugs and prediction of drug-drug interactions. It also provides a comprehensive overview and summary of the latest update in the role of PBPK modeling in prediction of intestinal metabolism and DDIs in humans. Expert opinion: The contribution of intestinal first-pass metabolism in the oral bioavailability of drugs and prediction of DDIs has become more evident over the last few years. Several in vitro, in situ, and in vivo models have been developed to evaluate the role of first-pass metabolism and to predict DDIs. Currently, physiologically based pharmacokinetic modeling is considered the most valuable tool for the prediction of intestinal first-pass metabolism and DDIs.

  12. Accurate and robust genomic prediction of celiac disease using statistical learning.

    PubMed

    Abraham, Gad; Tye-Din, Jason A; Bhalala, Oneil G; Kowalczyk, Adam; Zobel, Justin; Inouye, Michael

    2014-02-01

    Practical application of genomic-based risk stratification to clinical diagnosis is appealing yet performance varies widely depending on the disease and genomic risk score (GRS) method. Celiac disease (CD), a common immune-mediated illness, is strongly genetically determined and requires specific HLA haplotypes. HLA testing can exclude diagnosis but has low specificity, providing little information suitable for clinical risk stratification. Using six European cohorts, we provide a proof-of-concept that statistical learning approaches which simultaneously model all SNPs can generate robust and highly accurate predictive models of CD based on genome-wide SNP profiles. The high predictive capacity replicated both in cross-validation within each cohort (AUC of 0.87-0.89) and in independent replication across cohorts (AUC of 0.86-0.9), despite differences in ethnicity. The models explained 30-35% of disease variance and up to ∼43% of heritability. The GRS's utility was assessed in different clinically relevant settings. Comparable to HLA typing, the GRS can be used to identify individuals without CD with ≥99.6% negative predictive value however, unlike HLA typing, fine-scale stratification of individuals into categories of higher-risk for CD can identify those that would benefit from more invasive and costly definitive testing. The GRS is flexible and its performance can be adapted to the clinical situation by adjusting the threshold cut-off. Despite explaining a minority of disease heritability, our findings indicate a genomic risk score provides clinically relevant information to improve upon current diagnostic pathways for CD and support further studies evaluating the clinical utility of this approach in CD and other complex diseases.

  13. Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism

    PubMed Central

    2015-01-01

    Background Enzymes are known as the molecular machines that drive the metabolism of an organism; hence identification of the full enzyme complement of an organism is essential to build the metabolic blueprint of that species as well as to understand the interplay of multiple species in an ecosystem. Experimental characterization of the enzymatic reactions of all enzymes in a genome is a tedious and expensive task. The problem is more pronounced in the metagenomic samples where even the species are not adequately cultured or characterized. Enzymes encoded by the gut microbiota play an essential role in the host metabolism; thus, warranting the need to accurately identify and annotate the full enzyme complements of species in the genomic and metagenomic projects. To fulfill this need, we develop and apply a method called ECemble, an ensemble approach to identify enzymes and enzyme classes and study the human gut metabolic pathways. Results ECemble method uses an ensemble of machine-learning methods to accurately model and predict enzymes from protein sequences and also identifies the enzyme classes and subclasses at the finest resolution. A tenfold cross-validation result shows accuracy between 97 and 99% at different levels in the hierarchy of enzyme classification, which is superior to comparable methods. We applied ECemble to predict the entire complements of enzymes from ten sequenced proteomes including the human proteome. We also applied this method to predict enzymes encoded by the human gut microbiome from gut metagenomic samples, and to study the role played by the microbe-derived enzymes in the human metabolism. After mapping the known and predicted enzymes to canonical human pathways, we identified 48 pathways that have at least one bacteria-encoded enzyme, which demonstrates the complementary role of gut microbiome in human gut metabolism. These pathways are primarily involved in metabolizing dietary nutrients such as carbohydrates, amino acids, lipids

  14. Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism.

    PubMed

    Mohammed, Akram; Guda, Chittibabu

    2015-01-01

    Enzymes are known as the molecular machines that drive the metabolism of an organism; hence identification of the full enzyme complement of an organism is essential to build the metabolic blueprint of that species as well as to understand the interplay of multiple species in an ecosystem. Experimental characterization of the enzymatic reactions of all enzymes in a genome is a tedious and expensive task. The problem is more pronounced in the metagenomic samples where even the species are not adequately cultured or characterized. Enzymes encoded by the gut microbiota play an essential role in the host metabolism; thus, warranting the need to accurately identify and annotate the full enzyme complements of species in the genomic and metagenomic projects. To fulfill this need, we develop and apply a method called ECemble, an ensemble approach to identify enzymes and enzyme classes and study the human gut metabolic pathways. ECemble method uses an ensemble of machine-learning methods to accurately model and predict enzymes from protein sequences and also identifies the enzyme classes and subclasses at the finest resolution. A tenfold cross-validation result shows accuracy between 97 and 99% at different levels in the hierarchy of enzyme classification, which is superior to comparable methods. We applied ECemble to predict the entire complements of enzymes from ten sequenced proteomes including the human proteome. We also applied this method to predict enzymes encoded by the human gut microbiome from gut metagenomic samples, and to study the role played by the microbe-derived enzymes in the human metabolism. After mapping the known and predicted enzymes to canonical human pathways, we identified 48 pathways that have at least one bacteria-encoded enzyme, which demonstrates the complementary role of gut microbiome in human gut metabolism. These pathways are primarily involved in metabolizing dietary nutrients such as carbohydrates, amino acids, lipids, cofactors and

  15. Chimeric mice transplanted with human hepatocytes as a model for prediction of human drug metabolism and pharmacokinetics.

    PubMed

    Sanoh, Seigo; Ohta, Shigeru

    2014-03-01

    Preclinical studies in animal models are used routinely during drug development, but species differences of pharmacokinetics (PK) between animals and humans have to be taken into account in interpreting the results. Human hepatocytes are also widely used to examine metabolic activities mediated by cytochrome P450 (P450) and other enzymes, but such in vitro metabolic studies also have limitations. Recently, chimeric mice with humanized liver (h-chimeric mice), generated by transplantation of human donor hepatocytes, have been developed as a model for the prediction of metabolism and PK in humans, using both in vitro and in vivo approaches. The expression of human-specific metabolic enzymes and metabolic activities was confirmed in humanized liver of h-chimeric mice with high replacement ratios, and several reports indicate that the profiles of P450 and non-P450 metabolism in these mice adequately reflect those in humans. Further, the combined use of h-chimeric mice and r-chimeric mice, in which endogenous hepatocytes are replaced with rat hepatocytes, is a promising approach for evaluation of species differences in drug metabolism. Recent work has shown that data obtained in h-chimeric mice enable the semi-quantitative prediction of not only metabolites, but also PK parameters, such as hepatic clearance, of drug candidates in humans, although some limitations remain because of differences in the metabolic activities, hepatic blood flow and liver structure between humans and mice. In addition, fresh h-hepatocytes can be isolated reproducibly from h-chimeric mice for metabolic studies. Copyright © 2013 John Wiley & Sons, Ltd.

  16. Warming alters the metabolic balance of ecosystems

    PubMed Central

    Yvon-Durocher, Gabriel; Jones, J. Iwan; Trimmer, Mark; Woodward, Guy; Montoya, Jose M.

    2010-01-01

    The carbon cycle modulates climate change, via the regulation of atmospheric CO2, and it represents one of the most important services provided by ecosystems. However, considerable uncertainties remain concerning potential feedback between the biota and the climate. In particular, it is unclear how global warming will affect the metabolic balance between the photosynthetic fixation and respiratory release of CO2 at the ecosystem scale. Here, we present a combination of experimental field data from freshwater mesocosms, and theoretical predictions derived from the metabolic theory of ecology to investigate whether warming will alter the capacity of ecosystems to absorb CO2. Our manipulative experiment simulated the temperature increases predicted for the end of the century and revealed that ecosystem respiration increased at a faster rate than primary production, reducing carbon sequestration by 13 per cent. These results confirmed our theoretical predictions based on the differential activation energies of these two processes. Using only the activation energies for whole ecosystem photosynthesis and respiration we provide a theoretical prediction that accurately quantified the precise magnitude of the reduction in carbon sequestration observed experimentally. We suggest the combination of whole-ecosystem manipulative experiments and ecological theory is one of the most promising and fruitful research areas to predict the impacts of climate change on key ecosystem services. PMID:20513719

  17. Feedback about More Accurate versus Less Accurate Trials: Differential Effects on Self-Confidence and Activation

    ERIC Educational Resources Information Center

    Badami, Rokhsareh; VaezMousavi, Mohammad; Wulf, Gabriele; Namazizadeh, Mahdi

    2012-01-01

    One purpose of the present study was to examine whether self-confidence or anxiety would be differentially affected by feedback from more accurate rather than less accurate trials. The second purpose was to determine whether arousal variations (activation) would predict performance. On Day 1, participants performed a golf putting task under one of…

  18. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

    PubMed

    Nemati, Shamim; Holder, Andre; Razmi, Fereshteh; Stanley, Matthew D; Clifford, Gari D; Buchman, Timothy G

    2018-04-01

    Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. Observational cohort study. Academic medical center from January 2013 to December 2015. Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. None. High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the

  19. Knowledge representation in metabolic pathway databases.

    PubMed

    Stobbe, Miranda D; Jansen, Gerbert A; Moerland, Perry D; van Kampen, Antoine H C

    2014-05-01

    The accurate representation of all aspects of a metabolic network in a structured format, such that it can be used for a wide variety of computational analyses, is a challenge faced by a growing number of researchers. Analysis of five major metabolic pathway databases reveals that each database has made widely different choices to address this challenge, including how to deal with knowledge that is uncertain or missing. In concise overviews, we show how concepts such as compartments, enzymatic complexes and the direction of reactions are represented in each database. Importantly, also concepts which a database does not represent are described. Which aspects of the metabolic network need to be available in a structured format and to what detail differs per application. For example, for in silico phenotype prediction, a detailed representation of gene-protein-reaction relations and the compartmentalization of the network is essential. Our analysis also shows that current databases are still limited in capturing all details of the biology of the metabolic network, further illustrated with a detailed analysis of three metabolic processes. Finally, we conclude that the conceptual differences between the databases, which make knowledge exchange and integration a challenge, have not been resolved, so far, by the exchange formats in which knowledge representation is standardized.

  20. Metabolic flexibility of mitochondrial respiratory chain disorders predicted by computer modelling.

    PubMed

    Zieliński, Łukasz P; Smith, Anthony C; Smith, Alexander G; Robinson, Alan J

    2016-11-01

    Mitochondrial respiratory chain dysfunction causes a variety of life-threatening diseases affecting about 1 in 4300 adults. These diseases are genetically heterogeneous, but have the same outcome; reduced activity of mitochondrial respiratory chain complexes causing decreased ATP production and potentially toxic accumulation of metabolites. Severity and tissue specificity of these effects varies between patients by unknown mechanisms and treatment options are limited. So far most research has focused on the complexes themselves, and the impact on overall cellular metabolism is largely unclear. To illustrate how computer modelling can be used to better understand the potential impact of these disorders and inspire new research directions and treatments, we simulated them using a computer model of human cardiomyocyte mitochondrial metabolism containing over 300 characterised reactions and transport steps with experimental parameters taken from the literature. Overall, simulations were consistent with patient symptoms, supporting their biological and medical significance. These simulations predicted: complex I deficiencies could be compensated using multiple pathways; complex II deficiencies had less metabolic flexibility due to impacting both the TCA cycle and the respiratory chain; and complex III and IV deficiencies caused greatest decreases in ATP production with metabolic consequences that parallel hypoxia. Our study demonstrates how results from computer models can be compared to a clinical phenotype and used as a tool for hypothesis generation for subsequent experimental testing. These simulations can enhance understanding of dysfunctional mitochondrial metabolism and suggest new avenues for research into treatment of mitochondrial disease and other areas of mitochondrial dysfunction. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  1. Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

    NASA Technical Reports Server (NTRS)

    Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

    2011-01-01

    A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

  2. Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

    DOE PAGES

    Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; ...

    2014-10-16

    Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genesmore » and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary

  3. Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models

    PubMed Central

    Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; Chia, Nicholas; Price, Nathan D.

    2014-01-01

    Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to

  4. Optical metabolic imaging measures early drug response in an allograft murine breast cancer model (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Sharick, Joe T.; Cook, Rebecca S.; Skala, Melissa C.

    2017-02-01

    Previous work has shown that cellular-level Optical Metabolic Imaging (OMI) of organoids derived from human breast cancer cell-line xenografts accurately and rapidly predicts in vivo response to therapy. To validate OMI as a predictive measure of treatment response in an immune-competent model, we used the polyomavirus middle-T (PyVmT) transgenic mouse breast cancer model. The PyVmT model includes intra-tumoral heterogeneity and a complex tumor microenvironment that can influence treatment responses. Three-dimensional organoids generated from primary PyVmT tumor tissue were treated with a chemotherapy (paclitaxel) and a PI3K inhibitor (XL147), each alone or in combination. Cellular subpopulations of response were measured using the OMI Index, a composite endpoint of metabolic response comprised of the optical redox ratio (ratio of the fluorescence intensities of metabolic co-enzymes NAD(P)H to FAD) as well as the fluorescence lifetimes of NAD(P)H and FAD. Combination treatment significantly decreased the OMI Index of PyVmT tumor organoids (p<0.0001) and in vivo tumors (p<0.0001) versus controls. Subpopulation analyses revealed a homogeneous response to combined therapy in both cultured organoids and in vivo tumors, while single agent treatment with XL147 alone or paclitaxel alone elicited heterogeneous responses in organoids. Tumor volume decreased with combination treatment through treatment day 30. These results indicate that OMI of organoids generated from PyVmT tumors can accurately reflect drug response in heterogeneous allografts with both innate and adaptive immunity. Thus, this method is promising for use in humans to predict long-term treatment responses accurately and rapidly, and could aid in clinical treatment planning.

  5. Ensemble-based docking: From hit discovery to metabolism and toxicity predictions.

    PubMed

    Evangelista, Wilfredo; Weir, Rebecca L; Ellingson, Sally R; Harris, Jason B; Kapoor, Karan; Smith, Jeremy C; Baudry, Jerome

    2016-10-15

    This paper describes and illustrates the use of ensemble-based docking, i.e., using a collection of protein structures in docking calculations for hit discovery, the exploration of biochemical pathways and toxicity prediction of drug candidates. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Metabolic Profiles Predict Adverse Events Following Coronary Artery Bypass Grafting

    PubMed Central

    Shah, Asad A.; Craig, Damian M.; Sebek, Jacqueline K.; Haynes, Carol; Stevens, Robert C.; Muehlbauer, Michael J.; Granger, Christopher B.; Hauser, Elizabeth R.; Newby, L. Kristin; Newgard, Christopher B.; Kraus, William E.; Hughes, G. Chad; Shah, Svati H.

    2012-01-01

    Objectives Clinical models incompletely predict outcomes following coronary artery bypass grafting. Novel molecular technologies may identify biomarkers to improve risk stratification. We examined whether metabolic profiles can predict adverse events in patients undergoing coronary artery bypass grafting. Methods The study population comprised 478 subjects from the CATHGEN biorepository of patients referred for cardiac catheterization who underwent coronary artery bypass grafting after enrollment. Targeted mass spectrometry-based profiling of 69 metabolites was performed in frozen, fasting plasma samples collected prior to surgery. Principal-components analysis and Cox proportional hazards regression modeling were used to assess the relation between metabolite factor levels and a composite outcome of post-coronary artery bypass grafting myocardial infarction, need for percutaneous coronary intervention, repeat coronary artery bypass grafting, or death. Results Over a mean follow-up of 4.3 ± 2.4 years, 126 subjects (26.4%) suffered an adverse event. Three principal-components analysis-derived factors were significantly associated with adverse outcome in univariable analysis: short-chain dicarboxylacylcarnitines (factor 2, P=0.001); ketone-related metabolites (factor 5, P=0.02); and short-chain acylcarnitines (factor 6, P=0.004). These three factors remained independently predictive of adverse outcome after multivariable adjustment: factor 2 (adjusted hazard ratio 1.23; 95% confidence interval [1.10-1.38]; P<0.001), factor 5 (1.17 [1.01-1.37], P=0.04), and factor 6 (1.14 [1.02-1.27], P=0.03). Conclusions Metabolic profiles are independently associated with adverse outcomes following coronary artery bypass grafting. These profiles may represent novel biomarkers of risk that augment existing tools for risk stratification of coronary artery bypass grafting patients and may elucidate novel biochemical pathways that mediate risk. PMID:22306227

  7. Expanded prediction equations of human sweat loss and water needs.

    PubMed

    Gonzalez, R R; Cheuvront, S N; Montain, S J; Goodman, D A; Blanchard, L A; Berglund, L G; Sawka, M N

    2009-08-01

    The Institute of Medicine expressed a need for improved sweating rate (msw) prediction models that calculate hourly and daily water needs based on metabolic rate, clothing, and environment. More than 25 years ago, the original Shapiro prediction equation (OSE) was formulated as msw (g.m(-2).h(-1))=27.9.Ereq.(Emax)(-0.455), where Ereq is required evaporative heat loss and Emax is maximum evaporative power of the environment; OSE was developed for a limited set of environments, exposures times, and clothing systems. Recent evidence shows that OSE often overpredicts fluid needs. Our study developed a corrected OSE and a new msw prediction equation by using independent data sets from a wide range of environmental conditions, metabolic rates (rest to 500 observations) by using a variety of metabolic rates over a range of environmental conditions (ambient temperature, 15-46 degrees C; water vapor pressure, 0.27-4.45 kPa; wind speed, 0.4-2.5 m/s), clothing, and equipment combinations and durations (2-8 h). Data are expressed as grams per square meter per hour and were analyzed using fuzzy piecewise regression. OSE overpredicted sweating rates (P<0.003) compared with observed msw. Both the correction equation (OSEC), msw=147.exp (0.0012.OSE), and a new piecewise (PW) equation, msw=147+1.527.Ereq-0.87.Emax were derived, compared with OSE, and then cross-validated against independent data (21 males and 9 females; >200 observations). OSEC and PW were more accurate predictors of sweating rate (58 and 65% more accurate, P<0.01) and produced minimal error (standard error estimate<100 g.m(-2).h(-1)) for conditions both within and outside the original OSE domain of validity. The new equations provide for more accurate sweat predictions over a broader range of conditions with applications to public health, military, occupational, and sports

  8. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

    PubMed Central

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

    2017-01-01

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

  9. Accurate First-Principles Spectra Predictions for Planetological and Astrophysical Applications at Various T-Conditions

    NASA Astrophysics Data System (ADS)

    Rey, M.; Nikitin, A. V.; Tyuterev, V.

    2014-06-01

    Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3. For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions (temperature, optical path length, pressure). On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions. There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range. The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are (i) accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and (ii) efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical

  10. Humanizing the zebrafish liver shifts drug metabolic profiles and improves pharmacokinetics of CYP3A4 substrates.

    PubMed

    Poon, Kar Lai; Wang, Xingang; Ng, Ashley S; Goh, Wei Huang; McGinnis, Claudia; Fowler, Stephen; Carney, Tom J; Wang, Haishan; Ingham, Phillip W

    2017-03-01

    Understanding and predicting whether new drug candidates will be safe in the clinic is a critical hurdle in pharmaceutical development, that relies in part on absorption, distribution, metabolism, excretion and toxicology studies in vivo. Zebrafish is a relatively new model system for drug metabolism and toxicity studies, offering whole organism screening coupled with small size and potential for high-throughput screening. Through toxicity and absorption analyses of a number of drugs, we find that zebrafish is generally predictive of drug toxicity, although assay outcomes are influenced by drug lipophilicity which alters drug uptake. In addition, liver microsome assays reveal specific differences in metabolism of compounds between human and zebrafish livers, likely resulting from the divergence of the cytochrome P450 superfamily between species. To reflect human metabolism more accurately, we generated a transgenic "humanized" zebrafish line that expresses the major human phase I detoxifying enzyme, CYP3A4, in the liver. Here, we show that this humanized line shows an elevated metabolism of CYP3A4-specific substrates compared to wild-type zebrafish. The generation of this first described humanized zebrafish liver suggests such approaches can enhance the accuracy of the zebrafish model for toxicity prediction.

  11. Accurate prediction of vaccine stability under real storage conditions and during temperature excursions.

    PubMed

    Clénet, Didier

    2018-04-01

    Due to their thermosensitivity, most vaccines must be kept refrigerated from production to use. To successfully carry out global immunization programs, ensuring the stability of vaccines is crucial. In this context, two important issues are critical, namely: (i) predicting vaccine stability and (ii) preventing product damage due to excessive temperature excursions outside of the recommended storage conditions (cold chain break). We applied a combination of advanced kinetics and statistical analyses on vaccine forced degradation data to accurately describe the loss of antigenicity for a multivalent freeze-dried inactivated virus vaccine containing three variants. The screening of large amounts of kinetic models combined with a statistical model selection approach resulted in the identification of two-step kinetic models. Predictions based on kinetic analysis and experimental stability data were in agreement, with approximately five percentage points difference from real values for long-term stability storage conditions, after excursions of temperature and during experimental shipments of freeze-dried products. Results showed that modeling a few months of forced degradation can be used to predict various time and temperature profiles endured by vaccines, i.e. long-term stability, short time excursions outside the labeled storage conditions or shipments at ambient temperature, with high accuracy. Pharmaceutical applications of the presented kinetics-based approach are discussed. Copyright © 2018 The Author. Published by Elsevier B.V. All rights reserved.

  12. Deviation from symmetrically self-similar branching in trees predicts altered hydraulics, mechanics, light interception and metabolic scaling.

    PubMed

    Smith, Duncan D; Sperry, John S; Enquist, Brian J; Savage, Van M; McCulloh, Katherine A; Bentley, Lisa P

    2014-01-01

    The West, Brown, Enquist (WBE) model derives symmetrically self-similar branching to predict metabolic scaling from hydraulic conductance, K, (a metabolism proxy) and tree mass (or volume, V). The original prediction was Kα V(0.75). We ask whether trees differ from WBE symmetry and if it matters for plant function and scaling. We measure tree branching and model how architecture influences K, V, mechanical stability, light interception and metabolic scaling. We quantified branching architecture by measuring the path fraction, Pf : mean/maximum trunk-to-twig pathlength. WBE symmetry produces the maximum, Pf = 1.0. We explored tree morphospace using a probability-based numerical model constrained only by biomechanical principles. Real tree Pf ranged from 0.930 (nearly symmetric) to 0.357 (very asymmetric). At each modeled tree size, a reduction in Pf led to: increased K; decreased V; increased mechanical stability; and decreased light absorption. When Pf was ontogenetically constant, strong asymmetry only slightly steepened metabolic scaling. The Pf ontogeny of real trees, however, was 'U' shaped, resulting in size-dependent metabolic scaling that exceeded 0.75 in small trees before falling below 0.65. Architectural diversity appears to matter considerably for whole-tree hydraulics, mechanics, photosynthesis and potentially metabolic scaling. Optimal architectures likely exist that maximize carbon gain per structural investment. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

  13. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

    DOE PAGES

    Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.; ...

    2017-06-05

    Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy,more » we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. Lastly, this opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.« less

  14. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.

    Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy,more » we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. Lastly, this opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.« less

  15. Accurate and dynamic predictive model for better prediction in medicine and healthcare.

    PubMed

    Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

    2018-05-01

    Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

  16. Accurate prediction of severe allergic reactions by a small set of environmental parameters (NDVI, temperature).

    PubMed

    Notas, George; Bariotakis, Michail; Kalogrias, Vaios; Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

    2015-01-01

    Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions.

  17. Accurate Prediction of Severe Allergic Reactions by a Small Set of Environmental Parameters (NDVI, Temperature)

    PubMed Central

    Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

    2015-01-01

    Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions. PMID:25794106

  18. A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

    PubMed Central

    Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

    2013-01-01

    Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637

  19. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

    PubMed

    Li, Fuyi; Li, Chen; Marquez-Lago, Tatiana T; Leier, André; Akutsu, Tatsuya; Purcell, Anthony W; Smith, A Ian; Lithgow, Trevor; Daly, Roger J; Song, Jiangning; Chou, Kuo-Chen

    2018-06-27

    Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase family-specific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis. In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase family-regulated phosphorylation sites. Quokka was developed by using a variety of sequence scoring functions combined with an optimized logistic regression algorithm. We evaluated Quokka based on well-prepared up-to-date benchmark and independent test datasets, curated from the Phospho.ELM and UniProt databases, respectively. The independent test demonstrates that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. In summary, our tool provides users with high-quality predicted human phosphorylation sites for hypothesis generation and biological validation. The Quokka webserver and datasets are freely available at http://quokka.erc.monash.edu/. Supplementary data are available at Bioinformatics online.

  20. Designing safer chemicals: predicting the rates of metabolism of halogenated alkanes.

    PubMed

    Yin, H; Anders, M W; Korzekwa, K R; Higgins, L; Thummel, K E; Kharasch, E D; Jones, J P

    1995-11-21

    A computational model is presented that can be used as a tool in the design of safer chemicals. This model predicts the rate of hydrogen-atom abstraction by cytochrome P450 enzymes. Excellent correlations between biotransformation rates and the calculated activation energies (delta Hact) of the cytochrome P450-mediated hydrogen-atom abstractions were obtained for the in vitro biotransformation of six halogenated alkanes (1-fluoro-1,1,2,2-tetrachloroethane, 1,1-difluoro-1,2,2-trichloroethane, 1,1,1-trifluro-2,2-dichloroethane, 1,1,1,2-tetrafluoro-2-chloroethane, 1,1,1,2,2,-pentafluoroethane, and 2-bromo-2-chloro-1,1,1-trifluoroethane) with both rat and human enzyme preparations: In(rate, rat liver microsomes) = 44.99 - 1.79(delta Hact), r2 = 0.86; In(rate, human CYP2E1) = 46.99 - 1.77(delta Hact), r2 = 0.97 (rates are in nmol of product per min per nmol of cytochrome P450 and energies are in kcal/mol). Correlations were also obtained for five inhalation anesthetics (enflurane, sevoflurane, desflurane, methoxyflurane, and isoflurane) for both in vivo and in vitro metabolism by humans: In[F(-)]peak plasma = 42.87 - 1.57(delta Hact), r2 = 0.86. To our knowledge, these are the first in vivo human metabolic rates to be quantitatively predicted. Furthermore, this is one of the first examples where computational predictions and in vivo and in vitro data have been shown to agree in any species. The model presented herein provides an archetype for the methodology that may be used in the future design of safer chemicals, particularly hydrochlorofluorocarbons and inhalation anesthetics.

  1. Designing safer chemicals: predicting the rates of metabolism of halogenated alkanes.

    PubMed Central

    Yin, H; Anders, M W; Korzekwa, K R; Higgins, L; Thummel, K E; Kharasch, E D; Jones, J P

    1995-01-01

    A computational model is presented that can be used as a tool in the design of safer chemicals. This model predicts the rate of hydrogen-atom abstraction by cytochrome P450 enzymes. Excellent correlations between biotransformation rates and the calculated activation energies (delta Hact) of the cytochrome P450-mediated hydrogen-atom abstractions were obtained for the in vitro biotransformation of six halogenated alkanes (1-fluoro-1,1,2,2-tetrachloroethane, 1,1-difluoro-1,2,2-trichloroethane, 1,1,1-trifluro-2,2-dichloroethane, 1,1,1,2-tetrafluoro-2-chloroethane, 1,1,1,2,2,-pentafluoroethane, and 2-bromo-2-chloro-1,1,1-trifluoroethane) with both rat and human enzyme preparations: In(rate, rat liver microsomes) = 44.99 - 1.79(delta Hact), r2 = 0.86; In(rate, human CYP2E1) = 46.99 - 1.77(delta Hact), r2 = 0.97 (rates are in nmol of product per min per nmol of cytochrome P450 and energies are in kcal/mol). Correlations were also obtained for five inhalation anesthetics (enflurane, sevoflurane, desflurane, methoxyflurane, and isoflurane) for both in vivo and in vitro metabolism by humans: In[F(-)]peak plasma = 42.87 - 1.57(delta Hact), r2 = 0.86. To our knowledge, these are the first in vivo human metabolic rates to be quantitatively predicted. Furthermore, this is one of the first examples where computational predictions and in vivo and in vitro data have been shown to agree in any species. The model presented herein provides an archetype for the methodology that may be used in the future design of safer chemicals, particularly hydrochlorofluorocarbons and inhalation anesthetics. PMID:7479940

  2. Current cytochrome P450 phenotyping methods applied to metabolic drug-drug interaction prediction in dogs.

    PubMed

    Mills, Beth Miskimins; Zaya, Matthew J; Walters, Rodney R; Feenstra, Kenneth L; White, Julie A; Gagne, Jason; Locuson, Charles W

    2010-03-01

    Recombinant cytochrome P450 (P450) phenotyping, different approaches for estimating fraction metabolized (f(m)), and multiple measures of in vivo inhibitor exposure were tested for their ability to predict drug interaction magnitude in dogs. In previous reports, midazolam-ketoconazole interaction studies in dogs have been attributed to inhibition of CYP3A pathways. However, in vitro phenotyping studies demonstrated higher apparent intrinsic clearances (CL(int,app)) of midazolam with canine CYP2B11 and CYP2C21. Application of activity correction factors and isoform hepatic abundance to liver microsome CL(int,app) values further implicated CYP2B11 (f(m) >or= 0.89) as the dog enzyme responsible for midazolam- and temazepam-ketoconazole interactions in vivo. Mean area under the curve (AUC) in the presence of the inhibitor/AUC ratios from intravenous and oral midazolam interaction studies were predicted well with unbound K(i) and estimates of unbound hepatic inlet inhibitor concentrations and intestinal metabolism using the AUC-competitive inhibitor relationship. No interactions were observed in vivo with bufuralol, although significant interactions with bufuralol were predicted with fluoxetine via CYP2D and CYP2C pathways (>2.45-fold) but not with clomipramine (<2-fold). The minor caffeine-fluvoxamine interaction (1.78-fold) was slightly higher than predicted values based on determination of a moderate f(m) value for CYP1A1, although CYP1A2 may also be involved in caffeine metabolism. The findings suggest promise for in vitro approaches to drug interaction assessment in dogs, but they also highlight the need to identify improved substrate and inhibitor probes for canine P450s.

  3. The metabolic syndrome: validity and utility of clinical definitions for cardiovascular disease and diabetes risk prediction.

    PubMed

    Cameron, Adrian

    2010-02-01

    The purpose of clinical definitions of the metabolic syndrome is frequently misunderstood. While the metabolic syndrome as a physiological process describes a clustering of numerous age-related metabolic abnormalities that together increase the risk for cardiovascular disease and type 2 diabetes, clinical definitions include obesity which is thought to be a cause rather than a consequence of metabolic disturbance, and several elements that are routinely measured in clinical practice, including high blood pressure, high blood glucose and dyslipidaemia. Obesity is frequently a central player in the development of the metabolic syndrome and should be considered a key component of clinical definitions. Previous clinical definitions have differed in the priority given to obesity. Perhaps more importantly than its role in a clinical definition, however, is obesity in isolation before the hallmarks of metabolic dysfunction that typify the syndrome have developed. This should be treated seriously as an opportunity to prevent the consequences of the global diabetes epidemic now apparent. Clinical definitions were designed to identify a population at high lifetime CVD and type 2 diabetes risk, but in the absence of several major risk factors for each condition, are not optimal risk prediction devices for either. Despite this, the metabolic syndrome has several properties that make it a useful construct, in conjunction with short-term risk prediction algorithms and sound clinical judgement, for the identification of those at high lifetime risk of CVD and diabetes. A recently published consensus definition provides some much needed clarity about what a clinical definition entails. Even this, however, remains a work in progress until more evidence becomes available, particularly in the area of ethnicity-specific waist cut-points. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

  4. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

    PubMed

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

    2017-01-09

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  5. Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction.

    PubMed

    Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

    2017-02-14

    Accurate energy ranking is a key facet to the problem of first-principles crystal-structure prediction (CSP) of molecular crystals. This work presents a systematic assessment of B86bPBE-XDM, a semilocal density functional combined with the exchange-hole dipole moment (XDM) dispersion model, for energy ranking using 14 compounds from the first five CSP blind tests. Specifically, the set of crystals studied comprises 11 rigid, planar compounds and 3 co-crystals. The experimental structure was correctly identified as the lowest in lattice energy for 12 of the 14 total crystals. One of the exceptions is 4-hydroxythiophene-2-carbonitrile, for which the experimental structure was correctly identified once a quasi-harmonic estimate of the vibrational free-energy contribution was included, evidencing the occasional importance of thermal corrections for accurate energy ranking. The other exception is an organic salt, where charge-transfer error (also called delocalization error) is expected to cause the base density functional to be unreliable. Provided the choice of base density functional is appropriate and an estimate of temperature effects is used, XDM-corrected density-functional theory is highly reliable for the energetic ranking of competing crystal structures.

  6. Repeated cis-regulatory tuning of a metabolic bottleneck gene during evolution.

    PubMed

    Kuang, Meihua Christina; Kominek, Jacek; Alexander, William G; Cheng, Jan-Fang; Wrobel, Russell L; Hittinger, Chris Todd

    2018-05-21

    Repeated evolutionary events imply underlying genetic constraints that can make evolutionary mechanisms predictable. Morphological traits are thought to evolve frequently through cis-regulatory changes because these mechanisms bypass constraints in pleiotropic genes that are reused during development. In contrast, the constraints acting on metabolic traits during evolution are less well studied. Here we show how a metabolic bottleneck gene has repeatedly adopted similar cis-regulatory solutions during evolution, likely due to its pleiotropic role integrating flux from multiple metabolic pathways. Specifically, the genes encoding phosphoglucomutase activity (PGM1/PGM2), which connect GALactose catabolism to glycolysis, have gained and lost direct regulation by the transcription factor Gal4 several times during yeast evolution. Through targeted mutations of predicted Gal4-binding sites in yeast genomes, we show this galactose-mediated regulation of PGM1/2 supports vigorous growth on galactose in multiple yeast species, including Saccharomyces uvarum and Lachancea kluyveri. Furthermore, the addition of galactose-inducible PGM1 alone is sufficient to improve the growth on galactose of multiple species that lack this regulation, including Saccharomyces cerevisiae. The strong association between regulation of PGM1/2 by Gal4 even enables remarkably accurate predictions of galactose growth phenotypes between closely related species. This repeated mode of evolution suggests that this specific cis-regulatory connection is a common way that diverse yeasts can govern flux through the pathway, likely due to the constraints imposed by this pleiotropic bottleneck gene. Since metabolic pathways are highly interconnected, we argue that cis-regulatory evolution might be widespread at pleiotropic genes that control metabolic bottlenecks and intersections.

  7. An Extrapolation of a Radical Equation More Accurately Predicts Shelf Life of Frozen Biological Matrices.

    PubMed

    De Vore, Karl W; Fatahi, Nadia M; Sass, John E

    2016-08-01

    Arrhenius modeling of analyte recovery at increased temperatures to predict long-term colder storage stability of biological raw materials, reagents, calibrators, and controls is standard practice in the diagnostics industry. Predicting subzero temperature stability using the same practice is frequently criticized but nevertheless heavily relied upon. We compared the ability to predict analyte recovery during frozen storage using 3 separate strategies: traditional accelerated studies with Arrhenius modeling, and extrapolation of recovery at 20% of shelf life using either ordinary least squares or a radical equation y = B1x(0.5) + B0. Computer simulations were performed to establish equivalence of statistical power to discern the expected changes during frozen storage or accelerated stress. This was followed by actual predictive and follow-up confirmatory testing of 12 chemistry and immunoassay analytes. Linear extrapolations tended to be the most conservative in the predicted percent recovery, reducing customer and patient risk. However, the majority of analytes followed a rate of change that slowed over time, which was fit best to a radical equation of the form y = B1x(0.5) + B0. Other evidence strongly suggested that the slowing of the rate was not due to higher-order kinetics, but to changes in the matrix during storage. Predicting shelf life of frozen products through extrapolation of early initial real-time storage analyte recovery should be considered the most accurate method. Although in this study the time required for a prediction was longer than a typical accelerated testing protocol, there are less potential sources of error, reduced costs, and a lower expenditure of resources. © 2016 American Association for Clinical Chemistry.

  8. Validity of the Inbody 520™ to predict metabolic rate in apparently healthy adults.

    PubMed

    Salacinski, Amanda J; Howell, Steven M; Hill, Danielle L

    2017-05-30

    The present study seeks to assess the validity of the InBody 520™ device to predict RMR in apparently healthy adults relative to a metabolic cart (the standard, yet time intensive, method for determining resting metabolic rate). Twenty-six apparently healthy adults participated in the study. Predicted RMR (pRMR) was calculated by the InBody 520™ and measured RMR (mRMR) was determined by 30-minute gas analysis and ventilated hood system. Of the 78 measurement trials conducted, 64 yielded acceptable measurement trials. A Pearson product-moment correlation was used to determine the relationship between pRMR and mRMR (r = .87, P < .001). No significant difference existed between the pRMR (1650.89 ± 295.96 kcal) and mRMR (1675.36 ± 278.69 kcal) values (P =.19). Study findings suggest that the InBody520™ provides valid measurements of RMR in apparently healthy adults and can be an effective and efficient method for collecting data in a clinical setting.

  9. Higher schizotypy predicts better metabolic profile in unaffected siblings of patients with schizophrenia.

    PubMed

    Atbasoglu, E Cem; Gumus-Akay, Guvem; Guloksuz, Sinan; Saka, Meram Can; Ucok, Alp; Alptekin, Koksal; Gullu, Sevim; van Os, Jim

    2018-04-01

    Type 2 diabetes (T2D) is more frequent in schizophrenia (Sz) than in the general population. This association is partly accounted for by shared susceptibility genetic variants. We tested the hypotheses that a genetic predisposition to Sz would be associated with higher likelihood of insulin resistance (IR), and that IR would be predicted by subthreshold psychosis phenotypes. Unaffected siblings of Sz patients (n = 101) were compared with a nonclinical sample (n = 305) in terms of IR, schizotypy (SzTy), and a behavioural experiment of "jumping to conclusions". The measures, respectively, were the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), Structured Interview for Schizotypy-Revised (SIS-R), and the Beads Task (BT). The likelihood of IR was examined in multiple regression models that included sociodemographic, metabolic, and cognitive parameters alongside group status, SIS-R scores, and BT performance. Insulin resistance was less frequent in siblings (31.7%) compared to controls (43.3%) (p < 0.05), and negatively associated with SzTy, as compared among the tertile groups for the latter (p < 0.001). The regression model that examined all relevant parameters included the tSzTy tertiles, TG and HDL-C levels, and BMI, as significant predictors of IR. Lack of IR was predicted by the highest as compared to the lowest SzTy tertile [OR (95%CI): 0.43 (0.21-0.85), p = 0.015]. Higher dopaminergic activity may contribute to both schizotypal features and a favourable metabolic profile in the same individual. This is compatible with dopamine's regulatory role in glucose metabolism via indirect central actions and a direct action on pancreatic insulin secretion. The relationship between dopaminergic activity and metabolic profile in Sz must be examined in longitudinal studies with younger unaffected siblings.

  10. SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

    PubMed Central

    2014-01-01

    Background Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them. Results SnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data. Conclusions SnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/. PMID:24980894

  11. Does the emergency surgery score accurately predict outcomes in emergent laparotomies?

    PubMed

    Peponis, Thomas; Bohnen, Jordan D; Sangji, Naveen F; Nandan, Anirudh R; Han, Kelsey; Lee, Jarone; Yeh, D Dante; de Moya, Marc A; Velmahos, George C; Chang, David C; Kaafarani, Haytham M A

    2017-08-01

    The emergency surgery score is a mortality-risk calculator for emergency general operation patients. We sought to examine whether the emergency surgery score predicts 30-day morbidity and mortality in a high-risk group of patients undergoing emergent laparotomy. Using the 2011-2012 American College of Surgeons National Surgical Quality Improvement Program database, we identified all patients who underwent emergent laparotomy using (1) the American College of Surgeons National Surgical Quality Improvement Program definition of "emergent," and (2) all Current Procedural Terminology codes denoting a laparotomy, excluding aortic aneurysm rupture. Multivariable logistic regression analyses were performed to measure the correlation (c-statistic) between the emergency surgery score and (1) 30-day mortality, and (2) 30-day morbidity after emergent laparotomy. As sensitivity analyses, the correlation between the emergency surgery score and 30-day mortality was also evaluated in prespecified subgroups based on Current Procedural Terminology codes. A total of 26,410 emergent laparotomy patients were included. Thirty-day mortality and morbidity were 10.2% and 43.8%, respectively. The emergency surgery score correlated well with mortality (c-statistic = 0.84); scores of 1, 11, and 22 correlated with mortalities of 0.4%, 39%, and 100%, respectively. Similarly, the emergency surgery score correlated well with morbidity (c-statistic = 0.74); scores of 0, 7, and 11 correlated with complication rates of 13%, 58%, and 79%, respectively. The morbidity rates plateaued for scores higher than 11. Sensitivity analyses demonstrated that the emergency surgery score effectively predicts mortality in patients undergoing emergent (1) splenic, (2) gastroduodenal, (3) intestinal, (4) hepatobiliary, or (5) incarcerated ventral hernia operation. The emergency surgery score accurately predicts outcomes in all types of emergent laparotomy patients and may prove valuable as a bedside decision

  12. Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

    PubMed Central

    Vongsangnak, Wanwipa; Olsen, Peter; Hansen, Kim; Krogsgaard, Steen; Nielsen, Jens

    2008-01-01

    . oryzae genome was performed and a genome-scale metabolic model of A. oryzae was reconstructed. The model accurately predicted the growth and biomass yield on different carbon sources. The model serves as an important resource for gaining further insight into our understanding of A. oryzae physiology. PMID:18500999

  13. A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

    PubMed

    Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

    2015-09-01

    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. A Physiologically Based Pharmacokinetic Model for Pregnant Women to Predict the Pharmacokinetics of Drugs Metabolized Via Several Enzymatic Pathways.

    PubMed

    Dallmann, André; Ince, Ibrahim; Coboeken, Katrin; Eissing, Thomas; Hempel, Georg

    2017-09-18

    Physiologically based pharmacokinetic modeling is considered a valuable tool for predicting pharmacokinetic changes in pregnancy to subsequently guide in-vivo pharmacokinetic trials in pregnant women. The objective of this study was to extend and verify a previously developed physiologically based pharmacokinetic model for pregnant women for the prediction of pharmacokinetics of drugs metabolized via several cytochrome P450 enzymes. Quantitative information on gestation-specific changes in enzyme activity available in the literature was incorporated in a pregnancy physiologically based pharmacokinetic model and the pharmacokinetics of eight drugs metabolized via one or multiple cytochrome P450 enzymes was predicted. The tested drugs were caffeine, midazolam, nifedipine, metoprolol, ondansetron, granisetron, diazepam, and metronidazole. Pharmacokinetic predictions were evaluated by comparison with in-vivo pharmacokinetic data obtained from the literature. The pregnancy physiologically based pharmacokinetic model successfully predicted the pharmacokinetics of all tested drugs. The observed pregnancy-induced pharmacokinetic changes were qualitatively and quantitatively reasonably well predicted for all drugs. Ninety-seven percent of the mean plasma concentrations predicted in pregnant women fell within a twofold error range and 63% within a 1.25-fold error range. For all drugs, the predicted area under the concentration-time curve was within a 1.25-fold error range. The presented pregnancy physiologically based pharmacokinetic model can quantitatively predict the pharmacokinetics of drugs that are metabolized via one or multiple cytochrome P450 enzymes by integrating prior knowledge of the pregnancy-related effect on these enzymes. This pregnancy physiologically based pharmacokinetic model may thus be used to identify potential exposure changes in pregnant women a priori and to eventually support informed decision making when clinical trials are designed in this

  15. Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising.

    PubMed

    Yu, Bin; Li, Shan; Qiu, Wen-Ying; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Wang, Ming-Hui; Zhang, Yan

    2017-12-08

    Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research.

  16. The association between the metabolic syndrome and metabolic syndrome score and pulmonary function in non-smoking adults.

    PubMed

    Yoon, Hyun; Gi, Mi Young; Cha, Ju Ae; Yoo, Chan Uk; Park, Sang Muk

    2018-03-01

    This study assessed the association of metabolic syndrome and metabolic syndrome score with the predicted forced vital capacity and predicted forced expiratory volume in 1 s (predicted forced expiratory volume in 1 s) values in Korean non-smoking adults. We analysed data obtained from 6684 adults during the 2013-2015 Korean National Health and Nutrition Examination Survey. After adjustment for related variables, metabolic syndrome ( p < 0.001) and metabolic syndrome score ( p < 0.001) were found to be inversely associated with the predicted forced vital capacity and forced expiratory volume in 1 s values. The odds ratios of restrictive pulmonary disease (the predicted forced vital capacity < 80.0% with forced expiratory volume in 1 s/FVC ⩾ 70.0%) by metabolic syndrome score with metabolic syndrome score 0 as a reference group showed no significance for metabolic syndrome score 1 [1.061 (95% confidence interval, 0.755-1.490)] and metabolic syndrome score 2 [1.247 (95% confidence interval, 0.890-1.747)], but showed significant for metabolic syndrome score 3 [1.433 (95% confidence interval, 1.010-2.033)] and metabolic syndrome score ⩾ 4 [1.760 (95% confidence interval, 1.216-2.550)]. In addition, the odds ratio of restrictive pulmonary disease of the metabolic syndrome [1.360 (95% confidence interval, 1.118-1.655)] was significantly higher than those of non-metabolic syndrome. Metabolic syndrome and metabolic syndrome score were inversely associated with the predicted forced vital capacity and forced expiratory volume in 1 s values in Korean non-smoking adults. In addition, metabolic syndrome and metabolic syndrome score were positively associated with the restrictive pulmonary disease.

  17. NMRDSP: an accurate prediction of protein shape strings from NMR chemical shifts and sequence data.

    PubMed

    Mao, Wusong; Cong, Peisheng; Wang, Zhiheng; Lu, Longjian; Zhu, Zhongliang; Li, Tonghua

    2013-01-01

    Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at http://cal.tongji.edu.cn/NMRDSP/index.jsp.

  18. A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica

    PubMed Central

    2012-01-01

    Background Yarrowia lipolytica is an oleaginous yeast which has emerged as an important microorganism for several biotechnological processes, such as the production of organic acids, lipases and proteases. It is also considered a good candidate for single-cell oil production. Although some of its metabolic pathways are well studied, its metabolic engineering is hindered by the lack of a genome-scale model that integrates the current knowledge about its metabolism. Results Combining in silico tools and expert manual curation, we have produced an accurate genome-scale metabolic model for Y. lipolytica. Using a scaffold derived from a functional metabolic model of the well-studied but phylogenetically distant yeast S. cerevisiae, we mapped conserved reactions, rewrote gene associations, added species-specific reactions and inserted specialized copies of scaffold reactions to account for species-specific expansion of protein families. We used physiological measures obtained under lab conditions to validate our predictions. Conclusions Y. lipolytica iNL895 represents the first well-annotated metabolic model of an oleaginous yeast, providing a base for future metabolic improvement, and a starting point for the metabolic reconstruction of other species in the Yarrowia clade and other oleaginous yeasts. PMID:22558935

  19. Fast and accurate predictions of covalent bonds in chemical space.

    PubMed

    Chang, K Y Samuel; Fias, Stijn; Ramakrishnan, Raghunathan; von Lilienfeld, O Anatole

    2016-05-07

    We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigated σ bonding to hydrogen, as well as σ and π bonding between main-group elements, occurring in small sets of iso-valence-electronic molecules with elements drawn from second to fourth rows in the p-block of the periodic table. Numerical evidence suggests that first order Taylor expansions of covalent bonding potentials can achieve high accuracy if (i) the alchemical interpolation is vertical (fixed geometry), (ii) it involves elements from the third and fourth rows of the periodic table, and (iii) an optimal reference geometry is used. This leads to near linear changes in the bonding potential, resulting in analytical predictions with chemical accuracy (∼1 kcal/mol). Second order estimates deteriorate the prediction. If initial and final molecules differ not only in composition but also in geometry, all estimates become substantially worse, with second order being slightly more accurate than first order. The independent particle approximation based second order perturbation theory performs poorly when compared to the coupled perturbed or finite difference approach. Taylor series expansions up to fourth order of the potential energy curve of highly symmetric systems indicate a finite radius of convergence, as illustrated for the alchemical stretching of H2 (+). Results are presented for (i) covalent bonds to hydrogen in 12 molecules with 8 valence electrons (CH4, NH3, H2O, HF, SiH4, PH3, H2S, HCl, GeH4, AsH3, H2Se, HBr); (ii) main-group single bonds in 9 molecules with 14 valence electrons (CH3F, CH3Cl, CH3Br, SiH3F, SiH3Cl, SiH3Br, GeH3F, GeH3Cl, GeH3Br); (iii) main-group double bonds in 9 molecules with 12 valence electrons (CH2O, CH2S, CH2Se, SiH2O, SiH2S, SiH2Se, GeH2O, GeH2S, GeH2Se); (iv) main-group triple bonds in 9 molecules with 10 valence electrons (HCN, HCP, HCAs, HSiN, HSi

  20. Does mesenteric venous imaging assessment accurately predict pathologic invasion in localized pancreatic ductal adenocarcinoma?

    PubMed

    Clanton, Jesse; Oh, Stephen; Kaplan, Stephen J; Johnson, Emily; Ross, Andrew; Kozarek, Richard; Alseidi, Adnan; Biehl, Thomas; Picozzi, Vincent J; Helton, William S; Coy, David; Dorer, Russell; Rocha, Flavio G

    2018-05-09

    Accurate prediction of mesenteric venous involvement in pancreatic ductal adenocarcinoma (PDAC) is necessary for adequate staging and treatment. A retrospective cohort study was conducted in PDAC patients at a single institution. All patients with resected PDAC and staging CT and EUS between 2003 and 2014 were included and sub-divided into "upfront resected" and "neoadjuvant chemotherapy (NAC)" groups. Independent imaging re-review was correlated to venous resection and venous invasion. Sensitivity, specificity, positive and negative predictive values were then calculated. A total of 109 patients underwent analysis, 60 received upfront resection, and 49 NAC. Venous resection (30%) and vein invasion (13%) was less common in patients resected upfront than those who received NAC (53% and 16%, respectively). Both CT and EUS had poor sensitivity (14-44%) but high specificity (75-95%) for detecting venous resection and vein invasion in patients resected upfront, whereas sensitivity was high (84-100%) and specificity was low (27-44%) after NAC. Preoperative CT and EUS in PDAC have similar efficacy but different predictive capacity in assessing mesenteric venous involvement depending on whether patients are resected upfront or received NAC. Both modalities appear to significantly overestimate true vascular involvement and should be interpreted in the appropriate clinical context. Copyright © 2018 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.

  1. Prediction of phospholipidosis-inducing potential of drugs by in vitro biochemical and physicochemical assays followed by multivariate analysis.

    PubMed

    Kuroda, Yukihiro; Saito, Madoka

    2010-03-01

    An in vitro method to predict phospholipidosis-inducing potential of cationic amphiphilic drugs (CADs) was developed using biochemical and physicochemical assays. The following parameters were applied to principal component analysis, as well as physicochemical parameters: pK(a) and clogP; dissociation constant of CADs from phospholipid, inhibition of enzymatic phospholipid degradation, and metabolic stability of CADs. In the score plot, phospholipidosis-inducing drugs (amiodarone, propranolol, imipramine, chloroquine) were plotted locally forming the subspace for positive CADs; while non-inducing drugs (chlorpromazine, chloramphenicol, disopyramide, lidocaine) were placed scattering out of the subspace, allowing a clear discrimination between both classes of CADs. CADs that often produce false results by conventional physicochemical or cell-based assay methods were accurately determined by our method. Basic and lipophilic disopyramide could be accurately predicted as a nonphospholipidogenic drug. Moreover, chlorpromazine, which is often falsely predicted as a phospholipidosis-inducing drug by in vitro methods, could be accurately determined. Because this method uses the pharmacokinetic parameters pK(a), clogP, and metabolic stability, which are usually obtained in the early stages of drug development, the method newly requires only the two parameters, binding to phospholipid, and inhibition of lipid degradation enzyme. Therefore, this method provides a cost-effective approach to predict phospholipidosis-inducing potential of a drug. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

  2. Accurate load prediction by BEM with airfoil data from 3D RANS simulations

    NASA Astrophysics Data System (ADS)

    Schneider, Marc S.; Nitzsche, Jens; Hennings, Holger

    2016-09-01

    In this paper, two methods for the extraction of airfoil coefficients from 3D CFD simulations of a wind turbine rotor are investigated, and these coefficients are used to improve the load prediction of a BEM code. The coefficients are extracted from a number of steady RANS simulations, using either averaging of velocities in annular sections, or an inverse BEM approach for determination of the induction factors in the rotor plane. It is shown that these 3D rotor polars are able to capture the rotational augmentation at the inner part of the blade as well as the load reduction by 3D effects close to the blade tip. They are used as input to a simple BEM code and the results of this BEM with 3D rotor polars are compared to the predictions of BEM with 2D airfoil coefficients plus common empirical corrections for stall delay and tip loss. While BEM with 2D airfoil coefficients produces a very different radial distribution of loads than the RANS simulation, the BEM with 3D rotor polars manages to reproduce the loads from RANS very accurately for a variety of load cases, as long as the blade pitch angle is not too different from the cases from which the polars were extracted.

  3. Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

    PubMed Central

    Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

    2015-01-01

    Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on α/β-mixed or β-structure–rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides. PMID:26038575

  4. Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles.

    PubMed

    Zou, Lingyun; Nan, Chonghan; Hu, Fuquan

    2013-12-15

    Various human pathogens secret effector proteins into hosts cells via the type IV secretion system (T4SS). These proteins play important roles in the interaction between bacteria and hosts. Computational methods for T4SS effector prediction have been developed for screening experimental targets in several isolated bacterial species; however, widely applicable prediction approaches are still unavailable In this work, four types of distinctive features, namely, amino acid composition, dipeptide composition, .position-specific scoring matrix composition and auto covariance transformation of position-specific scoring matrix, were calculated from primary sequences. A classifier, T4EffPred, was developed using the support vector machine with these features and their different combinations for effector prediction. Various theoretical tests were performed in a newly established dataset, and the results were measured with four indexes. We demonstrated that T4EffPred can discriminate IVA and IVB effectors in benchmark datasets with positive rates of 76.7% and 89.7%, respectively. The overall accuracy of 95.9% shows that the present method is accurate for distinguishing the T4SS effector in unidentified sequences. A classifier ensemble was designed to synthesize all single classifiers. Notable performance improvement was observed using this ensemble system in benchmark tests. To demonstrate the model's application, a genome-scale prediction of effectors was performed in Bartonella henselae, an important zoonotic pathogen. A number of putative candidates were distinguished. A web server implementing the prediction method and the source code are both available at http://bioinfo.tmmu.edu.cn/T4EffPred.

  5. Towards accurate cosmological predictions for rapidly oscillating scalar fields as dark matter

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Ureña-López, L. Arturo; Gonzalez-Morales, Alma X., E-mail: lurena@ugto.mx, E-mail: alma.gonzalez@fisica.ugto.mx

    2016-07-01

    As we are entering the era of precision cosmology, it is necessary to count on accurate cosmological predictions from any proposed model of dark matter. In this paper we present a novel approach to the cosmological evolution of scalar fields that eases their analytic and numerical analysis at the background and at the linear order of perturbations. The new method makes use of appropriate angular variables that simplify the writing of the equations of motion, and which also show that the usual field variables play a secondary role in the cosmological dynamics. We apply the method to a scalar fieldmore » endowed with a quadratic potential and revisit its properties as dark matter. Some of the results known in the literature are recovered, and a better understanding of the physical properties of the model is provided. It is confirmed that there exists a Jeans wavenumber k {sub J} , directly related to the suppression of linear perturbations at wavenumbers k > k {sub J} , and which is verified to be k {sub J} = a √ mH . We also discuss some semi-analytical results that are well satisfied by the full numerical solutions obtained from an amended version of the CMB code CLASS. Finally we draw some of the implications that this new treatment of the equations of motion may have in the prediction of cosmological observables from scalar field dark matter models.« less

  6. Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding

    PubMed Central

    Nissley, Daniel A.; Sharma, Ajeet K.; Ahmed, Nabeel; Friedrich, Ulrike A.; Kramer, Günter; Bukau, Bernd; O'Brien, Edward P.

    2016-01-01

    The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally—a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process. PMID:26887592

  7. Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets.

    PubMed

    Rienksma, Rienk A; Suarez-Diez, Maria; Spina, Lucie; Schaap, Peter J; Martins dos Santos, Vitor A P

    2014-12-01

    Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  8. A gene expression biomarker accurately predicts estrogen ...

    EPA Pesticide Factsheets

    The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1 screening tests. The ToxCast program currently includes 18 HTS in vitro assays that evaluate the ability of chemicals to modulate estrogen receptor α (ERα), an important endocrine target. We propose microarray-based gene expression profiling as a complementary approach to predict ERα modulation and have developed computational methods to identify ERα modulators in an existing database of whole-genome microarray data. The ERα biomarker consisted of 46 ERα-regulated genes with consistent expression patterns across 7 known ER agonists and 3 known ER antagonists. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression data sets from experiments in MCF-7 cells. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% or 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) OECD ER reference chemicals including “very weak” agonists and replicated predictions based on 18 in vitro ER-associated HTS assays. For 114 chemicals present in both the HTS data and the MCF-7 c

  9. Prediction of future risk of insulin resistance and metabolic syndrome based on Korean boy's metabolite profiling.

    PubMed

    Lee, AeJin; Jang, Han Byul; Ra, Moonjin; Choi, Youngshim; Lee, Hye-Ja; Park, Ju Yeon; Kang, Jae Heon; Park, Kyung-Hee; Park, Sang Ick; Song, Jihyun

    2015-01-01

    Childhood obesity is strongly related to future insulin resistance and metabolic syndrome. Thus, identifying early biomarkers of obesity-related diseases based on metabolic profiling is useful to control future metabolic disorders. We compared metabolic profiles between obese and normal-weight children and investigated specific biomarkers of future insulin resistance and metabolic syndrome. In all, 186 plasma metabolites were analysed at baseline and after 2 years in 109 Korean boys (age 10.5±0.4 years) from the Korean Child Obesity Cohort Study using the AbsoluteIDQ™ p180 Kit. We observed that levels of 41 metabolites at baseline and 40 metabolites at follow-up were significantly altered in obese children (p<0.05). Obese children showed significantly higher levels of branched-chain amino acids (BCAAs) and several acylcarnitines and lower levels of acyl-alkyl phosphatidylcholines. Also, baseline BCAAs were significantly positively correlated with both homeostasis model assessment for insulin resistance (HOMA-IR) and continuous metabolic risk score at the 2-year follow-up. In logistic regression analyses with adjustments for degree of obesity at baseline, baseline BCAA concentration, greater than the median value, was identified as a predictor of future risk of insulin resistance and metabolic syndrome. High BCAA concentration could be "early" biomarkers for predicting future metabolic diseases. Copyright © 2014 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

  10. PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

    PubMed

    Islam, S M Ashiqul; Sajed, Tanvir; Kearney, Christopher Michel; Baker, Erich J

    2015-07-05

    Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86%, 94.11%, 84.31%, 94.30% and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB. PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

  11. Microbial Communities and Their Predicted Metabolic Functions in Growth Laminae of a Unique Large Conical Mat from Lake Untersee, East Antarctica

    PubMed Central

    Koo, Hyunmin; Mojib, Nazia; Hakim, Joseph A.; Hawes, Ian; Tanabe, Yukiko; Andersen, Dale T.; Bej, Asim K.

    2017-01-01

    In this study, we report the distribution of microbial taxa and their predicted metabolic functions observed in the top (U1), middle (U2), and inner (U3) decadal growth laminae of a unique large conical microbial mat from perennially ice-covered Lake Untersee of East Antarctica, using NextGen sequencing of the 16S rRNA gene and bioinformatics tools. The results showed that the U1 lamina was dominated by cyanobacteria, specifically Phormidium sp., Leptolyngbya sp., and Pseudanabaena sp. The U2 and U3 laminae had high abundances of Actinobacteria, Verrucomicrobia, Proteobacteria, and Bacteroidetes. Closely related taxa within each abundant bacterial taxon found in each lamina were further differentiated at the highest taxonomic resolution using the oligotyping method. PICRUSt analysis, which determines predicted KEGG functional categories from the gene contents and abundances among microbial communities, revealed a high number of sequences belonging to carbon fixation, energy metabolism, cyanophycin, chlorophyll, and photosynthesis proteins in the U1 lamina. The functional predictions of the microbial communities in U2 and U3 represented signal transduction, membrane transport, zinc transport and amino acid-, carbohydrate-, and arsenic- metabolisms. The Nearest Sequenced Taxon Index (NSTI) values processed through PICRUSt were 0.10, 0.13, and 0.11 for U1, U2, and U3 laminae, respectively. These values indicated a close correspondence with the reference microbial genome database, implying high confidence in the predicted metabolic functions of the microbial communities in each lamina. The distribution of microbial taxa observed in each lamina and their predicted metabolic functions provides additional insight into the complex microbial ecosystem at Lake Untersee, and lays the foundation for studies that will enhance our understanding of the mechanisms responsible for the formation of these unique mat structures and their evolutionary significance. PMID:28824553

  12. Microbial Communities and Their Predicted Metabolic Functions in Growth Laminae of a Unique Large Conical Mat from Lake Untersee, East Antarctica.

    PubMed

    Koo, Hyunmin; Mojib, Nazia; Hakim, Joseph A; Hawes, Ian; Tanabe, Yukiko; Andersen, Dale T; Bej, Asim K

    2017-01-01

    In this study, we report the distribution of microbial taxa and their predicted metabolic functions observed in the top (U1), middle (U2), and inner (U3) decadal growth laminae of a unique large conical microbial mat from perennially ice-covered Lake Untersee of East Antarctica, using NextGen sequencing of the 16S rRNA gene and bioinformatics tools. The results showed that the U1 lamina was dominated by cyanobacteria, specifically Phormidium sp., Leptolyngbya sp., and Pseudanabaena sp. The U2 and U3 laminae had high abundances of Actinobacteria, Verrucomicrobia, Proteobacteria, and Bacteroidetes. Closely related taxa within each abundant bacterial taxon found in each lamina were further differentiated at the highest taxonomic resolution using the oligotyping method. PICRUSt analysis, which determines predicted KEGG functional categories from the gene contents and abundances among microbial communities, revealed a high number of sequences belonging to carbon fixation, energy metabolism, cyanophycin, chlorophyll, and photosynthesis proteins in the U1 lamina. The functional predictions of the microbial communities in U2 and U3 represented signal transduction, membrane transport, zinc transport and amino acid-, carbohydrate-, and arsenic- metabolisms. The Nearest Sequenced Taxon Index (NSTI) values processed through PICRUSt were 0.10, 0.13, and 0.11 for U1, U2, and U3 laminae, respectively. These values indicated a close correspondence with the reference microbial genome database, implying high confidence in the predicted metabolic functions of the microbial communities in each lamina. The distribution of microbial taxa observed in each lamina and their predicted metabolic functions provides additional insight into the complex microbial ecosystem at Lake Untersee, and lays the foundation for studies that will enhance our understanding of the mechanisms responsible for the formation of these unique mat structures and their evolutionary significance.

  13. PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

    PubMed

    Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

    2014-01-01

    Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

  14. The predictive ability of triglycerides and waist (hypertriglyceridemic waist) in assessing metabolic triad change in obese children and adolescents.

    PubMed

    Hobkirk, James P; King, Roderick F; Gately, Paul; Pemberton, Philip; Smith, Alexander; Barth, Julian H; Harman, Nicola; Davies, Ian; Carroll, Sean

    2013-10-01

    The metabolic triad [fasting insulin, apolipoprotein B, and low-density lipoporotein (LDL) peak particle density] is characteristic of increased intra-abdominal adipose tissue and insulin resistance and can be predicted by the simple and adoptable screening tool, the hypertriglyceridemic waist. The associations between hypertriglyceridemic waist components [fasting triglycerides (TG) and waist circumference cut-points derived from a child-specific metabolic syndrome definition] with the metabolic triad were examined in obese youth before and after weight loss. A continuous metabolic triad score (MTS) was calculated as a cumulative and standardized residual score of fasting insulin, apolipoprotein B, and LDL peak particle density (z-scores of the metabolic triad variables regressed onto age and sex). The predictive ability of TG and waist in assessing metabolic triad change was undertaken in 75 clinically obese boys and girls, aged 8-18, body mass index (BMI) 34.2±6.4 kg/m(2) before and after weight loss. Fasting TG concentrations (r(2)=0.216, P<0.0001) and waist circumference (r(2)=0.049, P=0.019) were both significant independent predictors of the cumulative MTS, together accounting for 26.5% of its total variance. All cardiometabolic risk factors [except a reduction in high-density lipoprotein cholesterol (HDL-C)] were favorably modified following weight loss. Fasting TG change was the only significant predictor of the MTS change (r(2)=0.177, P<0.0001). Waist circumference was not a significant predictor of MTS change. The reduction in fasting TG concentration (but not waist circumference) was the only significant predictor of MTS change. Fasting TG may be the most important metabolic syndrome component to best characterize the metabolic heterogeneity in obese cohorts and the changes in metabolic risk in clinically obese youth.

  15. Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome.

    PubMed

    Mallory, Emily K; Acharya, Ambika; Rensi, Stefano E; Turnbaugh, Peter J; Bright, Roselie A; Altman, Russ B

    2018-01-01

    Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response. Discovery and validation of drug metabolism via bacterial enzymes has yielded >50 drugs after nearly a century of experimental research. However, there are limited computational tools for screening drugs for potential metabolism by the gut microbiome. We developed a pipeline for comparing and characterizing chemical transformations using continuous vector representations of molecular structure learned using unsupervised representation learning. We applied this pipeline to chemical reaction data from MetaCyc to characterize the utility of vector representations for chemical reaction transformations. After clustering molecular and reaction vectors, we performed enrichment analyses and queries to characterize the space. We detected enriched enzyme names, Gene Ontology terms, and Enzyme Consortium (EC) classes within reaction clusters. In addition, we queried reactions against drug-metabolite transformations known to be metabolized by the human gut microbiome. The top results for these known drug transformations contained similar substructure modifications to the original drug pair. This work enables high throughput screening of drugs and their resulting metabolites against chemical reactions common to gut bacteria.

  16. The evolution of metabolic networks of E. coli

    PubMed Central

    2011-01-01

    -scale metabolic model based on conserved ortholog groups in all 16 E. coli genomes was also constructed, reflecting the conserved ancestral core of E. coli metabolism (iEco1053_core). Comparative analysis of all six strain-specific E. coli models revealed that some of the pathogenic E. coli strains possess reactions in their metabolic networks enabling higher biomass yields on glucose. Finally the lineage-specific metabolic traits were compared to the ancestral core model predictions to derive new insight into the evolution of metabolism within this species. Conclusion Our findings demonstrate that a pangenome-scale metabolic model can be used to rapidly construct additional E. coli strain-specific models, and that quantitative models of different strains of E. coli can accurately predict strain-specific phenotypes. Such pangenome and strain-specific models can be further used to engineer metabolic phenotypes of interest, such as designing new industrial E. coli strains. PMID:22044664

  17. A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses

    DOE PAGES

    Imam, Saheed; Schäuble, Sascha; Valenzuela, Jacob; ...

    2015-10-20

    Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems-level understanding of their metabolism, which can be fundamentally achieved through large-scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome-scale metabolic model for the widely-studied microalga, Chlamydomonas reinhardtii. The model, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 encompasses a broad range of metabolic functions encoded across the nuclear, chloroplast and mitochondrial genomes accounting formore » 1355 genes (1460 transcripts), 2394 and 1133 metabolites. We found improved performance over the previous metabolic model based on comparisons of predictive accuracy across 306 phenotypes (from 81 mutants), lipid yield analysis and growth rates derived from chemostat-grown cells (under three conditions). Measurement of macronutrient uptake revealed carbon and phosphate to be good predictors of growth rate, while nitrogen consumption appeared to be in excess. We analyzed high-resolution time series transcriptomics data using iCre1355 to uncover dynamic pathway-level changes that occur in response to nitrogen starvation and changes in light intensity. This approach enabled accurate prediction of growth rates, the cessation of growth and accumulation of triacylglycerols during nitrogen starvation, and the temporal response of different growth-associated pathways to increased light intensity. Thus, iCre1355 represents an experimentally validated genome-scale reconstruction of C. reinhardtii metabolism that should serve as a useful resource for studying the metabolic processes of this and related microalgae.« less

  18. Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment.

    PubMed

    Young, Jonathan; Modat, Marc; Cardoso, Manuel J; Mendelson, Alex; Cash, Dave; Ourselin, Sebastien

    2013-01-01

    Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy

  19. Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism

    PubMed Central

    Chang, Roger L; Ghamsari, Lila; Manichaikul, Ani; Hom, Erik F Y; Balaji, Santhanam; Fu, Weiqi; Shen, Yun; Hao, Tong; Palsson, Bernhard Ø; Salehi-Ashtiani, Kourosh; Papin, Jason A

    2011-01-01

    Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. PMID:21811229

  20. Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

    PubMed Central

    Ando, David; Singh, Jahnavi; Keasling, Jay D.; García Martín, Héctor

    2018-01-01

    Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis (13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S-13C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13C MFA or 2S-13C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https

  1. Low-Turnover Drug Molecules: A Current Challenge for Drug Metabolism Scientists.

    PubMed

    Hutzler, J Matthew; Ring, Barbara J; Anderson, Shelby R

    2015-12-01

    In vitro assays using liver subcellular fractions or suspended hepatocytes for characterizing the metabolism of drug candidates play an integral role in the optimization strategy employed by medicinal chemists. However, conventional in vitro assays have limitations in their ability to predict clearance and generate metabolites for low-turnover (slowly metabolized) drug molecules. Due to a rapid loss in the activity of the drug-metabolizing enzymes, in vitro incubations are typically performed for a maximum of 1 hour with liver microsomes to 4 hours with suspended hepatocytes. Such incubations are insufficient to generate a robust metabolic response for compounds that are slowly metabolized. Thus, the challenge of accurately estimating low human clearance with confidence has emerged to be among the top challenges that drug metabolism scientists are confronted with today. In response, investigators have evaluated novel methodologies to extend incubation times and more sufficiently measure metabolism of low-turnover drugs. These methods include plated human hepatocytes in monoculture, and a novel in vitro methodology using a relay of sequential incubations with suspended cryopreserved hepatocytes. In addition, more complex in vitro cellular models, such as HepatoPac (Hepregen, Medford, MA), a micropatterned hepatocyte-fibroblast coculture system, and the HµREL (Beverley Hills, CA) hepatic coculture system, have been developed and characterized that demonstrate prolonged enzyme activity. In this review, the advantages and disadvantages of each of these in vitro methodologies as it relates to the prediction of clearance and metabolite identification will be described in an effort to provide drug metabolism scientists with the most up-to-date experimental options for dealing with the complex issue of low-turnover drug candidates. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

  2. A Quantitative Study of Oxygen as a Metabolic Regulator

    NASA Technical Reports Server (NTRS)

    Radhakrishnan, Krishnan; LaManna, Joseph C.; Cabera, Marco E.

    2000-01-01

    An acute reduction in oxygen delivery to a tissue is associated with metabolic changes aimed at maintaining ATP homeostasis. However, given the complexity of the human bio-energetic system, it is difficult to determine quantitatively how cellular metabolic processes interact to maintain ATP homeostasis during stress (e.g., hypoxia, ischemia, and exercise). In particular, we are interested in determining mechanisms relating cellular oxygen concentration to observed metabolic responses at the cellular, tissue, organ, and whole body levels and in quantifying how changes in tissue oxygen availability affect the pathways of ATP synthesis and the metabolites that control these pathways. In this study; we extend a previously developed mathematical model of human bioenergetics, to provide a physicochemical framework that permits quantitative understanding of oxygen as a metabolic regulator. Specifically, the enhancement - sensitivity analysis - permits studying the effects of variations in tissue oxygenation and parameters controlling cellular respiration on glycolysis, lactate production, and pyruvate oxidation. The analysis can distinguish between parameters that must be determined accurately and those that require less precision, based on their effects on model predictions. This capability may prove to be important in optimizing experimental design, thus reducing use of animals.

  3. Improved kinetic model of Escherichia coli central carbon metabolism in batch and continuous cultures.

    PubMed

    Kurata, Hiroyuki; Sugimoto, Yurie

    2018-02-01

    Many kinetic models of Escherichia coli central metabolism have been built, but few models accurately reproduced the dynamic behaviors of wild type and multiple genetic mutants. In 2016, our latest kinetic model improved problems of existing models to reproduce the cell growth and glucose uptake of wild type, ΔpykA:pykF and Δpgi in a batch culture, while it overestimated the glucose uptake and cell growth rates of Δppc and hardly captured the typical characteristics of the glyoxylate and TCA cycle fluxes for Δpgi and Δppc. Such discrepancies between the simulated and experimental data suggested biological complexity. In this study, we overcame these problems by assuming critical mechanisms regarding the OAA-regulated isocitrate dehydrogenase activity, aceBAK gene regulation and growth suppression. The present model accurately predicts the extracellular and intracellular dynamics of wild type and many gene knockout mutants in batch and continuous cultures. It is now the most accurate, detailed kinetic model of E. coli central carbon metabolism and will contribute to advances in mathematical modeling of cell factories. Copyright © 2017 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

  4. Fat-to-muscle Ratio: A New Anthropometric Indicator for Predicting Metabolic Syndrome in the Han and Bouyei Populations from Guizhou Province, China.

    PubMed

    Xu, Ke; Zhu, Hui Juan; Chen, Shi; Chen, Lu; Wang, Xin; Zhang, Li Yuan; Pan, Li; Wang, Li; Feng, Kui; Wang, Ke; Dong, Fen; Wang, Ding Ming; Yu, Yang Wen; Pan, Hui; Shan, Guang Liang

    2018-04-01

    To investigate the prevalence and possible factors influencing metabolic syndrome in people from Guizhou Province and to explore the predictive value of the fat-to-muscle ratio in diagnosing metabolic syndrome. A multistage stratified sampling method was used in this cross-sectional study of 20-80 years old Han and Bouyei populations from Guizhou Province, southwestern China, from October-December 2012. The study included 4,553 cases of metabolic syndrome, that was defined according to 2005 International Diabetes Federation criteria. The receiver operating characteristic curve was used for determining the sensitivity, specificity, and predictive ability of the fat-to-muscle ratio for the diagnosis of metabolic syndrome. The age-standardized prevalence of metabolic syndrome was 11.38% (men: 9.76%; women: 12.72%) for Han and 4.78% (men: 4.43%; women: 5.30%) for Bouyei populations. In Guizhou Province, the cut-off value for the men fat-to-muscle ratio was 0.34, the area under the curve was 0.95, and the sensitivity and specificity were 0.94 and 0.85, respectively. The cut-off value for the women fat-to-muscle ratio was 0.55, the area under the curve was 0.91, and the sensitivity and specificity were 0.93 and 0.79, respectively. The fat-to-muscle ratio is highly predictive of metabolic syndrome in Guizhou Province, and a useful reference indicator. Copyright © 2018 The Editorial Board of Biomedical and Environmental Sciences. Published by China CDC. All rights reserved.

  5. A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis.

    PubMed

    Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin; Brehm Christensen, Peer; Langeland, Nina; Buhl, Mads Rauning; Pedersen, Court; Mørch, Kristine; Wejstål, Rune; Norkrans, Gunnar; Lindh, Magnus; Färkkilä, Martti; Westin, Johan

    2014-01-01

    Diagnosis of liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV) infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV infection. A stepwise multivariate logistic model selection was performed with liver cirrhosis, defined as Ishak fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI) in the paper, was based on the model: Log-odds (predicting cirrhosis) = -12.17+ (age × 0.11) + (BMI (kg/m(2)) × 0.23) + (D7-lathosterol (μg/100 mg cholesterol)×(-0.013)) + (Platelet count (x10(9)/L) × (-0.018)) + (Prothrombin-INR × 3.69). The area under the ROC curve (AUROC) for prediction of cirrhosis was 0.91 (95% CI 0.86-0.96). The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98). In conclusion, the new index may complement other methods in diagnosing cirrhosis in patients with chronic HCV infection.

  6. Determination of species-difference in microsomal metabolism of amitriptyline using a predictive MRM-IDA-EPI method.

    PubMed

    Lee, Ji-Yoon; Lee, Sang Yoon; Lee, KiHo; Oh, Soo Jin; Kim, Sang Kyum

    2015-03-05

    We investigated to compare species differences in amitriptyline (AMI) metabolism among mouse, rat, dog, and human liver microsomes. We developed a method for simultaneous determination of metabolic stability and metabolite profiling using predictive multiple reaction monitoring information-dependent acquisition-enhanced product ion (MRM-IDA-EPI) scanning. In the cofactor-dependent microsomal metabolism study, AMI was metabolized more rapidly in rat and human liver microsomes incubated with NADPH than UDPGA. AMI incubated with NADPH+UDPGA in rat, dog, or mouse liver microsomes disappeared rapidly with a half-life of 3.5, 8.4, or 9.2 min, respectively, but slowly in human liver microsomes with a half-life of 96 min. In total, 9, 10, 11, and 6 putative metabolites of AMI were detected in mouse, rat, dog, and human liver microsomes, respectively, based on mass spectrometric analyses. Kinetic analysis of metabolites in liver microsomes from each species over 120 min showed common metabolic routes of AMI, such as N-demethylation, hydroxylation, and glucuronidation, and subtle interspecies differences in AMI metabolism. The main metabolic routes in mouse, rat, dog, and human liver microsomes were hydroxylation followed by glucuronide conjugation, methyl hydroxylation, and N-demethylation, respectively. The MRM-IDA-EPI method can provide quantitative and qualitative information about metabolic stability and metabolite profiling simultaneously. Moreover, time course analysis of metabolites can not only eliminate false identification of metabolites, but also provide a rationale for proposed metabolic pathways. The MRM-IDA-EPI method combined with time course analysis of metabolites is useful for investigating drug metabolism at the early drug discovery stage. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  7. Towards more accurate vegetation mortality predictions

    DOE PAGES

    Sevanto, Sanna Annika; Xu, Chonggang

    2016-09-26

    Predicting the fate of vegetation under changing climate is one of the major challenges of the climate modeling community. Here, terrestrial vegetation dominates the carbon and water cycles over land areas, and dramatic changes in vegetation cover resulting from stressful environmental conditions such as drought feed directly back to local and regional climate, potentially leading to a vicious cycle where vegetation recovery after a disturbance is delayed or impossible.

  8. Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data.

    PubMed

    Gu, Deqing; Jian, Xingxing; Zhang, Cheng; Hua, Qiang

    2017-01-01

    Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named logical transformation of model (LTM) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.

  9. A high order accurate finite element algorithm for high Reynolds number flow prediction

    NASA Technical Reports Server (NTRS)

    Baker, A. J.

    1978-01-01

    A Galerkin-weighted residuals formulation is employed to establish an implicit finite element solution algorithm for generally nonlinear initial-boundary value problems. Solution accuracy, and convergence rate with discretization refinement, are quantized in several error norms, by a systematic study of numerical solutions to several nonlinear parabolic and a hyperbolic partial differential equation characteristic of the equations governing fluid flows. Solutions are generated using selective linear, quadratic and cubic basis functions. Richardson extrapolation is employed to generate a higher-order accurate solution to facilitate isolation of truncation error in all norms. Extension of the mathematical theory underlying accuracy and convergence concepts for linear elliptic equations is predicted for equations characteristic of laminar and turbulent fluid flows at nonmodest Reynolds number. The nondiagonal initial-value matrix structure introduced by the finite element theory is determined intrinsic to improved solution accuracy and convergence. A factored Jacobian iteration algorithm is derived and evaluated to yield a consequential reduction in both computer storage and execution CPU requirements while retaining solution accuracy.

  10. Metabolic Activity in the Insular Cortex and Hypothalamus Predicts Hot Flashes: An FDG-PET Study

    PubMed Central

    Deckersbach, Thilo; Lin, Nancy U.; Makris, Nikos; Skaar, Todd C.; Rauch, Scott L.; Dougherty, Darin D.; Hall, Janet E.

    2012-01-01

    Context: Hot flashes are a common side effect of adjuvant endocrine therapies (AET; leuprolide, tamoxifen, aromatase inhibitors) that reduce quality of life and treatment adherence in breast cancer patients. Because hot flashes affect only some women, preexisting neurobiological traits might predispose to their development. Previous studies have implicated the insula during the perception of hot flashes and the hypothalamus in thermoregulatory dysfunction. Objective: The aim of the study was to understand whether neurobiological factors predict hot flashes. Design: [18F]-Fluorodeoxyglucose (FDG) positron emission tomography (PET) brain scans coregistered with structural magnetic resonance imaging were used to determine whether metabolic activity in the insula and hypothalamic thermoregulatory and estrogen-feedback regions measured before and in response to AET predict hot flashes. Findings were correlated with CYP2D6 genotype because of CYP2D6 polymorphism associations with tamoxifen-induced hot flashes. Outcome Measures: We measured regional cerebral metabolic rate of glucose uptake (rCMRglu) in the insula and hypothalamus on FDG-PET. Results: Of 18 women without hot flashes who began AET, new-onset hot flashes were reported by 10 (55.6%) and were detected objectively in nine (50%) participants. Prior to the use of all AET, rCMRglu in the insula (P ≤ 0.01) and hypothalamic thermoregulatory (P = 0.045) and estrogen-feedback (P = 0.007) regions was lower in women who reported developing hot flashes. In response to AET, rCMRglu was further reduced in the insula in women developing hot flashes (P ≤ 0.02). Insular and hypothalamic rCMRglu levels were lower in intermediate than extensive CYP2D6 metabolizers. Conclusions: Trait neurobiological characteristics predict hot flashes. Genetic variability in CYP2D6 may underlie the neurobiological predisposition to hot flashes induced by AET. PMID:22723326

  11. Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

    NASA Astrophysics Data System (ADS)

    Rahmati, Mehdi

    2017-08-01

    Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed

  12. Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

    PubMed

    Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

    2016-01-01

    Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

  13. Hyperandrogenemia predicts metabolic phenotype in polycystic ovary syndrome: the utility of serum androstenedione.

    PubMed

    O'Reilly, Michael W; Taylor, Angela E; Crabtree, Nicola J; Hughes, Beverly A; Capper, Farfia; Crowley, Rachel K; Stewart, Paul M; Tomlinson, Jeremy W; Arlt, Wiebke

    2014-03-01

    Polycystic ovary syndrome (PCOS) is a triad of anovulation, insulin resistance, and hyperandrogenism. Androgen excess may correlate with metabolic risk and PCOS consensus criteria define androgen excess on the basis of serum T. Here we studied the utility of the androgen precursor serum androstenedione (A) in conjunction with serum T for predicting metabolic dysfunction in PCOS. Eighty-six PCOS patients fulfilling Rotterdam diagnostic consensus criteria and 43 age- and body mass index-matched controls underwent measurement of serum androgens by tandem mass spectrometry and an oral glucose tolerance test with homeostatic model assessment of insulin resistance and insulin sensitivity index calculation. We analyzed 24-hour urine androgen excretion by gas chromatography/mass spectrometry. PCOS patients had higher levels of serum androgens and urinary androgen metabolites than controls (all P < .001). Within the PCOS cohort, both serum A and T were positively correlated with the free androgen index (T × 100/SHBG) and total androgen metabolite excretion (all P < .001). All subjects with T above the normal reference range [high T (HT)] also had high A (HA/HT group, n = 56). However, the remaining 30 patients had normal T levels, either in the presence of HA (HA/NT; n = 20) or normal A (NA/NT; n = 10). The groups did not differ in age or BMI. The HA/HT and HA/NT groups had higher total androgen excretion than NA/NT (P < .01 and P < .05, respectively). Multiple linear regression showed a strong negative association between serum androstenedione and insulin sensitivity. The incidence of dysglycemia according to an oral glucose tolerance test increased with the severity of androgen phenotype (NA/NT, 0%; HA/NT, 14%; HA/HT, 25%, P = .03). Simultaneous measurement of serum T and A represents a useful tool for predicting metabolic risk in PCOS women. HA levels are a sensitive indicator of PCOS-related androgen excess.

  14. Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising

    PubMed Central

    Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Wang, Ming-Hui; Zhang, Yan

    2017-01-01

    Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research. PMID:29296195

  15. A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions.

    PubMed

    Cheung, C Y Maurice; Williams, Thomas C R; Poolman, Mark G; Fell, David A; Ratcliffe, R George; Sweetlove, Lee J

    2013-09-01

    Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade-off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper-osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper-osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance. © 2013 The Authors The Plant Journal © 2013 John Wiley & Sons Ltd.

  16. Predicted osteotomy planes are accurate when using patient-specific instrumentation for total knee arthroplasty in cadavers: a descriptive analysis.

    PubMed

    Kievit, A J; Dobbe, J G G; Streekstra, G J; Blankevoort, L; Schafroth, M U

    2018-06-01

    Malalignment of implants is a major source of failure during total knee arthroplasty. To achieve more accurate 3D planning and execution of the osteotomy cuts during surgery, the Signature (Biomet, Warsaw) patient-specific instrumentation (PSI) was used to produce pin guides for the positioning of the osteotomy blocks by means of computer-aided manufacture based on CT scan images. The research question of this study is: what is the transfer accuracy of osteotomy planes predicted by the Signature PSI system for preoperative 3D planning and intraoperative block-guided pin placement to perform total knee arthroplasty procedures? The transfer accuracy achieved by using the Signature PSI system was evaluated by comparing the osteotomy planes predicted preoperatively with the osteotomy planes seen intraoperatively in human cadaveric legs. Outcomes were measured in terms of translational and rotational errors (varus, valgus, flexion, extension and axial rotation) for both tibia and femur osteotomies. Average translational errors between the osteotomy planes predicted using the Signature system and the actual osteotomy planes achieved was 0.8 mm (± 0.5 mm) for the tibia and 0.7 mm (± 4.0 mm) for the femur. Average rotational errors in relation to predicted and achieved osteotomy planes were 0.1° (± 1.2°) of varus and 0.4° (± 1.7°) of anterior slope (extension) for the tibia, and 2.8° (± 2.0°) of varus and 0.9° (± 2.7°) of flexion and 1.4° (± 2.2°) of external rotation for the femur. The similarity between osteotomy planes predicted using the Signature system and osteotomy planes actually achieved was excellent for the tibia although some discrepancies were seen for the femur. The use of 3D system techniques in TKA surgery can provide accurate intraoperative guidance, especially for patients with deformed bone, tailored to individual patients and ensure better placement of the implant.

  17. CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts.

    PubMed

    Testa, Alison C; Hane, James K; Ellwood, Simon R; Oliver, Richard P

    2015-03-11

    The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against

  18. Prediction of exercise-mediated changes in metabolic markers by gene polymorphism.

    PubMed

    Kahara, Toshio; Takamura, Toshinari; Hayakawa, Tetsuo; Nagai, Yukihiro; Yamaguchi, Hiromi; Katsuki, Tatsuo; Katsuki, Ken-ichi; Katsuki, Michio; Kobayashi, Ken-ichi

    2002-08-01

    The effects of regular physical exercise on obesity-associated metabolic abnormalities vary for each individual. In this study, we investigated whether genotypes of genes associated with obesity can predict the effects of exercise on changes in metabolic markers in healthy men. Healthy Japanese men (n=106) performed the exercise program at 50% of their maximal heart rate for 20-60 min a day, 2-3 days each week for 3 months. The levels of fasting plasma glucose (FPG) and serum leptin significantly decreased after the exercise program. Polymorphisms of the beta3-adrenergic receptor (beta3AR) and uncoupling protein-1 (UCP-1) genes were analyzed with RFLP methods. In the Trp/Trp genotype of the beta3AR gene, the levels of serum leptin, FPG and fructosamine (FrAm) decreased significantly after the exercise program, but not in the Arg/Arg genotype. In the AG heterozygote and the GG homozygote of the UCP-1 gene, FPG and FrAm levels were significantly reduced, respectively. In conclusion, gene polymorphism of the beta3AR and UCP-1 was found to be associated with the exercise-mediated improvement in glucose tolerance and leptin resistance in healthy Japanese men.

  19. Metabolic enzyme microarray coupled with miniaturized cell-culture array technology for high-throughput toxicity screening.

    PubMed

    Lee, Moo-Yeal; Dordick, Jonathan S; Clark, Douglas S

    2010-01-01

    Due to poor drug candidate safety profiles that are often identified late in the drug development process, the clinical progression of new chemical entities to pharmaceuticals remains hindered, thus resulting in the high cost of drug discovery. To accelerate the identification of safer drug candidates and improve the clinical progression of drug candidates to pharmaceuticals, it is important to develop high-throughput tools that can provide early-stage predictive toxicology data. In particular, in vitro cell-based systems that can accurately mimic the human in vivo response and predict the impact of drug candidates on human toxicology are needed to accelerate the assessment of drug candidate toxicity and human metabolism earlier in the drug development process. The in vitro techniques that provide a high degree of human toxicity prediction will be perhaps more important in cosmetic and chemical industries in Europe, as animal toxicity testing is being phased out entirely in the immediate future.We have developed a metabolic enzyme microarray (the Metabolizing Enzyme Toxicology Assay Chip, or MetaChip) and a miniaturized three-dimensional (3D) cell-culture array (the Data Analysis Toxicology Assay Chip, or DataChip) for high-throughput toxicity screening of target compounds and their metabolic enzyme-generated products. The human or rat MetaChip contains an array of encapsulated metabolic enzymes that is designed to emulate the metabolic reactions in the human or rat liver. The human or rat DataChip contains an array of 3D human or rat cells encapsulated in alginate gels for cell-based toxicity screening. By combining the DataChip with the complementary MetaChip, in vitro toxicity results are obtained that correlate well with in vivo rat data.

  20. Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production

    PubMed Central

    2010-01-01

    Background Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405) is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous. Results Here we present a genome-scale model of C. thermocellum metabolism, iSR432, for the purpose of establishing a computational tool to study the metabolic network of C. thermocellum and facilitate efforts to engineer C. thermocellum for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the iSR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production. Conclusions By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of C. thermocellum and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of C. thermocellum and highlight remaining gaps

  1. Deformation, Failure, and Fatigue Life of SiC/Ti-15-3 Laminates Accurately Predicted by MAC/GMC

    NASA Technical Reports Server (NTRS)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2002-01-01

    NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) (ref.1) has been extended to enable fully coupled macro-micro deformation, failure, and fatigue life predictions for advanced metal matrix, ceramic matrix, and polymer matrix composites. Because of the multiaxial nature of the code's underlying micromechanics model, GMC--which allows the incorporation of complex local inelastic constitutive models--MAC/GMC finds its most important application in metal matrix composites, like the SiC/Ti-15-3 composite examined here. Furthermore, since GMC predicts the microscale fields within each constituent of the composite material, submodels for local effects such as fiber breakage, interfacial debonding, and matrix fatigue damage can and have been built into MAC/GMC. The present application of MAC/GMC highlights the combination of these features, which has enabled the accurate modeling of the deformation, failure, and life of titanium matrix composites.

  2. Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens Using Proteomic Data from a Field Biostimulation Experiment

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Fang, Yilin; Wilkins, Michael J.; Yabusaki, Steven B.

    2012-12-12

    Biomass and shotgun global proteomics data that reflected relative protein abundances from samples collected during the 2008 experiment at the U.S. Department of Energy Integrated Field-Scale Subsurface Research Challenge site in Rifle, Colorado, provided an unprecedented opportunity to validate a genome-scale metabolic model of Geobacter metallireducens and assess its performance with respect to prediction of metal reduction, biomass yield, and growth rate under dynamic field conditions. Reconstructed from annotated genomic sequence, biochemical, and physiological data, the constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes.more » Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low fluxes through amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.« less

  3. A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses.

    PubMed

    Imam, Saheed; Schäuble, Sascha; Valenzuela, Jacob; López García de Lomana, Adrián; Carter, Warren; Price, Nathan D; Baliga, Nitin S

    2015-12-01

    Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems-level understanding of their metabolism, which can be fundamentally achieved through large-scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome-scale metabolic model for the widely-studied microalga, Chlamydomonas reinhardtii. The model, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 encompasses a broad range of metabolic functions encoded across the nuclear, chloroplast and mitochondrial genomes accounting for 1355 genes (1460 transcripts), 2394 and 1133 metabolites. We found improved performance over the previous metabolic model based on comparisons of predictive accuracy across 306 phenotypes (from 81 mutants), lipid yield analysis and growth rates derived from chemostat-grown cells (under three conditions). Measurement of macronutrient uptake revealed carbon and phosphate to be good predictors of growth rate, while nitrogen consumption appeared to be in excess. We analyzed high-resolution time series transcriptomics data using iCre1355 to uncover dynamic pathway-level changes that occur in response to nitrogen starvation and changes in light intensity. This approach enabled accurate prediction of growth rates, the cessation of growth and accumulation of triacylglycerols during nitrogen starvation, and the temporal response of different growth-associated pathways to increased light intensity. Thus, iCre1355 represents an experimentally validated genome-scale reconstruction of C. reinhardtii metabolism that should serve as a useful resource for studying the metabolic processes of this and related microalgae. © 2015 The Authors The Plant Journal © 2015 John Wiley & Sons Ltd.

  4. Accurate prediction of cardiorespiratory fitness using cycle ergometry in minimally disabled persons with relapsing-remitting multiple sclerosis.

    PubMed

    Motl, Robert W; Fernhall, Bo

    2012-03-01

    To examine the accuracy of predicting peak oxygen consumption (VO(2peak)) primarily from peak work rate (WR(peak)) recorded during a maximal, incremental exercise test on a cycle ergometer among persons with relapsing-remitting multiple sclerosis (RRMS) who had minimal disability. Cross-sectional study. Clinical research laboratory. Women with RRMS (n=32) and sex-, age-, height-, and weight-matched healthy controls (n=16) completed an incremental exercise test on a cycle ergometer to volitional termination. Not applicable. Measured and predicted VO(2peak) and WR(peak). There were strong, statistically significant associations between measured and predicted VO(2peak) in the overall sample (R(2)=.89, standard error of the estimate=127.4 mL/min) and subsamples with (R(2)=.89, standard error of the estimate=131.3 mL/min) and without (R(2)=.85, standard error of the estimate=126.8 mL/min) multiple sclerosis (MS) based on the linear regression analyses. Based on the 95% confidence limits for worst-case errors, the equation predicted VO(2peak) within 10% of its true value in 95 of every 100 subjects with MS. Peak VO(2) can be accurately predicted in persons with RRMS who have minimal disability as it is in controls by using established equations and WR(peak) recorded from a maximal, incremental exercise test on a cycle ergometer. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  5. Improving Predictions of Tree Drought Mortality in the Community Land Model Using Hydraulic Physiology Theory and its Effects on Carbon Metabolism

    NASA Astrophysics Data System (ADS)

    McNellis, B.; Hudiburg, T. W.

    2017-12-01

    Tree mortality due to drought is predicted to have increasing impacts on ecosystem structure and function during the 21st century. Models can attempt to predict which forests are most at risk from drought, but novel environments may preclude analysis that relies on past observations. The inclusion of more mechanistic detail may reduce uncertainty in predictions, but can also compound model complexity, especially in global models. The Community Land Model version 5 (CLM5), itself a component of the Community Earth System Model (CESM), has recently integrated cohort-based demography into its dynamic vegetation component and is in the process of coupling this demography to a model of plant hydraulic physiology (FATES-Hydro). Previous treatment of drought stress and plant mortality within CLM has been relatively broad, but a detailed hydraulics module represents a key step towards accurate mortality prognosis. Here, we examine the structure of FATES-Hydro with respect to two key physiological attributes: tissue osmotic potentials and embolism refilling. Specifically, we ask how FATES-Hydro captures mechanistic realism within each attribute and how much support there is within the physiological literature for its further elaboration within the model structure. Additionally, connections to broader aspects of carbon metabolism within FATES are explored to better resolve emergent consequences of drought stress on ecosystem function and tree demographics. An on-going field experiment in managed stands of Pinus ponderosa and mixed conifers is assessed for model parameterization and performance across PNW forests, with important implications for future forest management strategy.

  6. [ABDOMINAL BIOELECTRICAL IMPEDANCE ANALYSIS AND ANTHROPOMETRY FOR PREDICTING METABOLIC SYNDROME IN MIDDLE AGED MEN].

    PubMed

    Fernández-Vázquez, Rosalía; Millán Romero, Ángel; Barbancho, Miguel Ángel; Alvero-Cruz, José Ramón

    2015-09-01

    central obesity has a higher risk of metabolic syndrome. The present work aimed to study the relationship of trunk fat and the visceral fat index, and other anthropometric indices in relation to the metabolic syndrome in middle aged male Methods: design: transversal descriptive and correlational study. 75 male, volunteers who have access to a medical assessment, with an age range of 21 to 59 years, from different professions. Weight, height, body mass index, waist circumference, gluteal circumference, waist-to-hip ratio, waist-to-height ratio, trunk fat and visceral fat level by bioelectrical abdominal impedance analysis with Tanita AB-140 (ViScan) and biochemical markers: fasting glucose, total cholesterol, and triglycerides. Likewise, the systolic and diastolic blood pressure was measured. there are significant correlations of anthropometric measurements with trunk fat and visceral fat level and the same with biochemical variables. Receptor-operator curves (ROC curve) analysis shows that the cutoff points from which arises the metabolic syndrome are 32.7% of trunk fat and a level of visceral fat of 13 with a high sensitivity and specificity, attaining the same cut-off points for the metabolic syndrome and obesity status. trunk fat and visceral fat levels determined by bioelectrical abdominal impedance analysis, values are variables very sensitive and specific for the detection of metabolic syndrome and obesity, though not over the variables and anthropometric indices. In the condition of the overweight, trunk fat and visceral fat level are more predictive than anthropometric measures. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

  7. Extending the Derek-Meteor Workflow to Predict Chemical-Toxicity Space Impacted by Metabolism: Application to ToxCast and Tox21 Chemical Inventories

    EPA Science Inventory

    A central aim of EPA’s ToxCast project is to use in vitro high-throughput screening (HTS) profiles to build predictive models of in vivo toxicity. Where assays lack metabolic capability, such efforts may need to anticipate the role of metabolic activation (or deactivation). A wo...

  8. Micropatterned coculture of hepatocytes on electrospun fibers as a potential in vitro model for predictive drug metabolism.

    PubMed

    Liu, Yaowen; Wei, Jiaojun; Lu, Jinfu; Lei, Dongmei; Yan, Shili; Li, Xiaohong

    2016-06-01

    The liver is the major organ of importance to determine drug dispositions in the body, thus the development of hepatocyte culture systems is of great scientific and practical interests to provide reliable and predictable models for in vitro drug screening. In the current study, to address the challenges of a rapid function loss of primary hepatocytes, the coculture of hepatocytes with fibroblasts and endothelial cells (Hep-Fib-EC) was established on micropatterned fibrous scaffolds. Liver-specific functions, such as the albumin secretion and urea synthesis, were well maintained in the coculture system, accompanied by a rapid formation of multicellular hepatocyte spheroids. The activities of phase I (CYP3A11 and CYP2C9) and phase II enzymes indicated a gradual increase for cocultured hepatocytes, and a maximum level was achieved after 5 days and maintained throughout 15 days of culture. The metabolism testing on model drugs indicated that the scaled clearance rates for hepatocytes in the Hep-Fib-EC coculture system were significantly higher than those of other culture methods, and a linear regression analysis indicated good correlations between the observed data of rats and in vitro predicted values during 15 days of culture. In addition, the enzyme activities and drug clearance rates of hepatocytes in the Hep-Fib-EC coculture model experienced sensitive responsiveness to the inducers and inhibitors of metabolizing enzymes. These results demonstrated the feasibility of micropatterned coculture of hepatocytes as a potential in vitro testing model for the prediction of in vivo drug metabolism. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Do Skilled Elementary Teachers Hold Scientific Conceptions and Can They Accurately Predict the Type and Source of Students' Preconceptions of Electric Circuits?

    ERIC Educational Resources Information Center

    Lin, Jing-Wen

    2016-01-01

    Holding scientific conceptions and having the ability to accurately predict students' preconceptions are a prerequisite for science teachers to design appropriate constructivist-oriented learning experiences. This study explored the types and sources of students' preconceptions of electric circuits. First, 438 grade 3 (9 years old) students were…

  10. In vitro enantioselective human liver microsomal metabolism and prediction of in vivo pharmacokinetic parameters of tetrabenazine by DLLME-CE.

    PubMed

    Bocato, Mariana Zuccherato; de Lima Moreira, Fernanda; de Albuquerque, Nayara Cristina Perez; de Gaitani, Cristiane Masetto; de Oliveira, Anderson Rodrigo Moraes

    2016-09-05

    A new capillary electrophoresis method for the enantioselective analysis of cis- and trans- dihydrotetrabenazine (diHTBZ) after in vitro metabolism by human liver microsomes (HLMs) was developed. The chiral electrophoretic separations were performed by using tris-phosphate buffer (pH 2.5) containing 1% (w/v) carboxymethyl-β-CD as background electrolyte with an applied voltage of +15kV and capillary temperature kept at 15°C. Dispersive liquid-liquid microextraction was employed to extract the analytes from HLMs. Dichloromethane was used as extraction solvent (75μL) and acetone as disperser solvent (150μL). The method was validated according to official guidelines and showed to be linear over the concentration range of 0.29-19.57μmolL(-1) (r=0.9955) for each metabolite enantiomer. Within- and between-day precision and accuracy evaluated by relative standard deviation and relative error were lower than 15% for all enantiomers. The stability assay showed that the analytes kept stable under handling, storage and in metabolism conditions. After method validation, an enantioselective in vitro metabolism and in vivo pharmacokinetic prediction was carried out. This study showed a stereoselective metabolism and the observed kinetic profile indicated a substrate inhibition behavior. DiHTBZ enantiomers were catalyzed mainly by CYP2C19 and the predicted clearance suggests that liver metabolism is the main route for TBZ elimination which supports the literature data. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Anthropometric cutoff values for predicting metabolic syndrome in a Saudi community: from the SAUDI-DM study.

    PubMed

    Al-Rubean, Khalid; Youssef, Amira M; AlFarsi, Yousuf; Al-Sharqawi, Ahmad H; Bawazeer, Nahla; AlOtaibi, Mohammad T; AlRumaih, Fahd Issa; Zaidi, Muhammad Shoaib

    2017-01-01

    The prevalence of metabolic syndrome varies widely by ethnicity and by the criteria used in its definition. To identify the optimal cutoff values for waist circumference (WC), waist-to-hip ratio (WHR) and body mass index (BMI) for identifying metabolic syndrome among the Saudi population. Nationwide household cross-sectional population-based survey. Thirteen health sectors in Saudi Arabia. We used data for subjects in the Saudi Abnormal Glucose Metabolism and Diabetes Impact Study (SAUDI-DM), which was conducted from 2007 to 2009. Using International Diabetes Federation (IDF) criteria, metabolic syndrome and its different components were assessed using anthropometric measurements, blood pressure, fasting plasma glucose, triglycerides and HDL cholesterol. Receiver operating characteristic (ROC) curves were generated to assess sensitivity and specificity for different cutoff values of WC, WHR, and BMI. The Youden index was used to calculate the optimal cutoff value for each anthropometric measurement. Optimal cutoff value for WC, WHR, and BMI for identifying the risk of metabolic syndrome. The prevalence of two or more risk factors for metabolic syndrome was observed in 43.42% of the total cohort of 12126 study participants >=18 years of age. The presence of two or more risk factors were significantly higher among men (46.81%) than women (40.53%) (P < .001). The optimal cutoff values for WC, WHR, and BMI were 92 cm, 0.89, and 25 kg/m2 for men and 87 cm, 0.81 and 28 kg/m2 for women for identifying the risk of metabolic syndrome. The prevalence of elevated triglycerides, blood pressure, and fasting plasma glucose significantly increased with age for both genders. The proposed WC cutoff values were better than WHR and BMI in predicting metabolic syndrome and could be used for screening people at high risk for metabolic syndrome in the Saudi population. No direct measure of body fatness and fat distribution, cross-sectional design.

  12. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review.

    PubMed

    Frankenfield, David; Roth-Yousey, Lori; Compher, Charlene

    2005-05-01

    An assessment of energy needs is a necessary component in the development and evaluation of a nutrition care plan. The metabolic rate can be measured or estimated by equations, but estimation is by far the more common method. However, predictive equations might generate errors large enough to impact outcome. Therefore, a systematic review of the literature was undertaken to document the accuracy of predictive equations preliminary to deciding on the imperative to measure metabolic rate. As part of a larger project to determine the role of indirect calorimetry in clinical practice, an evidence team identified published articles that examined the validity of various predictive equations for resting metabolic rate (RMR) in nonobese and obese people and also in individuals of various ethnic and age groups. Articles were accepted based on defined criteria and abstracted using evidence analysis tools developed by the American Dietetic Association. Because these equations are applied by dietetics practitioners to individuals, a key inclusion criterion was research reports of individual data. The evidence was systematically evaluated, and a conclusion statement and grade were developed. Four prediction equations were identified as the most commonly used in clinical practice (Harris-Benedict, Mifflin-St Jeor, Owen, and World Health Organization/Food and Agriculture Organization/United Nations University [WHO/FAO/UNU]). Of these equations, the Mifflin-St Jeor equation was the most reliable, predicting RMR within 10% of measured in more nonobese and obese individuals than any other equation, and it also had the narrowest error range. No validation work concentrating on individual errors was found for the WHO/FAO/UNU equation. Older adults and US-residing ethnic minorities were underrepresented both in the development of predictive equations and in validation studies. The Mifflin-St Jeor equation is more likely than the other equations tested to estimate RMR to within 10% of

  13. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

    NASA Astrophysics Data System (ADS)

    Xie, Tian; Grossman, Jeffrey C.

    2018-04-01

    The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

  14. Challenges and Opportunities with Predicting in Vivo Phase II Metabolism via Glucuronidation from in Vitro Data

    PubMed Central

    Ge, Shufan; Tu, Yifan; Hu, Ming

    2017-01-01

    Glucuronidation is the most important phase II metabolic pathway which is responsible for the clearance of many endogenous and exogenous compounds. To better understand the elimination process for compounds undergoing glucuronidation and identify compounds with desirable in vivo pharmacokinetic properties, many efforts have been made to predict in vivo glucuronidation using in vitro data. In this article, we reviewed typical approaches used in previous predictions. The problems and challenges in prediction of glucuronidation were discussed. Besides that different incubation conditions can affect the prediction accuracy, other factors including efflux / uptake transporters, enterohepatic recycling, and deglucuronidation reactions also contribute to the disposition of glucuronides and make the prediction more difficult. PBPK modeling, which can describe more complicated process in vivo, is a promising prediction strategy which may greatly improve the prediction of glucuronidation and potential DDIs involving glucuronidation. Based on previous studies, we proposed a transport-glucuronidation classification system, which was built based on the kinetics of both glucuronidation and transport of the glucuronide. This system could be a very useful tool to achieve better in vivo predictions. PMID:28966903

  15. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can bemore » rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.« less

  16. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory

    DOE PAGES

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; ...

    2017-03-03

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can bemore » rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.« less

  17. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory.

    PubMed

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; Drisdell, Walter S; Shirley, Eric L; Prendergast, David

    2017-03-03

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can be rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.

  18. Metabolite profiling stratifies pancreatic ductal adenocarcinomas into subtypes with distinct sensitivities to metabolic inhibitors

    PubMed Central

    Daemen, Anneleen; Peterson, David; Sahu, Nisebita; McCord, Ron; Du, Xiangnan; Liu, Bonnie; Kowanetz, Katarzyna; Hong, Rebecca; Moffat, John; Gao, Min; Boudreau, Aaron; Mroue, Rana; Corson, Laura; O’Brien, Thomas; Qing, Jing; Sampath, Deepak; Merchant, Mark; Yauch, Robert; Manning, Gerard; Settleman, Jeffrey; Hatzivassiliou, Georgia; Evangelista, Marie

    2015-01-01

    Although targeting cancer metabolism is a promising therapeutic strategy, clinical success will depend on an accurate diagnostic identification of tumor subtypes with specific metabolic requirements. Through broad metabolite profiling, we successfully identified three highly distinct metabolic subtypes in pancreatic ductal adenocarcinoma (PDAC). One subtype was defined by reduced proliferative capacity, whereas the other two subtypes (glycolytic and lipogenic) showed distinct metabolite levels associated with glycolysis, lipogenesis, and redox pathways, confirmed at the transcriptional level. The glycolytic and lipogenic subtypes showed striking differences in glucose and glutamine utilization, as well as mitochondrial function, and corresponded to differences in cell sensitivity to inhibitors of glycolysis, glutamine metabolism, lipid synthesis, and redox balance. In PDAC clinical samples, the lipogenic subtype associated with the epithelial (classical) subtype, whereas the glycolytic subtype strongly associated with the mesenchymal (QM-PDA) subtype, suggesting functional relevance in disease progression. Pharmacogenomic screening of an additional ∼200 non-PDAC cell lines validated the association between mesenchymal status and metabolic drug response in other tumor indications. Our findings highlight the utility of broad metabolite profiling to predict sensitivity of tumors to a variety of metabolic inhibitors. PMID:26216984

  19. Prediction of the presence of insulin resistance using general health checkup data in Japanese employees with metabolic risk factors.

    PubMed

    Takahara, Mitsuyoshi; Katakami, Naoto; Kaneto, Hideaki; Noguchi, Midori; Shimomura, Iichiro

    2014-01-01

    The aim of the current study was to develop a predictive model of insulin resistance using general health checkup data in Japanese employees with one or more metabolic risk factors. We used a database of 846 Japanese employees with one or more metabolic risk factors who underwent general health checkup and a 75-g oral glucose tolerance test (OGTT). Logistic regression models were developed to predict existing insulin resistance evaluated using the Matsuda index. The predictive performance of these models was assessed using the C statistic. The C statistics of body mass index (BMI), waist circumference and their combined use were 0.743, 0.732 and 0.749, with no significant differences. The multivariate backward selection model, in which BMI, the levels of plasma glucose, high-density lipoprotein (HDL) cholesterol, log-transformed triglycerides and log-transformed alanine aminotransferase and hypertension under treatment remained, had a C statistic of 0.816, with a significant difference compared to the combined use of BMI and waist circumference (p<0.01). The C statistic was not significantly reduced when the levels of log-transformed triglycerides and log-transformed alanine aminotransferase and hypertension under treatment were simultaneously excluded from the multivariate model (p=0.14). On the other hand, further exclusion of any of the remaining three variables significantly reduced the C statistic (all p<0.01). When predicting the presence of insulin resistance using general health checkup data in Japanese employees with metabolic risk factors, it is important to take into consideration the BMI and fasting plasma glucose and HDL cholesterol levels.

  20. Does Foot Anthropometry Predict Metabolic Cost During Running?

    PubMed

    van Werkhoven, Herman; Piazza, Stephen J

    2017-10-01

    Several recent investigations have linked running economy to heel length, with shorter heels being associated with less metabolic energy consumption. It has been hypothesized that shorter heels require larger plantar flexor muscle forces, thus increasing tendon energy storage and reducing metabolic cost. The goal of this study was to investigate this possible mechanism for metabolic cost reduction. Fifteen male subjects ran at 16 km⋅h -1 on a treadmill and subsequently on a force-plate instrumented runway. Measurements of oxygen consumption, kinematics, and ground reaction forces were collected. Correlational analyses were performed between oxygen consumption and anthropometric and kinetic variables associated with the ankle and foot. Correlations were also computed between kinetic variables (peak joint moment and peak tendon force) and heel length. Estimated peak Achilles tendon force normalized to body weight was found to be strongly correlated with heel length normalized to body height (r = -.751, p = .003). Neither heel length nor any other measured or calculated variable were correlated with oxygen consumption, however. Subjects with shorter heels experienced larger Achilles tendon forces, but these forces were not associated with reduced metabolic cost. No other anthropometric and kinetic variables considered explained the variance in metabolic cost across individuals.

  1. Accurate prediction of acute fish toxicity of fragrance chemicals with the RTgill-W1 cell assay.

    PubMed

    Natsch, Andreas; Laue, Heike; Haupt, Tina; von Niederhäusern, Valentin; Sanders, Gordon

    2018-03-01

    Testing for acute fish toxicity is an integral part of the environmental safety assessment of chemicals. A true replacement of primary fish tissue was recently proposed using cell viability in a fish gill cell line (RTgill-W1) as a means of predicting acute toxicity, showing good predictivity on 35 chemicals. To promote regulatory acceptance, the predictivity and applicability domain of novel tests need to be carefully evaluated on chemicals with existing high-quality in vivo data. We applied the RTgill-W1 cell assay to 38 fragrance chemicals with a wide range of both physicochemical properties and median lethal concentration (LC50) values and representing a diverse range of chemistries. A strong correlation (R 2  = 0.90-0.94) between the logarithmic in vivo LC50 values, based on fish mortality, and the logarithmic in vitro median effect concentration (EC50) values based on cell viability was observed. A leave-one-out analysis illustrates a median under-/overprediction from in vitro EC50 values to in vivo LC50 values by a factor of 1.5. This assay offers a simple, accurate, and reliable alternative to in vivo acute fish toxicity testing for chemicals, presumably acting mainly by a narcotic mode of action. Furthermore, the present study provides validation of the predictivity of the RTgill-W1 assay on a completely independent set of chemicals that had not been previously tested and indicates that fragrance chemicals are clearly within the applicability domain. Environ Toxicol Chem 2018;37:931-941. © 2017 SETAC. © 2017 SETAC.

  2. Hierarchical Status Predicts Behavioral Vulnerability and Nucleus Accumbens Metabolic Profile Following Chronic Social Defeat Stress.

    PubMed

    Larrieu, Thomas; Cherix, Antoine; Duque, Aranzazu; Rodrigues, João; Lei, Hongxia; Gruetter, Rolf; Sandi, Carmen

    2017-07-24

    Extensive data highlight the existence of major differences in individuals' susceptibility to stress [1-4]. While genetic factors [5, 6] and exposure to early life stress [7, 8] are key components for such neurobehavioral diversity, intriguing observations revealed individual differences in response to stress in inbred mice [9-12]. This raised the possibility that other factors might be critical in stress vulnerability. A key challenge in the field is to identify non-invasively risk factors for vulnerability to stress. Here, we investigated whether behavioral factors, emerging from preexisting dominance hierarchies, could predict vulnerability to chronic stress [9, 13-16]. We applied a chronic social defeat stress (CSDS) model of depression in C57BL/6J mice to investigate the predictive power of hierarchical status to pinpoint which individuals will exhibit susceptibility to CSDS. Given that the high social status of dominant mice would be the one particularly challenged by CSDS, we predicted and found that dominant individuals were the ones showing a strong susceptibility profile as indicated by strong social avoidance following CSDS, while subordinate mice were not affected. Data from 1 H-NMR spectroscopy revealed that the metabolic profile in the nucleus accumbens (NAc) relates to social status and vulnerability to stress. Under basal conditions, subordinates show lower levels of energy-related metabolites compared to dominants. In subordinates, but not dominants, levels of these metabolites were increased after exposure to CSDS. To the best of our knowledge, this is the first study that identifies non-invasively the origin of behavioral risk factors predictive of stress-induced depression-like behaviors associated with metabolic changes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Resting metabolic rate in elite rowers and canoeists: difference between indirect calorimetry and prediction.

    PubMed

    Carlsohn, Anja; Scharhag-Rosenberger, Friederike; Cassel, Michael; Mayer, Frank

    2011-01-01

    Athletes may differ in their resting metabolic rate (RMR) from the general population. However, to estimate the RMR in athletes, prediction equations that have not been validated in athletes are often used. The purpose of this study was therefore to verify the applicability of commonly used RMR predictions for use in athletes. The RMR was measured by indirect calorimetry in 17 highly trained rowers and canoeists of the German national teams (BMI 24 ± 2 kg/m(2), fat-free mass 69 ± 15 kg). In addition, the RMR was predicted using Cunningham (CUN) and Harris-Benedict (HB) equations. A two-way repeated measures ANOVA was calculated to test for differences between predicted and measured RMR (α = 0.05). The root mean square percentage error (RMSPE) was calculated and the Bland-Altman procedure was used to quantify the bias for each prediction. Prediction equations significantly underestimated the RMR in males (p < 0.001). The RMSPE was calculated to be 18.4% (CUN) and 20.9% (HB) in the entire group. The bias was 133 kcal/24 h for CUN and 202 kcal/24 h for HB. Predictions significantly underestimate the RMR in male heavyweight endurance athletes but not in females. In athletes with a high fat-free mass, prediction equations might therefore not be applicable to estimate energy requirements. Instead, measurement of the resting energy expenditure or specific prediction equations might be needed for the individual heavyweight athlete. Copyright © 2011 S. Karger AG, Basel.

  4. A cross-race effect in metamemory: Predictions of face recognition are more accurate for members of our own race

    PubMed Central

    Hourihan, Kathleen L.; Benjamin, Aaron S.; Liu, Xiping

    2012-01-01

    The Cross-Race Effect (CRE) in face recognition is the well-replicated finding that people are better at recognizing faces from their own race, relative to other races. The CRE reveals systematic limitations on eyewitness identification accuracy and suggests that some caution is warranted in evaluating cross-race identification. The CRE is a problem because jurors value eyewitness identification highly in verdict decisions. In the present paper, we explore how accurate people are in predicting their ability to recognize own-race and other-race faces. Caucasian and Asian participants viewed photographs of Caucasian and Asian faces, and made immediate judgments of learning during study. An old/new recognition test replicated the CRE: both groups displayed superior discriminability of own-race faces, relative to other-race faces. Importantly, relative metamnemonic accuracy was also greater for own-race faces, indicating that the accuracy of predictions about face recognition is influenced by race. This result indicates another source of concern when eliciting or evaluating eyewitness identification: people are less accurate in judging whether they will or will not recognize a face when that face is of a different race than they are. This new result suggests that a witness’s claim of being likely to recognize a suspect from a lineup should be interpreted with caution when the suspect is of a different race than the witness. PMID:23162788

  5. Characteristics of Resting Metabolic Rate in Critically Ill, Mechanically Ventilated Adults With Cystic Fibrosis.

    PubMed

    Frankenfield, David C; Ashcraft, Christine M; Drasher, Tammy L; Reid, Elizabeth K; Vender, Robert L

    2017-05-01

    Critically ill patients with cystic fibrosis may be especially sensitive to the negative consequences of overfeeding and underfeeding, yet there is almost no information available about the energy needs of these patients. The purpose of this study was to characterize the metabolic rate of critically ill adult patients with cystic fibrosis requiring mechanical ventilation. This was an observational study in which the resting metabolic rate, oxygen consumption, and carbon dioxide production of adult patients with cystic fibrosis requiring critical care, sedation, and mechanical ventilation were measured with indirect calorimetry. This group was compared with a cohort of adult critical care patients without cystic fibrosis. Twelve patients with cystic fibrosis were identified and measured. These were compared with a control group of 25 critically ill patients. Both groups were underweight (body mass index, 17.4 ± 4.0 kg/m 2 in cystic fibrosis and 18.4 ± 2.3 kg/m 2 in control). Adjusting for differences in age, sex, height, and weight, there was no difference in resting metabolic rate between the cystic fibrosis and control groups (1702 ± 193 vs 1642 ± 194 kcal/d, P = .388). Measured resting metabolic rate matched predicted values 58% of the time in cystic fibrosis and 60% of the time in control. The resting metabolic rate of sedated adult patients with cystic fibrosis being assisted with mechanical ventilation is not different from that of adult critical care patients without cystic fibrosis. In both these underweight groups, accurate prediction of resting metabolic rate is difficult to obtain.

  6. Comparison of Lipid Accumulation Product Index with Body Mass Index and Waist Circumference as a Predictor of Metabolic Syndrome in Indian Population.

    PubMed

    Ray, Lopamudra; Ravichandran, Kandasamy; Nanda, Sunil Kumar

    2018-06-01

    Metabolic syndrome (MetS), which confers a high risk for cardiovascular diseases, needs early diagnosis and treatment to reduce morbidity and mortality. Lipid accumulation product index has been reported to be an inexpensive marker of visceral fat and metabolic syndrome. This study aimed to evaluate lipid accumulation product index as a marker for metabolic syndrome in the Indian population where the prevalence of the condition is steadily increasing. A hospital-based, case-control study was conducted with 72 diagnosed cases of metabolic syndrome and 79 control subjects. In all the participants, body mass index (BMI) and lipid accumulation product index were calculated. The difference between cases and controls in BMI, waist circumference (WC), and lipid accumulation product index was assessed by Mann-Whitney U test/unpaired t-test. Associations of BMI, WC, and lipid accumulation product index with metabolic syndrome were compared by multiple logistic regression analysis and receiver operating characteristic analysis. BMI, WC, and lipid accumulation product index were significantly higher in metabolic syndrome (P < 0.05). Although all were independently associated with metabolic syndrome, lipid accumulation product index had the highest prediction accuracy. The parameter also had a high area under curve of 0.901 (95% confidence interval 0.85-0.95) and a high sensitivity (76.4%), specificity (91.1%), positive predictive value (88.7%), and negative predictive value (80.9%) for detection of metabolic syndrome. In the Indian population, lipid accumulation product index is a better predictor of metabolic syndrome compared to BMI and WC and should be incorporated in laboratory reports as early, accurate, and inexpensive indicator of metabolic syndrome.

  7. Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble.

    PubMed

    Zhang, Ming; Xu, Yan; Li, Lei; Liu, Zi; Yang, Xibei; Yu, Dong-Jun

    2018-06-01

    RNA 5-methylcytosine (m 5 C) is an important post-transcriptional modification that plays an indispensable role in biological processes. The accurate identification of m 5 C sites from primary RNA sequences is especially useful for deeply understanding the mechanisms and functions of m 5 C. Due to the difficulty and expensive costs of identifying m 5 C sites with wet-lab techniques, developing fast and accurate machine-learning-based prediction methods is urgently needed. In this study, we proposed a new m 5 C site predictor, called M5C-HPCR, by introducing a novel heuristic nucleotide physicochemical property reduction (HPCR) algorithm and classifier ensemble. HPCR extracts multiple reducts of physical-chemical properties for encoding discriminative features, while the classifier ensemble is applied to integrate multiple base predictors, each of which is trained based on a separate reduct of the physical-chemical properties obtained from HPCR. Rigorous jackknife tests on two benchmark datasets demonstrate that M5C-HPCR outperforms state-of-the-art m 5 C site predictors, with the highest values of MCC (0.859) and AUC (0.962). We also implemented the webserver of M5C-HPCR, which is freely available at http://cslab.just.edu.cn:8080/M5C-HPCR/. Copyright © 2018 Elsevier Inc. All rights reserved.

  8. Dynamic metabolic modeling of heterotrophic and mixotrophic microalgal growth on fermentative wastes

    PubMed Central

    Baroukh, Caroline; Turon, Violette; Bernard, Olivier

    2017-01-01

    Microalgae are promising microorganisms for the production of numerous molecules of interest, such as pigments, proteins or triglycerides that can be turned into biofuels. Heterotrophic or mixotrophic growth on fermentative wastes represents an interesting approach to achieving higher biomass concentrations, while reducing cost and improving the environmental footprint. Fermentative wastes generally consist of a blend of diverse molecules and it is thus crucial to understand microalgal metabolism in such conditions, where switching between substrates might occur. Metabolic modeling has proven to be an efficient tool for understanding metabolism and guiding the optimization of biomass or target molecule production. Here, we focused on the metabolism of Chlorella sorokiniana growing heterotrophically and mixotrophically on acetate and butyrate. The metabolism was represented by 172 metabolic reactions. The DRUM modeling framework with a mildly relaxed quasi-steady-state assumption was used to account for the switching between substrates and the presence of light. Nine experiments were used to calibrate the model and nine experiments for the validation. The model efficiently predicted the experimental data, including the transient behavior during heterotrophic, autotrophic, mixotrophic and diauxic growth. It shows that an accurate model of metabolism can now be constructed, even in dynamic conditions, with the presence of several carbon substrates. It also opens new perspectives for the heterotrophic and mixotrophic use of microalgae, especially for biofuel production from wastes. PMID:28582469

  9. Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering

    PubMed Central

    Baghalian, Kambiz; Hajirezaei, Mohammad-Reza; Schreiber, Falk

    2014-01-01

    Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology. PMID:25344492

  10. Hyperandrogenemia Predicts Metabolic Phenotype in Polycystic Ovary Syndrome: The Utility of Serum Androstenedione

    PubMed Central

    O'Reilly, Michael W.; Taylor, Angela E.; Crabtree, Nicola J.; Hughes, Beverly A.; Capper, Farfia; Crowley, Rachel K.; Stewart, Paul M.; Tomlinson, Jeremy W.

    2014-01-01

    Context: Polycystic ovary syndrome (PCOS) is a triad of anovulation, insulin resistance, and hyperandrogenism. Androgen excess may correlate with metabolic risk and PCOS consensus criteria define androgen excess on the basis of serum T. Here we studied the utility of the androgen precursor serum androstenedione (A) in conjunction with serum T for predicting metabolic dysfunction in PCOS. Patients and Methods: Eighty-six PCOS patients fulfilling Rotterdam diagnostic consensus criteria and 43 age- and body mass index-matched controls underwent measurement of serum androgens by tandem mass spectrometry and an oral glucose tolerance test with homeostatic model assessment of insulin resistance and insulin sensitivity index calculation. We analyzed 24-hour urine androgen excretion by gas chromatography/mass spectrometry. Results: PCOS patients had higher levels of serum androgens and urinary androgen metabolites than controls (all P < .001). Within the PCOS cohort, both serum A and T were positively correlated with the free androgen index (T × 100/SHBG) and total androgen metabolite excretion (all P < .001). All subjects with T above the normal reference range [high T (HT)] also had high A (HA/HT group, n = 56). However, the remaining 30 patients had normal T levels, either in the presence of HA (HA/NT; n = 20) or normal A (NA/NT; n = 10). The groups did not differ in age or BMI. The HA/HT and HA/NT groups had higher total androgen excretion than NA/NT (P < .01 and P < .05, respectively). Multiple linear regression showed a strong negative association between serum androstenedione and insulin sensitivity. The incidence of dysglycemia according to an oral glucose tolerance test increased with the severity of androgen phenotype (NA/NT, 0%; HA/NT, 14%; HA/HT, 25%, P = .03). Conclusion: Simultaneous measurement of serum T and A represents a useful tool for predicting metabolic risk in PCOS women. HA levels are a sensitive indicator of PCOS-related androgen excess. PMID

  11. Models Predictive of Metabolic Syndrome Components in Obese Pediatric Patients.

    PubMed

    Ortega-Cortes, Rosa; Trujillo, Xóchitl; Hurtado López, Erika Fabiola; López Beltrán, Ana Laura; Colunga Rodríguez, Cecilia; Barrera-de Leon, Juan Carlos; Tlacuilo-Parra, Alberto

    2016-01-01

    Components of metabolic syndrome (MetS) are complications caused by abdominal obesity and insulin resistance (IR). Diagnosis of MetS by clinical indicators could help to identify patients at risk of cardiovascular disease and type 2 diabetes. We undertook this study to propose predictive indicators of MetS in obese children and adolescents. A cross-sectional study was carried out. After obtaining informed consent and the registration of the study with an institutional research committee, 172 obese patients from an Obesity Clinic, aged 6-15 years, were included. Variables included were waist circumference (WC), glucose, high-density lipoprotein (HDL), triglycerides (TGL), blood pressure, insulin resistance (by homeostatic model assessment HOMA-index), acanthosis nigricans (AN), uric acid, serum glutamic oxaloacetic transaminase (GOT) and alanine transaminase, and hepatic sonogram. International standards for age and sex variables were used. Multivariate analysis was applied. Variables predicted components of MetS in children: HOMA-IR (insulin resistance by HOMA index) was increased by 2.4 in hepatic steatosis, by 0.6 for each unit of SUA (serum uric acid), and by 0.009 for every mg/dL of triglycerides. In adolescents, every cm of waist circumference increased systolic blood pressure by 0.6 mmHg, and each unit of SUA increased it by 2.9 mmHg. Serum uric acid and waist circumference are useful and accessible variables that can predict an increased risk of cardiovascular disease in obese pediatric patients. Copyright © 2016 IMSS. Published by Elsevier Inc. All rights reserved.

  12. Extent of cutaneous metabolism during percutaneous absorption of xenobiotics.

    PubMed

    Bronaugh, R L; Stewart, R F; Storm, J E

    1989-07-01

    In vitro percutaneous absorption studies generally do not determine whether biotransformation occurs during passage of a substance through the skin. Since it has recently been demonstrated that several chemicals are metabolized during skin permeation, we investigated the metabolism of five additional compounds (14C-labeled) after application to fuzzy rat skin: caffeine, p,p'-DDT, butylated hydroxytoluene (BHT), salicylic acid, and acetyl ethyl tetramethyltetralin (AETT). The viability of skin was maintained with a tissue culture medium. Radioactivity of each substrate and any metabolites in skin and receptor fluid was measured so that the absorption and metabolism of water-insoluble compounds would be accurately determined. Percutaneous absorption ranged from a low of 13% of the applied dose for BHT to a high of 49% for DDT. BHT was metabolized in skin to 4-hydroxy-BHT and an unknown metabolite. Of the absorbed radioisotope, 6.6% was isolated in biotransformed products found mainly in the receptor fluid. AETT was also metabolized during absorption, with 1.9% of the absorbed radioisotope found in two unknown peaks. Caffeine, DDT, and salicylic acid were not metabolized during skin permeation. Skin and liver microsomal metabolism was measured for all compounds except DDT. Metabolism in skin was observed only for the compounds also biotransformed in the diffusion cell; BHT and AETT were metabolized at 113 and 2.5 pmol/min/mg protein, respectively. In this study, as in others, skin metabolism was substantially less than the corresponding metabolism in liver. Therefore, a low rate of liver metabolism such as that found for caffeine, salicylic acid, and DDT might often be predictive of the absence of measurable metabolism during skin permeation. It seems likely that for many compounds, the biotransformations in skin will be small in terms of the percentage of absorbed material that is metabolized. Nevertheless, with potent compounds, even small quantities of a metabolite

  13. The NAFLD Index: A Simple and Accurate Screening Tool for the Prediction of Non-Alcoholic Fatty Liver Disease.

    PubMed

    Ichino, Naohiro; Osakabe, Keisuke; Sugimoto, Keiko; Suzuki, Koji; Yamada, Hiroya; Takai, Hiroji; Sugiyama, Hiroko; Yukitake, Jun; Inoue, Takashi; Ohashi, Koji; Hata, Tadayoshi; Hamajima, Nobuyuki; Nishikawa, Toru; Hashimoto, Senju; Kawabe, Naoto; Yoshioka, Kentaro

    2015-01-01

    Non-alcoholic fatty liver disease (NAFLD) is a common debilitating condition in many industrialized countries that increases the risk of cardiovascular disease. The aim of this study was to derive a simple and accurate screening tool for the prediction of NAFLD in the Japanese population. A total of 945 participants, 279 men and 666 women living in Hokkaido, Japan, were enrolled among residents who attended a health check-up program from 2010 to 2014. Participants with an alcohol consumption > 20 g/day and/or a chronic liver disease, such as chronic hepatitis B, chronic hepatitis C or autoimmune hepatitis, were excluded from this study. Clinical and laboratory data were examined to identify predictive markers of NAFLD. A new predictive index for NAFLD, the NAFLD index, was constructed for men and for women. The NAFLD index for men = -15.5693+0.3264 [BMI] +0.0134 [triglycerides (mg/dl)], and for women = -31.4686+0.3683 [BMI] +2.5699 [albumin (g/dl)] +4.6740[ALT/AST] -0.0379 [HDL cholesterol (mg/dl)]. The AUROC of the NAFLD index for men and for women was 0.87(95% CI 0.88-1.60) and 0.90 (95% CI 0.66-1.02), respectively. The cut-off point of -5.28 for men predicted NAFLD with an accuracy of 82.8%. For women, the cut-off point of -7.65 predicted NAFLD with an accuracy of 87.7%. A new index for the non-invasive prediction of NAFLD, the NAFLD index, was constructed using available clinical and laboratory data. This index is a simple screening tool to predict the presence of NAFLD.

  14. A genome-scale metabolic flux model of Escherichia coli K–12 derived from the EcoCyc database

    PubMed Central

    2014-01-01

    Background Constraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology. Results We present EcoCyc–18.0–GEM, a genome-scale model of the E. coli K–12 MG1655 metabolic network. The model is automatically generated from the current state of EcoCyc using the MetaFlux software, enabling the release of multiple model updates per year. EcoCyc–18.0–GEM encompasses 1445 genes, 2286 unique metabolic reactions, and 1453 unique metabolites. We demonstrate a three-part validation of the model that breaks new ground in breadth and accuracy: (i) Comparison of simulated growth in aerobic and anaerobic glucose culture with experimental results from chemostat culture and simulation results from the E. coli modeling literature. (ii) Essentiality prediction for the 1445 genes represented in the model, in which EcoCyc–18.0–GEM achieves an improved accuracy of 95.2% in predicting the growth phenotype of experimental gene knockouts. (iii) Nutrient utilization predictions under 431 different media conditions, for which the model achieves an overall accuracy of 80.7%. The model’s derivation from EcoCyc enables query and visualization via the EcoCyc website, facilitating model reuse and validation by inspection. We present an extensive investigation of disagreements between EcoCyc–18.0–GEM predictions and experimental data to highlight areas of interest to E. coli modelers and experimentalists, including 70 incorrect predictions of gene essentiality on glucose, 80 incorrect predictions of gene essentiality on glycerol, and 83 incorrect predictions of nutrient utilization. Conclusion Significant

  15. A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

    PubMed

    Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

    2018-06-01

    The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  16. Branched-chain amino acids to tyrosine ratio (BTR) predicts intrahepatic distant recurrence and survival for early hepatocellular carcinoma.

    PubMed

    Ishikawa, Toru; Kubota, Tomoyuki; Horigome, Ryoko; Kimura, Naruhiro; Honda, Hiroki; Iwanaga, Akito; Seki, Keiichi; Honma, Terasu; Yoshida, Toshiaki

    2013-01-01

    The Child-Pugh classification system is the most widely used system for assessing hepatic functional reserve in HCC treatment. In the Child-Pugh classification system, serum albumin levels are used to accurately assess the status of protein metabolism and nutrition. To date, a lack of attention has been given to amino acid metabolism. In the present study, we investigated whether the branched-chain amino acids to tyrosine ratio (BTR) as an indicator of amino acid metabolism can serve as both a prognostic factor for early HCC and a predictive factor for recurrence. We conducted a cohort study of 50 patients with stage I/II HCC enrolled between May 2002 and December 2010. It was investigated whether BTR can serve as both a prognostic factor and a predictive factor for HCC recurrence. Overall survival rates were significantly higher in patients with high baseline BTR than in those with low BTR. Multivariate analysis showed that both BTR and serum albumin were prognostic factors, and that BTR was the best predictive factor for recurrence. BTR was a prognostic factor for early HCC and the most predictive factor for intrahepatic distant recurrence and contributing factors for survival.

  17. Genome-wide gene expression and RNA half-life measurements allow predictions of regulation and metabolic behavior in Methanosarcina acetivorans

    DOE PAGES

    Peterson, Joseph R.; Thor, ShengShee; Kohler, Lars; ...

    2016-11-16

    Here, while a few studies on the variations in mRNA expression and half-lives measured under different growth conditions have been used to predict patterns of regulation in bacterial organisms, the extent to which this information can also play a role in defining metabolic phenotypes has yet to be examined systematically. Here we present the first comprehensive study for a model methanogen. As a result, we use expression and half-life data for the methanogen Methanosarcina acetivorans growing on fast- and slow-growth substrates to examine the regulation of its genes. Unlike Escherichia coli where only small shifts in half-lives were observed, wemore » found that most mRNA have significantly longer half-lives for slow growth on acetate compared to fast growth on methanol or trimethylamine. Interestingly, half-life shifts are not uniform across functional classes of enzymes, suggesting the existence of a selective stabilization mechanism for mRNAs. Using the transcriptomics data we determined whether transcription or degradation rate controls the change in transcript abundance. Degradation was found to control abundance for about half of the metabolic genes underscoring its role in regulating metabolism. Genes involved in half of the metabolic reactions were found to be differentially expressed among the substrates suggesting the existence of drastically different metabolic phenotypes that extend beyond just the methanogenesis pathways. By integrating expression data with an updated metabolic model of the organism (iST807) significant differences in pathway flux and production of metabolites were predicted for the three growth substrates. In conclusion, this study provides the first global picture of differential expression and half-lives for a class II methanogen, as well as provides the first evidence in a single organism that drastic genome-wide shifts in RNA half-lives can be modulated by growth substrate. We determined which genes in each metabolic pathway

  18. Genome-wide gene expression and RNA half-life measurements allow predictions of regulation and metabolic behavior in Methanosarcina acetivorans

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Peterson, Joseph R.; Thor, ShengShee; Kohler, Lars

    Here, while a few studies on the variations in mRNA expression and half-lives measured under different growth conditions have been used to predict patterns of regulation in bacterial organisms, the extent to which this information can also play a role in defining metabolic phenotypes has yet to be examined systematically. Here we present the first comprehensive study for a model methanogen. As a result, we use expression and half-life data for the methanogen Methanosarcina acetivorans growing on fast- and slow-growth substrates to examine the regulation of its genes. Unlike Escherichia coli where only small shifts in half-lives were observed, wemore » found that most mRNA have significantly longer half-lives for slow growth on acetate compared to fast growth on methanol or trimethylamine. Interestingly, half-life shifts are not uniform across functional classes of enzymes, suggesting the existence of a selective stabilization mechanism for mRNAs. Using the transcriptomics data we determined whether transcription or degradation rate controls the change in transcript abundance. Degradation was found to control abundance for about half of the metabolic genes underscoring its role in regulating metabolism. Genes involved in half of the metabolic reactions were found to be differentially expressed among the substrates suggesting the existence of drastically different metabolic phenotypes that extend beyond just the methanogenesis pathways. By integrating expression data with an updated metabolic model of the organism (iST807) significant differences in pathway flux and production of metabolites were predicted for the three growth substrates. In conclusion, this study provides the first global picture of differential expression and half-lives for a class II methanogen, as well as provides the first evidence in a single organism that drastic genome-wide shifts in RNA half-lives can be modulated by growth substrate. We determined which genes in each metabolic pathway

  19. Lipid Metabolic Versatility in Malassezia spp. Yeasts Studied through Metabolic Modeling

    PubMed Central

    Triana, Sergio; de Cock, Hans; Ohm, Robin A.; Danies, Giovanna; Wösten, Han A. B.; Restrepo, Silvia; González Barrios, Andrés F.; Celis, Adriana

    2017-01-01

    Malassezia species are lipophilic and lipid-dependent yeasts belonging to the human and animal microbiota. Typically, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis, pityriasis versicolor, atopic dermatitis, and folliculitis. The genomes of Malassezia globosa, Malassezia sympodialis, and Malassezia pachydermatis lack the genes related to fatty acid synthesis. Here, the lipid-synthesis pathways of these species, as well as of Malassezia furfur, and of an atypical M. furfur variant were reconstructed using genome data and Constraints Based Reconstruction and Analysis. To this end, the genomes of M. furfur CBS 1878 and the atypical M. furfur 4DS were sequenced and annotated. The resulting Enzyme Commission numbers and predicted reactions were similar to the other Malassezia strains despite the differences in their genome size. Proteomic profiling was utilized to validate flux distributions. Flux differences were observed in the production of steroids in M. furfur and in the metabolism of butanoate in M. pachydermatis. The predictions obtained via these metabolic reconstructions also suggested defects in the assimilation of palmitic acid in M. globosa, M. sympodialis, M. pachydermatis, and the atypical variant of M. furfur, but not in M. furfur. These predictions were validated via physiological characterization, showing the predictive power of metabolic network reconstructions to provide new clues about the metabolic versatility of Malassezia. PMID:28959251

  20. Lipid Metabolic Versatility in Malassezia spp. Yeasts Studied through Metabolic Modeling.

    PubMed

    Triana, Sergio; de Cock, Hans; Ohm, Robin A; Danies, Giovanna; Wösten, Han A B; Restrepo, Silvia; González Barrios, Andrés F; Celis, Adriana

    2017-01-01

    Malassezia species are lipophilic and lipid-dependent yeasts belonging to the human and animal microbiota. Typically, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis, pityriasis versicolor, atopic dermatitis, and folliculitis. The genomes of Malassezia globosa , Malassezia sympodialis , and Malassezia pachydermatis lack the genes related to fatty acid synthesis. Here, the lipid-synthesis pathways of these species, as well as of Malassezia furfur , and of an atypical M. furfur variant were reconstructed using genome data and Constraints Based Reconstruction and Analysis. To this end, the genomes of M. furfur CBS 1878 and the atypical M. furfur 4DS were sequenced and annotated. The resulting Enzyme Commission numbers and predicted reactions were similar to the other Malassezia strains despite the differences in their genome size. Proteomic profiling was utilized to validate flux distributions. Flux differences were observed in the production of steroids in M. furfur and in the metabolism of butanoate in M. pachydermatis . The predictions obtained via these metabolic reconstructions also suggested defects in the assimilation of palmitic acid in M. globosa , M. sympodialis , M. pachydermatis , and the atypical variant of M. furfur , but not in M. furfur. These predictions were validated via physiological characterization, showing the predictive power of metabolic network reconstructions to provide new clues about the metabolic versatility of Malassezia .

  1. APPLICATION OF ADVANCED IN VITRO TECHNIQUES TO MEASURE, UNDERSTAND AND PREDICT THE KINETICS AND MECHANISMS OF XENOBIOTIC METABOLISM

    EPA Science Inventory

    We have developed a research program in metabolism that involves numerous collaborators across EPA as well as other federal and academic labs. A primary goal is to develop and apply advanced in vitro techniques to measure, understand and predict the kinetics and mechanisms of xen...

  2. Metabolic network modeling with model organisms.

    PubMed

    Yilmaz, L Safak; Walhout, Albertha Jm

    2017-02-01

    Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions. Published by Elsevier Ltd.

  3. Metabolic network modeling with model organisms

    PubMed Central

    Yilmaz, L. Safak; Walhout, Albertha J.M.

    2017-01-01

    Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions. PMID:28088694

  4. Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

    PubMed

    Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

    2018-03-01

    Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t

  5. Towards more accurate and reliable predictions for nuclear applications

    NASA Astrophysics Data System (ADS)

    Goriely, Stephane; Hilaire, Stephane; Dubray, Noel; Lemaître, Jean-François

    2017-09-01

    The need for nuclear data far from the valley of stability, for applications such as nuclear astrophysics or future nuclear facilities, challenges the robustness as well as the predictive power of present nuclear models. Most of the nuclear data evaluation and prediction are still performed on the basis of phenomenological nuclear models. For the last decades, important progress has been achieved in fundamental nuclear physics, making it now feasible to use more reliable, but also more complex microscopic or semi-microscopic models in the evaluation and prediction of nuclear data for practical applications. Nowadays mean-field models can be tuned at the same level of accuracy as the phenomenological models, renormalized on experimental data if needed, and therefore can replace the phenomenological inputs in the evaluation of nuclear data. The latest achievements to determine nuclear masses within the non-relativistic HFB approach, including the related uncertainties in the model predictions, are discussed. Similarly, recent efforts to determine fission observables within the mean-field approach are described and compared with more traditional existing models.

  6. Genome-scale metabolic modeling of Mucor circinelloides and comparative analysis with other oleaginous species.

    PubMed

    Vongsangnak, Wanwipa; Klanchui, Amornpan; Tawornsamretkit, Iyarest; Tatiyaborwornchai, Witthawin; Laoteng, Kobkul; Meechai, Asawin

    2016-06-01

    We present a novel genome-scale metabolic model iWV1213 of Mucor circinelloides, which is an oleaginous fungus for industrial applications. The model contains 1213 genes, 1413 metabolites and 1326 metabolic reactions across different compartments. We demonstrate that iWV1213 is able to accurately predict the growth rates of M. circinelloides on various nutrient sources and culture conditions using Flux Balance Analysis and Phenotypic Phase Plane analysis. Comparative analysis of three oleaginous genome-scale models, including M. circinelloides (iWV1213), Mortierella alpina (iCY1106) and Yarrowia lipolytica (iYL619_PCP) revealed that iWV1213 possesses a higher number of genes involved in carbohydrate, amino acid, and lipid metabolisms that might contribute to its versatility in nutrient utilization. Moreover, the identification of unique and common active reactions among the Zygomycetes oleaginous models using Flux Variability Analysis unveiled a set of gene/enzyme candidates as metabolic engineering targets for cellular improvement. Thus, iWV1213 offers a powerful metabolic engineering tool for multi-level omics analysis, enabling strain optimization as a cell factory platform of lipid-based production. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Partial volume correction and image segmentation for accurate measurement of standardized uptake value of grey matter in the brain.

    PubMed

    Bural, Gonca; Torigian, Drew; Basu, Sandip; Houseni, Mohamed; Zhuge, Ying; Rubello, Domenico; Udupa, Jayaram; Alavi, Abass

    2015-12-01

    Our aim was to explore a novel quantitative method [based upon an MRI-based image segmentation that allows actual calculation of grey matter, white matter and cerebrospinal fluid (CSF) volumes] for overcoming the difficulties associated with conventional techniques for measuring actual metabolic activity of the grey matter. We included four patients with normal brain MRI and fluorine-18 fluorodeoxyglucose (F-FDG)-PET scans (two women and two men; mean age 46±14 years) in this analysis. The time interval between the two scans was 0-180 days. We calculated the volumes of grey matter, white matter and CSF by using a novel segmentation technique applied to the MRI images. We measured the mean standardized uptake value (SUV) representing the whole metabolic activity of the brain from the F-FDG-PET images. We also calculated the white matter SUV from the upper transaxial slices (centrum semiovale) of the F-FDG-PET images. The whole brain volume was calculated by summing up the volumes of the white matter, grey matter and CSF. The global cerebral metabolic activity was calculated by multiplying the mean SUV with total brain volume. The whole brain white matter metabolic activity was calculated by multiplying the mean SUV for the white matter by the white matter volume. The global cerebral metabolic activity only reflects those of the grey matter and the white matter, whereas that of the CSF is zero. We subtracted the global white matter metabolic activity from that of the whole brain, resulting in the global grey matter metabolism alone. We then divided the grey matter global metabolic activity by grey matter volume to accurately calculate the SUV for the grey matter alone. The brain volumes ranged between 1546 and 1924 ml. The mean SUV for total brain was 4.8-7. Total metabolic burden of the brain ranged from 5565 to 9617. The mean SUV for white matter was 2.8-4.1. On the basis of these measurements we generated the grey matter SUV, which ranged from 8.1 to 11.3. The

  8. Accurate electrical prediction of memory array through SEM-based edge-contour extraction using SPICE simulation

    NASA Astrophysics Data System (ADS)

    Shauly, Eitan; Rotstein, Israel; Peltinov, Ram; Latinski, Sergei; Adan, Ofer; Levi, Shimon; Menadeva, Ovadya

    2009-03-01

    The continues transistors scaling efforts, for smaller devices, similar (or larger) drive current/um and faster devices, increase the challenge to predict and to control the transistor off-state current. Typically, electrical simulators like SPICE, are using the design intent (as-drawn GDS data). At more sophisticated cases, the simulators are fed with the pattern after lithography and etch process simulations. As the importance of electrical simulation accuracy is increasing and leakage is becoming more dominant, there is a need to feed these simulators, with more accurate information extracted from physical on-silicon transistors. Our methodology to predict changes in device performances due to systematic lithography and etch effects was used in this paper. In general, the methodology consists on using the OPCCmaxTM for systematic Edge-Contour-Extraction (ECE) from transistors, taking along the manufacturing and includes any image distortions like line-end shortening, corner rounding and line-edge roughness. These measurements are used for SPICE modeling. Possible application of this new metrology is to provide a-head of time, physical and electrical statistical data improving time to market. In this work, we applied our methodology to analyze a small and large array's of 2.14um2 6T-SRAM, manufactured using Tower Standard Logic for General Purposes Platform. 4 out of the 6 transistors used "U-Shape AA", known to have higher variability. The predicted electrical performances of the transistors drive current and leakage current, in terms of nominal values and variability are presented. We also used the methodology to analyze an entire SRAM Block array. Study of an isolation leakage and variability are presented.

  9. An objective function exploiting suboptimal solutions in metabolic networks

    PubMed Central

    2013-01-01

    Background Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power. Results We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation. Conclusion Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network. PMID:24088221

  10. Plant metabolic modeling: achieving new insight into metabolism and metabolic engineering.

    PubMed

    Baghalian, Kambiz; Hajirezaei, Mohammad-Reza; Schreiber, Falk

    2014-10-01

    Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology. © 2014 American Society of Plant Biologists. All rights reserved.

  11. Physiologically based pharmacokinetics model predicts the lack of inhibition by repaglinide on the metabolism of pioglitazone.

    PubMed

    Xiao, Qingqing; Tang, Liling; Xu, Ruijuan; Qian, Wei; Yang, Jin

    2015-12-01

    Repaglinide and pioglitazone are both CYP2C8 and CYP3A4 substrates. This study was to determine whether repaglinide has an inhibitory effect on the metabolism of pioglitazone in vitro, in silico and in vivo. In vitro, the effect of repaglinide on the metabolism of pioglitazone was assessed in pooled human liver microsomes. In silico, an IVIVE-PBPK linked model was built with Simcyp®. Then, a randomized, 2-phase cross-over clinical study was conducted in 12 healthy volunteers. Repaglinide showed a strong inhibitory effect on the metabolism of pioglitazone in vitro (Ki  = 0.0757 µm), [I]/Ki  > 0.1. The Simcyp® prediction ratios of AUC and Cmax between the two treatment groups were both about 1.01. The pharmacokinetics of pioglitazone in clinical trials showed no significant difference between these two treatment groups (p > 0.05). Repaglinide has no significant inhibitory effect on the metabolism of pioglitazone in vivo, which is inconsistent with the in vitro results. The lack of an inhibitory effect was partly due to extensive plasma protein binding and to the high in vivo clearance of repaglinide, for the concentration of repaglinide in vivo was far smaller than in vitro. Copyright © 2015 John Wiley & Sons, Ltd.

  12. Estimating energy expenditure in vascular surgery patients: Are predictive equations accurate enough?

    PubMed

    Suen, J; Thomas, J M; Delaney, C L; Spark, J I; Miller, M D

    2016-12-01

    Malnutrition is prevalent in vascular surgical patients who commonly seek tertiary care at advanced stages of disease. Adjunct nutrition support is therefore pertinent to optimise patient outcomes. To negate consequences related to excessive or suboptimal dietary energy intake, it is essential to accurately determine energy expenditure and subsequent requirements. This study aims to compare resting energy expenditure (REE) measured by indirect calorimetry, a commonly used comparator, to REE estimated by predictive equations (Schofield, Harris-Benedict equations and Miller equation) to determine the most suitable equation for vascular surgery patients. Data were collected from four studies that measured REE in 77 vascular surgery patients. Bland-Altman analyses were conducted to explore agreement. Presence of fixed or proportional bias was assessed by linear regression analyses. In comparison to measured REE, on average REE was overestimated when Schofield (+857 kJ/day), Harris-Benedict (+801 kJ/day) and Miller (+71 kJ/day) equations were used. Wide limits of agreement led to an over or underestimation from 1552 to 1755 kJ. Proportional bias was absent in Schofield (R 2  = 0.005, p = 0.54) and Harris-Benedict equations (R 2  = 0.045, p = 0.06) but was present in the Miller equation (R 2  = 0.210, p < 0.01) even after logarithmic transformation (R 2  = 0.213, p < 0.01). Whilst the Miller equation tended to overestimate resting energy expenditure and was affected by proportional bias, the limits of agreement and mean bias were smaller compared to Schofield and Harris-Benedict equations. This suggested that it is the preferred predictive equation for vascular surgery patients. Future research to refine the Miller equation to improve its overall accuracy will better inform the provision of nutritional support for vascular surgery patients and subsequently improve outcomes. Alternatively, an equation might be developed specifically for use with

  13. Quality of Relationships with Parents and Friends in Adolescence Predicts Metabolic Risk in Young Adulthood

    PubMed Central

    Ehrlich, Katherine B.; Hoyt, Lindsay T.; Sumner, Jennifer A.; McDade, Thomas W.; Adam, Emma K.

    2015-01-01

    Objective The present study was designed to examine whether family and peer relationships in adolescence predict the emergence of metabolic risk factors in young adulthood. Methods Participants from a large, nationally representative cohort study (N = 11,617 for these analyses) reported on their relationship experiences with parents and close friends during adolescence. Fourteen years later, interviewers collected blood samples, as well as anthropometric and blood pressure measurements. Blood samples were analyzed for HbA1c. Results Ordered logistic regressions revealed that for females, supportive parent-child relationships and close male friendships in adolescence were associated with reduced odds of having elevated metabolic risk markers in young adulthood. These effects remained significant even after controlling for baseline measures of body mass index (BMI) and health and demographic covariates. The protective effects of close relationships were not significant for males, however. Exploratory analyses with two-parent families revealed that supportive father-child relationships were especially protective for females. Conclusions These findings suggest that, for females, close and supportive relationships with parents and male friends in adolescence may reduce the risk of metabolic dysregulation in adulthood. PMID:25689301

  14. Quality of relationships with parents and friends in adolescence predicts metabolic risk in young adulthood.

    PubMed

    Ehrlich, Katherine B; Hoyt, Lindsay Till; Sumner, Jennifer A; McDade, Thomas W; Adam, Emma K

    2015-09-01

    This study was designed to examine whether family and peer relationships in adolescence predict the emergence of metabolic risk factors in young adulthood. Participants from a large, nationally representative cohort study (N = 11,617 for these analyses) reported on their relationship experiences with parents and close friends during adolescence. Fourteen years later, interviewers collected blood samples, as well as anthropometric and blood pressure measurements. Blood samples were analyzed for HbA1c. Ordered logistic regressions revealed that for females, supportive parent-child relationships and close male friendships in adolescence were associated with reduced odds of having elevated metabolic risk markers in young adulthood. These effects remained significant even after controlling for baseline measures of body mass index (BMI) and health and demographic covariates. The protective effects of close relationships were not significant for males, however. Exploratory analyses with 2-parent families revealed that supportive father-child relationships were especially protective for females. These findings suggest that, for females, close and supportive relationships with parents and male friends in adolescence may reduce the risk of metabolic dysregulation in adulthood. (c) 2015 APA, all rights reserved).

  15. Cerebral glucose metabolic prediction from amnestic mild cognitive impairment to Alzheimer's dementia: a meta-analysis.

    PubMed

    Ma, Hai Rong; Sheng, Li Qin; Pan, Ping Lei; Wang, Gen Di; Luo, Rong; Shi, Hai Cun; Dai, Zhen Yu; Zhong, Jian Guo

    2018-01-01

    Brain 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) has been utilized to monitor disease conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer's dementia (AD). However, the conversion patterns of FDG-PET metabolism across studies are not conclusive. We conducted a voxel-wise meta-analysis using Seed-based d Mapping that included 10 baseline voxel-wise FDG-PET comparisons between 93 aMCI converters and 129 aMCI non-converters from nine longitudinal studies. The most robust and reliable metabolic alterations that predicted conversion from aMCI to AD were localized in the left posterior cingulate cortex (PCC)/precuneus. Furthermore, meta-regression analyses indicated that baseline mean age and severity of cognitive impairment, and follow-up duration were significant moderators for metabolic alterations in aMCI converters. Our study revealed hypometabolism in the left PCC/precuneus as an early feature in the development of AD. This finding has important implications in understanding the neural substrates for AD conversion and could serve as a potential imaging biomarker for early detection of AD as well as for tracking disease progression at the predementia stage.

  16. Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation.

    PubMed

    Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha; Theriot, Casey M; Young, Vincent B; Jansson, Janet K; Fredricks, David N; Borenstein, Elhanan

    2016-01-01

    Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites' abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. Studies

  17. Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation

    PubMed Central

    Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha; Theriot, Casey M.; Young, Vincent B.; Jansson, Janet K.; Fredricks, David N.

    2016-01-01

    ABSTRACT Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease

  18. Metabolism and disposition of ABT-894, a novel α4β2 neuronal acetylcholine receptor agonist, in mice and monkeys.

    PubMed

    Liu, Hong; Fu, Wentao; Wetter, Jill; Xu, Hongyu; Guan, Zhiwen; Stuart, Patricia

    2014-06-01

    1.  Metabolism and disposition of ABT-894 was investigated in hepatocytes, in mice and monkeys receiving [(14)C]ABT-894. 2.  In hepatocytes, turnover rate of ABT-894 was slow in all species with more than 90% of parent remaining. M3 (carbamoyl glucuronide) and M6 (mono-oxidation) were detected across species. 3.  ABT-894 showed species-specific disposition profiles. ABT-894 was primarily eliminated by renal secretion in mice. Whereas, monkey mainly cleared ABT-894 metabolically. 4.  ABT-894 underwent two primary routes of metabolism in monkeys: N-carbamoyl glucuronidation to form M3 and oxidation product M1. M3 was the major metabolite in monkey excreta. M3 was observed in mice urine. Circulating levels of M3 in terms of M3/ABT-894 ratios were essentially absent in mice, but were high in monkeys. 5.  Understanding the species difference in the clearance mechanism is the key to the accurate projection of the human clearance and preclinical safety assessment. Lack of species difference in the metabolism of ABT-894 in hepatocytes certainly creates a challenge in predicting its metabolism and pharmacokinetics in human. Based on available metabolism and pharmacokinetic data of ABT-894 in human, monkey is the preferred species in predicting human clearance since it presents a similar clearance mechanism from that observed in human.

  19. A fast and accurate method to predict 2D and 3D aerodynamic boundary layer flows

    NASA Astrophysics Data System (ADS)

    Bijleveld, H. A.; Veldman, A. E. P.

    2014-12-01

    A quasi-simultaneous interaction method is applied to predict 2D and 3D aerodynamic flows. This method is suitable for offshore wind turbine design software as it is a very accurate and computationally reasonably cheap method. This study shows the results for a NACA 0012 airfoil. The two applied solvers converge to the experimental values when the grid is refined. We also show that in separation the eigenvalues remain positive thus avoiding the Goldstein singularity at separation. In 3D we show a flow over a dent in which separation occurs. A rotating flat plat is used to show the applicability of the method for rotating flows. The shown capabilities of the method indicate that the quasi-simultaneous interaction method is suitable for design methods for offshore wind turbine blades.

  20. Ability among adolescents for the metabolic syndrome to predict elevations in factors associated with type 2 diabetes and cardiovascular disease: data from the national health and nutrition examination survey 1999-2006.

    PubMed

    DeBoer, Mark D; Gurka, Matthew J

    2010-08-01

    The aim of this study was to compare currently proposed sets of pediatric metabolic syndrome criteria for the ability to predict elevations in "surrogate" factors that are associated with metabolic syndrome and with future cardiovascular disease and type 2 diabetes mellitus. These surrogate factors were fasting insulin, hemoglobin A1c (HbA1c), high-sensitivity C-reactive protein (hsCRP), and uric acid. Waist circumference (WC), blood pressure, triglycerides, high-density lipoprotein cholesterol (HDL-C), fasting glucose, fasting insulin, HbA1c, hsCRP, and uric acid measurements were obtained from 2,624 adolescent (12-18 years old) participants of the 1999-2006 National Health and Nutrition Examination Surveys. We identified children with metabolic syndrome as defined by six commonly used sets of pediatric metabolic syndrome criteria. We then defined elevations in the surrogate factors as values in the top 5% for the cohort and calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each set of metabolic syndrome criteria and for each surrogate factor. Current pediatric metabolic syndrome criteria exhibited variable sensitivity and specificity for surrogate predictions. Metabolic syndrome criteria had the highest sensitivity for predicting fasting insulin (40-70%), followed by uric acid (31-54%), hsCRP (13-31%), and HbA1c (7-21%). The criteria of de Ferranti (which includes children with WC >75(th) percentile, compared to all other sets including children with WC >90(th) percentile) exhibited the highest sensitivity for predicting each of the surrogates, with only modest decrease in specificity compared to the other sets of criteria. However, the de Ferranti criteria also exhibited the lowest PPV values. Conversely, the pediatric International Diabetes Federation criteria exhibited the lowest sensitivity and the highest specificity. Pediatric metabolic syndrome criteria exhibit moderate sensitivity for detecting

  1. Ensemble MD simulations restrained via crystallographic data: Accurate structure leads to accurate dynamics

    PubMed Central

    Xue, Yi; Skrynnikov, Nikolai R

    2014-01-01

    Currently, the best existing molecular dynamics (MD) force fields cannot accurately reproduce the global free-energy minimum which realizes the experimental protein structure. As a result, long MD trajectories tend to drift away from the starting coordinates (e.g., crystallographic structures). To address this problem, we have devised a new simulation strategy aimed at protein crystals. An MD simulation of protein crystal is essentially an ensemble simulation involving multiple protein molecules in a crystal unit cell (or a block of unit cells). To ensure that average protein coordinates remain correct during the simulation, we introduced crystallography-based restraints into the MD protocol. Because these restraints are aimed at the ensemble-average structure, they have only minimal impact on conformational dynamics of the individual protein molecules. So long as the average structure remains reasonable, the proteins move in a native-like fashion as dictated by the original force field. To validate this approach, we have used the data from solid-state NMR spectroscopy, which is the orthogonal experimental technique uniquely sensitive to protein local dynamics. The new method has been tested on the well-established model protein, ubiquitin. The ensemble-restrained MD simulations produced lower crystallographic R factors than conventional simulations; they also led to more accurate predictions for crystallographic temperature factors, solid-state chemical shifts, and backbone order parameters. The predictions for 15N R1 relaxation rates are at least as accurate as those obtained from conventional simulations. Taken together, these results suggest that the presented trajectories may be among the most realistic protein MD simulations ever reported. In this context, the ensemble restraints based on high-resolution crystallographic data can be viewed as protein-specific empirical corrections to the standard force fields. PMID:24452989

  2. Short communication: Use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks.

    PubMed

    Ehret, A; Hochstuhl, D; Krattenmacher, N; Tetens, J; Klein, M S; Gronwald, W; Thaller, G

    2015-01-01

    Subclinical ketosis is one of the most prevalent metabolic disorders in high-producing dairy cows during early lactation. This renders its early detection and prevention important for both economical and animal-welfare reasons. Construction of reliable predictive models is challenging, because traits like ketosis are commonly affected by multiple factors. In this context, machine learning methods offer great advantages because of their universal learning ability and flexibility in integrating various sorts of data. Here, an artificial-neural-network approach was applied to investigate the utility of metabolic, genetic, and milk performance data for the prediction of milk levels of β-hydroxybutyrate within and across consecutive weeks postpartum. Data were collected from 218 dairy cows during their first 5wk in milk. All animals were genotyped with a 50,000 SNP panel, and weekly information on the concentrations of the milk metabolites glycerophosphocholine and phosphocholine as well as milk composition data (milk yield, fat and protein percentage) was available. The concentration of β-hydroxybutyric acid in milk was used as target variable in all prediction models. Average correlations between observed and predicted target values up to 0.643 could be obtained, if milk metabolite and routine milk recording data were combined for prediction at the same day within weeks. Predictive performance of metabolic as well as milk performance-based models was higher than that of models based on genetic information. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  3. Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

    PubMed Central

    Yang, Jing; He, Bao-Ji; Jang, Richard; Zhang, Yang; Shen, Hong-Bin

    2015-01-01

    Abstract Motivation: Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g. >3 bonds, is too low to effectively assist structure assembly simulations. Results: We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins. Availability and implementation: http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/ Contact: zhng@umich.edu or hbshen@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26254435

  4. A demonstration of the uncertainty in predicting the estrogenic ...

    EPA Pesticide Factsheets

    In vitro estrogen receptor assays are valuable screening tools for identifying environmental samples and chemicals that display estrogenic activity. However, in vitro potency cannot necessarily be extrapolated to estimates of in vivo potency because in vitro assays are currently unable to fully account for adsorption, distribution, metabolism, and excretion. To explore this issue, we calculated relative potency factors (RPF) for several chemicals and mixtures in the T47D-KBluc estrogen receptor transactivation assay. The in vitro RPF values were then used to predict rat uterotrophic assay responses following oral administration of individual chemicals and mixtures. 17β-estradiol (E2), 17α-ethinyl estradiol (EE2), benzyl-butyl phthalate (BBP), bisphenol-A (BPA), bisphenol-AF (BPAF), bisphenol-C (BPC), bisphenol-S (BPS), and methoxychlor (MET) were tested individually, while BPS+MET, BPAF+MET, and BPAF+BPC+BPS+EE2+MET were tested as equipotent mixtures. In vivo ED50 values for BPA, BPAF, and BPC were accurately predicted using in vitro data; however, E2 was less potent than predicted, BBP was a false positive, and BPS and MET were 76.6 and 368.3-fold more active in vivo than predicted from the in vitro potency assessment, respectively. Further, mixture ED50 values were more accurately predicted by the dose addition model using individual chemical in vivo uterotrophic data (0.7-1.5-fold difference from observed) than in vitro data (1.4-86.8-fold). Overall,

  5. Development of a New Model for Accurate Prediction of Cloud Water Deposition on Vegetation

    NASA Astrophysics Data System (ADS)

    Katata, G.; Nagai, H.; Wrzesinsky, T.; Klemm, O.; Eugster, W.; Burkard, R.

    2006-12-01

    Scarcity of water resources in arid and semi-arid areas is of great concern in the light of population growth and food shortages. Several experiments focusing on cloud (fog) water deposition on the land surface suggest that cloud water plays an important role in water resource in such regions. A one-dimensional vegetation model including the process of cloud water deposition on vegetation has been developed to better predict cloud water deposition on the vegetation. New schemes to calculate capture efficiency of leaf, cloud droplet size distribution, and gravitational flux of cloud water were incorporated in the model. Model calculations were compared with the data acquired at the Norway spruce forest at the Waldstein site, Germany. High performance of the model was confirmed by comparisons of calculated net radiation, sensible and latent heat, and cloud water fluxes over the forest with measurements. The present model provided a better prediction of measured turbulent and gravitational fluxes of cloud water over the canopy than the Lovett model, which is a commonly used cloud water deposition model. Detailed calculations of evapotranspiration and of turbulent exchange of heat and water vapor within the canopy and the modifications are necessary for accurate prediction of cloud water deposition. Numerical experiments to examine the dependence of cloud water deposition on the vegetation species (coniferous and broad-leaved trees, flat and cylindrical grasses) and structures (Leaf Area Index (LAI) and canopy height) are performed using the presented model. The results indicate that the differences of leaf shape and size have a large impact on cloud water deposition. Cloud water deposition also varies with the growth of vegetation and seasonal change of LAI. We found that the coniferous trees whose height and LAI are 24 m and 2.0 m2m-2, respectively, produce the largest amount of cloud water deposition in all combinations of vegetation species and structures in the

  6. Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction: COMMUNITY DATA-DRIVEN METABOLIC NETWORK MODELING

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Henry, Christopher S.; Bernstein, Hans C.; Weisenhorn, Pamela

    Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the networkmore » reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources.« less

  7. Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging.

    PubMed

    Hughes, Timothy J; Kandathil, Shaun M; Popelier, Paul L A

    2015-02-05

    As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging. Models were built at three levels of theory: HF/6-31G(**), B3LYP/aug-cc-pVDZ and M06-2X/aug-cc-pVDZ. The quality of the kriging models was measured by their ability to predict the electrostatic interaction energy between atoms in external test examples for which the true energies are known. At all levels of theory, >90% of test cases for small van der Waals complexes were predicted within 1 kJ mol(-1), decreasing to 60-70% of test cases for larger base pair complexes. Models built on moments obtained at B3LYP and M06-2X level generally outperformed those at HF level. For all systems the individual interactions were predicted with a mean unsigned error of less than 1 kJ mol(-1). Copyright © 2013 Elsevier B.V. All rights reserved.

  8. Impact of the Triglyceride/High-Density Lipoprotein Cholesterol Ratio and the Hypertriglyceremic-Waist Phenotype to Predict the Metabolic Syndrome and Insulin Resistance.

    PubMed

    von Bibra, Helene; Saha, Sarama; Hapfelmeier, Alexander; Müller, Gabriele; Schwarz, Peter E H

    2017-07-01

    Insulin resistance is the underlying mechanism for the metabolic syndrome and associated dyslipidaemia that theoretically implies a practical tool for identifying individuals at risk for cardiovascular disease and type-2-diabetes. Another screening tool is the hypertriglyceremic-waist phenotype (HTW). There is important impact of the ethnic background but a lack of studied European populations for the association of the triglyceride/high-density lipoprotein cholesterol (HDL-C) ratio and insulin resistance. This observational, retrospective study evaluated lipid ratios and the HTW for predicting the metabolic syndrome/insulin resistance in 1932 non-diabetic individuals from Germany in the fasting state and during a glucose tolerance test. The relations of triglyceride/HDL-C, total-cholesterol/HDL-C, and low-density lipoprotein cholesterol/HDL-C with 5 surrogate estimates of insulin resistance/sensitivity and metabolic syndrome were analysed by linear regression analysis and receiver operating characteristics (ROC) in participants with normal (n=1 333) or impaired fasting glucose (n=599), also for the impact of gender. Within the lipid ratios, triglyceride/HDL-C had the strongest associations with insulin resistance/sensitivity markers. In the prediction of metabolic syndrome, diagnostic accuracy was good for triglyceride/HDL-C (area under the ROC curve 0.817) with optimal cut-off points (in mg/dl units) of 2.8 for men (80% sensitivity, 71% specificity) and 1.9 for women (80% sensitivity, 75% specificity) and fair for HTW and HOMA-IR (area under the curve 0.773 and 0.761). These data suggest the triglyceride/HDL-C ratio as a physiologically relevant and practical index for predicting the concomitant presence of metabolic syndrome, insulin resistance and dyslipidaemia for therapeutic and preventive care in apparently healthy European populations. © Georg Thieme Verlag KG Stuttgart · New York.

  9. Metabolic and hemodynamic events following changes in neuronal activity: current hypotheses, theoretical predictions and in vivo NMR experimental findings

    PubMed Central

    Mangia, Silvia; Giove, Federico; Tkáč, Ivan; Logothetis, Nikos K.; Henry, Pierre-Gilles; Olman, Cheryl A.; Maraviglia, Bruno; Di Salle, Francesco; Uğurbil, Kâmil

    2009-01-01

    Unraveling the energy metabolism and the hemodynamic outcomes of excitatory and inhibitory neuronal activity is critical not only for our basic understanding of overall brain function, but also for the understanding of many brain disorders. Methodologies of magnetic resonance spectroscopy (MRS) and magnetic resonance imaging (MRI) are powerful tools for the non-invasive investigation of brain metabolism and physiology. However, the temporal and spatial resolution of in vivo MRS and MRI is not suitable to provide direct evidence for hypotheses that involve metabolic compartmentalization between different cell types, or to untangle the complex neuronal micro-circuitry which results in changes of electrical activity. This review aims at describing how the current models of brain metabolism, mainly built on the basis of in vitro evidence, relate to experimental findings recently obtained in vivo by 1H MRS, 13C MRS and MRI. The hypotheses related to the role of different metabolic substrates, the metabolic neuron-glia interactions, along with the available theoretical predictions of the energy budget of neurotransmission, will be discussed. In addition, the cellular and network mechanisms that characterize different types of increased and suppressed neuronal activity will be considered within the sensitivity-constraints of MRS and MRI. PMID:19002199

  10. Prevalence of metabolic syndrome and prediction of diabetes using IDF versus ATPIII criteria in a Middle East population.

    PubMed

    Hajat, Cother; Shather, Zainab

    2012-12-01

    This study investigates the prevalence and predictive value of metabolic syndrome in Abu Dhabi, using data from the population-based, programme, 'Weqaya'. The Weqaya screen included family history of cardiovascular disease (CVD), CVD risk factors, random blood glucose (RBG) and HbA1c. Those not previously diabetic but at high risk of diabetes (RBG ≥11.1 mmol/l or HbA1c ≥6.1%) and randomly selected normal subjects were followed-up with fasting glucose, oral glucose tolerance test (OGTT), HbA1c and fasting triglycerides. In 760 subjects, 325 (42.8%) were male with a mean age of 41.9 years (SD 13.8). Using World Health Organisation (WHO) criteria, 149 (20.2%) were newly diagnosed diabetics. Using International Diabetes Foundation (IDF) and Adult Treatment Panel III (ATPIII) criteria, metabolic syndrome was present in 29.0% vs. 31.5% of non-diabetics, 63.4% vs. 64.2% with impaired glucose handling and 78.6% vs. 79.8% with new-onset diabetes using OGTT (P<.001). IDF criteria better predicted pre-diabetes and diabetes (OR 3.4 P<.001; OR 6.4 P<.001, respectively); ATPIII criteria better predicted high CVD risk scores (OR 13.6 P<.001). Whilst IDF and APTIII definitions provide similar prevalence rates, they differentially predict prediabetes, diabetes and CVD. This ongoing study will enable the longitudinal investigation of the development of diabetes and cardiovascular events in this population. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  11. Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

    NASA Technical Reports Server (NTRS)

    Voorhies, Coerte V.; Conrad, Joy

    1996-01-01

    The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field

  12. Accurate prediction of pregnancy viability by means of a simple scoring system.

    PubMed

    Bottomley, Cecilia; Van Belle, Vanya; Kirk, Emma; Van Huffel, Sabine; Timmerman, Dirk; Bourne, Tom

    2013-01-01

    What is the performance of a simple scoring system to predict whether women will have an ongoing viable intrauterine pregnancy beyond the first trimester? A simple scoring system using demographic and initial ultrasound variables accurately predicts pregnancy viability beyond the first trimester with an area under the curve (AUC) in a receiver operating characteristic curve of 0.924 [95% confidence interval (CI) 0.900-0.947] on an independent test set. Individual demographic and ultrasound factors, such as maternal age, vaginal bleeding and gestational sac size, are strong predictors of miscarriage. Previous mathematical models have combined individual risk factors with reasonable performance. A simple scoring system derived from a mathematical model that can be easily implemented in clinical practice has not previously been described for the prediction of ongoing viability. This was a prospective observational study in a single early pregnancy assessment centre during a 9-month period. A cohort of 1881 consecutive women undergoing transvaginal ultrasound scan at a gestational age <84 days were included. Women were excluded if the first trimester outcome was not known. Demographic features, symptoms and ultrasound variables were tested for their influence on ongoing viability. Logistic regression was used to determine the influence on first trimester viability from demographics and symptoms alone, ultrasound findings alone and then from all the variables combined. Each model was developed on a training data set, and a simple scoring system was derived from this. This scoring system was tested on an independent test data set. The final outcome based on a total of 1435 participants was an ongoing viable pregnancy in 885 (61.7%) and early pregnancy loss in 550 (38.3%) women. The scoring system using significant demographic variables alone (maternal age and amount of bleeding) to predict ongoing viability gave an AUC of 0.724 (95% CI = 0.692-0.756) in the training set

  13. Temporal Expression-based Analysis of Metabolism

    PubMed Central

    Segrè, Daniel

    2012-01-01

    Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques. PMID:23209390

  14. Mathematical model of cycad cones' thermogenic temperature responses: inverse calorimetry to estimate metabolic heating rates.

    PubMed

    Roemer, R B; Booth, D; Bhavsar, A A; Walter, G H; Terry, L I

    2012-12-21

    A mathematical model based on conservation of energy has been developed and used to simulate the temperature responses of cones of the Australian cycads Macrozamia lucida and Macrozamia. macleayi during their daily thermogenic cycle. These cones generate diel midday thermogenic temperature increases as large as 12 °C above ambient during their approximately two week pollination period. The cone temperature response model is shown to accurately predict the cones' temperatures over multiple days as based on simulations of experimental results from 28 thermogenic events from 3 different cones, each simulated for either 9 or 10 sequential days. The verified model is then used as the foundation of a new, parameter estimation based technique (termed inverse calorimetry) that estimates the cones' daily metabolic heating rates from temperature measurements alone. The inverse calorimetry technique's predictions of the major features of the cones' thermogenic metabolism compare favorably with the estimates from conventional respirometry (indirect calorimetry). Because the new technique uses only temperature measurements, and does not require measurements of oxygen consumption, it provides a simple, inexpensive and portable complement to conventional respirometry for estimating metabolic heating rates. It thus provides an additional tool to facilitate field and laboratory investigations of the bio-physics of thermogenic plants. Copyright © 2012 Elsevier Ltd. All rights reserved.

  15. Robust and accurate decoding of motoneuron behavior and prediction of the resulting force output.

    PubMed

    Thompson, Christopher K; Negro, Francesco; Johnson, Michael D; Holmes, Matthew R; McPherson, Laura Miller; Powers, Randall K; Farina, Dario; Heckman, Charles J

    2018-05-03

    The spinal alpha motoneuron is the only cell in the human CNS whose discharge can be routinely recorded in humans. We have reengineered motor unit collection and decomposition approaches, originally developed in humans, to measure the neural drive to muscle and estimate muscle force generation in the decerebrate cat model. Experimental, computational, and predictive approaches are used to demonstrate the validity of this approach across a wide range of modes to activate the motor pool. The utility of this approach is shown through the ability to track individual motor units across trials, allowing for better predictions of muscle force than the electromyography signal, and providing insights in to the stereotypical discharge characteristics in response to synaptic activation of the motor pool. This approach now allows for a direct link between the intracellular data of single motoneurons, the discharge properties of motoneuron populations, and muscle force generation in the same preparation. The discharge of a spinal alpha motoneuron and the resulting contraction of its muscle fibers represents the functional quantum of the motor system. Recent advances in the recording and decomposition of the electromyographic signal allows for the identification of several tens of concurrently active motor units. These detailed population data provide the potential to achieve deep insights into the synaptic organization of motor commands. Yet most of our understanding of the synaptic input to motoneurons is derived from intracellular recordings in animal preparations. Thus, it is necessary to extend the new electrode and decomposition methods to recording of motor unit populations in these same preparations. To achieve this goal, we use high-density electrode arrays and decomposition techniques, analogous to those developed for humans, to record and decompose the activity of tens of concurrently active motor units in a hindlimb muscle in the decerebrate cat. Our results showed

  16. Accurate perception of negative emotions predicts functional capacity in schizophrenia.

    PubMed

    Abram, Samantha V; Karpouzian, Tatiana M; Reilly, James L; Derntl, Birgit; Habel, Ute; Smith, Matthew J

    2014-04-30

    Several studies suggest facial affect perception (FAP) deficits in schizophrenia are linked to poorer social functioning. However, whether reduced functioning is associated with inaccurate perception of specific emotional valence or a global FAP impairment remains unclear. The present study examined whether impairment in the perception of specific emotional valences (positive, negative) and neutrality were uniquely associated with social functioning, using a multimodal social functioning battery. A sample of 59 individuals with schizophrenia and 41 controls completed a computerized FAP task, and measures of functional capacity, social competence, and social attainment. Participants also underwent neuropsychological testing and symptom assessment. Regression analyses revealed that only accurately perceiving negative emotions explained significant variance (7.9%) in functional capacity after accounting for neurocognitive function and symptoms. Partial correlations indicated that accurately perceiving anger, in particular, was positively correlated with functional capacity. FAP for positive, negative, or neutral emotions were not related to social competence or social attainment. Our findings were consistent with prior literature suggesting negative emotions are related to functional capacity in schizophrenia. Furthermore, the observed relationship between perceiving anger and performance of everyday living skills is novel and warrants further exploration. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  17. Accurate prediction of collapse temperature using optical coherence tomography-based freeze-drying microscopy.

    PubMed

    Greco, Kristyn; Mujat, Mircea; Galbally-Kinney, Kristin L; Hammer, Daniel X; Ferguson, R Daniel; Iftimia, Nicusor; Mulhall, Phillip; Sharma, Puneet; Kessler, William J; Pikal, Michael J

    2013-06-01

    The objective of this study was to assess the feasibility of developing and applying a laboratory tool that can provide three-dimensional product structural information during freeze-drying and which can accurately characterize the collapse temperature (Tc ) of pharmaceutical formulations designed for freeze-drying. A single-vial freeze dryer coupled with optical coherence tomography freeze-drying microscopy (OCT-FDM) was developed to investigate the structure and Tc of formulations in pharmaceutically relevant products containers (i.e., freeze-drying in vials). OCT-FDM was used to measure the Tc and eutectic melt of three formulations in freeze-drying vials. The Tc as measured by OCT-FDM was found to be predictive of freeze-drying with a batch of vials in a conventional laboratory freeze dryer. The freeze-drying cycles developed using OCT-FDM data, as compared with traditional light transmission freeze-drying microscopy (LT-FDM), resulted in a significant reduction in primary drying time, which could result in a substantial reduction of manufacturing costs while maintaining product quality. OCT-FDM provides quantitative data to justify freeze-drying at temperatures higher than the Tc measured by LT-FDM and provides a reliable upper limit to setting a product temperature in primary drying. Copyright © 2013 Wiley Periodicals, Inc.

  18. Hindered rotor models with variable kinetic functions for accurate thermodynamic and kinetic predictions

    NASA Astrophysics Data System (ADS)

    Reinisch, Guillaume; Leyssale, Jean-Marc; Vignoles, Gérard L.

    2010-10-01

    We present an extension of some popular hindered rotor (HR) models, namely, the one-dimensional HR (1DHR) and the degenerated two-dimensional HR (d2DHR) models, allowing for a simple and accurate treatment of internal rotations. This extension, based on the use of a variable kinetic function in the Hamiltonian instead of a constant reduced moment of inertia, is extremely suitable in the case of rocking/wagging motions involved in dissociation or atom transfer reactions. The variable kinetic function is first introduced in the framework of a classical 1DHR model. Then, an effective temperature and potential dependent constant is proposed in the cases of quantum 1DHR and classical d2DHR models. These methods are finally applied to the atom transfer reaction SiCl3+BCl3→SiCl4+BCl2. We show, for this particular case, that a proper accounting of internal rotations greatly improves the accuracy of thermodynamic and kinetic predictions. Moreover, our results confirm (i) that using a suitably defined kinetic function appears to be very adapted to such problems; (ii) that the separability assumption of independent rotations seems justified; and (iii) that a quantum mechanical treatment is not a substantial improvement with respect to a classical one.

  19. A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina

    PubMed Central

    Maturana, Matias I.; Apollo, Nicholas V.; Hadjinicolaou, Alex E.; Garrett, David J.; Cloherty, Shaun L.; Kameneva, Tatiana; Grayden, David B.; Ibbotson, Michael R.; Meffin, Hamish

    2016-01-01

    Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron’s electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy. PMID:27035143

  20. Neonatal metabolic acidosis at birth: In search of a reliable marker.

    PubMed

    Racinet, C; Ouellet, P; Charles, F; Daboval, T

    2016-06-01

    A newborn may present acidemia on the umbilical artery blood which can result from respiratory acidosis or metabolic acidosis or be of mixed origin. Currently, in the absence of a satisfactory definition, the challenge is to determine the most accurate marker for metabolic acidosis, which can be deleterious for the neonate. We reviewed the methodological and physiological aspects of the perinatal literature to search for the best marker of NMA. Base deficit and pH have been criticized as the standard criteria to predict outcome. The proposed threshold of pathogenicity is not based on convincing studies. The algorithms of various blood gas analyzers differ and do not take into account the specific neonatal acid-base profile. Birth-related neonatal eucapnic pH is described as the most pertinent marker of NMA at birth. The various means of calculating this value and the level below which it seems to play a possible pathogenic role are presented. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  1. 13C metabolic flux analysis at a genome-scale.

    PubMed

    Gopalakrishnan, Saratram; Maranas, Costas D

    2015-11-01

    Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism. They typically omit degradation pathways, complete cofactor balances, and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up mapping models to a genome-scale. The core mapping model employed in this study accounts for (75 reactions and 65 metabolites) primarily from central metabolism. The genome-scale metabolic mapping model (GSMM) (697 reaction and 595 metabolites) is constructed using as a basis the iAF1260 model upon eliminating reactions guaranteed not to carry flux based on growth and fermentation data for a minimal glucose growth medium. Labeling data for 17 amino acid fragments obtained from cells fed with glucose labeled at the second carbon was used to obtain fluxes and ranges. Metabolic fluxes and confidence intervals are estimated, for both core and genome-scale mapping models, by minimizing the sum of square of differences between predicted and experimentally measured labeling patterns using the EMU decomposition algorithm. Overall, we find that both topology and estimated values of the metabolic fluxes remain largely consistent between core and GSM model. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non

  2. Metabolic Syndrome Biomarkers Predict Lung Function Impairment

    PubMed Central

    Naveed, Bushra; Weiden, Michael D.; Kwon, Sophia; Gracely, Edward J.; Comfort, Ashley L.; Ferrier, Natalia; Kasturiarachchi, Kusali J.; Cohen, Hillel W.; Aldrich, Thomas K.; Rom, William N.; Kelly, Kerry; Prezant, David J.

    2012-01-01

    Rationale: Cross-sectional studies demonstrate an association between metabolic syndrome and impaired lung function. Objectives: To define if metabolic syndrome biomarkers are risk factors for loss of lung function after irritant exposure. Methods: A nested case-control study of Fire Department of New York personnel with normal pre–September 11th FEV1 and who presented for subspecialty pulmonary evaluation before March 10, 2008. We correlated metabolic syndrome biomarkers obtained within 6 months of World Trade Center dust exposure with subsequent FEV1. FEV1 at subspecialty pulmonary evaluation within 6.5 years defined disease status; cases had FEV1 less than lower limit of normal, whereas control subjects had FEV1 greater than or equal to lower limit of normal. Measurements and Main Results: Clinical data and serum sampled at the first monitoring examination within 6 months of September 11, 2001, assessed body mass index, heart rate, serum glucose, triglycerides and high-density lipoprotein (HDL), leptin, pancreatic polypeptide, and amylin. Cases and control subjects had significant differences in HDL less than 40 mg/dl with triglycerides greater than or equal to 150 mg/dl, heart rate greater than or equal to 66 bpm, and leptin greater than or equal to 10,300 pg/ml. Each increased the odds of abnormal FEV1 at pulmonary evaluation by more than twofold, whereas amylin greater than or equal to 116 pg/ml decreased the odds by 84%, in a multibiomarker model adjusting for age, race, body mass index, and World Trade Center arrival time. This model had a sensitivity of 41%, a specificity of 86%, and a receiver operating characteristic area under the curve of 0.77. Conclusions: Abnormal triglycerides and HDL and elevated heart rate and leptin are independent risk factors of greater susceptibility to lung function impairment after September 11, 2001, whereas elevated amylin is protective. Metabolic biomarkers are predictors of lung disease, and may be useful for assessing

  3. Combining Mean and Standard Deviation of Hounsfield Unit Measurements from Preoperative CT Allows More Accurate Prediction of Urinary Stone Composition Than Mean Hounsfield Units Alone.

    PubMed

    Tailly, Thomas; Larish, Yaniv; Nadeau, Brandon; Violette, Philippe; Glickman, Leonard; Olvera-Posada, Daniel; Alenezi, Husain; Amann, Justin; Denstedt, John; Razvi, Hassan

    2016-04-01

    The mineral composition of a urinary stone may influence its surgical and medical treatment. Previous attempts at identifying stone composition based on mean Hounsfield Units (HUm) have had varied success. We aimed to evaluate the additional use of standard deviation of HU (HUsd) to more accurately predict stone composition. We identified patients from two centers who had undergone urinary stone treatment between 2006 and 2013 and had mineral stone analysis and a computed tomography (CT) available. HUm and HUsd of the stones were compared with ANOVA. Receiver operative characteristic analysis with area under the curve (AUC), Youden index, and likelihood ratio calculations were performed. Data were available for 466 patients. The major components were calcium oxalate monohydrate (COM), uric acid, hydroxyapatite, struvite, brushite, cystine, and CO dihydrate (COD) in 41.4%, 19.3%, 12.4%, 7.5%, 5.8%, 5.4%, and 4.7% of patients, respectively. The HUm of UA and Br was significantly lower and higher than the HUm of any other stone type, respectively. HUm and HUsd were most accurate in predicting uric acid with an AUC of 0.969 and 0.851, respectively. The combined use of HUm and HUsd resulted in increased positive predictive value and higher likelihood ratios for identifying a stone's mineral composition for all stone types but COM. To the best of our knowledge, this is the first report of CT data aiding in the prediction of brushite stone composition. Both HUm and HUsd can help predict stone composition and their combined use results in higher likelihood ratios influencing probability.

  4. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

    PubMed Central

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-01-01

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car. PMID:26927108

  5. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

    PubMed

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-02-24

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

  6. An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features.

    PubMed

    Nandi, Sutanu; Subramanian, Abhishek; Sarkar, Ram Rup

    2017-07-25

    Prediction of essential genes helps to identify a minimal set of genes that are absolutely required for the appropriate functioning and survival of a cell. The available machine learning techniques for essential gene prediction have inherent problems, like imbalanced provision of training datasets, biased choice of the best model for a given balanced dataset, choice of a complex machine learning algorithm, and data-based automated selection of biologically relevant features for classification. Here, we propose a simple support vector machine-based learning strategy for the prediction of essential genes in Escherichia coli K-12 MG1655 metabolism that integrates a non-conventional combination of an appropriate sample balanced training set, a unique organism-specific genotype, phenotype attributes that characterize essential genes, and optimal parameters of the learning algorithm to generate the best machine learning model (the model with the highest accuracy among all the models trained for different sample training sets). For the first time, we also introduce flux-coupled metabolic subnetwork-based features for enhancing the classification performance. Our strategy proves to be superior as compared to previous SVM-based strategies in obtaining a biologically relevant classification of genes with high sensitivity and specificity. This methodology was also trained with datasets of other recent supervised classification techniques for essential gene classification and tested using reported test datasets. The testing accuracy was always high as compared to the known techniques, proving that our method outperforms known methods. Observations from our study indicate that essential genes are conserved among homologous bacterial species, demonstrate high codon usage bias, GC content and gene expression, and predominantly possess a tendency to form physiological flux modules in metabolism.

  7. Serum-based diagnostic prediction of oral submucous fibrosis using FTIR spectrometry

    NASA Astrophysics Data System (ADS)

    Rai, Vertika; Mukherjee, Rashmi; Routray, Aurobinda; Ghosh, Ananta Kumar; Roy, Seema; Ghosh, Barnali Paul; Mandal, Puspendu Bikash; Bose, Surajit; Chakraborty, Chandan

    2018-01-01

    Oral submucous fibrosis (OSF) is found to have the highest malignant potentiality among all other pre-cancerous lesions. However, its detection prior to tissue biopsy can be challenging in clinics. Moreover, biopsy examination is invasive and painful. Hence, there is an urgent need of new technology that facilitates accurate diagnostic prediction of OSF prior to biopsy. Here, we used FTIR spectroscopy coupled with chemometric techniques to distinguish the serum metabolic signatures of OSF patients (n = 30) and healthy controls (n = 30). Serum biochemical analyses have been performed to further support the FTIR findings. Absorbance intensities of 45 infrared wavenumbers differed significantly between OSF and normal serum FTIR spectra representing alterations in carbohydrates, proteins, lipids and nucleic acids. Nineteen prominent significant wavenumbers (P ≤ 0.001) at 1020, 1025, 1035, 1039, 1045, 1078, 1055, 1100, 1117, 1122, 1151, 1169, 1243, 1313, 1398, 1453, 1544, 1650 and 1725 cm- 1 provided excellent segregation of OSF spectra from normal using multivariate statistical techniques. These findings provided essential information on the metabolic features of blood serum of OSF patients and established that FTIR spectroscopy coupled with chemometric analysis can be potentially useful in the rapid and accurate preoperative screening/diagnosis of OSF.

  8. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

    PubMed

    Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael; Stryhn, Anette; Buus, Søren; Nielsen, Morten

    2015-11-01

    A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

  9. Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium

    PubMed Central

    Ryan, Natalia; Chorley, Brian; Tice, Raymond R.; Judson, Richard; Corton, J. Christopher

    2016-01-01

    Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including “very weak” agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. PMID:26865669

  10. Lost in translation: preclinical studies on 3,4-methylenedioxymethamphetamine provide information on mechanisms of action, but do not allow accurate prediction of adverse events in humans

    PubMed Central

    Green, AR; King, MV; Shortall, SE; Fone, KCF

    2012-01-01

    3,4-Methylenedioxymethamphetamine (MDMA) induces both acute adverse effects and long-term neurotoxic loss of brain 5-HT neurones in laboratory animals. However, when choosing doses, most preclinical studies have paid little attention to the pharmacokinetics of the drug in humans or animals. The recreational use of MDMA and current clinical investigations of the drug for therapeutic purposes demand better translational pharmacology to allow accurate risk assessment of its ability to induce adverse events. Recent pharmacokinetic studies on MDMA in animals and humans are reviewed and indicate that the risks following MDMA ingestion should be re-evaluated. Acute behavioural and body temperature changes result from rapid MDMA-induced monoamine release, whereas long-term neurotoxicity is primarily caused by metabolites of the drug. Therefore acute physiological changes in humans are fairly accurately mimicked in animals by appropriate dosing, although allometric dosing calculations have little value. Long-term changes require MDMA to be metabolized in a similar manner in experimental animals and humans. However, the rate of metabolism of MDMA and its major metabolites is slower in humans than rats or monkeys, potentially allowing endogenous neuroprotective mechanisms to function in a species specific manner. Furthermore acute hyperthermia in humans probably limits the chance of recreational users ingesting sufficient MDMA to produce neurotoxicity, unlike in the rat. MDMA also inhibits the major enzyme responsible for its metabolism in humans thereby also assisting in preventing neurotoxicity. These observations question whether MDMA alone produces long-term 5-HT neurotoxicity in human brain, although when taken in combination with other recreational drugs it may induce neurotoxicity. LINKED ARTICLES This article is commented on by Parrott, pp. 1518–1520 of this issue. To view this commentary visit http://dx.doi.org/10.1111/j.1476-5381.2012.01941.x and to view the the

  11. FDG-PET Response Prediction in Pediatric Hodgkin's Lymphoma: Impact of Metabolically Defined Tumor Volumes and Individualized SUV Measurements on the Positive Predictive Value.

    PubMed

    Hussien, Amr Elsayed M; Furth, Christian; Schönberger, Stefan; Hundsdoerfer, Patrick; Steffen, Ingo G; Amthauer, Holger; Müller, Hans-Wilhelm; Hautzel, Hubertus

    2015-01-28

    In pediatric Hodgkin's lymphoma (pHL) early response-to-therapy prediction is metabolically assessed by (18)F-FDG PET carrying an excellent negative predictive value (NPV) but an impaired positive predictive value (PPV). Aim of this study was to improve the PPV while keeping the optimal NPV. A comparison of different PET data analyses was performed applying individualized standardized uptake values (SUV), PET-derived metabolic tumor volume (MTV) and the product of both parameters, termed total lesion glycolysis (TLG); One-hundred-eight PET datasets (PET1, n = 54; PET2, n = 54) of 54 children were analysed by visual and semi-quantitative means. SUVmax, SUVmean, MTV and TLG were obtained the results of both PETs and the relative change from PET1 to PET2 (Δ in %) were compared for their capability of identifying responders and non-responders using receiver operating characteristics (ROC)-curves. In consideration of individual variations in noise and contrasts levels all parameters were additionally obtained after threshold correction to lean body mass and background; All semi-quantitative SUV estimates obtained at PET2 were significantly superior to the visual PET2 analysis. However, ΔSUVmax revealed the best results (area under the curve, 0.92; p < 0.001; sensitivity 100%; specificity 85.4%; PPV 46.2%; NPV 100%; accuracy, 87.0%) but was not significantly superior to SUVmax-estimation at PET2 and ΔTLGmax. Likewise, the lean body mass and background individualization of the datasets did not impove the results of the ROC analyses; Sophisticated semi-quantitative PET measures in early response assessment of pHL patients do not perform significantly better than the previously proposed ΔSUVmax. All analytical strategies failed to improve the impaired PPV to a clinically acceptable level while preserving the excellent NPV.

  12. Lipoprotein metabolism indicators improve cardiovascular risk prediction

    USDA-ARS?s Scientific Manuscript database

    Background: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to inves...

  13. Gestational dating by metabolic profile at birth: a California cohort study.

    PubMed

    Jelliffe-Pawlowski, Laura L; Norton, Mary E; Baer, Rebecca J; Santos, Nicole; Rutherford, George W

    2016-04-01

    Accurate gestational dating is a critical component of obstetric and newborn care. In the absence of early ultrasound, many clinicians rely on less accurate measures, such as last menstrual period or symphysis-fundal height during pregnancy, or Dubowitz scoring or the Ballard (or New Ballard) method at birth. These measures often underestimate or overestimate gestational age and can lead to misclassification of babies as born preterm, which has both short- and long-term clinical care and public health implications. We sought to evaluate whether metabolic markers in newborns measured as part of routine screening for treatable inborn errors of metabolism can be used to develop a population-level metabolic gestational dating algorithm that is robust despite intrauterine growth restriction and can be used when fetal ultrasound dating is not available. We focused specifically on the ability of these markers to differentiate preterm births (PTBs) (<37 weeks) from term births and to assign a specific gestational age in the PTB group. We evaluated a cohort of 729,503 singleton newborns with a California birth in 2005 through 2011 who had routine newborn metabolic screening and fetal ultrasound dating at 11-20 weeks' gestation. Using training and testing subsets (divided in a ratio of 3:1) we evaluated the association among PTB, target newborn characteristics, acylcarnitines, amino acids, thyroid-stimulating hormone, 17-hydroxyprogesterone, and galactose-1-phosphate-uridyl-transferase. We used multivariate backward stepwise regression to test for associations and linear discriminate analyses to create a linear function for PTB and to assign a specific week of gestation. We used sensitivity, specificity, and positive predictive value to evaluate the performance of linear functions. Along with birthweight and infant age at test, we included 35 of the 51 metabolic markers measured in the final multivariate model comparing PTBs and term births. Using a linear discriminate

  14. Two-Scale 13C Metabolic Flux Analysis for Metabolic Engineering.

    PubMed

    Ando, David; Garcia Martin, Hector

    2018-01-01

    Accelerating the Design-Build-Test-Learn (DBTL) cycle in synthetic biology is critical to achieving rapid and facile bioengineering of organisms for the production of, e.g., biofuels and other chemicals. The Learn phase involves using data obtained from the Test phase to inform the next Design phase. As part of the Learn phase, mathematical models of metabolic fluxes give a mechanistic level of comprehension to cellular metabolism, isolating the principle drivers of metabolic behavior from the peripheral ones, and directing future experimental designs and engineering methodologies. Furthermore, the measurement of intracellular metabolic fluxes is specifically noteworthy as providing a rapid and easy-to-understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis in the Learn phase of the DBTL cycle, where we show how one can take the isotope labeling data from a 13 C labeling experiment and immediately turn it into a determination of cellular fluxes that points in the direction of genetic engineering strategies that will advance the metabolic engineering process.For our modeling purposes we use the Joint BioEnergy Institute (JBEI) Quantitative Metabolic Modeling (jQMM) library, which provides an open-source, python-based framework for modeling internal metabolic fluxes and making actionable predictions on how to modify cellular metabolism for specific bioengineering goals. It presents a complete toolbox for performing different types of flux analysis such as Flux Balance Analysis, 13 C Metabolic Flux Analysis, and it introduces the capability to use 13 C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) [1]. In addition to several other capabilities, the jQMM is also able to predict the effects of knockouts using the MoMA and ROOM methodologies. The use of the jQMM library is

  15. Accurate thermoelastic tensor and acoustic velocities of NaCl

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Marcondes, Michel L., E-mail: michel@if.usp.br; Chemical Engineering and Material Science, University of Minnesota, Minneapolis, 55455; Shukla, Gaurav, E-mail: shukla@physics.umn.edu

    Despite the importance of thermoelastic properties of minerals in geology and geophysics, their measurement at high pressures and temperatures are still challenging. Thus, ab initio calculations are an essential tool for predicting these properties at extreme conditions. Owing to the approximate description of the exchange-correlation energy, approximations used in calculations of vibrational effects, and numerical/methodological approximations, these methods produce systematic deviations. Hybrid schemes combining experimental data and theoretical results have emerged as a way to reconcile available information and offer more reliable predictions at experimentally inaccessible thermodynamics conditions. Here we introduce a method to improve the calculated thermoelastic tensor bymore » using highly accurate thermal equation of state (EoS). The corrective scheme is general, applicable to crystalline solids with any symmetry, and can produce accurate results at conditions where experimental data may not exist. We apply it to rock-salt-type NaCl, a material whose structural properties have been challenging to describe accurately by standard ab initio methods and whose acoustic/seismic properties are important for the gas and oil industry.« less

  16. Predicting of perceived self efficacy in the amount of macronutrients intake in women with metabolic syndrome - 2012

    PubMed Central

    Mohebi, Siamak; Azadbakht, Leila; Feizi, Avat; Sharifirad, Gholamreza; Hozori, Mohammad

    2014-01-01

    Introduction: Metabolic syndrome is a collection of metabolic disorders, which can increase the mortality rates from 20% to 80%. One of strategies to control the disease is the attention to the dietary habits. Compliance with proper diet is one of the major challenges in the management of this syndrome. Due to this fact, that the patient is responsible for the adjustment of the daily diet, it is important to identify the factors affecting the adoption of nutritional self-care. Besides, self-efficacy is considered as an important pre-requisite for this behavior because it acts as an independent part of the basic skills. This study was carried out with the purpose of determining the predictive role of perceived self-efficacy on macronutrients intake in women with metabolic syndrome. Materials and Methods: In this descriptive study with correlational nature in 2012, there were 329 patients with the metabolic syndrome. The patients were covered by Isfahan oil industry medical centers and selected by a systematic method. In order to gather information on perceived self-efficacy, the questionnaires constructed by the researchers were used and the validity and reliability had been confirmed by the calculation of content validity indexand content validity ratio values and the indices of internal consistency and stability of the tool. The 24-h dietary recall questionnaire was also used for 3 days in order to investigate the nutritional behavior. The obtained data from the dietary recall questionnaire were analyzed by the N4 nutritional software. In this study, AMOS software version 16 was used for the structural model fitting by using the generalized least squares method besides the SPSS statistical software version 16. Results: These averages obtained from the results: 2512.37 kcal energy intake, 70.95 g protein, 420 g carbohydrates and 61.61 g of fat per day. The mean of perceived self-efficacy score was 47.89. The Pearson correlation coefficient was indicated a

  17. Predicting of perceived self efficacy in the amount of macronutrients intake in women with metabolic syndrome - 2012.

    PubMed

    Mohebi, Siamak; Azadbakht, Leila; Feizi, Avat; Sharifirad, Gholamreza; Hozori, Mohammad

    2014-01-01

    Metabolic syndrome is a collection of metabolic disorders, which can increase the mortality rates from 20% to 80%. One of strategies to control the disease is the attention to the dietary habits. Compliance with proper diet is one of the major challenges in the management of this syndrome. Due to this fact, that the patient is responsible for the adjustment of the daily diet, it is important to identify the factors affecting the adoption of nutritional self-care. Besides, self-efficacy is considered as an important pre-requisite for this behavior because it acts as an independent part of the basic skills. This study was carried out with the purpose of determining the predictive role of perceived self-efficacy on macronutrients intake in women with metabolic syndrome. In this descriptive study with correlational nature in 2012, there were 329 patients with the metabolic syndrome. The patients were covered by Isfahan oil industry medical centers and selected by a systematic method. In order to gather information on perceived self-efficacy, the questionnaires constructed by the researchers were used and the validity and reliability had been confirmed by the calculation of content validity indexand content validity ratio values and the indices of internal consistency and stability of the tool. The 24-h dietary recall questionnaire was also used for 3 days in order to investigate the nutritional behavior. The obtained data from the dietary recall questionnaire were analyzed by the N4 nutritional software. In this study, AMOS software version 16 was used for the structural model fitting by using the generalized least squares method besides the SPSS statistical software version 16. THESE AVERAGES OBTAINED FROM THE RESULTS: 2512.37 kcal energy intake, 70.95 g protein, 420 g carbohydrates and 61.61 g of fat per day. The mean of perceived self-efficacy score was 47.89. The Pearson correlation coefficient was indicated a significant inverse relationship between the perceived

  18. Multi-modal imaging predicts memory performance in normal aging and cognitive decline.

    PubMed

    Walhovd, K B; Fjell, A M; Dale, A M; McEvoy, L K; Brewer, J; Karow, D S; Salmon, D P; Fennema-Notestine, C

    2010-07-01

    This study (n=161) related morphometric MR imaging, FDG-PET and APOE genotype to memory scores in normal controls (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD). Stepwise regression analyses focused on morphometric and metabolic characteristics of the episodic memory network: hippocampus, entorhinal, parahippocampal, retrosplenial, posterior cingulate, precuneus, inferior parietal, and lateral orbitofrontal cortices. In NC, hippocampal metabolism predicted learning; entorhinal metabolism predicted recognition; and hippocampal metabolism predicted recall. In MCI, thickness of the entorhinal and precuneus cortices predicted learning, while parahippocampal metabolism predicted recognition. In AD, posterior cingulate cortical thickness predicted learning, while APOE genotype predicted recognition. In the total sample, hippocampal volume and metabolism, cortical thickness of the precuneus, and inferior parietal metabolism predicted learning; hippocampal volume and metabolism, parahippocampal thickness and APOE genotype predicted recognition. Imaging methods appear complementary and differentially sensitive to memory in health and disease. Medial temporal and parietal metabolism and morphometry best explained memory variance. Medial temporal characteristics were related to learning, recall and recognition, while parietal structures only predicted learning. Copyright 2008. Published by Elsevier Inc.

  19. Prediction of acute GVHD and relapse by metabolic biomarkers after allogeneic hematopoietic stem cell transplantation.

    PubMed

    Wu, Xiaojin; Xie, Yiyu; Wang, Chang; Han, Yue; Bao, Xiebing; Ma, Shoubao; Yilmaz, Ahmet; Yang, Bingyu; Ji, Yuhan; Xu, Jinge; Liu, Hong; Chen, Suning; Zhang, Jianying; Yu, Jianhua; Wu, Depei

    2018-05-03

    There are very few studies investigating metabolic biomarkers to predict acute graft-versus-host disease (aGVHD) after allogeneic hematopoietic stem cell transplantation (HSCT). Metabolic models can provide a framework for analyzing the information-rich omics data sets in this setting. Four hundred and fifty-six samples from one hundred and fourteen consecutive patients who underwent HSCT from January 2012 to May 2014 were collected for this study. The changes in serum metabolite levels were investigated using a gas chromatography-mass spectrometry-based metabolomics approach and underwent statistical analysis. Significant metabolic changes were observed on day 7. The stearic acid/palmitic acid (SA/PA) ratio was effective in the diagnosis of grade II-IV aGVHD. Multivariate analysis showed that patients with high SA/PA ratios on day 7 after HSCT were less likely to develop II-IV aGVHD than patients with low SA/PA ratios (odds ratio [OR] = 0.06, 95% CI 0.02-0.18, P < 0.001). After the adjustment for clinical characteristics, the SA/PA ratio had no significant effect on overall survival (hazard ratio [HR] = 1.95, 95% CI 0.92-4.14, P = 0.08), and patients in the high SA/PA ratio group were significantly more likely to relapse than those in the low ratio group (HR = 2.26, 95% CI 1.04-4.91, P = 0.04). Our findings suggest that the SA/PA ratio on day 7 after HSCT is an excellent biomarker to predict both aGVHD and relapse. The serum SA/PA ratio measured on day 7 after transplantation may improve risk stratification for aGVHD and relapse after allogeneic stem cell transplantation. National Natural Science Foundation of China (81470346, 81773361), Priority Academic Program Development of Jiangsu Higher Education Institutions, Jiangsu Natural Science Foundation (BK20161204), Innovation Capability Development Project of Jiangsu Province (BM2015004), Jiangsu Medical Junior Talent Person award (QNRC2016707), and NIH (AI129582 and NS106170).

  20. Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

    PubMed

    Reynolds, Sheila M; Bilmes, Jeff A; Noble, William Stafford

    2010-07-08

    DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

  1. Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens

    PubMed Central

    Reynolds, Sheila M.; Bilmes, Jeff A.; Noble, William Stafford

    2010-01-01

    DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence—301 base pairs, centered at the position to be scored—with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

  2. Accurate predictions of population-level changes in sequence and structural properties of HIV-1 Env using a volatility-controlled diffusion model

    PubMed Central

    DeLeon, Orlando; Hodis, Hagit; O’Malley, Yunxia; Johnson, Jacklyn; Salimi, Hamid; Zhai, Yinjie; Winter, Elizabeth; Remec, Claire; Eichelberger, Noah; Van Cleave, Brandon; Puliadi, Ramya; Harrington, Robert D.; Stapleton, Jack T.; Haim, Hillel

    2017-01-01

    The envelope glycoproteins (Envs) of HIV-1 continuously evolve in the host by random mutations and recombination events. The resulting diversity of Env variants circulating in the population and their continuing diversification process limit the efficacy of AIDS vaccines. We examined the historic changes in Env sequence and structural features (measured by integrity of epitopes on the Env trimer) in a geographically defined population in the United States. As expected, many Env features were relatively conserved during the 1980s. From this state, some features diversified whereas others remained conserved across the years. We sought to identify “clues” to predict the observed historic diversification patterns. Comparison of viruses that cocirculate in patients at any given time revealed that each feature of Env (sequence or structural) exists at a defined level of variance. The in-host variance of each feature is highly conserved among individuals but can vary between different HIV-1 clades. We designate this property “volatility” and apply it to model evolution of features as a linear diffusion process that progresses with increasing genetic distance. Volatilities of different features are highly correlated with their divergence in longitudinally monitored patients. Volatilities of features also correlate highly with their population-level diversification. Using volatility indices measured from a small number of patient samples, we accurately predict the population diversity that developed for each feature over the course of 30 years. Amino acid variants that evolved at key antigenic sites are also predicted well. Therefore, small “fluctuations” in feature values measured in isolated patient samples accurately describe their potential for population-level diversification. These tools will likely contribute to the design of population-targeted AIDS vaccines by effectively capturing the diversity of currently circulating strains and addressing properties

  3. Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

    NASA Astrophysics Data System (ADS)

    Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

    2008-11-01

    The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

  4. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models

    NASA Astrophysics Data System (ADS)

    Blackman, Jonathan; Field, Scott E.; Galley, Chad R.; Szilágyi, Béla; Scheel, Mark A.; Tiglio, Manuel; Hemberger, Daniel A.

    2015-09-01

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic -2Yℓm waveform modes resolved by the NR code up to ℓ=8 . We compare our surrogate model to effective one body waveforms from 50 M⊙ to 300 M⊙ for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

  5. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models.

    PubMed

    Blackman, Jonathan; Field, Scott E; Galley, Chad R; Szilágyi, Béla; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

    2015-09-18

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic _{-2}Y_{ℓm} waveform modes resolved by the NR code up to ℓ=8. We compare our surrogate model to effective one body waveforms from 50M_{⊙} to 300M_{⊙} for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

  6. Use of Multiple Metabolic and Genetic Markers to Improve the Prediction of Type 2 Diabetes: the EPIC-Potsdam Study

    PubMed Central

    Schulze, Matthias B.; Weikert, Cornelia; Pischon, Tobias; Bergmann, Manuela M.; Al-Hasani, Hadi; Schleicher, Erwin; Fritsche, Andreas; Häring, Hans-Ulrich; Boeing, Heiner; Joost, Hans-Georg

    2009-01-01

    OBJECTIVE We investigated whether metabolic biomarkers and single nucleotide polymorphisms (SNPs) improve diabetes prediction beyond age, anthropometry, and lifestyle risk factors. RESEARCH DESIGN AND METHODS A case-cohort study within a prospective study was designed. We randomly selected a subcohort (n = 2,500) from 26,444 participants, of whom 1,962 were diabetes free at baseline. Of the 801 incident type 2 diabetes cases identified in the cohort during 7 years of follow-up, 579 remained for analyses after exclusions. Prediction models were compared by receiver operatoring characteristic (ROC) curve and integrated discrimination improvement. RESULTS Case-control discrimination by the lifestyle characteristics (ROC-AUC: 0.8465) improved with plasma glucose (ROC-AUC: 0.8672, P < 0.001) and A1C (ROC-AUC: 0.8859, P < 0.001). ROC-AUC further improved with HDL cholesterol, triglycerides, γ-glutamyltransferase, and alanine aminotransferase (0.9000, P = 0.002). Twenty SNPs did not improve discrimination beyond these characteristics (P = 0.69). CONCLUSIONS Metabolic markers, but not genotyping for 20 diabetogenic SNPs, improve discrimination of incident type 2 diabetes beyond lifestyle risk factors. PMID:19720844

  7. Identifying metabolic enzymes with multiple types of association evidence

    PubMed Central

    Kharchenko, Peter; Chen, Lifeng; Freund, Yoav; Vitkup, Dennis; Church, George M

    2006-01-01

    Background Existing large-scale metabolic models of sequenced organisms commonly include enzymatic functions which can not be attributed to any gene in that organism. Existing computational strategies for identifying such missing genes rely primarily on sequence homology to known enzyme-encoding genes. Results We present a novel method for identifying genes encoding for a specific metabolic function based on a local structure of metabolic network and multiple types of functional association evidence, including clustering of genes on the chromosome, similarity of phylogenetic profiles, gene expression, protein fusion events and others. Using E. coli and S. cerevisiae metabolic networks, we illustrate predictive ability of each individual type of association evidence and show that significantly better predictions can be obtained based on the combination of all data. In this way our method is able to predict 60% of enzyme-encoding genes of E. coli metabolism within the top 10 (out of 3551) candidates for their enzymatic function, and as a top candidate within 43% of the cases. Conclusion We illustrate that a combination of genome context and other functional association evidence is effective in predicting genes encoding metabolic enzymes. Our approach does not rely on direct sequence homology to known enzyme-encoding genes, and can be used in conjunction with traditional homology-based metabolic reconstruction methods. The method can also be used to target orphan metabolic activities. PMID:16571130

  8. Assessment of in vivo organ-uptake and in silico prediction of CYP mediated metabolism of DA-Phen, a new dopaminergic agent.

    PubMed

    Sutera, Flavia Maria; Giannola, Libero Italo; Murgia, Denise; De Caro, Viviana

    2017-12-01

    The drug development process strives to predict metabolic fate of a drug candidate, together with its uptake in major organs, whether they act as target, deposit or metabolism sites, to the aim of establish a relationship between the pharmacodynamics and the pharmacokinetics and highlight the potential toxicity of the drug candidate. The present study was aimed at evaluating the in vivo uptake of 2-Amino-N-[2-(3,4-dihydroxy-phenyl)-ethyl]-3-phenyl-propionamide (DA-Phen) - a new dopaminergic neurotransmission modulator, in target and non-target organs of animal subjects and integrating these data with SMARTCyp results, an in silico method that predicts the sites of cytochrome P450-mediated metabolism of drug-like molecules. Wistar rats, subjected to two different behavioural studies in which DA-Phen was intraperitoneally administrated at a dose equal to 0.03mmol/kg, were sacrificed after the experimental protocols and their major organs were analysed to quantify the drug uptake. The data obtained were integrated with in silico prediction of potential metabolites of DA-Phen using the SmartCYP predictive tool. DA-Phen reached quantitatively the Central Nervous System and the results showed that the amide bond of the DA-Phen is scarcely hydrolysed as it was found intact in analyzed organs. As a consequence, it is possible to assume that DA-Phen acts as dopaminergic modulator per se and not as a Dopamine prodrug, thus avoiding peripheral release and toxic side effects due to the endogenous neurotransmitter. Furthermore the identification of potential metabolites related to biotransformation of the drug candidate leads to a more careful evaluation of the appropriate route of administration for future intended therapeutic aims and potential translation into clinical studies. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. A NEW CLINICAL PREDICTION CRITERION ACCURATELY DETERMINES A SUBSET OF PATIENTS WITH BILATERAL PRIMARY ALDOSTERONISM BEFORE ADRENAL VENOUS SAMPLING.

    PubMed

    Kocjan, Tomaz; Janez, Andrej; Stankovic, Milenko; Vidmar, Gaj; Jensterle, Mojca

    2016-05-01

    Adrenal venous sampling (AVS) is the only available method to distinguish bilateral from unilateral primary aldosteronism (PA). AVS has several drawbacks, so it is reasonable to avoid this procedure when the results would not affect clinical management. Our objective was to identify a clinical criterion that can reliably predict nonlateralized AVS as a surrogate for bilateral PA that is not treated surgically. A retrospective diagnostic cross-sectional study conducted at Slovenian national endocrine referral center included 69 consecutive patients (mean age 56 ± 8 years, 21 females) with PA who underwent AVS. PA was confirmed with the saline infusion test (SIT). AVS was performed sequentially during continuous adrenocorticotrophic hormone (ACTH) infusion. The main outcome measures were variables associated with nonlateralized AVS to derive a clinical prediction rule. Sixty-seven (97%) patients had a successful AVS and were included in the statistical analysis. A total of 39 (58%) patients had nonlateralized AVS. The combined criterion of serum potassium ≥3.5 mmol/L, post-SIT aldosterone <18 ng/dL, and either no or bilateral tumor found on computed tomography (CT) imaging had perfect estimated specificity (and thus 100% positive predictive value) for bilateral PA, saving an estimated 16% of the patients (11/67) from unnecessary AVS. The best overall classification accuracy (50/67 = 75%) was achieved using the post-SIT aldosterone level <18 ng/dL alone, which yielded 74% sensitivity and 75% specificity for predicting nonlateralized AVS. Our clinical prediction criterion appears to accurately determine a subset of patients with bilateral PA who could avoid unnecessary AVS and immediately commence with medical treatment.

  10. Towards improved models for predicting bioconcentration of well-metabolized compounds by rainbow trout using measured rates of in vitro intrinsic clearance

    EPA Science Inventory

    Consensus models were developed to predict the bioconcentration of well-metabolized chemicals by rainbow trout. The models employ intrinsic clearance data from in vitro studies with liver S9 fractions or isolated hepatocytes to estimate a liver clearance rate which is extrapolat...

  11. Metabolic Analysis

    NASA Astrophysics Data System (ADS)

    Tolstikov, Vladimir V.

    Analysis of the metabolome with coverage of all of the possibly detectable components in the sample, rather than analysis of each individual metabolite at a given time, can be accomplished by metabolic analysis. Targeted and/or nontargeted approaches are applied as needed for particular experiments. Monitoring hundreds or more metabolites at a given time requires high-throughput and high-end techniques that enable screening for relative changes in, rather than absolute concentrations of, compounds within a wide dynamic range. Most of the analytical techniques useful for these purposes use GC or HPLC/UPLC separation modules coupled to a fast and accurate mass spectrometer. GC separations require chemical modification (derivatization) before analysis, and work efficiently for the small molecules. HPLC separations are better suited for the analysis of labile and nonvolatile polar and nonpolar compounds in their native form. Direct infusion and NMR-based techniques are mostly used for fingerprinting and snap phenotyping, where applicable. Discovery and validation of metabolic biomarkers are exciting and promising opportunities offered by metabolic analysis applied to biological and biomedical experiments. We have demonstrated that GC-TOF-MS, HPLC/UPLC-RP-MS and HILIC-LC-MS techniques used for metabolic analysis offer sufficient metabolome mapping providing researchers with confident data for subsequent multivariate analysis and data mining.

  12. In Silico Strategies for Modeling Stereoselective Metabolism of Pyrethroids

    EPA Science Inventory

    In silico methods are invaluable tools to researchers seeking to understand and predict metabolic processes within PBPK models. Even though these methods have been successfully utilized to predict and quantify metabolic processes, there are many challenges involved. Stereochemica...

  13. Accurate prediction of complex free surface flow around a high speed craft using a single-phase level set method

    NASA Astrophysics Data System (ADS)

    Broglia, Riccardo; Durante, Danilo

    2017-11-01

    This paper focuses on the analysis of a challenging free surface flow problem involving a surface vessel moving at high speeds, or planing. The investigation is performed using a general purpose high Reynolds free surface solver developed at CNR-INSEAN. The methodology is based on a second order finite volume discretization of the unsteady Reynolds-averaged Navier-Stokes equations (Di Mascio et al. in A second order Godunov—type scheme for naval hydrodynamics, Kluwer Academic/Plenum Publishers, Dordrecht, pp 253-261, 2001; Proceedings of 16th international offshore and polar engineering conference, San Francisco, CA, USA, 2006; J Mar Sci Technol 14:19-29, 2009); air/water interface dynamics is accurately modeled by a non standard level set approach (Di Mascio et al. in Comput Fluids 36(5):868-886, 2007a), known as the single-phase level set method. In this algorithm the governing equations are solved only in the water phase, whereas the numerical domain in the air phase is used for a suitable extension of the fluid dynamic variables. The level set function is used to track the free surface evolution; dynamic boundary conditions are enforced directly on the interface. This approach allows to accurately predict the evolution of the free surface even in the presence of violent breaking waves phenomena, maintaining the interface sharp, without any need to smear out the fluid properties across the two phases. This paper is aimed at the prediction of the complex free-surface flow field generated by a deep-V planing boat at medium and high Froude numbers (from 0.6 up to 1.2). In the present work, the planing hull is treated as a two-degree-of-freedom rigid object. Flow field is characterized by the presence of thin water sheets, several energetic breaking waves and plungings. The computational results include convergence of the trim angle, sinkage and resistance under grid refinement; high-quality experimental data are used for the purposes of validation, allowing to

  14. Metabolic pathways for the whole community.

    PubMed

    Hanson, Niels W; Konwar, Kishori M; Hawley, Alyse K; Altman, Tomer; Karp, Peter D; Hallam, Steven J

    2014-07-22

    A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical levels from genomes to biomes. Holistic understanding of biological systems requires agile software tools that permit comparative analyses across multiple information levels (DNA, RNA, protein, and metabolites) to identify emergent properties, diagnose system states, or predict responses to environmental change. Here we adopt the MetaPathways annotation and analysis pipeline and Pathway Tools to construct environmental pathway/genome databases (ePGDBs) that describe microbial community metabolism using MetaCyc, a highly curated database of metabolic pathways and components covering all domains of life. We evaluate Pathway Tools' performance on three datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series. We define accuracy and sensitivity relationships between read length, coverage and pathway recovery and evaluate the impact of taxonomic pruning on ePGDB construction and interpretation. Resulting ePGDBs provide interactive metabolic maps, predict emergent metabolic pathways associated with biosynthesis and energy production and differentiate between genomic potential and phenotypic expression across defined environmental gradients. This multi-tiered analysis provides the user community with specific operating guidelines, performance metrics and prediction hazards for more reliable ePGDB construction and interpretation. Moreover, it demonstrates the power of Pathway Tools in predicting metabolic interactions in natural and engineered ecosystems.

  15. Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.

    PubMed

    Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Li, Li-Ping; Huang, De-Shuang; Yan, Gui-Ying; Nie, Ru; Huang, Yu-An

    2017-04-04

    Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.

  16. Metabolism of captopril carboxyl ester derivatives for percutaneous absorption.

    PubMed

    Gullick, Darren R; Ingram, Matthew J; Pugh, W John; Cox, Paul A; Gard, Paul; Smart, John D; Moss, Gary P

    2009-02-01

    To determine the metabolism of captopril n-carboxyl derivatives and how this may impact on their use as transdermal prodrugs. The pharmacological activity of the ester derivatives was also characterised in order to compare the angiotensin converting enzyme inhibitory potency of the derivatives compared with the parent drug, captopril. The metabolism rates of the ester derivatives were determined in vitro (using porcine liver esterase and porcine ear skin) and in silico (using molecular modelling to investigate the potential to predict metabolism). Relatively slow pseudo first-order metabolism of the prodrugs was observed, with the ethyl ester displaying the highest rate of metabolism. A strong relationship was established between in-vitro methods, while in-silico methods support the use of in-vitro methods and highlight the potential of in-silico techniques to predict metabolism. All the prodrugs behaved as angiotensin converting enzyme inhibitors, with the methyl ester displaying optimum inhibition. In-vitro porcine liver esterase metabolism rates inform in-vitro skin rates well, and in-silico interaction energies relate well to both. Thus, in-silico methods may be developed that include interaction energies to predict metabolism rates.

  17. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

    PubMed Central

    Schellenberger, Jan; Que, Richard; Fleming, Ronan M. T.; Thiele, Ines; Orth, Jeffrey D.; Feist, Adam M.; Zielinski, Daniel C.; Bordbar, Aarash; Lewis, Nathan E.; Rahmanian, Sorena; Kang, Joseph; Hyduke, Daniel R.; Palsson, Bernhard Ø.

    2012-01-01

    Over the past decade, a growing community of researchers has emerged around the use of COnstraint-Based Reconstruction and Analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a significant update of this in silico ToolBox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include: (1) network gap filling, (2) 13C analysis, (3) metabolic engineering, (4) omics-guided analysis, and (5) visualization. As with the first version, the COBRA Toolbox reads and writes Systems Biology Markup Language formatted models. In version 2.0, we improved performance, usability, and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the Toolbox and validate results. This Toolbox lowers the barrier of entry to use powerful COBRA methods. PMID:21886097

  18. Association between the abdominal obesity anthropometric indicators and metabolic disorders in a Chinese population.

    PubMed

    Dong, J; Ni, Y-Q; Chu, X; Liu, Y-Q; Liu, G-X; Zhao, J; Yang, Y-B; Yan, Y-X

    2016-02-01

    Obesity has become a major health problem in contemporary society and it is closely related to many chronic diseases, so it is an important issue for measuring adiposity accurately and predicting its future. Prevention and treatment of overweight and obesity has become one of the key prevention and treatment of metabolic disorders. In this study, we compared the ability of the four anthropometric indicators (body mass index, waist circumstance, waist-height ratio, waist-to-hip ratio) to identify metabolic disorders (hypertension, hyperlipidaemia, hyperglycemia and hyperuricemia) by receiver operating characteristic (ROC) curve analyses and to provide evidence for clinical practice. In this large scale cross-sectional study, 13,275 Han adults (including 7595 males and 5680 females) received physical examination between January, 2009 and January, 2010 in Xuanwu Hospital of Capital Medical University were investigated by the means of questionnaire, Meanwhile, the physical examination and serological results were recorded. A package known as Statistical Package for Social Scientist (SPSS) was employed to analyse the responses while t-test, one-way analysis of variance (ANOVA), ROC analysis and chi-square statistical methods were used to test the hypotheses. WC, WHtR, WHR and BMI were all significantly (P < 0.001) correlated with all metabolic risk factors regardless of gender. And the area under the curve (AUC) of WHtR was significantly greater than that of WC, BMI or WHR in the prediction of hypertension, hyperlipidaemia, hyperglycemia and hyperuricemia. Our data show that WHtR was the best predictor of various metabolic disorders. The diagnostic value in descending order was WHtR > WHR > WC > BMI. Therefore we recommend WHtR in assessment of obese patients, in order to better assess the risks of their metabolic diseases. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

  19. Gestational dating by metabolic profile at birth: a California cohort study

    PubMed Central

    Jelliffe-Pawlowski, Laura L.; Norton, Mary E.; Baer, Rebecca J.; Santos, Nicole; Rutherford, George W.

    2016-01-01

    Background Accurate gestational dating is a critical component of obstetric and newborn care. In the absence of early ultrasound, many clinicians rely on less accurate measures, such as last menstrual period or symphysis-fundal height during pregnancy, or Dubowitz scoring or the Ballard (or New Ballard) method at birth. These measures often underestimate or overestimate gestational age and can lead to misclassification of babies as born preterm, which has both short- and long-term clinical care and public health implications. Objective We sought to evaluate whether metabolic markers in newborns measured as part of routine screening for treatable inborn errors of metabolism can be used to develop a population-level metabolic gestational dating algorithm that is robust despite intrauterine growth restriction and can be used when fetal ultrasound dating is not available. We focused specifically on the ability of these markers to differentiate preterm births (PTBs) (<37 weeks) from term births and to assign a specific gestational age in the PTB group. Study Design We evaluated a cohort of 729,503 singleton newborns with a California birth in 2005 through 2011 who had routine newborn metabolic screening and fetal ultrasound dating at 11–20 weeks’ gestation. Using training and testing subsets (divided in a ratio of 3:1) we evaluated the association among PTB, target newborn characteristics, acylcarnitines, amino acids, thyroid-stimulating hormone, 17-hydroxyprogesterone, and galactose-1-phosphate-uridyl-transferase. We used multivariate backward stepwise regression to test for associations and linear discriminate analyses to create a linear function for PTB and to assign a specific week of gestation. We used sensitivity, specificity, and positive predictive value to evaluate the performance of linear functions. Results Along with birthweight and infant age at test, we included 35 of the 51 metabolic markers measured in the final multivariate model comparing PTBs and

  20. Metabolic health assessment of zoo elephants: Management factors predicting leptin levels and the glucose-to-insulin ratio and their associations with health parameters

    PubMed Central

    Brown, Janine L.

    2017-01-01

    Screening for metabolic-related health problems can enhance animal welfare, so the purpose of this study was to conduct the first metabolic health assessment of zoo elephants and use epidemiological methods to determine how factors in the captive environment were associated with metabolic hormone concentrations. In addition, we examined relationships between metabolic status and several fitness parameters: foot health, musculoskeletal health, reproductive cyclicity, and body condition. Two blood samples were collected 2 weeks apart from 87 Asian (Elephas maximus) and 105 African (Loxodonta africana) elephants managed by zoos accredited by the Association of Zoos and Aquariums for analysis of serum leptin, insulin, glucose and the glucose-to-insulin ratio (G:I). In females, mean (± SD) leptin concentrations and the G:I were lower (P<0.05) in Asian (3.93 ± 2.21 ng/ml and 110 ± 86 units) compared to African (4.37 ± 2.89 ng/ml and 208 ± 133 units) elephants, respectively. For males, mean leptin and the G:I were 4.99 ± 3.61 ng/ml and 253 ± 181 units for Asian, and 3.72 ± 2.00 ng/ml and 326 ± 231 units for African elephants, respectively, with no differences between species (P>0.05). As mean leptin concentration increased there was an increase in the odds of a female being non-cycling (P = 0.0083). The G:I was associated inversely with body condition (P = 0.0002); as the G:I increased there was a decreased risk of BCS = 4 or 5 as compared to the ideal, or BCS = 3. Neither leptin nor G:I were predictive of foot or musculoskeletal health scores. Factors related to walking and feeding practices were most influential in predicting metabolic status, whereas social and housing factors showed smaller, but significant effects. The metabolic health benefits of walking were detected if the time spent in staff-directed walking was 7 hours or more per week. The most protective feeding practices included implementing a random rather than predictable feeding schedule and

  1. Metabolic health assessment of zoo elephants: Management factors predicting leptin levels and the glucose-to-insulin ratio and their associations with health parameters.

    PubMed

    Morfeld, Kari A; Brown, Janine L

    2017-01-01

    Screening for metabolic-related health problems can enhance animal welfare, so the purpose of this study was to conduct the first metabolic health assessment of zoo elephants and use epidemiological methods to determine how factors in the captive environment were associated with metabolic hormone concentrations. In addition, we examined relationships between metabolic status and several fitness parameters: foot health, musculoskeletal health, reproductive cyclicity, and body condition. Two blood samples were collected 2 weeks apart from 87 Asian (Elephas maximus) and 105 African (Loxodonta africana) elephants managed by zoos accredited by the Association of Zoos and Aquariums for analysis of serum leptin, insulin, glucose and the glucose-to-insulin ratio (G:I). In females, mean (± SD) leptin concentrations and the G:I were lower (P<0.05) in Asian (3.93 ± 2.21 ng/ml and 110 ± 86 units) compared to African (4.37 ± 2.89 ng/ml and 208 ± 133 units) elephants, respectively. For males, mean leptin and the G:I were 4.99 ± 3.61 ng/ml and 253 ± 181 units for Asian, and 3.72 ± 2.00 ng/ml and 326 ± 231 units for African elephants, respectively, with no differences between species (P>0.05). As mean leptin concentration increased there was an increase in the odds of a female being non-cycling (P = 0.0083). The G:I was associated inversely with body condition (P = 0.0002); as the G:I increased there was a decreased risk of BCS = 4 or 5 as compared to the ideal, or BCS = 3. Neither leptin nor G:I were predictive of foot or musculoskeletal health scores. Factors related to walking and feeding practices were most influential in predicting metabolic status, whereas social and housing factors showed smaller, but significant effects. The metabolic health benefits of walking were detected if the time spent in staff-directed walking was 7 hours or more per week. The most protective feeding practices included implementing a random rather than predictable feeding schedule and

  2. Anthropometric Variables Accurately Predict Dual Energy X-Ray Absorptiometric-Derived Body Composition and Can Be Used to Screen for Diabetes

    PubMed Central

    Yavari, Reza; McEntee, Erin; McEntee, Michael; Brines, Michael

    2011-01-01

    The current world-wide epidemic of obesity has stimulated interest in developing simple screening methods to identify individuals with undiagnosed diabetes mellitus type 2 (DM2) or metabolic syndrome (MS). Prior work utilizing body composition obtained by sophisticated technology has shown that the ratio of abdominal fat to total fat is a good predictor for DM2 or MS. The goals of this study were to determine how well simple anthropometric variables predict the fat mass distribution as determined by dual energy x-ray absorptometry (DXA), and whether these are useful to screen for DM2 or MS within a population. To accomplish this, the body composition of 341 females spanning a wide range of body mass indices and with a 23% prevalence of DM2 and MS was determined using DXA. Stepwise linear regression models incorporating age, weight, height, waistline, and hipline predicted DXA body composition (i.e., fat mass, trunk fat, fat free mass, and total mass) with good accuracy. Using body composition as independent variables, nominal logistic regression was then performed to estimate the probability of DM2. The results show good discrimination with the receiver operating characteristic (ROC) having an area under the curve (AUC) of 0.78. The anthropometrically-derived body composition equations derived from the full DXA study group were then applied to a group of 1153 female patients selected from a general endocrinology practice. Similar to the smaller study group, the ROC from logistical regression using body composition had an AUC of 0.81 for the detection of DM2. These results are superior to screening based on questionnaires and compare favorably with published data derived from invasive testing, e.g., hemoglobin A1c. This anthropometric approach offers promise for the development of simple, inexpensive, non-invasive screening to identify individuals with metabolic dysfunction within large populations. PMID:21915276

  3. Predicting nutrient excretion of aquatic animals with metabolic ecology and ecological stoichiometry: a global synthesis.

    PubMed

    Vanni, Michael J; McIntyre, Peter B

    2016-12-01

    The metabolic theory of ecology (MTE) and ecological stoichiometry (ES) are both prominent frameworks for understanding energy and nutrient budgets of organisms. We tested their separate and joint power to predict nitrogen (N) and phosphorus (P) excretion rates of ectothermic aquatic invertebrate and vertebrate animals (10,534 observations worldwide). MTE variables (body size, temperature) performed better than ES variables (trophic guild, vertebrate classification, body N:P) in predicting excretion rates, but the best models included variables from both frameworks. Size scaling coefficients were significantly lower than predicted by MTE (<0.75), were lower for P than N, and varied greatly among species. Contrary to expectations under ES, vertebrates excreted both N and P at higher rates than invertebrates despite having more nutrient-rich bodies, and primary consumers excreted as much nutrients as carnivores despite having nutrient-poor diets. Accounting for body N:P hardly improved upon predictions from treating vertebrate classification categorically. We conclude that basic data on body size, water temperature, trophic guild, and vertebrate classification are sufficient to make general estimates of nutrient excretion rates for any animal taxon or aquatic ecosystem. Nonetheless, dramatic interspecific variation in size-scaling coefficients and counter-intuitive patterns with respect to diet and body composition underscore the need for field data on consumption and egestion rates. Together, MTE and ES provide a powerful conceptual basis for interpreting and predicting nutrient recycling rates of aquatic animals worldwide. © 2016 by the Ecological Society of America.

  4. Myofiber metabolic type determination by mass spectrometry imaging.

    PubMed

    Centeno, Delphine; Vénien, Annie; Pujos-Guillot, Estelle; Astruc, Thierry; Chambon, Christophe; Théron, Laëtitia

    2017-08-01

    Matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging is a powerful tool that opens new research opportunities in the field of biology. In this work, predictive model was developed to discriminate metabolic myofiber types using the MALDI spectral data. Rat skeletal muscles are constituted of type I and type IIA fiber, which have an oxidative metabolism for glycogen degradation, and type IIX and type IIB fiber which have a glycolytic metabolism, present in different proportions according to the muscle function and physiological state. So far, myofiber type is determined by histological methods that are time consuming. Thanks to the predictive model, we were able to predict not only the metabolic fiber type but also their location, on the same muscle section that was used for MALDI imaging. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  5. Using radiance predicted by the P3 approximation in a spherical geometry to predict tissue optical properties

    NASA Astrophysics Data System (ADS)

    Dickey, Dwayne J.; Moore, Ronald B.; Tulip, John

    2001-01-01

    For photodynamic therapy of solid tumors, such as prostatic carcinoma, to be achieved, an accurate model to predict tissue parameters and light dose must be found. Presently, most analytical light dosimetry models are fluence based and are not clinically viable for tissue characterization. Other methods of predicting optical properties, such as Monet Carlo, are accurate but far too time consuming for clinical application. However, radiance predicted by the P3-Approximation, an anaylitical solution to the transport equation, may be a viable and accurate alternative. The P3-Approximation accurately predicts optical parameters in intralipid/methylene blue based phantoms in a spherical geometry. The optical parameters furnished by the radiance, when introduced into fluence predicted by both P3- Approximation and Grosjean Theory, correlate well with experimental data. The P3-Approximation also predicts the optical properties of prostate tissue, agreeing with documented optical parameters. The P3-Approximation could be the clinical tool necessary to facilitate PDT of solid tumors because of the limited number of invasive measurements required and the speed in which accurate calculations can be performed.

  6. Accurate density functional prediction of molecular electron affinity with the scaling corrected Kohn–Sham frontier orbital energies

    NASA Astrophysics Data System (ADS)

    Zhang, DaDi; Yang, Xiaolong; Zheng, Xiao; Yang, Weitao

    2018-04-01

    Electron affinity (EA) is the energy released when an additional electron is attached to an atom or a molecule. EA is a fundamental thermochemical property, and it is closely pertinent to other important properties such as electronegativity and hardness. However, accurate prediction of EA is difficult with density functional theory methods. The somewhat large error of the calculated EAs originates mainly from the intrinsic delocalisation error associated with the approximate exchange-correlation functional. In this work, we employ a previously developed non-empirical global scaling correction approach, which explicitly imposes the Perdew-Parr-Levy-Balduz condition to the approximate functional, and achieve a substantially improved accuracy for the calculated EAs. In our approach, the EA is given by the scaling corrected Kohn-Sham lowest unoccupied molecular orbital energy of the neutral molecule, without the need to carry out the self-consistent-field calculation for the anion.

  7. Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers

    NASA Astrophysics Data System (ADS)

    Afzal, Mohammad Atif Faiz; Cheng, Chong; Hachmann, Johannes

    2018-06-01

    Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3-1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that can guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and a highly economical path to determining the RI values for a wide range of organic polymers.

  8. Lower NIH stroke scale scores are required to accurately predict a good prognosis in posterior circulation stroke.

    PubMed

    Inoa, Violiza; Aron, Abraham W; Staff, Ilene; Fortunato, Gilbert; Sansing, Lauren H

    2014-01-01

    The NIH stroke scale (NIHSS) is an indispensable tool that aids in the determination of acute stroke prognosis and decision making. Patients with posterior circulation (PC) strokes often present with lower NIHSS scores, which may result in the withholding of thrombolytic treatment from these patients. However, whether these lower initial NIHSS scores predict better long-term prognoses is uncertain. We aimed to assess the utility of the NIHSS at presentation for predicting the functional outcome at 3 months in anterior circulation (AC) versus PC strokes. This was a retrospective analysis of a large prospectively collected database of adults with acute ischemic stroke. Univariate and multivariate analyses were conducted to identify factors associated with outcome. Additional analyses were performed to determine the receiver operating characteristic (ROC) curves for NIHSS scores and outcomes in AC and PC infarctions. Both the optimal cutoffs for maximal diagnostic accuracy and the cutoffs to obtain >80% sensitivity for poor outcomes were determined in AC and PC strokes. The analysis included 1,197 patients with AC stroke and 372 with PC stroke. The median initial NIHSS score for patients with AC strokes was 7 and for PC strokes it was 2. The majority (71%) of PC stroke patients had baseline NIHSS scores ≤4, and 15% of these 'minor' stroke patients had a poor outcome at 3 months. ROC analysis identified that the optimal NIHSS cutoff for outcome prediction after infarction in the AC was 8 and for infarction in the PC it was 4. To achieve >80% sensitivity for detecting patients with a subsequent poor outcome, the NIHSS cutoff for infarctions in the AC was 4 and for infarctions in the PC it was 2. The NIHSS cutoff that most accurately predicts outcomes is 4 points higher in AC compared to PC infarctions. There is potential for poor outcomes in patients with PC strokes and low NIHSS scores, suggesting that thrombolytic treatment should not be withheld from these patients

  9. A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189

    PubMed Central

    Suthers, Patrick F.; Dasika, Madhukar S.; Kumar, Vinay Satish; Denisov, Gennady; Glass, John I.; Maranas, Costas D.

    2009-01-01

    With a genome size of ∼580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms. PMID:19214212

  10. New analytical model for the ozone electronic ground state potential surface and accurate ab initio vibrational predictions at high energy range.

    PubMed

    Tyuterev, Vladimir G; Kochanov, Roman V; Tashkun, Sergey A; Holka, Filip; Szalay, Péter G

    2013-10-07

    An accurate description of the complicated shape of the potential energy surface (PES) and that of the highly excited vibration states is of crucial importance for various unsolved issues in the spectroscopy and dynamics of ozone and remains a challenge for the theory. In this work a new analytical representation is proposed for the PES of the ground electronic state of the ozone molecule in the range covering the main potential well and the transition state towards the dissociation. This model accounts for particular features specific to the ozone PES for large variations of nuclear displacements along the minimum energy path. The impact of the shape of the PES near the transition state (existence of the "reef structure") on vibration energy levels was studied for the first time. The major purpose of this work was to provide accurate theoretical predictions for ozone vibrational band centres at the energy range near the dissociation threshold, which would be helpful for understanding the very complicated high-resolution spectra and its analyses currently in progress. Extended ab initio electronic structure calculations were carried out enabling the determination of the parameters of a minimum energy path PES model resulting in a new set of theoretical vibrational levels of ozone. A comparison with recent high-resolution spectroscopic data on the vibrational levels gives the root-mean-square deviations below 1 cm(-1) for ozone band centres up to 90% of the dissociation energy. New ab initio vibrational predictions represent a significant improvement with respect to all previously available calculations.

  11. Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium.

    PubMed

    Ryan, Natalia; Chorley, Brian; Tice, Raymond R; Judson, Richard; Corton, J Christopher

    2016-05-01

    Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. Published by Oxford University Press on behalf of the Society of Toxicology 2016. This work is written by US Government employees and is in the public domain in the US.

  12. A Quantitative Study of Oxygen as a Metabolic Regulator

    NASA Technical Reports Server (NTRS)

    Radhakrishnan, Krishnan; LaManna, Joseph C.; Cabrera, Marco E.

    1999-01-01

    ). Sensitivity analysis establishes relationships between model predictions and problem parameters (i.e., initial concentrations, rate coefficients, etc). It helps determine the effects of uncertainties or changes in these input parameters on the predictions, which ultimately are compared with experimental observations in order to validate the model. Sensitivity analysis can identify parameters that must be determined accurately because of their large effect on the model predictions and parameters that need not be known with great precision because they have little or no effect on the solution. This capability may prove to be important in optimizing the design of experiments, thereby reducing the use of animals. This approach can be applied to study the metabolic effects of reduced oxygen delivery to cardiac muscle due to local myocardial ischemia and the effects of acute hypoxia on brain metabolism. Other important applications of sensitivity analysis include identification of quantitatively relevant pathways and biochemical species within an overall mechanism, when examining the effects of a genetic anomaly or pathological state on energetic system components and whole system behavior.

  13. Development of an In Silico Metabolic Simulator and Searchable Metabolism Database for Chemical Risk Assessments

    EPA Science Inventory

    The US EPA is faced with long lists of chemicals that need to be assessed for hazard, and a gap in evaluating chemical risk is accounting for metabolic activation resulting in increased toxicity. The goals of this project are to develop a capability to predict metabolic maps of x...

  14. Metabolism drives distribution and abundance in extremophile fish

    PubMed Central

    McHugh, Peter A.; Glover, Chris N.; McIntosh, Angus R.

    2017-01-01

    Differences in population density between species of varying size are frequently attributed to metabolic rates which are assumed to scale with body size with a slope of 0.75. This assumption is often criticised on the grounds that 0.75 scaling of metabolic rate with body size is not universal and can vary significantly depending on species and life-history. However, few studies have investigated how interspecific variation in metabolic scaling relationships affects population density in different sized species. Here we predict inter-specific differences in metabolism from niche requirements, thereby allowing metabolic predictions of species distribution and abundance at fine spatial scales. Due to the differences in energetic efficiency required along harsh-benign gradients, an extremophile fish (brown mudfish, Neochanna apoda) living in harsh environments had slower metabolism, and thus higher population densities, compared to a fish species (banded kōkopu, Galaxias fasciatus) in physiologically more benign habitats. Interspecific differences in the intercepts for the relationship between body and density disappeared when species mass-specific metabolic rates, rather than body sizes, were used to predict density, implying population energy use was equivalent between mudfish and kōkopu. Nevertheless, despite significant interspecific differences in the slope of the metabolic scaling relationships, mudfish and kōkopu had a common slope for the relationship between body size and population density. These results support underlying logic of energetic equivalence between different size species implicit in metabolic theory. However, the precise slope of metabolic scaling relationships, which is the subject of much debate, may not be a reliable indicator of population density as expected under metabolic theory. PMID:29176819

  15. Genome-scale metabolic reconstructions and theoretical investigation of methane conversion in Methylomicrobium buryatense strain 5G(B1).

    PubMed

    de la Torre, Andrea; Metivier, Aisha; Chu, Frances; Laurens, Lieve M L; Beck, David A C; Pienkos, Philip T; Lidstrom, Mary E; Kalyuzhnaya, Marina G

    2015-11-25

    Methane-utilizing bacteria (methanotrophs) are capable of growth on methane and are attractive systems for bio-catalysis. However, the application of natural methanotrophic strains to large-scale production of value-added chemicals/biofuels requires a number of physiological and genetic alterations. An accurate metabolic model coupled with flux balance analysis can provide a solid interpretative framework for experimental data analyses and integration. A stoichiometric flux balance model of Methylomicrobium buryatense strain 5G(B1) was constructed and used for evaluating metabolic engineering strategies for biofuels and chemical production with a methanotrophic bacterium as the catalytic platform. The initial metabolic reconstruction was based on whole-genome predictions. Each metabolic step was manually verified, gapfilled, and modified in accordance with genome-wide expression data. The final model incorporates a total of 841 reactions (in 167 metabolic pathways). Of these, up to 400 reactions were recruited to produce 118 intracellular metabolites. The flux balance simulations suggest that only the transfer of electrons from methanol oxidation to methane oxidation steps can support measured growth and methane/oxygen consumption parameters, while the scenario employing NADH as a possible source of electrons for particulate methane monooxygenase cannot. Direct coupling between methane oxidation and methanol oxidation accounts for most of the membrane-associated methane monooxygenase activity. However the best fit to experimental results is achieved only after assuming that the efficiency of direct coupling depends on growth conditions and additional NADH input (about 0.1-0.2 mol of incremental NADH per one mol of methane oxidized). The additional input is proposed to cover loss of electrons through inefficiency and to sustain methane oxidation at perturbations or support uphill electron transfer. Finally, the model was used for testing the carbon conversion

  16. Microbial communities and their predicted metabolic characteristics in deep fracture groundwaters of the crystalline bedrock at Olkiluoto, Finland

    NASA Astrophysics Data System (ADS)

    Bomberg, Malin; Lamminmäki, Tiina; Itävaara, Merja

    2016-11-01

    The microbial diversity in oligotrophic isolated crystalline Fennoscandian Shield bedrock fracture groundwaters is high, but the core community has not been identified. Here we characterized the bacterial and archaeal communities in 12 water conductive fractures situated at depths between 296 and 798 m by high throughput amplicon sequencing using the Illumina HiSeq platform. Between 1.7 × 104 and 1.2 × 106 bacterial or archaeal sequence reads per sample were obtained. These sequences revealed that up to 95 and 99 % of the bacterial and archaeal sequences obtained from the 12 samples, respectively, belonged to only a few common species, i.e. the core microbiome. However, the remaining rare microbiome contained over 3- and 6-fold more bacterial and archaeal taxa. The metabolic properties of the microbial communities were predicted using PICRUSt. The approximate estimation showed that the metabolic pathways commonly included fermentation, fatty acid oxidation, glycolysis/gluconeogenesis, oxidative phosphorylation, and methanogenesis/anaerobic methane oxidation, but carbon fixation through the Calvin cycle, reductive TCA cycle, and the Wood-Ljungdahl pathway was also predicted. The rare microbiome is an unlimited source of genomic functionality in all ecosystems. It may consist of remnants of microbial communities prevailing in earlier environmental conditions, but could also be induced again if changes in their living conditions occur.

  17. DESIGN AND PERFORMANCE OF A XENOBIOTIC METABOLISM DATABASE MANAGER FOR METABOLIC SIMULATOR ENHANCEMENT AND CHEMICAL RISK ANALYSIS

    EPA Science Inventory

    A major uncertainty that has long been recognized in evaluating chemical toxicity is accounting for metabolic activation of chemicals resulting in increased toxicity. In silico approaches to predict chemical metabolism and to subsequently screen and prioritize chemicals for risk ...

  18. Shedding light on the variability of optical skin properties: finding a path towards more accurate prediction of light propagation in human cutaneous compartments

    PubMed Central

    Mignon, C.; Tobin, D. J.; Zeitouny, M.; Uzunbajakava, N. E.

    2018-01-01

    Finding a path towards a more accurate prediction of light propagation in human skin remains an aspiration of biomedical scientists working on cutaneous applications both for diagnostic and therapeutic reasons. The objective of this study was to investigate variability of the optical properties of human skin compartments reported in literature, to explore the underlying rational of this variability and to propose a dataset of values, to better represent an in vivo case and recommend a solution towards a more accurate prediction of light propagation through cutaneous compartments. To achieve this, we undertook a novel, logical yet simple approach. We first reviewed scientific articles published between 1981 and 2013 that reported on skin optical properties, to reveal the spread in the reported quantitative values. We found variations of up to 100-fold. Then we extracted the most trust-worthy datasets guided by a rule that the spectral properties should reflect the specific biochemical composition of each of the skin layers. This resulted in the narrowing of the spread in the calculated photon densities to 6-fold. We conclude with a recommendation to use the identified most robust datasets when estimating light propagation in human skin using Monte Carlo simulations. Alternatively, otherwise follow our proposed strategy to screen any new datasets to determine their biological relevance. PMID:29552418

  19. Enzyme clustering accelerates processing of intermediates through metabolic channeling

    PubMed Central

    Castellana, Michele; Wilson, Maxwell Z.; Xu, Yifan; Joshi, Preeti; Cristea, Ileana M.; Rabinowitz, Joshua D.; Gitai, Zemer; Wingreen, Ned S.

    2015-01-01

    We present a quantitative model to demonstrate that coclustering multiple enzymes into compact agglomerates accelerates the processing of intermediates, yielding the same efficiency benefits as direct channeling, a well-known mechanism in which enzymes are funneled between enzyme active sites through a physical tunnel. The model predicts the separation and size of coclusters that maximize metabolic efficiency, and this prediction is in agreement with previously reported spacings between coclusters in mammalian cells. For direct validation, we study a metabolic branch point in Escherichia coli and experimentally confirm the model prediction that enzyme agglomerates can accelerate the processing of a shared intermediate by one branch, and thus regulate steady-state flux division. Our studies establish a quantitative framework to understand coclustering-mediated metabolic channeling and its application to both efficiency improvement and metabolic regulation. PMID:25262299

  20. Integrating Crystallography into Early Metabolism Studies

    NASA Astrophysics Data System (ADS)

    Cruciani, Gabriele; Aristei, Yasmin; Goracci, Laura; Carosati, Emanuele

    Since bioavailability, activity, toxicity, distribution, and final elimination all depend on metabolic biotransformations, it would be extremely advantageous if this information to be produced early in the discovery phase. Once obtained, researchers can judge whether or not a potential candidate should be eliminated from the pipeline, or modified to improve chemical stability or safety. The use of in silico methods to predict the site of metabolism in Phase I cytochrome-mediated reactions is a starting point in any metabolic pathway prediction. This paper presents a new method, which provides the site of metabolism for any CYP-mediated reaction acting on unknown substrates. The methodology can be applied automatically to all the cytochromes whose Xray 3D structure is known, but can be also applied to homology model 3D structures. The fully automated procedure can be used to detect positions that should be protected in order to avoid metabolic degradation, or to check the suitability of a new scaffold or pro-drug. Therefore the procedure is also a valuable new tool in early ADME-Tox, where drug-safety and metabolic profile patterns must be evaluated as soon, and as early, as possible.

  1. Predicting ecological roles in the rhizosphere using metabolome and transportome modeling

    DOE PAGES

    Larsen, Peter E.; Collart, Frank R.; Dai, Yang; ...

    2015-09-02

    The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. New algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad’s ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter ofmore » plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism’s transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.« less

  2. Predicting Ecological Roles in the Rhizosphere Using Metabolome and Transportome Modeling

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Larsen, Peter E.; Collart, Frank R.; Dai, Yang

    2015-09-02

    The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. However, new algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad's ecological role in the rhizosphere: a biofilm, biocontrol agent, promotermore » of plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism's transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.« less

  3. A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE).

    PubMed

    Stacey, R Greg; Skinnider, Michael A; Scott, Nichollas E; Foster, Leonard J

    2017-10-23

    An organism's protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome. Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE , where usage instructions can be found. An example dataset and output are also provided for testing purposes. PrInCE is the first fast and easy

  4. Using open source computational tools for predicting human metabolic stability and additional absorption, distribution, metabolism, excretion, and toxicity properties.

    PubMed

    Gupta, Rishi R; Gifford, Eric M; Liston, Ted; Waller, Chris L; Hohman, Moses; Bunin, Barry A; Ekins, Sean

    2010-11-01

    Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

  5. Hounsfield unit density accurately predicts ESWL success.

    PubMed

    Magnuson, William J; Tomera, Kevin M; Lance, Raymond S

    2005-01-01

    Extracorporeal shockwave lithotripsy (ESWL) is a commonly used non-invasive treatment for urolithiasis. Helical CT scans provide much better and detailed imaging of the patient with urolithiasis including the ability to measure density of urinary stones. In this study we tested the hypothesis that density of urinary calculi as measured by CT can predict successful ESWL treatment. 198 patients were treated at Alaska Urological Associates with ESWL between January 2002 and April 2004. Of these 101 met study inclusion with accessible CT scans and stones ranging from 5-15 mm. Follow-up imaging demonstrated stone freedom in 74.2%. The overall mean Houndsfield density value for stone-free compared to residual stone groups were significantly different ( 93.61 vs 122.80 p < 0.0001). We determined by receiver operator curve (ROC) that HDV of 93 or less carries a 90% or better chance of stone freedom following ESWL for upper tract calculi between 5-15mm.

  6. Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

    NASA Astrophysics Data System (ADS)

    Theologou, I.; Patelaki, M.; Karantzalos, K.

    2015-04-01

    Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn't establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.

  7. A Computational Model of Torque Generation: Neural, Contractile, Metabolic and Musculoskeletal Components

    PubMed Central

    Callahan, Damien M.; Umberger, Brian R.; Kent-Braun, Jane A.

    2013-01-01

    The pathway of voluntary joint torque production includes motor neuron recruitment and rate-coding, sarcolemmal depolarization and calcium release by the sarcoplasmic reticulum, force generation by motor proteins within skeletal muscle, and force transmission by tendon across the joint. The direct source of energetic support for this process is ATP hydrolysis. It is possible to examine portions of this physiologic pathway using various in vivo and in vitro techniques, but an integrated view of the multiple processes that ultimately impact joint torque remains elusive. To address this gap, we present a comprehensive computational model of the combined neuromuscular and musculoskeletal systems that includes novel components related to intracellular bioenergetics function. Components representing excitatory drive, muscle activation, force generation, metabolic perturbations, and torque production during voluntary human ankle dorsiflexion were constructed, using a combination of experimentally-derived data and literature values. Simulation results were validated by comparison with torque and metabolic data obtained in vivo. The model successfully predicted peak and submaximal voluntary and electrically-elicited torque output, and accurately simulated the metabolic perturbations associated with voluntary contractions. This novel, comprehensive model could be used to better understand impact of global effectors such as age and disease on various components of the neuromuscular system, and ultimately, voluntary torque output. PMID:23405245

  8. Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low-density SNP panels: Evidence that long-range LD is a major contributing factor.

    PubMed

    Vallejo, Roger L; Silva, Rafael M O; Evenhuis, Jason P; Gao, Guangtu; Liu, Sixin; Parsons, James E; Martin, Kyle E; Wiens, Gregory D; Lourenco, Daniela A L; Leeds, Timothy D; Palti, Yniv

    2018-06-05

    Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium-density single nucleotide polymorphism (SNP) array. Here, the impact of lower-density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)-flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single-step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree-based prediction (0.50-0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL-flanking SNP (0.65-0.72) was similar to the panel with 35K SNP (0.65-0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r 2  ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long-range LD likely contributed to the accurate genomic predictions with the low-density SNP panels. Population structure analysis supported the hypothesis that long-range LD in this population may be caused by admixture. Results suggest that lower-cost, low-density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs. © 2018 The Authors. This article is a U.S. Government work and is in the public domain in the USA. Journal of Animal Breeding and Genetics published by Blackwell Verlag GmbH.

  9. Pretherapeutic FDG-PET total metabolic tumor volume predicts response to induction therapy in pediatric Hodgkin's lymphoma.

    PubMed

    Rogasch, Julian M M; Hundsdoerfer, Patrick; Hofheinz, Frank; Wedel, Florian; Schatka, Imke; Amthauer, Holger; Furth, Christian

    2018-05-03

    Standardized treatment in pediatric patients with Hodgkin's lymphoma (HL) follows risk stratification by tumor stage, erythrocyte sedimentation rate and tumor bulk. We aimed to identify quantitative parameters from pretherapeutic FDG-PET to assist prediction of response to induction chemotherapy. Retrospective analysis in 50 children with HL (f:18; m:32; median age, 14.8 [4-18] a) consecutively treated according to EuroNet-PHL-C1 (n = 42) or -C2 treatment protocol (n = 8). Total metabolic tumor volume (MTV) in pretherapeutic FDG-PET was defined using a semi-automated, background-adapted threshold. Metabolic (SUVmax, SUVmean, SUVpeak, total lesion glycolysis [MTV*SUVmean]) and heterogeneity parameters (asphericity [ASP], entropy, contrast, local homogeneity, energy, and cumulative SUV-volume histograms) were derived. Early response assessment (ERA) was performed after 2 cycles of induction chemotherapy according to treatment protocol and verified by reference rating. Prediction of inadequate response (IR) in ERA was based on ROC analysis separated by stage I/II (1 and 26 patients) and stage III/IV disease (7 and 16 patients) or treatment group/level (TG/TL) 1 to 3. IR was seen in 28/50 patients (TG/TL 1, 6/12 patients; TG/TL 2, 10/17; TG/TL 3, 12/21). Among all PET parameters, MTV best predicted IR; ASP was the best heterogeneity parameter. AUC of MTV was 0.84 (95%-confidence interval, 0.69-0.99) in stage I/II and 0.86 (0.7-1.0) in stage III/IV. In patients of TG/TL 1, AUC of MTV was 0.92 (0.74-1.0); in TG/TL 2 0.71 (0.44-0.99), and in TG/TL 3 0.85 (0.69-1.0). Patients with high vs. low MTV had IR in 86 vs. 0% in TG/TL 1, 80 vs. 29% in TG/TL 2, and 90 vs. 27% in TG/TL 3 (cut-off, > 80 ml, > 160 ml, > 410 ml). In this explorative study, high total MTV best predicted inadequate response to induction therapy in pediatric HL of all pretherapeutic FDG-PET parameters - in both low and high stages as well as the 3 different TG/TL. Ethics committee number

  10. Drug Metabolism by the Host and Gut Microbiota: A Partnership or Rivalry?

    PubMed Central

    2015-01-01

    The importance of the gut microbiome in determining not only overall health, but also in the metabolism of drugs and xenobiotics, is rapidly emerging. It is becoming increasingly clear that the gut microbiota can act in concert with the host cells to maintain intestinal homeostasis, cometabolize drugs and xenobiotics, and alter the expression levels of drug-metabolizing enzymes and transporters and the expression and activity levels of nuclear receptors. In this myriad of activities, the impact of the microbiota may be beneficial or detrimental to the host. Given that the interplay between the gut microbiota and host cells is likely subject to high interindividual variability, this work has tremendous implications for our ability to predict accurately a particular drug’s pharmacokinetics and a given patient population’s response to drugs. In this issue of Drug Metabolism and Disposition, a series of articles is presented that illustrate the progress and challenges that lie ahead as we unravel the intricacies associated with drug and xenobiotic metabolism by the gut microbiota. These articles highlight the underlying mechanisms that are involved and the use of in vivo and in vitro approaches that are currently available for elucidating the role of the gut microbiota in drug and xenobiotic metabolism. These articles also shed light on exciting new avenues of research that may be pursued as we consider the role of the gut microbiota as an endocrine organ, a component of the brain-gut axis, and whether the gut microbiota is an appropriate and amenable target for new drugs. PMID:26261284

  11. Drug Metabolism by the Host and Gut Microbiota: A Partnership or Rivalry?

    PubMed

    Swanson, Hollie I

    2015-10-01

    The importance of the gut microbiome in determining not only overall health, but also in the metabolism of drugs and xenobiotics, is rapidly emerging. It is becoming increasingly clear that the gut microbiota can act in concert with the host cells to maintain intestinal homeostasis, cometabolize drugs and xenobiotics, and alter the expression levels of drug-metabolizing enzymes and transporters and the expression and activity levels of nuclear receptors. In this myriad of activities, the impact of the microbiota may be beneficial or detrimental to the host. Given that the interplay between the gut microbiota and host cells is likely subject to high interindividual variability, this work has tremendous implications for our ability to predict accurately a particular drug's pharmacokinetics and a given patient population's response to drugs. In this issue of Drug Metabolism and Disposition, a series of articles is presented that illustrate the progress and challenges that lie ahead as we unravel the intricacies associated with drug and xenobiotic metabolism by the gut microbiota. These articles highlight the underlying mechanisms that are involved and the use of in vivo and in vitro approaches that are currently available for elucidating the role of the gut microbiota in drug and xenobiotic metabolism. These articles also shed light on exciting new avenues of research that may be pursued as we consider the role of the gut microbiota as an endocrine organ, a component of the brain-gut axis, and whether the gut microbiota is an appropriate and amenable target for new drugs. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

  12. Rapid prediction of chemical metabolism by human UDP-glucuronosyltransferase isoforms using quantum chemical descriptors derived with the electronegativity equalization method.

    PubMed

    Sorich, Michael J; McKinnon, Ross A; Miners, John O; Winkler, David A; Smith, Paul A

    2004-10-07

    This study aimed to evaluate in silico models based on quantum chemical (QC) descriptors derived using the electronegativity equalization method (EEM) and to assess the use of QC properties to predict chemical metabolism by human UDP-glucuronosyltransferase (UGT) isoforms. Various EEM-derived QC molecular descriptors were calculated for known UGT substrates and nonsubstrates. Classification models were developed using support vector machine and partial least squares discriminant analysis. In general, the most predictive models were generated with the support vector machine. Combining QC and 2D descriptors (from previous work) using a consensus approach resulted in a statistically significant improvement in predictivity (to 84%) over both the QC and 2D models and the other methods of combining the descriptors. EEM-derived QC descriptors were shown to be both highly predictive and computationally efficient. It is likely that EEM-derived QC properties will be generally useful for predicting ADMET and physicochemical properties during drug discovery.

  13. In silico method for modelling metabolism and gene product expression at genome scale

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem

    2012-07-03

    Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome andmore » transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.« less

  14. Hypertriglyceridemic waist might be an alternative to metabolic syndrome for predicting future diabetes mellitus.

    PubMed

    He, Sen; Zheng, Yi; Shu, Yan; He, Jiyun; Wang, Yong; Chen, Xiaoping

    2013-01-01

    In some cross-sectional studies, hypertriglyceridemic waist (HTGW) has been recommended as an alternative to metabolic syndrome (MetS) for screening individuals at high risk for diabetes mellitus (DM). However, little information is about the predictive power of HTGW for future DM. The aims of the study were to assess the DM predictive power of HTGW compared with MetS based on the follow-up data over 15 years collected from a general Chinese population. And Findings: The data were collected in 1992 and then again in 2007 from the same group of 687 individuals without DM in 1992. For the whole population (n =687), multivariate analysis showed presence of HTGW was associated with a 4.1-fold (95%CI: 2.4-7.0, p < 0.001) increased risk and presence of MetS was associated with a 3.7-fold (95%CI: 2.2-6.2, p < 0.001) increased risk for future DM. For the population without elevated fasting plasma glucose (n = 650), multivariate analysis showed presence of HTGW was associated with a 3.9-fold (95%CI: 2.2-7.0, p < 0.001) increased risk and presence of MetS was associated with a 3.7-fold (95%CI: 2.1-6.6, p < 0.001) increased risk for future DM. HTGW could predict future DM independently, and the predictive power was similar to MetS. HTGW might be an alternative to MetS for predicting future DM. For simpler and fewer components, HTGW might be more practical than MetS, and it might be recommended in most clinical practices. This finding might be more useful for the individuals who only have elevated WC and TG. Although these individuals are without MetS, they are still at high risk for future DM, similarly to the individuals with MetS.

  15. Accurate Prediction of Inducible Transcription Factor Binding Intensities In Vivo

    PubMed Central

    Siepel, Adam; Lis, John T.

    2012-01-01

    DNA sequence and local chromatin landscape act jointly to determine transcription factor (TF) binding intensity profiles. To disentangle these influences, we developed an experimental approach, called protein/DNA binding followed by high-throughput sequencing (PB–seq), that allows the binding energy landscape to be characterized genome-wide in the absence of chromatin. We applied our methods to the Drosophila Heat Shock Factor (HSF), which inducibly binds a target DNA sequence element (HSE) following heat shock stress. PB–seq involves incubating sheared naked genomic DNA with recombinant HSF, partitioning the HSF–bound and HSF–free DNA, and then detecting HSF–bound DNA by high-throughput sequencing. We compared PB–seq binding profiles with ones observed in vivo by ChIP–seq and developed statistical models to predict the observed departures from idealized binding patterns based on covariates describing the local chromatin environment. We found that DNase I hypersensitivity and tetra-acetylation of H4 were the most influential covariates in predicting changes in HSF binding affinity. We also investigated the extent to which DNA accessibility, as measured by digital DNase I footprinting data, could be predicted from MNase–seq data and the ChIP–chip profiles for many histone modifications and TFs, and found GAGA element associated factor (GAF), tetra-acetylation of H4, and H4K16 acetylation to be the most predictive covariates. Lastly, we generated an unbiased model of HSF binding sequences, which revealed distinct biophysical properties of the HSF/HSE interaction and a previously unrecognized substructure within the HSE. These findings provide new insights into the interplay between the genomic sequence and the chromatin landscape in determining transcription factor binding intensity. PMID:22479205

  16. A link prediction approach to cancer drug sensitivity prediction.

    PubMed

    Turki, Turki; Wei, Zhi

    2017-10-03

    Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

  17. Breathing metabolic simulator

    NASA Technical Reports Server (NTRS)

    Bartlett, R. G.; Hendricks, C. M.; Morison, W. B.

    1972-01-01

    The development of a breathing metabolic simulator (BMS) is reported. This BMS simulates all of the breathing and metabolic parameters required for complete evaluation and test of life support and resuscitation equipment. It is also useful for calibrating and validating mechanical and gaseous pulmonary function test procedures. Breathing rate, breathing depth, breath velocity contour, oxygen uptake, and carbon dioxide release are all variable over wide ranges simulating conditions from sleep to hard work with respiratory exchange ratios covering the range from hypoventilation. In addition, all of these parameters are remotely controllable to facilitate use of the device in hostile or remote environments. The exhaled breath is also maintained at body temperature and a high humidity. The simulation is accurate to the extent of having a variable functional residual capacity independent of other parameters.

  18. FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately.

    PubMed

    Budowski-Tal, Inbal; Nov, Yuval; Kolodny, Rachel

    2010-02-23

    Fast identification of protein structures that are similar to a specified query structure in the entire Protein Data Bank (PDB) is fundamental in structure and function prediction. We present FragBag: An ultrafast and accurate method for comparing protein structures. We describe a protein structure by the collection of its overlapping short contiguous backbone segments, and discretize this set using a library of fragments. Then, we succinctly represent the protein as a "bags-of-fragments"-a vector that counts the number of occurrences of each fragment-and measure the similarity between two structures by the similarity between their vectors. Our representation has two additional benefits: (i) it can be used to construct an inverted index, for implementing a fast structural search engine of the entire PDB, and (ii) one can specify a structure as a collection of substructures, without combining them into a single structure; this is valuable for structure prediction, when there are reliable predictions only of parts of the protein. We use receiver operating characteristic curve analysis to quantify the success of FragBag in identifying neighbor candidate sets in a dataset of over 2,900 structures. The gold standard is the set of neighbors found by six state of the art structural aligners. Our best FragBag library finds more accurate candidate sets than the three other filter methods: The SGM, PRIDE, and a method by Zotenko et al. More interestingly, FragBag performs on a par with the computationally expensive, yet highly trusted structural aligners STRUCTAL and CE.

  19. Prediction model with metabolic syndrome to predict recurrent vascular events in patients with clinically manifest vascular diseases.

    PubMed

    Wassink, Annemarie M; van der Graaf, Yolanda; Janssen, Kristel J; Cook, Nancy R; Visseren, Frank L

    2012-12-01

    Although the overall average 10-year cardiovascular risk for patients with manifest atherosclerosis is considered to be more than 20%, actual risk for individual patients ranges from much lower to much higher. We investigated whether information on metabolic syndrome (MetS) or its individual components improves cardiovascular risk stratification in these patients. We conducted a prospective cohort study in 3679 patients with clinical manifest atherosclerosis from the Secondary Manifestations of ARTerial disease (SMART) study. Primary outcome was defined as any cardiovascular event (cardiovascular death, ischemic stroke or myocardial infarction). Three pre-specified prediction models were derived, all including information on established MetS components. The association between outcome and predictors was quantified using a Cox proportional hazard analysis. Model performance was assessed using global goodness-of-fit fit (χ(2)), discrimination (C-index) and ability to improve risk stratification. A total of 417 cardiovascular events occurred among 3679 patients with 15,102 person-years of follow-up (median follow-up 3.7 years, range 1.6-6.4 years). Compared to a model with age and gender only, all MetS-based models performed slightly better in terms of global model fit (χ(2)) but not C-index. The Net Reclassification Index associated with the addition of MetS (yes/no), the dichotomous MetS-components or the continuous MetS-components on top of age and gender was 2.1% (p = 0.29), 2.3% (p = 0.31) and 7.5% (p = 0.01), respectively. Prediction models incorporating age, gender and MetS can discriminate between patients with clinical manifest atherosclerosis at the highest vascular risk and those at lower risk. The addition of MetS components to a model with age and gender correctly reclassifies only a small proportion of patients into higher- and lower-risk categories. The clinical utility of a prediction model with MetS is therefore limited.

  20. Accurate interatomic force fields via machine learning with covariant kernels

    NASA Astrophysics Data System (ADS)

    Glielmo, Aldo; Sollich, Peter; De Vita, Alessandro

    2017-06-01

    We present a novel scheme to accurately predict atomic forces as vector quantities, rather than sets of scalar components, by Gaussian process (GP) regression. This is based on matrix-valued kernel functions, on which we impose the requirements that the predicted force rotates with the target configuration and is independent of any rotations applied to the configuration database entries. We show that such covariant GP kernels can be obtained by integration over the elements of the rotation group SO (d ) for the relevant dimensionality d . Remarkably, in specific cases the integration can be carried out analytically and yields a conservative force field that can be recast into a pair interaction form. Finally, we show that restricting the integration to a summation over the elements of a finite point group relevant to the target system is sufficient to recover an accurate GP. The accuracy of our kernels in predicting quantum-mechanical forces in real materials is investigated by tests on pure and defective Ni, Fe, and Si crystalline systems.

  1. Neck circumference as a new anthropometric indicator for prediction of insulin resistance and components of metabolic syndrome in adolescents: Brazilian Metabolic Syndrome Study

    PubMed Central

    da Silva, Cleliani de Cassia; Zambon, Mariana Porto; Vasques, Ana Carolina J.; Rodrigues, Ana Maria de B.; Camilo, Daniella Fernandes; Antonio, Maria Ângela R. de G. M.; Cassani, Roberta Soares L.; Geloneze, Bruno

    2014-01-01

    OBJECTIVE: To evaluate the correlation between neck circumference and insulin resistance and components of metabolic syndrome in adolescents with different adiposity levels and pubertal stages, as well as to determine the usefulness of neck circumference to predict insulin resistance in adolescents. METHODS: Cross-sectional study with 388 adolescents of both genders from ten to 19 years old. The adolescents underwent anthropometric and body composition assessment, including neck and waist circumferences, and biochemical evaluation. The pubertal stage was obtained by self-assessment, and the blood pressure, by auscultation. Insulin resistance was evaluated by the Homeostasis Model Assessment-Insulin Resistance. The correlation between two variables was evaluated by partial correlation coefficient adjusted for the percentage of body fat and pubertal stage. The performance of neck circumference to identify insulin resistance was tested by Receiver Operating Characteristic Curve. RESULTS: After the adjustment for percentage body fat and pubertal stage, neck circumference correlated with waist circumference, blood pressure, triglycerides and markers of insulin resistance in both genders. CONCLUSIONS: The results showed that the neck circumference is a useful tool for the detection of insulin resistance and changes in the indicators of metabolic syndrome in adolescents. The easiness of application and low cost of this measure may allow its use in Public Health services. PMID:25119754

  2. Knotty: Efficient and Accurate Prediction of Complex RNA Pseudoknot Structures.

    PubMed

    Jabbari, Hosna; Wark, Ian; Montemagno, Carlo; Will, Sebastian

    2018-06-01

    The computational prediction of RNA secondary structure by free energy minimization has become an important tool in RNA research. However in practice, energy minimization is mostly limited to pseudoknot-free structures or rather simple pseudoknots, not covering many biologically important structures such as kissing hairpins. Algorithms capable of predicting sufficiently complex pseudoknots (for sequences of length n) used to have extreme complexities, e.g. Pknots (Rivas and Eddy, 1999) has O(n6) time and O(n4) space complexity. The algorithm CCJ (Chen et al., 2009) dramatically improves the asymptotic run time for predicting complex pseudoknots (handling almost all relevant pseudoknots, while being slightly less general than Pknots), but this came at the cost of large constant factors in space and time, which strongly limited its practical application (∼200 bases already require 256GB space). We present a CCJ-type algorithm, Knotty, that handles the same comprehensive pseudoknot class of structures as CCJ with improved space complexity of Θ(n3 + Z)-due to the applied technique of sparsification, the number of "candidates", Z, appears to grow significantly slower than n4 on our benchmark set (which include pseudoknotted RNAs up to 400 nucleotides). In terms of run time over this benchmark, Knotty clearly outperforms Pknots and the original CCJ implementation, CCJ 1.0; Knotty's space consumption fundamentally improves over CCJ 1.0, being on a par with the space-economic Pknots. By comparing to CCJ 2.0, our unsparsified Knotty variant, we demonstrate the isolated effect of sparsification. Moreover, Knotty employs the state-of-the-art energy model of "HotKnots DP09", which results in superior prediction accuracy over Pknots. Our software is available at https://github.com/HosnaJabbari/Knotty. will@tbi.unvie.ac.at. Supplementary data are available at Bioinformatics online.

  3. Accurate Prediction of Protein Contact Maps by Coupling Residual Two-Dimensional Bidirectional Long Short-Term Memory with Convolutional Neural Networks.

    PubMed

    Hanson, Jack; Paliwal, Kuldip; Litfin, Thomas; Yang, Yuedong; Zhou, Yaoqi

    2018-06-19

    Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to be state-of-the-art in the latest Critical Assessment of Structure Prediction techniques (CASP12, (Schaarschmidt et al., 2018)) for protein contact map prediction by attempting to provide a protein-wide context at each residue pair. Recurrent neural networks have seen great success in recent protein residue classification problems due to their ability to propagate information through long protein sequences, especially Long Short-Term Memory (LSTM) cells. Here we propose a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two-dimensional evolutionary coupling-based information. We show that the proposed method achieves a robust performance over validation and independent test sets with the Area Under the receiver operating characteristic Curve (AUC)>0.95 in all tests. When compared to several state-of-the-art methods for independent testing of 228 proteins, the method yields an AUC value of 0.958, whereas the next-best method obtains an AUC of 0.909. More importantly, the improvement is over contacts at all sequence-position separations. Specifically, a 8.95%, 5.65% and 2.84% increase in precision were observed for the top L∕10 predictions over the next best for short, medium and long-range contacts, respectively. This confirms the usefulness of ResNets to congregate the short-range relations and 2D-BRLSTM to propagate the long-range dependencies throughout the entire protein contact map 'image'. SPOT-Contact server url: http://sparks-lab.org/jack/server/SPOT-Contact/. Supplementary data is available at Bioinformatics online.

  4. Warming reduces metabolic rate in marine snails: adaptation to fluctuating high temperatures challenges the metabolic theory of ecology.

    PubMed

    Marshall, David J; McQuaid, Christopher D

    2011-01-22

    The universal temperature-dependence model (UTD) of the metabolic theory of ecology (MTE) proposes that temperature controls mass-scaled, whole-animal resting metabolic rate according to the first principles of physics (Boltzmann kinetics). Controversy surrounds the model's implication of a mechanistic basis for metabolism that excludes the effects of adaptive regulation, and it is unclear how this would apply to organisms that live in fringe environments and typically show considerable metabolic adaptation. We explored thermal scaling of metabolism in a rocky-shore eulittoral-fringe snail (Echinolittorina malaccana) that experiences constrained energy gain and fluctuating high temperatures (between 25°C and approximately 50°C) during prolonged emersion (weeks). In contrast to the prediction of the UTD model, metabolic rate was often negatively related to temperature over a benign range (30-40°C), the relationship depending on (i) the temperature range, (ii) the degree of metabolic depression (related to the quiescent period), and (iii) whether snails were isolated within their shells. Apparent activation energies (E) varied between 0.05 and -0.43 eV, deviating excessively from the UTD's predicted range of between 0.6 and 0.7 eV. The lowering of metabolism when heated should improve energy conservation in a high-temperature environment and challenges both the theory's generality and its mechanistic basis.

  5. Local Debonding and Fiber Breakage in Composite Materials Modeled Accurately

    NASA Technical Reports Server (NTRS)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2001-01-01

    A prerequisite for full utilization of composite materials in aerospace components is accurate design and life prediction tools that enable the assessment of component performance and reliability. Such tools assist both structural analysts, who design and optimize structures composed of composite materials, and materials scientists who design and optimize the composite materials themselves. NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) software package (http://www.grc.nasa.gov/WWW/LPB/mac) addresses this need for composite design and life prediction tools by providing a widely applicable and accurate approach to modeling composite materials. Furthermore, MAC/GMC serves as a platform for incorporating new local models and capabilities that are under development at NASA, thus enabling these new capabilities to progress rapidly to a stage in which they can be employed by the code's end users.

  6. Urinary Squamous Epithelial Cells Do Not Accurately Predict Urine Culture Contamination, but May Predict Urinalysis Performance in Predicting Bacteriuria.

    PubMed

    Mohr, Nicholas M; Harland, Karisa K; Crabb, Victoria; Mutnick, Rachel; Baumgartner, David; Spinosi, Stephanie; Haarstad, Michael; Ahmed, Azeemuddin; Schweizer, Marin; Faine, Brett

    2016-03-01

    The presence of squamous epithelial cells (SECs) has been advocated to identify urinary contamination despite a paucity of evidence supporting this practice. We sought to determine the value of using quantitative SECs as a predictor of urinalysis contamination. Retrospective cross-sectional study of adults (≥18 years old) presenting to a tertiary academic medical center who had urinalysis with microscopy and urine culture performed. Patients with missing or implausible demographic data were excluded (2.5% of total sample). The primary analysis aimed to determine an SEC threshold that predicted urine culture contamination using receiver operating characteristics (ROC) curve analysis. The a priori secondary analysis explored how demographic variables (age, sex, body mass index) may modify the SEC test performance and whether SECs impacted traditional urinalysis indicators of bacteriuria. A total of 19,328 records were included. ROC curve analysis demonstrated that SEC count was a poor predictor of urine culture contamination (area under the ROC curve = 0.680, 95% confidence interval [CI] = 0.671 to 0.689). In secondary analysis, the positive likelihood ratio (LR+) of predicting bacteriuria via urinalysis among noncontaminated specimens was 4.98 (95% CI = 4.59 to 5.40) in the absence of SECs, but the LR+ fell to 2.35 (95% CI = 2.17 to 2.54) for samples with more than 8 SECs/low-powered field (lpf). In an independent validation cohort, urinalysis samples with fewer than 8 SECs/lpf predicted bacteriuria better (sensitivity = 75%, specificity = 84%) than samples with more than 8 SECs/lpf (sensitivity = 86%, specificity = 70%; diagnostic odds ratio = 17.5 [14.9 to 20.7] vs. 8.7 [7.3 to 10.5]). Squamous epithelial cells are a poor predictor of urine culture contamination, but may predict poor predictive performance of traditional urinalysis measures. © 2016 by the Society for Academic Emergency Medicine.

  7. Adult Attachment Interview Discourse Patterns Predict Metabolic Syndrome in Midlife

    PubMed Central

    Davis, Cynthia R.; Usher, Nicole; Dearing, Eric; Barkai, Ayelet R.; Crowell-Doom, Cindy; Mantzoros, Christos S.; Crowell, Judith A.

    2017-01-01

    Objective Adult attachment discourse patterns and current family relationship quality were examined as predictors of health behaviors and number of Metabolic Syndrome (MetS) criteria met. Methods A sample of 215 White/European American and Black/African American adults, aged 35 to 55, were examined cross-sectionally. Discourse was assessed with the Adult Attachment Interview (AAI), specifically: 1) coherence, a marker of attachment security, 2) unresolved trauma/loss, a marker of disorganized and distorted cognition related to trauma, and 3) idealization, the tendency to minimize the impact of stressful experiences. Health behaviors of diet, exercise, smoking and alcohol use were also assessed, as were adverse childhood experiences, current depressive symptoms and relationship functioning. MetS includes hypertension, hyperglycemia, high triglycerides, low HDL cholesterol, and obesity. Results Using path analysis and accounting for childhood adversity and depressive symptoms, AAI coherence and unresolved trauma or loss were directly linked to number of MetS criteria met (β = −.22 and .21 respectively). Idealization was indirectly linked to MetS through poor diet (β = −.26 and −.36 respectively), predicting 21% of the variance in number of MetS criteria met. Conclusions Attachment representations related to stress appraisal and care-seeking behaviors appear to serve as cognitive mechanisms increasing risk of MetS. PMID:25264975

  8. Characterization of the in Vitro Metabolic Profile of Evodiamine in Human Liver Microsomes and Hepatocytes by UHPLC-Q Exactive Mass Spectrometer

    PubMed Central

    Zhang, Zhaowei; Fang, Tianzi; Zhou, Hongyun; Yuan, Jie; Liu, Qingwang

    2018-01-01

    Evodiamine is an indoloquinazoline alkaloid isolated from the fruit of Evodia rutaecarpa, which has a wide range of pharmacological effects like anti-tumor and anti-inflammatory effects. This study was intended to investigate the metabolic characteristics of evodiamine in human liver microsomes and hepatocytes by ultra-high performance liquid chromatography coupled with a Q Exactive mass spectrometer. A total of 12 phase I metabolites were detected in human liver microsomes; whereas in human hepatocytes 19 metabolites, including seven phase II metabolites were detected. The structures of the metabolites were characterized based on their accurate masses, fragment ions, and chromatographic retention times. Four metabolites (M1, M2, M5, and M7) were further unambiguously confirmed by matching their retention times, accurate masses, and fragment ions with those of their reference standards. Among these metabolites, 12 metabolites are first identified (M2, M5–M8, M10–M13, and M17–M19). The current study revealed that oxygenation, N-demethylation, dehydrogenation, glucuronidation, and GSH conjugation were the major metabolic pathways for evodiamine. This study elucidated the detailed metabolite profiles of evodiamine, which is helpful in predicting in vivo metabolism of evodiamine in human and in understanding the elimination mechanism of evodiamine and in turn, the effectiveness and toxicity. PMID:29520234

  9. Modeling Neisseria meningitidis metabolism: from genome to metabolic fluxes

    PubMed Central

    Baart, Gino JE; Zomer, Bert; de Haan, Alex; van der Pol, Leo A; Beuvery, E Coen; Tramper, Johannes; Martens, Dirk E

    2007-01-01

    Background Neisseria meningitidis is a human pathogen that can infect diverse sites within the human host. The major diseases caused by N. meningitidis are responsible for death and disability, especially in young infants. In general, most of the recent work on N. meningitidis focuses on potential antigens and their functions, immunogenicity, and pathogenicity mechanisms. Very little work has been carried out on Neisseria primary metabolism over the past 25 years. Results Using the genomic database of N. meningitidis serogroup B together with biochemical and physiological information in the literature we constructed a genome-scale flux model for the primary metabolism of N. meningitidis. The validity of a simplified metabolic network derived from the genome-scale metabolic network was checked using flux-balance analysis in chemostat cultures. Several useful predictions were obtained from in silico experiments, including substrate preference. A minimal medium for growth of N. meningitidis was designed and tested succesfully in batch and chemostat cultures. Conclusion The verified metabolic model describes the primary metabolism of N. meningitidis in a chemostat in steady state. The genome-scale model is valuable because it offers a framework to study N. meningitidis metabolism as a whole, or certain aspects of it, and it can also be used for the purpose of vaccine process development (for example, the design of growth media). The flux distribution of the main metabolic pathways (that is, the pentose phosphate pathway and the Entner-Douderoff pathway) indicates that the major part of pyruvate (69%) is synthesized through the ED-cleavage, a finding that is in good agreement with literature. PMID:17617894

  10. Next-generation genome-scale models for metabolic engineering.

    PubMed

    King, Zachary A; Lloyd, Colton J; Feist, Adam M; Palsson, Bernhard O

    2015-12-01

    Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed--encompassing many biological processes and simulation strategies-and next-generation models enable new types of predictions. Here, three key examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. Global dynamic optimization approach to predict activation in metabolic pathways.

    PubMed

    de Hijas-Liste, Gundián M; Klipp, Edda; Balsa-Canto, Eva; Banga, Julio R

    2014-01-06

    During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary

  12. Large-scale structure prediction by improved contact predictions and model quality assessment.

    PubMed

    Michel, Mirco; Menéndez Hurtado, David; Uziela, Karolis; Elofsson, Arne

    2017-07-15

    Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known. We present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these, 415 have not been reported before. Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/ . All programs used here are freely available. arne@bioinfo.se. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  13. Comparison of Three Adiposity Indexes and Cutoff Values to Predict Metabolic Syndrome Among University Students.

    PubMed

    Correa-Bautista, Jorge Enrique; González-Ruíz, Katherine; Vivas, Andrés; Triana-Reina, Héctor Reynaldo; Martínez-Torres, Javier; Prieto-Benavides, Daniel Humberto; Carrillo, Hugo Alejandro; Ramos-Sepúlveda, Jeison Alexander; Afanador-Rodríguez, María Isabel; Villa-González, Emilio; García-Hermoso, Antonio; Ramírez-Vélez, Robinson

    2017-09-01

    Obesity and high body fat are related to diabetes and metabolic syndrome (MetS) in all ethnic groups. Based on the International Diabetes Federation (IDF) definition of MetS, the aim of the present study was to compare body adiposity indexes (BAIs) and to assess their various cutoff values for the prediction of MetS in university students from Colombia. A cross-sectional study was conducted on 886 volunteers (51.9% woman; age mean 21.4 years). Anthropometric characteristics (height, weight, waist circumference [WC], and hip circumference [HC]) were measured, and body composition was assessed by bioelectrical impedance analysis. MetS was defined as including ≥3 of the metabolic abnormalities (WC, high-density lipoprotein cholesterol [HDL-C], triglycerides, fasting glucose, and systolic and diastolic blood pressure [BP]) in the definition provided by the IDF. The BAIs (i.e., BAI-HC [BAI], BAI-WC [BAI-w], and [BAI-p]) were calculated from formulas taking into account, height, weight, and WC, and for the visceral adiposity indexes, a formula, including WC, HDL-C, and triglycerides, was used. The overall prevalence of MetS was 5.9%, higher in men than in women. The most prevalent components were low HDL-C, high triglyceride levels, WC, and BP levels. The receiver operating characteristic curves analysis showed that BAI, BAI-w, and BAI-p could be useful tools to predict MetS in this population. For women, the optimal MetS threshold was found to be 30.34 (area under curve [AUC] = 0.720-0.863), 19.10 (AUC = 0.799-0.925), and 29.68 (AUC = 0.779-0.901), for BAI, BAI-w, and BAI-p, respectively. For men, the optimal MetS threshold was found to be 27.83 (AUC = 0.726-0.873), 21.48 (AUC = 0.755-0.906), and 26.18 (AUC = 0.766-0.894), for BAI, BAI-w, and BAI-p, respectively. The three indexes can be useful tools to predict MetS according to the IDF criteria in university students from Colombia. Data on larger samples are needed.

  14. Systems metabolic engineering: genome-scale models and beyond.

    PubMed

    Blazeck, John; Alper, Hal

    2010-07-01

    The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches--based on the data collected with high throughput technologies--to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems.

  15. A Prochlorococcus proving ground for constraint-based metabolic modeling and multi-`omics data integration

    NASA Astrophysics Data System (ADS)

    Casey, J.; Ji, B.; Shaoie, S.; Mardinoglu, A.; Sarathi Sen, P.; Jahn, O.; Reda, K.; Leigh, J.; Follows, M. J.; Nielsen, J.; Karl, D. M.

    2016-02-01

    Representatives of the oligotrophic marine cyanobacterium Prochlorococcus marinus are the smallest free-living photosynthetic organisms, both in terms of physical size and genome size, yet are the most abundant photoautotrophic microbes in the oceans and profoundly influence global biogeochemical cycles. Physiological and regulatory control of nutrient and light stress has been observed in MED4 in culture and in its closely related `ecotype' eMED4 in the field, however its metabolism has not been investigated in detail. We present a genome-scale metabolic network reconstruction of the high-light adapted axenic strain MED4ax ("iJCMED4") for the quantitative analysis of a range of its metabolic phenotypes. The resulting structure is a proving ground for the incorporation of enzyme kinetics, biochemical and elemental compositional data, transcriptomic, proteomic, metabolomic, and fluxomic datasets which can be implemented within a constraint-based metabolic modeling environment. The iJCMED4 stoichiometric model consists of 523 metabolic genes encoding 787 reactions with 673 unique metabolites distributed in 5 sub-cellular compartments and is mass, charge, and thermodynamically balanced. Several variants of flux balance analysis were used to simulate growth and metabolic fluxes over the diel cycle, under various stress conditions (e.g., nitrogen, phosphorus, light), and within the framework of a global biogeochemical model (DARWIN). Model simulations accurately predicted growth rates in culture under a variety of defined medium compositions and there was close agreement of photosynthetic performance, biomass and energy yields and efficiencies, and transporter fluxes for iJCMED4 and culture experiments. In addition to a nearly optimal photosynthetic quotient and central carbon metabolism efficiency, MED4 has made dramatic alterations to redox and phosphorus metabolism across biosynthetic and intermediate pathways. We propose that reductions in phosphate reaction

  16. Omics Approaches To Probe Microbiota and Drug Metabolism Interactions.

    PubMed

    Nichols, Robert G; Hume, Nicole E; Smith, Philip B; Peters, Jeffrey M; Patterson, Andrew D

    2016-12-19

    The drug metabolism field has long recognized the beneficial and sometimes deleterious influence of microbiota in the absorption, distribution, metabolism, and excretion of drugs. Early pioneering work with the sulfanilamide precursor prontosil pointed toward the necessity not only to better understand the metabolic capabilities of the microbiota but also, importantly, to identify the specific microbiota involved in the generation and metabolism of drugs. However, technological limitations important for cataloging the microbiota community as well as for understanding and/or predicting their metabolic capabilities hindered progress. Current advances including mass spectrometry-based metabolite profiling as well as culture-independent sequence-based identification and functional analysis of microbiota have begun to shed light on microbial metabolism. In this review, case studies will be presented to highlight key aspects (e.g., microbiota identification, metabolic function and prediction, metabolite identification, and profiling) that have helped to clarify how the microbiota might impact or be impacted by drug metabolism. Lastly, a perspective of the future of this field is presented that takes into account what important knowledge is lacking and how to tackle these problems.

  17. Association of Lipid Accumulation Product with Cardio-Metabolic Risk Factors in Postmenopausal Women.

    PubMed

    Namazi Shabestari, Alireza; Asadi, Mojgan; Jouyandeh, Zahra; Qorbani, Mostafa; Kelishadi, Roya

    2016-06-01

    The lipid accumulation product is a novel, safe and inexpensive index of central lipid over accumulation based on waist circumference and fasting concentration of circulating triglycerides. This study was designed to investigate the ability of lipid accumulation product to predict Cardio-metabolic risk factors in postmenopausal women. In this Cross-sectional study, 264 postmenopausal women by using convenience sampling method were selected from menopause clinic in Tehran. Cardio-metabolic risk factors were measured, and lipid accumulation product (waist-58×triglycerides [nmol/L]) was calculated. Optimal cut-off point of lipid accumulation product for predicting metabolic syndrome was estimated by ROC (Receiver-operating characteristic) curve analysis. Metabolic syndrome was diagnosed in 41.2% of subjects. Optimal cut-off point of lipid accumulation product for predicting metabolic syndrome was 47.63 (sensitivity:75%; specificity:77.9%). High lipid accumulation product increases risk of all Cardio-metabolic risk factors except overweight, high Total Cholesterol, high Low Density Lipoprotein Cholesterol and high Fasting Blood Sugar in postmenopausal women. Our findings show that lipid accumulation product is associated with metabolic syndrome and some Cardio-metabolic risk factors Also lipid accumulation product may have been a useful tool for predicting cardiovascular disease and metabolic syndrome risk in postmenopausal women.

  18. Accurate and scalable social recommendation using mixed-membership stochastic block models.

    PubMed

    Godoy-Lorite, Antonia; Guimerà, Roger; Moore, Cristopher; Sales-Pardo, Marta

    2016-12-13

    With increasing amounts of information available, modeling and predicting user preferences-for books or articles, for example-are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.

  19. Accurate and scalable social recommendation using mixed-membership stochastic block models

    PubMed Central

    Godoy-Lorite, Antonia; Moore, Cristopher

    2016-01-01

    With increasing amounts of information available, modeling and predicting user preferences—for books or articles, for example—are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users’ ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user’s and item’s groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets. PMID:27911773

  20. Aligning Metabolic Pathways Exploiting Binary Relation of Reactions.

    PubMed

    Huang, Yiran; Zhong, Cheng; Lin, Hai Xiang; Huang, Jing

    2016-01-01

    Metabolic pathway alignment has been widely used to find one-to-one and/or one-to-many reaction mappings to identify the alternative pathways that have similar functions through different sets of reactions, which has important applications in reconstructing phylogeny and understanding metabolic functions. The existing alignment methods exhaustively search reaction sets, which may become infeasible for large pathways. To address this problem, we present an effective alignment method for accurately extracting reaction mappings between two metabolic pathways. We show that connected relation between reactions can be formalized as binary relation of reactions in metabolic pathways, and the multiplications of zero-one matrices for binary relations of reactions can be accomplished in finite steps. By utilizing the multiplications of zero-one matrices for binary relation of reactions, we efficiently obtain reaction sets in a small number of steps without exhaustive search, and accurately uncover biologically relevant reaction mappings. Furthermore, we introduce a measure of topological similarity of nodes (reactions) by comparing the structural similarity of the k-neighborhood subgraphs of the nodes in aligning metabolic pathways. We employ this similarity metric to improve the accuracy of the alignments. The experimental results on the KEGG database show that when compared with other state-of-the-art methods, in most cases, our method obtains better performance in the node correctness and edge correctness, and the number of the edges of the largest common connected subgraph for one-to-one reaction mappings, and the number of correct one-to-many reaction mappings. Our method is scalable in finding more reaction mappings with better biological relevance in large metabolic pathways.

  1. Chimeric mice with humanized liver: Application in drug metabolism and pharmacokinetics studies for drug discovery.

    PubMed

    Naritomi, Yoichi; Sanoh, Seigo; Ohta, Shigeru

    2018-02-01

    Predicting human drug metabolism and pharmacokinetics (PK) is key to drug discovery. In particular, it is important to predict human PK, metabolite profiles and drug-drug interactions (DDIs). Various methods have been used for such predictions, including in vitro metabolic studies using human biological samples, such as hepatic microsomes and hepatocytes, and in vivo studies using experimental animals. However, prediction studies using these methods are often inconclusive due to discrepancies between in vitro and in vivo results, and interspecies differences in drug metabolism. Further, the prediction methods have changed from qualitative to quantitative to solve these issues. Chimeric mice with humanized liver have been developed, in which mouse liver cells are mostly replaced with human hepatocytes. Since human drug metabolizing enzymes are expressed in the liver of these mice, they are regarded as suitable models for mimicking the drug metabolism and PK observed in humans; therefore, these mice are useful for predicting human drug metabolism and PK. In this review, we discuss the current state, issues, and future directions of predicting human drug metabolism and PK using chimeric mice with humanized liver in drug discovery. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

  2. Modelling the metabolism of protein secretion through the Tat route in Streptomyces lividans.

    PubMed

    Valverde, José R; Gullón, Sonia; Mellado, Rafael P

    2018-06-14

    variability analysis predicts a large potential for protein overproduction. This work provides a detailed look to metabolic changes associated to Tat-dependent protein secretion reproducing experimental observations and identifying changes that are specific to each secretory route, presenting a novel, improved, more accurate and strain-independent model of S. lividans, thus opening the way for enhanced metabolic engineering of protein overproduction in S. lividans.

  3. Raoult's law revisited: accurately predicting equilibrium relative humidity points for humidity control experiments.

    PubMed

    Bowler, Michael G; Bowler, David R; Bowler, Matthew W

    2017-04-01

    The humidity surrounding a sample is an important variable in scientific experiments. Biological samples in particular require not just a humid atmosphere but often a relative humidity (RH) that is in equilibrium with a stabilizing solution required to maintain the sample in the same state during measurements. The controlled dehydration of macromolecular crystals can lead to significant increases in crystal order, leading to higher diffraction quality. Devices that can accurately control the humidity surrounding crystals while monitoring diffraction have led to this technique being increasingly adopted, as the experiments become easier and more reproducible. Matching the RH to the mother liquor is the first step in allowing the stable mounting of a crystal. In previous work [Wheeler, Russi, Bowler & Bowler (2012). Acta Cryst. F 68 , 111-114], the equilibrium RHs were measured for a range of concentrations of the most commonly used precipitants in macromolecular crystallography and it was shown how these related to Raoult's law for the equilibrium vapour pressure of water above a solution. However, a discrepancy between the measured values and those predicted by theory could not be explained. Here, a more precise humidity control device has been used to determine equilibrium RH points. The new results are in agreement with Raoult's law. A simple argument in statistical mechanics is also presented, demonstrating that the equilibrium vapour pressure of a solvent is proportional to its mole fraction in an ideal solution: Raoult's law. The same argument can be extended to the case where the solvent and solute molecules are of different sizes, as is the case with polymers. The results provide a framework for the correct maintenance of the RH surrounding a sample.

  4. Estrogen Metabolism and Exposure in a Genotypic-Phenotypic Model for Breast Cancer Risk Prediction

    PubMed Central

    Crooke, Philip S.; Justenhoven, Christina; Brauch, Hiltrud; Dawling, Sheila; Roodi, Nady; Higginbotham, Kathryn S. P.; Plummer, W. Dale; Schuyler, Peggy A.; Sanders, Melinda E; Page, David L.; Smith, Jeffrey R.; Dupont, William D.; Parl, Fritz F.

    2012-01-01

    Background Current models of breast cancer risk prediction do not directly reflect mammary estrogen metabolism or genetic variability in exposure to carcinogenic estrogen metabolites. Methods We developed a model that simulates the kinetic effect of genetic variants of the enzymes CYP1A1, CYP1B1, and COMT on the production of the main carcinogenic estrogen metabolite, 4-hydroxyestradiol (4-OHE2), expressed as area under the curve metric (4-OHE2-AUC). The model also incorporates phenotypic factors (age, body mass index, hormone replacement therapy, oral contraceptives, family history), which plausibly influence estrogen metabolism and the production of 4-OHE2. We applied the model to two independent, population-based breast cancer case-control groups, the German GENICA study (967 cases, 971 controls) and the Nashville Breast Cohort (NBC; 465 cases, 885 controls). Results In the GENICA study, premenopausal women at the 90th percentile of 4-OHE2-AUC among control subjects had a risk of breast cancer that was 2.30 times that of women at the 10th control 4-OHE2-AUC percentile (95% CI 1.7 – 3.2, P = 2.9 × 10−7). This relative risk was 1.89 (95% CI 1.5 – 2.4, P = 2.2 × 10−8) in postmenopausal women. In the NBC, this relative risk in postmenopausal women was 1.81 (95% CI 1.3 – 2.6, P = 7.6 × 10−4), which increased to 1.83 (95% CI 1.4 – 2.3, P = 9.5 × 10−7) when a history of proliferative breast disease was included in the model. Conclusions The model combines genotypic and phenotypic factors involved in carcinogenic estrogen metabolite production and cumulative estrogen exposure to predict breast cancer risk. Impact The estrogen carcinogenesis-based model has the potential to provide personalized risk estimates. PMID:21610218

  5. A discrete mathematical model applied to genetic regulation and metabolic networks.

    PubMed

    Asenjo, A J; Ramirez, P; Rapaport, I; Aracena, J; Goles, E; Andrews, B A

    2007-03-01

    This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-à-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an inrtegrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 (2(3)) fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under

  6. How Accurately Can We Predict Eclipses for Algol? (Poster abstract)

    NASA Astrophysics Data System (ADS)

    Turner, D.

    2016-06-01

    (Abstract only) beta Persei, or Algol, is a very well known eclipsing binary system consisting of a late B-type dwarf that is regularly eclipsed by a GK subgiant every 2.867 days. Eclipses, which last about 8 hours, are regular enough that predictions for times of minima are published in various places, Sky & Telescope magazine and The Observer's Handbook, for example. But eclipse minimum lasts for less than a half hour, whereas subtle mistakes in the current ephemeris for the star can result in predictions that are off by a few hours or more. The Algol system is fairly complex, with the Algol A and Algol B eclipsing system also orbited by Algol C with an orbital period of nearly 2 years. Added to that are complex long-term O-C variations with a periodicity of almost two centuries that, although suggested by Hoffmeister to be spurious, fit the type of light travel time variations expected for a fourth star also belonging to the system. The AB sub-system also undergoes mass transfer events that add complexities to its O-C behavior. Is it actually possible to predict precise times of eclipse minima for Algol months in advance given such complications, or is it better to encourage ongoing observations of the star so that O-C variations can be tracked in real time?

  7. Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

    PubMed

    Sun, Meijian; Wang, Xia; Zou, Chuanxin; He, Zenghui; Liu, Wei; Li, Honglin

    2016-06-07

    RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

  8. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

    NASA Astrophysics Data System (ADS)

    An, Zhe; Rey, Daniel; Ye, Jingxin; Abarbanel, Henry D. I.

    2017-01-01

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

  9. Predicting Next Year's Resources--Short-Term Enrollment Forecasting for Accurate Budget Planning. AIR Forum Paper 1978.

    ERIC Educational Resources Information Center

    Salley, Charles D.

    Accurate enrollment forecasts are a prerequisite for reliable budget projections. This is because tuition payments make up a significant portion of a university's revenue, and anticipated revenue is the immediate constraint on current operating expenditures. Accurate forecasts are even more critical to revenue projections when a university's…

  10. Dynamic optimization of metabolic networks coupled with gene expression.

    PubMed

    Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

    2015-01-21

    The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle. Copyright

  11. Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism.

    PubMed

    Nemenman, Ilya; Escola, G Sean; Hlavacek, William S; Unkefer, Pat J; Unkefer, Clifford J; Wall, Michael E

    2007-12-01

    We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For benchmarking purposes, we generate synthetic metabolic profiles based on a well-established model for red blood cell metabolism. A variety of data sets are generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We use ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, to predict metabolic interactions from these data sets. We find that the performance of ARACNE on metabolic data is comparable to that on gene expression data.

  12. Improved Ecosystem Predictions of the California Current System via Accurate Light Calculations

    DTIC Science & Technology

    2011-09-30

    System via Accurate Light Calculations Curtis D. Mobley Sequoia Scientific, Inc. 2700 Richards Road, Suite 107 Bellevue, WA 98005 phone: 425...7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Sequoia Scientific, Inc,2700 Richards Road, Suite 107,Bellevue,WA,98005 8. PERFORMING...EcoLight-S 1.0 Users’ Guide and Technical Documentation. Sequoia Scientific, Inc., Bellevue, WA, 38 pages. Mobley, C. D., 2011. Fast light calculations

  13. Time-Accurate Numerical Prediction of Free Flight Aerodynamics of a Finned Projectile

    DTIC Science & Technology

    2005-09-01

    develop (with fewer dollars) more lethal and effective munitions. The munitions must stay abreast of the latest technology available to our...consuming. Computer simulations can and have provided an effective means of determining the unsteady aerodynamics and flight mechanics of guided projectile...Recently, the time-accurate technique was used to obtain improved results for Magnus moment and roll damping moment of a spinning projectile at transonic

  14. A physiologically based pharmacokinetic model to predict disposition of CYP2D6 and CYP1A2 metabolized drugs in pregnant women.

    PubMed

    Ke, Alice Ban; Nallani, Srikanth C; Zhao, Ping; Rostami-Hodjegan, Amin; Isoherranen, Nina; Unadkat, Jashvant D

    2013-04-01

    Conducting pharmacokinetic (PK) studies in pregnant women is challenging. Therefore, we asked if a physiologically based pharmacokinetic (PBPK) model could be used to evaluate different dosing regimens for pregnant women. We refined and verified our previously published pregnancy PBPK model by incorporating cytochrome P450 CYP1A2 suppression (based on caffeine PK) and CYP2D6 induction (based on metoprolol PK) into the model. This model accounts for gestational age-dependent changes in maternal physiology and hepatic CYP3A activity. For verification, the disposition of CYP1A2-metabolized drug theophylline (THEO) and CYP2D6-metabolized drugs paroxetine (PAR), dextromethorphan (DEX), and clonidine (CLO) during pregnancy was predicted. Our PBPK model successfully predicted THEO disposition during the third trimester (T3). Predicted mean postpartum to third trimester (PP:T3) ratios of THEO area under the curve (AUC), maximum plasma concentration, and minimum plasma concentration were 0.76, 0.95, and 0.66 versus observed values 0.75, 0.89, and 0.72, respectively. The predicted mean PAR steady-state plasma concentration (Css) ratio (PP:T3) was 7.1 versus the observed value 3.7. Predicted mean DEX urinary ratio (UR) (PP:T3) was 2.9 versus the observed value 1.9. Predicted mean CLO AUC ratio (PP:T3) was 2.2 versus the observed value 1.7. Sensitivity analysis suggested that a 100% induction of CYP2D6 during T3 was required to recover the observed PP:T3 ratios of PAR Css, DEX UR, and CLO AUC. Based on these data, it is prudent to conclude that the magnitude of hepatic CYP2D6 induction during T3 ranges from 100 to 200%. Our PBPK model can predict the disposition of CYP1A2, 2D6, and 3A drugs during pregnancy.

  15. Biogeochemical metabolic modeling of methanogenesis by Methanosarcina barkeri

    NASA Astrophysics Data System (ADS)

    Jensvold, Z. D.; Jin, Q.

    2015-12-01

    Methanogenesis, the biological process of methane production, is the final step of natural organic matter degradation. In studying natural methanogenesis, important questions include how fast methanogenesis proceeds and how methanogens adapt to the environment. To address these questions, we propose a new approach - biogeochemical reaction modeling - by simulating the metabolic networks of methanogens. Biogeochemical reaction modeling combines geochemical reaction modeling and genome-scale metabolic modeling. Geochemical reaction modeling focuses on the speciation of electron donors and acceptors in the environment, and therefore the energy available to methanogens. Genome-scale metabolic modeling predicts microbial rates and metabolic strategies. Specifically, this approach describes methanogenesis using an enzyme network model, and computes enzyme rates by accounting for both the kinetics and thermodynamics. The network model is simulated numerically to predict enzyme abundances and rates of methanogen metabolism. We applied this new approach to Methanosarcina barkeri strain fusaro, a model methanogen that makes methane by reducing carbon dioxide and oxidizing dihydrogen. The simulation results match well with the results of previous laboratory experiments, including the magnitude of proton motive force and the kinetic parameters of Methanosarcina barkeri. The results also predict that in natural environments, the configuration of methanogenesis network, including the concentrations of enzymes and metabolites, differs significantly from that under laboratory settings.

  16. Metabolic Compensation and Circadian Resilience in Prokaryotic Cyanobacteria

    PubMed Central

    Johnson, Carl Hirschie; Egli, Martin

    2014-01-01

    For a biological oscillator to function as a circadian pacemaker that confers a fitness advantage, its timing functions must be stable in response to environmental and metabolic fluctuations. One such stability enhancer, temperature compensation, has long been a defining characteristic of these timekeepers. However, an accurate biological timekeeper must also resist changes in metabolism, and this review suggests that temperature compensation is actually a subset of a larger phenomenon, namely metabolic compensation, which maintains the frequency of circadian oscillators in response to a host of factors that impinge on metabolism and would otherwise destabilize these clocks. The circadian system of prokaryotic cyanobacteria is an illustrative model because it is composed of transcriptional and nontranscriptional oscillators that are coupled to promote resilience. Moreover, the cyanobacterial circadian program regulates gene activity and metabolic pathways, and it can be manipulated to improve the expression of bioproducts that have practical value. PMID:24905782

  17. Multiple Applications of Alamar Blue as an Indicator of Metabolic Function and Cellular Health in Cell Viability Bioassays

    PubMed Central

    Rampersad, Sephra N.

    2012-01-01

    Accurate prediction of the adverse effects of test compounds on living systems, detection of toxic thresholds, and expansion of experimental data sets to include multiple toxicity end-point analysis are required for any robust screening regime. Alamar Blue is an important redox indicator that is used to evaluate metabolic function and cellular health. The Alamar Blue bioassay has been utilized over the past 50 years to assess cell viability and cytotoxicity in a range of biological and environmental systems and in a number of cell types including bacteria, yeast, fungi, protozoa and cultured mammalian and piscine cells. It offers several advantages over other metabolic indicators and other cytotoxicity assays. However, as with any bioassay, suitability must be determined for each application and cell model. This review seeks to highlight many of the important considerations involved in assay use and design in addition to the potential pitfalls. PMID:23112716

  18. Ontogenetic body-mass scaling of resting metabolic rate covaries with species-specific metabolic level and body size in spiders and snakes.

    PubMed

    Glazier, Douglas S

    2009-08-01

    According to common belief, metabolic rate usually scales with body mass to the 3/4-power, which is considered by some to be a universal law of nature. However, substantial variation in the metabolic scaling exponent (b) exists, much of which can be related to the overall metabolic level (L) of various taxonomic groups of organisms, as predicted by the recently proposed metabolic-level boundaries (MLB) hypothesis. Here the MLB hypothesis was tested using data for intraspecific (ontogenetic) body-mass scaling of resting metabolic rate in spiders and boid snakes. As predicted, in both animal groups b varies mostly between 2/3 and 1, and is significantly negatively related to L. L is, in turn, negatively related to species-specific body mass (M(m): estimated as the mass at the midpoint of a scaling relationship), and as a result, larger species tend to have steeper metabolic scaling slopes (b) than smaller species. After adjusting for the effects of M(m), b and L are still negatively related, though significantly only in the spiders, which exhibit a much wider range of L than the snakes. Therefore, in spiders and snakes the intraspecific scaling of metabolic rate with body mass itself scales with interspecific variation in both metabolic level and body mass.

  19. Fluid Dynamic Modeling to Support the Development of Flow-Based Hepatocyte Culture Systems for Metabolism Studies

    PubMed Central

    Pedersen, Jenny M.; Shim, Yoo-Sik; Hans, Vaibhav; Phillips, Martin B.; Macdonald, Jeffrey M.; Walker, Glenn; Andersen, Melvin E.; Clewell, Harvey J.; Yoon, Miyoung

    2016-01-01

    Accurate prediction of metabolism is a significant outstanding challenge in toxicology. The best predictions are based on experimental data from in vitro systems using primary hepatocytes. The predictivity of the primary hepatocyte-based culture systems, however, is still limited due to well-known phenotypic instability and rapid decline of metabolic competence within a few hours. Dynamic flow bioreactors for three-dimensional cell cultures are thought to be better at recapitulating tissue microenvironments and show potential to improve in vivo extrapolations of chemical or drug toxicity based on in vitro test results. These more physiologically relevant culture systems hold potential for extending metabolic competence of primary hepatocyte cultures as well. In this investigation, we used computational fluid dynamics to determine the optimal design of a flow-based hepatocyte culture system for evaluating chemical metabolism in vitro. The main design goals were (1) minimization of shear stress experienced by the cells to maximize viability, (2) rapid establishment of a uniform distribution of test compound in the chamber, and (3) delivery of sufficient oxygen to cells to support aerobic respiration. Two commercially available flow devices – RealBio® and QuasiVivo® (QV) – and a custom developed fluidized bed bioreactor were simulated, and turbulence, flow characteristics, test compound distribution, oxygen distribution, and cellular oxygen consumption were analyzed. Experimental results from the bioreactors were used to validate the simulation results. Our results indicate that maintaining adequate oxygen supply is the most important factor to the long-term viability of liver bioreactor cultures. Cell density and system flow patterns were the major determinants of local oxygen concentrations. The experimental results closely corresponded to the in silico predictions. Of the three bioreactors examined in this study, we were able to optimize the experimental

  20. Analysis of optimality in natural and perturbed metabolic networks

    PubMed Central

    Segrè, Daniel; Vitkup, Dennis; Church, George M.

    2002-01-01

    An important goal of whole-cell computational modeling is to integrate detailed biochemical information with biological intuition to produce testable predictions. Based on the premise that prokaryotes such as Escherichia coli have maximized their growth performance along evolution, flux balance analysis (FBA) predicts metabolic flux distributions at steady state by using linear programming. Corroborating earlier results, we show that recent intracellular flux data for wild-type E. coli JM101 display excellent agreement with FBA predictions. Although the assumption of optimality for a wild-type bacterium is justifiable, the same argument may not be valid for genetically engineered knockouts or other bacterial strains that were not exposed to long-term evolutionary pressure. We address this point by introducing the method of minimization of metabolic adjustment (MOMA), whereby we test the hypothesis that knockout metabolic fluxes undergo a minimal redistribution with respect to the flux configuration of the wild type. MOMA employs quadratic programming to identify a point in flux space, which is closest to the wild-type point, compatibly with the gene deletion constraint. Comparing MOMA and FBA predictions to experimental flux data for E. coli pyruvate kinase mutant PB25, we find that MOMA displays a significantly higher correlation than FBA. Our method is further supported by experimental data for E. coli knockout growth rates. It can therefore be used for predicting the behavior of perturbed metabolic networks, whose growth performance is in general suboptimal. MOMA and its possible future extensions may be useful in understanding the evolutionary optimization of metabolism. PMID:12415116