Design of experiments for identification of complex biochemical systems with applications to mitochondrial bioenergetics.

PubMed

Vinnakota, Kalyan C; Beard, Daniel A; Dash, Ranjan K

2009-01-01

Identification of a complex biochemical system model requires appropriate experimental data. Models constructed on the basis of data from the literature often contain parameters that are not identifiable with high sensitivity and therefore require additional experimental data to identify those parameters. Here we report the application of a local sensitivity analysis to design experiments that will improve the identifiability of previously unidentifiable model parameters in a model of mitochondrial oxidative phosphorylation and tricaboxylic acid cycle. Experiments were designed based on measurable biochemical reactants in a dilute suspension of purified cardiac mitochondria with experimentally feasible perturbations to this system. Experimental perturbations and variables yielding the most number of parameters above a 5% sensitivity level are presented and discussed.

An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge.

PubMed

Nassif, Houssam; Al-Ali, Hassan; Khuri, Sawsan; Keirouz, Walid; Page, David

2010-01-01

Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. They incorporate different parts of these findings in predictive black-box models. We investigate the empirical support for biochemical findings by comparing Inductive Logic Programming (ILP) induced rules to actual biochemical results. We mine the Protein Data Bank for a representative data set of hexose binding sites, non-hexose binding sites and surface grooves. We build an ILP model of hexose-binding sites and evaluate our results against several baseline machine learning classifiers. Our method achieves an accuracy similar to that of other black-box classifiers while providing insight into the discriminating process. In addition, it confirms wet-lab findings and reveals a previously unreported Trp-Glu amino acids dependency.

Classification model based on Raman spectra of selected morphological and biochemical tissue constituents for identification of atherosclerosis in human coronary arteries.

PubMed

Peres, Marines Bertolo; Silveira, Landulfo; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu Tavares; Pasqualucci, Carlos Augusto

2011-09-01

This study presents the results of Raman spectroscopy applied to the classification of arterial tissue based on a simplified model using basal morphological and biochemical information extracted from the Raman spectra of arteries. The Raman spectrograph uses an 830-nm diode laser, imaging spectrograph, and a CCD camera. A total of 111 Raman spectra from arterial fragments were used to develop the model, and those spectra were compared to the spectra of collagen, fat cells, smooth muscle cells, calcification, and cholesterol in a linear fit model. Non-atherosclerotic (NA), fatty and fibrous-fatty atherosclerotic plaques (A) and calcified (C) arteries exhibited different spectral signatures related to different morphological structures presented in each tissue type. Discriminant analysis based on Mahalanobis distance was employed to classify the tissue type with respect to the relative intensity of each compound. This model was subsequently tested prospectively in a set of 55 spectra. The simplified diagnostic model showed that cholesterol, collagen, and adipocytes were the tissue constituents that gave the best classification capability and that those changes were correlated to histopathology. The simplified model, using spectra obtained from a few tissue morphological and biochemical constituents, showed feasibility by using a small amount of variables, easily extracted from gross samples.

Automatic network coupling analysis for dynamical systems based on detailed kinetic models.

PubMed

Lebiedz, Dirk; Kammerer, Julia; Brandt-Pollmann, Ulrich

2005-10-01

We introduce a numerical complexity reduction method for the automatic identification and analysis of dynamic network decompositions in (bio)chemical kinetics based on error-controlled computation of a minimal model dimension represented by the number of (locally) active dynamical modes. Our algorithm exploits a generalized sensitivity analysis along state trajectories and subsequent singular value decomposition of sensitivity matrices for the identification of these dominant dynamical modes. It allows for a dynamic coupling analysis of (bio)chemical species in kinetic models that can be exploited for the piecewise computation of a minimal model on small time intervals and offers valuable functional insight into highly nonlinear reaction mechanisms and network dynamics. We present results for the identification of network decompositions in a simple oscillatory chemical reaction, time scale separation based model reduction in a Michaelis-Menten enzyme system and network decomposition of a detailed model for the oscillatory peroxidase-oxidase enzyme system.

Polyhydroxyalkanoates in waste activated sludge enhances anaerobic methane production through improving biochemical methane potential instead of hydrolysis rate.

PubMed

Wang, Qilin; Sun, Jing; Zhang, Chang; Xie, Guo-Jun; Zhou, Xu; Qian, Jin; Yang, Guojing; Zeng, Guangming; Liu, Yiqi; Wang, Dongbo

2016-01-21

Anaerobic sludge digestion is the main technology for sludge reduction and stabilization prior to sludge disposal. Nevertheless, methane production from anaerobic digestion of waste activated sludge (WAS) is often restricted by the poor biochemical methane potential and slow hydrolysis rate of WAS. This work systematically investigated the effect of PHA levels of WAS on anaerobic methane production, using both experimental and mathematical modeling approaches. Biochemical methane potential tests showed that methane production increased with increased PHA levels in WAS. Model-based analysis suggested that the PHA-based method enhanced methane production by improving biochemical methane potential of WAS, with the highest enhancement being around 40% (from 192 to 274 L CH4/kg VS added; VS: volatile solid) when the PHA levels increased from 21 to 143 mg/g VS. In contrast, the hydrolysis rate (approximately 0.10 d(-1)) was not significantly affected by the PHA levels. Economic analysis suggested that the PHA-based method could save $1.2/PE/y (PE: population equivalent) in a typical wastewater treatment plant (WWTP). The PHA-based method can be easily integrated into the current WWTP to enhance methane production, thereby providing a strong support to the on-going paradigm shift in wastewater management from pollutant removal to resource recovery.

Polyhydroxyalkanoates in waste activated sludge enhances anaerobic methane production through improving biochemical methane potential instead of hydrolysis rate

PubMed Central

Wang, Qilin; Sun, Jing; Zhang, Chang; Xie, Guo-Jun; Zhou, Xu; Qian, Jin; Yang, Guojing; Zeng, Guangming; Liu, Yiqi; Wang, Dongbo

2016-01-01

Anaerobic sludge digestion is the main technology for sludge reduction and stabilization prior to sludge disposal. Nevertheless, methane production from anaerobic digestion of waste activated sludge (WAS) is often restricted by the poor biochemical methane potential and slow hydrolysis rate of WAS. This work systematically investigated the effect of PHA levels of WAS on anaerobic methane production, using both experimental and mathematical modeling approaches. Biochemical methane potential tests showed that methane production increased with increased PHA levels in WAS. Model-based analysis suggested that the PHA-based method enhanced methane production by improving biochemical methane potential of WAS, with the highest enhancement being around 40% (from 192 to 274 L CH4/kg VS added; VS: volatile solid) when the PHA levels increased from 21 to 143 mg/g VS. In contrast, the hydrolysis rate (approximately 0.10 d−1) was not significantly affected by the PHA levels. Economic analysis suggested that the PHA-based method could save $1.2/PE/y (PE: population equivalent) in a typical wastewater treatment plant (WWTP). The PHA-based method can be easily integrated into the current WWTP to enhance methane production, thereby providing a strong support to the on-going paradigm shift in wastewater management from pollutant removal to resource recovery. PMID:26791952

Polyhydroxyalkanoates in waste activated sludge enhances anaerobic methane production through improving biochemical methane potential instead of hydrolysis rate

NASA Astrophysics Data System (ADS)

Wang, Qilin; Sun, Jing; Zhang, Chang; Xie, Guo-Jun; Zhou, Xu; Qian, Jin; Yang, Guojing; Zeng, Guangming; Liu, Yiqi; Wang, Dongbo

2016-01-01

Anaerobic sludge digestion is the main technology for sludge reduction and stabilization prior to sludge disposal. Nevertheless, methane production from anaerobic digestion of waste activated sludge (WAS) is often restricted by the poor biochemical methane potential and slow hydrolysis rate of WAS. This work systematically investigated the effect of PHA levels of WAS on anaerobic methane production, using both experimental and mathematical modeling approaches. Biochemical methane potential tests showed that methane production increased with increased PHA levels in WAS. Model-based analysis suggested that the PHA-based method enhanced methane production by improving biochemical methane potential of WAS, with the highest enhancement being around 40% (from 192 to 274 L CH4/kg VS added; VS: volatile solid) when the PHA levels increased from 21 to 143 mg/g VS. In contrast, the hydrolysis rate (approximately 0.10 d-1) was not significantly affected by the PHA levels. Economic analysis suggested that the PHA-based method could save $1.2/PE/y (PE: population equivalent) in a typical wastewater treatment plant (WWTP). The PHA-based method can be easily integrated into the current WWTP to enhance methane production, thereby providing a strong support to the on-going paradigm shift in wastewater management from pollutant removal to resource recovery.

Complete integrability of information processing by biochemical reactions

PubMed Central

Agliari, Elena; Barra, Adriano; Dello Schiavo, Lorenzo; Moro, Antonio

2016-01-01

Statistical mechanics provides an effective framework to investigate information processing in biochemical reactions. Within such framework far-reaching analogies are established among (anti-) cooperative collective behaviors in chemical kinetics, (anti-)ferromagnetic spin models in statistical mechanics and operational amplifiers/flip-flops in cybernetics. The underlying modeling – based on spin systems – has been proved to be accurate for a wide class of systems matching classical (e.g. Michaelis–Menten, Hill, Adair) scenarios in the infinite-size approximation. However, the current research in biochemical information processing has been focusing on systems involving a relatively small number of units, where this approximation is no longer valid. Here we show that the whole statistical mechanical description of reaction kinetics can be re-formulated via a mechanical analogy – based on completely integrable hydrodynamic-type systems of PDEs – which provides explicit finite-size solutions, matching recently investigated phenomena (e.g. noise-induced cooperativity, stochastic bi-stability, quorum sensing). The resulting picture, successfully tested against a broad spectrum of data, constitutes a neat rationale for a numerically effective and theoretically consistent description of collective behaviors in biochemical reactions. PMID:27812018

Complete integrability of information processing by biochemical reactions

NASA Astrophysics Data System (ADS)

Agliari, Elena; Barra, Adriano; Dello Schiavo, Lorenzo; Moro, Antonio

2016-11-01

Statistical mechanics provides an effective framework to investigate information processing in biochemical reactions. Within such framework far-reaching analogies are established among (anti-) cooperative collective behaviors in chemical kinetics, (anti-)ferromagnetic spin models in statistical mechanics and operational amplifiers/flip-flops in cybernetics. The underlying modeling - based on spin systems - has been proved to be accurate for a wide class of systems matching classical (e.g. Michaelis-Menten, Hill, Adair) scenarios in the infinite-size approximation. However, the current research in biochemical information processing has been focusing on systems involving a relatively small number of units, where this approximation is no longer valid. Here we show that the whole statistical mechanical description of reaction kinetics can be re-formulated via a mechanical analogy - based on completely integrable hydrodynamic-type systems of PDEs - which provides explicit finite-size solutions, matching recently investigated phenomena (e.g. noise-induced cooperativity, stochastic bi-stability, quorum sensing). The resulting picture, successfully tested against a broad spectrum of data, constitutes a neat rationale for a numerically effective and theoretically consistent description of collective behaviors in biochemical reactions.

Complete integrability of information processing by biochemical reactions.

PubMed

Agliari, Elena; Barra, Adriano; Dello Schiavo, Lorenzo; Moro, Antonio

2016-11-04

Statistical mechanics provides an effective framework to investigate information processing in biochemical reactions. Within such framework far-reaching analogies are established among (anti-) cooperative collective behaviors in chemical kinetics, (anti-)ferromagnetic spin models in statistical mechanics and operational amplifiers/flip-flops in cybernetics. The underlying modeling - based on spin systems - has been proved to be accurate for a wide class of systems matching classical (e.g. Michaelis-Menten, Hill, Adair) scenarios in the infinite-size approximation. However, the current research in biochemical information processing has been focusing on systems involving a relatively small number of units, where this approximation is no longer valid. Here we show that the whole statistical mechanical description of reaction kinetics can be re-formulated via a mechanical analogy - based on completely integrable hydrodynamic-type systems of PDEs - which provides explicit finite-size solutions, matching recently investigated phenomena (e.g. noise-induced cooperativity, stochastic bi-stability, quorum sensing). The resulting picture, successfully tested against a broad spectrum of data, constitutes a neat rationale for a numerically effective and theoretically consistent description of collective behaviors in biochemical reactions.

Computing molecular fluctuations in biochemical reaction systems based on a mechanistic, statistical theory of irreversible processes.

PubMed

Kulasiri, Don

2011-01-01

We discuss the quantification of molecular fluctuations in the biochemical reaction systems within the context of intracellular processes associated with gene expression. We take the molecular reactions pertaining to circadian rhythms to develop models of molecular fluctuations in this chapter. There are a significant number of studies on stochastic fluctuations in intracellular genetic regulatory networks based on single cell-level experiments. In order to understand the fluctuations associated with the gene expression in circadian rhythm networks, it is important to model the interactions of transcriptional factors with the E-boxes in the promoter regions of some of the genes. The pertinent aspects of a near-equilibrium theory that would integrate the thermodynamical and particle dynamic characteristics of intracellular molecular fluctuations would be discussed, and the theory is extended by using the theory of stochastic differential equations. We then model the fluctuations associated with the promoter regions using general mathematical settings. We implemented ubiquitous Gillespie's algorithms, which are used to simulate stochasticity in biochemical networks, for each of the motifs. Both the theory and the Gillespie's algorithms gave the same results in terms of the time evolution of means and variances of molecular numbers. As biochemical reactions occur far away from equilibrium-hence the use of the Gillespie algorithm-these results suggest that the near-equilibrium theory should be a good approximation for some of the biochemical reactions. © 2011 Elsevier Inc. All rights reserved.

[Simulation of vegetation indices optimizing under retrieval of vegetation biochemical parameters based on PROSPECT + SAIL model].

PubMed

Wu, Ling; Liu, Xiang-Nan; Zhou, Bo-Tian; Liu, Chuan-Hao; Li, Lu-Feng

2012-12-01

This study analyzed the sensitivities of three vegetation biochemical parameters [chlorophyll content (Cab), leaf water content (Cw), and leaf area index (LAI)] to the changes of canopy reflectance, with the effects of each parameter on the wavelength regions of canopy reflectance considered, and selected three vegetation indices as the optimization comparison targets of cost function. Then, the Cab, Cw, and LAI were estimated, based on the particle swarm optimization algorithm and PROSPECT + SAIL model. The results showed that retrieval efficiency with vegetation indices as the optimization comparison targets of cost function was better than that with all spectral reflectance. The correlation coefficients (R2) between the measured and estimated values of Cab, Cw, and LAI were 90.8%, 95.7%, and 99.7%, and the root mean square errors of Cab, Cw, and LAI were 4.73 microg x cm(-2), 0.001 g x cm(-2), and 0.08, respectively. It was suggested that to adopt vegetation indices as the optimization comparison targets of cost function could effectively improve the efficiency and precision of the retrieval of biochemical parameters based on PROSPECT + SAIL model.

Multiple Ion Binding Equilibria, Reaction Kinetics, and Thermodynamics in Dynamic Models of Biochemical Pathways

PubMed Central

Vinnakota, Kalyan C.; Wu, Fan; Kushmerick, Martin J.; Beard, Daniel A.

2009-01-01

The operation of biochemical systems in vivo and in vitro is strongly influenced by complex interactions between biochemical reactants and ions such as H+, Mg2+, K+, and Ca2+. These are important second messengers in metabolic and signaling pathways that directly influence the kinetics and thermodynamics of biochemical systems. Herein we describe the biophysical theory and computational methods to account for multiple ion binding to biochemical reactants and demonstrate the crucial effects of ion binding on biochemical reaction kinetics and thermodynamics. In simulations of realistic systems, the concentrations of these ions change with time due to dynamic buffering and competitive binding. In turn, the effective thermodynamic properties vary as functions of cation concentrations and important environmental variables such as temperature and overall ionic strength. Physically realistic simulations of biochemical systems require incorporating all of these phenomena into a coherent mathematical description. Several applications to physiological systems are demonstrated based on this coherent simulation framework. PMID:19216922

Guidance for the application of a population modeling framework in coordination with field based monitoring studies for multiple species and sites

EPA Science Inventory

A modeling framework was developed that can be applied in conjunction with field based monitoring efforts (e.g., through effects-based monitoring programs) to link chemically-induced alterations in molecular and biochemical endpoints to adverse outcomes in whole organisms and pop...

Incorporating extrinsic noise into the stochastic simulation of biochemical reactions: A comparison of approaches

NASA Astrophysics Data System (ADS)

Thanh, Vo Hong; Marchetti, Luca; Reali, Federico; Priami, Corrado

2018-02-01

The stochastic simulation algorithm (SSA) has been widely used for simulating biochemical reaction networks. SSA is able to capture the inherently intrinsic noise of the biological system, which is due to the discreteness of species population and to the randomness of their reciprocal interactions. However, SSA does not consider other sources of heterogeneity in biochemical reaction systems, which are referred to as extrinsic noise. Here, we extend two simulation approaches, namely, the integration-based method and the rejection-based method, to take extrinsic noise into account by allowing the reaction propensities to vary in time and state dependent manner. For both methods, new efficient implementations are introduced and their efficiency and applicability to biological models are investigated. Our numerical results suggest that the rejection-based method performs better than the integration-based method when the extrinsic noise is considered.

Biochemical methane potential prediction of plant biomasses: Comparing chemical composition versus near infrared methods and linear versus non-linear models.

PubMed

Godin, Bruno; Mayer, Frédéric; Agneessens, Richard; Gerin, Patrick; Dardenne, Pierre; Delfosse, Philippe; Delcarte, Jérôme

2015-01-01

The reliability of different models to predict the biochemical methane potential (BMP) of various plant biomasses using a multispecies dataset was compared. The most reliable prediction models of the BMP were those based on the near infrared (NIR) spectrum compared to those based on the chemical composition. The NIR predictions of local (specific regression and non-linear) models were able to estimate quantitatively, rapidly, cheaply and easily the BMP. Such a model could be further used for biomethanation plant management and optimization. The predictions of non-linear models were more reliable compared to those of linear models. The presentation form (green-dried, silage-dried and silage-wet form) of biomasses to the NIR spectrometer did not influence the performances of the NIR prediction models. The accuracy of the BMP method should be improved to enhance further the BMP prediction models. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling languages for biochemical network simulation: reaction vs equation based approaches.

PubMed

Wiechert, Wolfgang; Noack, Stephan; Elsheikh, Atya

2010-01-01

Biochemical network modeling and simulation is an essential task in any systems biology project. The systems biology markup language (SBML) was established as a standardized model exchange language for mechanistic models. A specific strength of SBML is that numerous tools for formulating, processing, simulation and analysis of models are freely available. Interestingly, in the field of multidisciplinary simulation, the problem of model exchange between different simulation tools occurred much earlier. Several general modeling languages like Modelica have been developed in the 1990s. Modelica enables an equation based modular specification of arbitrary hierarchical differential algebraic equation models. Moreover, libraries for special application domains can be rapidly developed. This contribution compares the reaction based approach of SBML with the equation based approach of Modelica and explains the specific strengths of both tools. Several biological examples illustrating essential SBML and Modelica concepts are given. The chosen criteria for tool comparison are flexibility for constraint specification, different modeling flavors, hierarchical, modular and multidisciplinary modeling. Additionally, support for spatially distributed systems, event handling and network analysis features is discussed. As a major result it is shown that the choice of the modeling tool has a strong impact on the expressivity of the specified models but also strongly depends on the requirements of the application context.

Reconstructing biochemical pathways from time course data.

PubMed

Srividhya, Jeyaraman; Crampin, Edmund J; McSharry, Patrick E; Schnell, Santiago

2007-03-01

Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.

Molecular Data for a Biochemical Model of DNA Radiation Damage: Electron Impact Ionization and Dissociative Ionization of DNA Bases and Sugar-Phosphate Backbone

NASA Technical Reports Server (NTRS)

Dateo, Christopher E.; Fletcher, Graham D.

2004-01-01

As part of the database for building up a biochemical model of DNA radiation damage, electron impact ionization cross sections of sugar-phosphate backbone and DNA bases have been calculated using the improved binary-encounter dipole (iBED) model. It is found that the total ionization cross sections of C3'- and C5'-deoxyribose-phospate, two conformers of the sugar-phosphate backbone, are close to each other. Furthermore, the sum of the ionization cross sections of the separate deoxyribose and phosphate fragments is in close agreement with the C3'- and C5'-deoxyribose-phospate cross sections, differing by less than 10%. Of the four DNA bases, the ionization cross section of guanine is the largest, then in decreasing order, adenine, thymine, and cytosine. The order is in accordance with the known propensity of oxidation of the bases by ionizing radiation. Dissociative ionization (DI), a process that both ionizes and dissociates a molecule, is investigated for cytosine. The DI cross section for the formation of H and (cytosine-Hl)(+), with the cytosine ion losing H at the 1 position, is also reported. The threshold of this process is calculated to be 17.1 eV. Detailed analysis of ionization products such as in DI is important to trace the sequential steps in the biochemical process of DNA damage.

Developmental and Life-Stage Physiologically-Based Pharmacokinetic (PBPK) Models in Humans and Animal Models.

EPA Science Inventory

PBPK models provide a computational framework for incorporating pertinent physiological and biochemical information to estimate in vivo levels of xenobiotics in biological tissues. In general, PBPK models are used to correlate exposures to target tissue levels of chemicals and th...

Prediction of non-biochemical recurrence rate after radical prostatectomy in a Japanese cohort: development of a postoperative nomogram.

PubMed

Okubo, Hidenori; Ohori, Makoto; Ohno, Yoshio; Nakashima, Jun; Inoue, Rie; Nagao, Toshitaka; Tachibana, Masaaki

2014-05-01

To develop a nomogram based on postoperative factors and prostate-specific antigen levels to predict the non-biochemical recurrence rate after radical prostatectomy ina Japanese cohort. A total of 606 Japanese patients with T1-3N0M0 prostate cancer who underwent radical prostatectomy and pelvic lymph node dissection at Tokyo Medical University hospital from 2000 to 2010 were studied. A nomogram was constructed based on Cox hazard regression analysis evaluating the prognostic significance of serum prostate-specific antigen and pathological factors in the radical prostatectomy specimens. The discriminating ability of the nomogram was assessed by the concordance index (C-index), and the predicted and actual outcomes were compared with a bootstrapped calibration plot. With a mean follow up of 60.0 months, a total of 187 patients (30.9%) experienced biochemical recurrence, with a 5-year non-biochemical recurrence rate of 72.3%. Based on a Cox hazard regression model, a nomogram was constructed to predict non-biochemical recurrence using serum prostate-specific antigen level and pathological features in radical prostatectomy specimens. The concordance index was 0.77, and the calibration plots appeared to be accurate. The postoperative nomogram described here can provide valuable information regarding the need for adjuvant/salvage radiation or hormonal therapy in patients after radical prostatectomy.

Timescale analysis of rule-based biochemical reaction networks

PubMed Central

Klinke, David J.; Finley, Stacey D.

2012-01-01

The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed upon reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of Interleukin-12 (IL-12) signaling in näive CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based upon the available data. The analysis correctly predicted that reactions associated with JAK2 and TYK2 binding to their corresponding receptor exist at a pseudo-equilibrium. In contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. PMID:21954150

Simulation and sensitivity analysis of carbon storage and fluxes in the New Jersey Pinelands

Treesearch

Zewei Miao; Richard G. Lathrop; Ming Xu; Inga P. La Puma; Kenneth L. Clark; John Hom; Nicholas Skowronski; Steve Van Tuyl

2011-01-01

A major challenge in modeling the carbon dynamics of vegetation communities is the proper parameterization and calibration of eco-physiological variables that are critical determinants of the ecosystem process-based model behavior. In this study, we improved and calibrated a biochemical process-based WxBGC model by using in situ AmeriFlux eddy covariance tower...

Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

PubMed

Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

2016-08-01

Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous epididymal sperm spiration; RBFN: radical basis function network; SRNN: simple recurrent neural network; SVM: support vector machines; TSE: testicular sperm extraction; WHO: World Health Organization.

Analytical considerations and dimensionless analysis for a description of particle interactions in high pressure processes

NASA Astrophysics Data System (ADS)

Rauh, Cornelia; Delgado, Antonio

2010-12-01

High pressures of up to several hundreds of MPa are utilized in a wide range of applications in chemical, bio-, and food engineering, aiming at selective control of (bio-)chemical reactions. Non-uniformity of process conditions may threaten the safety and quality of the resulting products because processing conditions such as pressure, temperature, and treatment history are crucial for the course of (bio-)chemical reactions. Therefore, thermofluid-dynamical phenomena during the high pressure process have to be examined, and numerical tools to predict process uniformity and to optimize the processes have to be developed. Recently applied mathematical models and numerical simulations of laboratory and industrial scale high pressure processes investigating the mentioned crucial phenomena are based on continuum balancing models of thermofluid dynamics. Nevertheless, biological systems are complex fluids containing the relevant (bio-)chemical compounds (enzymes and microorganisms). These compounds are particles that interact with the surrounding medium and between each other. This contribution deals with thermofluid-dynamical interactions of the relevant particulate (bio-)chemical compounds (enzymes and microorganisms) with the surrounding fluid. By consideration of characteristic time and length scales and particle forces, the motion of the (bio-)chemical compounds is characterized.

Discriminating model for diagnosis of basal cell carcinoma and melanoma in vitro based on the Raman spectra of selected biochemicals

NASA Astrophysics Data System (ADS)

Silveira, Landulfo; Silveira, Fabrício Luiz; Bodanese, Benito; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu T.

2012-07-01

Raman spectroscopy has been employed to identify differences in the biochemical constitution of malignant [basal cell carcinoma (BCC) and melanoma (MEL)] cells compared to normal skin tissues, with the goal of skin cancer diagnosis. We collected Raman spectra from compounds such as proteins, lipids, and nucleic acids, which are expected to be represented in human skin spectra, and developed a linear least-squares fitting model to estimate the contributions of these compounds to the tissue spectra. We used a set of 145 spectra from biopsy fragments of normal (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues, collected using a near-infrared Raman spectrometer (830 nm, 50 to 200 mW, and 20 s exposure time) coupled to a Raman probe. We applied the best-fitting model to the spectra of biochemicals and tissues, hypothesizing that the relative spectral contribution of each compound to the tissue Raman spectrum changes according to the disease. We verified that actin, collagen, elastin, and triolein were the most important biochemicals representing the spectral features of skin tissues. A classification model applied to the relative contribution of collagen III, elastin, and melanin using Euclidean distance as a discriminator could differentiate normal from BCC and MEL.

Programming biological models in Python using PySB.

PubMed

Lopez, Carlos F; Muhlich, Jeremy L; Bachman, John A; Sorger, Peter K

2013-01-01

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.

Programming biological models in Python using PySB

PubMed Central

Lopez, Carlos F; Muhlich, Jeremy L; Bachman, John A; Sorger, Peter K

2013-01-01

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis. PMID:23423320

A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand

NASA Astrophysics Data System (ADS)

Noori, Roohollah; Safavi, Salman; Nateghi Shahrokni, Seyyed Afshin

2013-07-01

The five-day biochemical oxygen demand (BOD5) is one of the key parameters in water quality management. In this study, a novel approach, i.e., reduced-order adaptive neuro-fuzzy inference system (ROANFIS) model was developed for rapid estimation of BOD5. In addition, an uncertainty analysis of adaptive neuro-fuzzy inference system (ANFIS) and ROANFIS models was carried out based on Monte-Carlo simulation. Accuracy analysis of ANFIS and ROANFIS models based on both developed discrepancy ratio and threshold statistics revealed that the selected ROANFIS model was superior. Pearson correlation coefficient (R) and root mean square error for the best fitted ROANFIS model were 0.96 and 7.12, respectively. Furthermore, uncertainty analysis of the developed models indicated that the selected ROANFIS had less uncertainty than the ANFIS model and accurately forecasted BOD5 in the Sefidrood River Basin. Besides, the uncertainty analysis also showed that bracketed predictions by 95% confidence bound and d-factor in the testing steps for the selected ROANFIS model were 94% and 0.83, respectively.

ESTIMATING GASEOUS EXCHANGES BETWEEN THE ATMOSPHERE AND PLANTS USING A COUPLED BIOCHEMICAL DRY DEPOSITION MODEL

EPA Science Inventory

To study gaseous exchanges between the soil, biosphere and atmosphere, a biochemical model was coupled with the latest version of Meyers Multi-Layer Deposition Model. The biochemical model describes photosynthesis and respiration and their coupling with stomatal resistance for...

Temperature-dependence of biomass accumulation rates during secondary succession.

PubMed

Anderson, Kristina J; Allen, Andrew P; Gillooly, James F; Brown, James H

2006-06-01

Rates of ecosystem recovery following disturbance affect many ecological processes, including carbon cycling in the biosphere. Here, we present a model that predicts the temperature dependence of the biomass accumulation rate following disturbances in forests. Model predictions are derived based on allometric and biochemical principles that govern plant energetics and are tested using a global database of 91 studies of secondary succession compiled from the literature. The rate of biomass accumulation during secondary succession increases with average growing season temperature as predicted based on the biochemical kinetics of photosynthesis in chloroplasts. In addition, the rate of biomass accumulation is greater in angiosperm-dominated communities than in gymnosperm-dominated ones and greater in plantations than in naturally regenerating stands. By linking the temperature-dependence of photosynthesis to the rate of whole-ecosystem biomass accumulation during secondary succession, our model and results provide one example of how emergent, ecosystem-level rate processes can be predicted based on the kinetics of individual metabolic rate.

Learning Petri net models of non-linear gene interactions.

PubMed

Mayo, Michael

2005-10-01

Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or "explanation" of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene-gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene-gene interactions recently reported in the literature.

Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks.

PubMed

Flassig, R J; Sundmacher, K

2012-12-01

Biochemical reaction networks in the form of coupled ordinary differential equations (ODEs) provide a powerful modeling tool for understanding the dynamics of biochemical processes. During the early phase of modeling, scientists have to deal with a large pool of competing nonlinear models. At this point, discrimination experiments can be designed and conducted to obtain optimal data for selecting the most plausible model. Since biological ODE models have widely distributed parameters due to, e.g. biologic variability or experimental variations, model responses become distributed. Therefore, a robust optimal experimental design (OED) for model discrimination can be used to discriminate models based on their response probability distribution functions (PDFs). In this work, we present an optimal control-based methodology for designing optimal stimulus experiments aimed at robust model discrimination. For estimating the time-varying model response PDF, which results from the nonlinear propagation of the parameter PDF under the ODE dynamics, we suggest using the sigma-point approach. Using the model overlap (expected likelihood) as a robust discrimination criterion to measure dissimilarities between expected model response PDFs, we benchmark the proposed nonlinear design approach against linearization with respect to prediction accuracy and design quality for two nonlinear biological reaction networks. As shown, the sigma-point outperforms the linearization approach in the case of widely distributed parameter sets and/or existing multiple steady states. Since the sigma-point approach scales linearly with the number of model parameter, it can be applied to large systems for robust experimental planning. An implementation of the method in MATLAB/AMPL is available at http://www.uni-magdeburg.de/ivt/svt/person/rf/roed.html. flassig@mpi-magdeburg.mpg.de Supplementary data are are available at Bioinformatics online.

JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

PubMed

Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D

2017-01-25

Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open source, available under an MIT license, and can be installed using the Julia package manager from the JuPOETs GitHub repository.

Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network

PubMed Central

2012-01-01

Background Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature. Results Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3. Conclusions Yeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file 4 and Additional file 5 and at http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible. PMID:22663945

Analyzing the performance of PROSPECT model inversion based on different spectral information for leaf biochemical properties retrieval

NASA Astrophysics Data System (ADS)

Sun, Jia; Shi, Shuo; Yang, Jian; Du, Lin; Gong, Wei; Chen, Biwu; Song, Shalei

2018-01-01

Leaf biochemical constituents provide useful information about major ecological processes. As a fast and nondestructive method, remote sensing techniques are critical to reflect leaf biochemistry via models. PROSPECT model has been widely applied in retrieving leaf traits by providing hemispherical reflectance and transmittance. However, the process of measuring both reflectance and transmittance can be time-consuming and laborious. Contrary to use reflectance spectrum alone in PROSPECT model inversion, which has been adopted by many researchers, this study proposes to use transmission spectrum alone, with the increasing availability of the latter through various remote sensing techniques. Then we analyzed the performance of PROSPECT model inversion with (1) only transmission spectrum, (2) only reflectance and (3) both reflectance and transmittance, using synthetic datasets (with varying levels of random noise and systematic noise) and two experimental datasets (LOPEX and ANGERS). The results show that (1) PROSPECT-5 model inversion based solely on transmission spectrum is viable with results generally better than that based solely on reflectance spectrum; (2) leaf dry matter can be better estimated using only transmittance or reflectance than with both reflectance and transmittance spectra.

Evolution of the patient characteristics of candidates for radical prostatectomy and the results obtained with the technique.

PubMed

Sanchís-Bonet, A; Arribas-Gómez, I; Sánchez-Rodríguez, C; Sánchez-Chapado, M

2015-03-01

To evaluate the oncological profile and risk of biochemical recurrence of patients with prostate cancer who underwent radical prostatectomy based on the time period in which the patients were operated. To evaluate the differences in prostate-specific antigen (PSA) at diagnosis of patients with or without biochemical recurrence based on these time periods. Observation carried forward study of a cohort of 972 radical prostatectomies performed during 3 time periods (1994-2000, 2001-2006, 2007-2011). The importance of PSA at diagnosis on the time periods and on biochemical recurrence was assessed using a generalized linear model. The independent predictive behavior of biochemical recurrence was analyzed using Cox regression. The median follow-up was 38 (16-76) months. PSA levels at diagnosis were higher in the period 1994-2000 (12.97ng/mL, P<.001). Seventy-two percent of the patients from the period 2007-2011 were diagnosed as clinical stage T1c (P<.001), compared with 55% from the period 1994-2000. The percentage of extracapsular extension in the specimen decreased from 27% to 18% from the period 1994-2000 to the period 2007-2011 (p<.001). The percentage of patients with biochemical recurrence went from 38% to 14% from the first to the third period (P>.001). The difference between PSA levels at diagnosis for the patients with or without biochemical recurrence was independent of the period (P=.84). The period during which surgery was performed was not an independent predictive factor for biochemical recurrence (P=.09). Patients from the 2007-2011 period had less extracapsular disease in the radical prostatectomy. The period was not an independent predictive factor for biochemical recurrence. Copyright © 2014 AEU. Publicado por Elsevier España, S.L.U. All rights reserved.

Automated visualization of rule-based models

PubMed Central

Tapia, Jose-Juan; Faeder, James R.

2017-01-01

Frameworks such as BioNetGen, Kappa and Simmune use “reaction rules” to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size. Also, inferring design motifs that emerge from biochemical interactions is an open problem, so current approaches to visualize model architecture rely on manual interpretation of the model. Here, we present three new visualization tools that constitute an automated visualization framework for rule-based models: (i) a compact rule visualization that efficiently displays each rule, (ii) the atom-rule graph that conveys regulatory interactions in the model as a bipartite network, and (iii) a tunable compression pipeline that incorporates expert knowledge and produces compact diagrams of model architecture when applied to the atom-rule graph. The compressed graphs convey network motifs and architectural features useful for understanding both small and large rule-based models, as we show by application to specific examples. Our tools also produce more readable diagrams than current approaches, as we show by comparing visualizations of 27 published models using standard graph metrics. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are general and can be applied to rule-based models from the Kappa and Simmune frameworks also. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into comprehensive whole-cell models. PMID:29131816

How important is thermodynamics for identifying elementary flux modes?

PubMed Central

Peres, Sabine; Jolicœur, Mario; Moulin, Cécile

2017-01-01

We present a method for computing thermodynamically feasible elementary flux modes (tEFMs) using equilibrium constants without need of internal metabolite concentrations. The method is compared with the method based on a binary distinction between reversible and irreversible reactions. When all reactions are reversible, adding the constraints based on equilibrium constants reduces the number of elementary flux modes (EFMs) by a factor of two. Declaring in advance some reactions as irreversible, based on reliable biochemical expertise, can in general reduce the number of EFMs by a greater factor. But, even in this case, computing tEFMs can rule out some EFMs which are biochemically irrelevant. We applied our method to two published models described with binary distinction: the monosaccharide metabolism and the central carbon metabolism of Chinese hamster ovary cells. The results show that the binary distinction is in good agreement with biochemical observations. Moreover, the suppression of the EFMs that are not consistent with the equilibrium constants appears to be biologically relevant. PMID:28222104

Modeling transformation of soil organic matter through the soil enzyme activity

NASA Astrophysics Data System (ADS)

Tregubova, Polina; Vladimirov, Artem; Vasilyeva, Nadezda

2017-04-01

The sensitivity of soil heterotrophic respiration to changing environmental conditions is widely investigated nowadays but still remain extremely controversial. The mechanisms are still needed to reveal. In this work we model soil C and N biogeochemical cycles based on general principles of soil carbon and nitrogen dynamics with focusing on biochemical processes occurring in the soil based on well known classes of enzymes and organic compounds that they can transform. According to classic theories, exoenzymes and endoenzymes of bacteria and fungi as stable over a long period catalytic components play a significant role in degradation of plant and animal residues, decomposition of biopolymers of different sizes, humification processes and in releasing of labile compounds essential for the microorganism and plant growth and germination. We test the model regimes sensitivity to such environmental factors as temperature and moisture. Modeling the directions and patterns of soil biochemical activity is important for evaluation of soil agricultural productivity as well as its ecological functions.

Modelling the influence of land-use changes on biophysical and biochemical interactions at regional and global scales.

PubMed

Devaraju, N; Bala, G; Nemani, R

2015-09-01

Land-use changes since the start of the industrial era account for nearly one-third of the cumulative anthropogenic CO2 emissions. In addition to the greenhouse effect of CO2 emissions, changes in land use also affect climate via changes in surface physical properties such as albedo, evapotranspiration and roughness length. Recent modelling studies suggest that these biophysical components may be comparable with biochemical effects. In regard to climate change, the effects of these two distinct processes may counterbalance one another both regionally and, possibly, globally. In this article, through hypothetical large-scale deforestation simulations using a global climate model, we contrast the implications of afforestation on ameliorating or enhancing anthropogenic contributions from previously converted (agricultural) land surfaces. Based on our review of past studies on this subject, we conclude that the sum of both biophysical and biochemical effects should be assessed when large-scale afforestation is used for countering global warming, and the net effect on global mean temperature change depends on the location of deforestation/afforestation. Further, although biochemical effects trigger global climate change, biophysical effects often cause strong local and regional climate change. The implication of the biophysical effects for adaptation and mitigation of climate change in agriculture and agroforestry sectors is discussed. © 2014 John Wiley & Sons Ltd.

Modelling and analysis of the sugar cataract development process using stochastic hybrid systems.

PubMed

Riley, D; Koutsoukos, X; Riley, K

2009-05-01

Modelling and analysis of biochemical systems such as sugar cataract development (SCD) are critical because they can provide new insights into systems, which cannot be easily tested with experiments; however, they are challenging problems due to the highly coupled chemical reactions that are involved. The authors present a stochastic hybrid system (SHS) framework for modelling biochemical systems and demonstrate the approach for the SCD process. A novel feature of the framework is that it allows modelling the effect of drug treatment on the system dynamics. The authors validate the three sugar cataract models by comparing trajectories computed by two simulation algorithms. Further, the authors present a probabilistic verification method for computing the probability of sugar cataract formation for different chemical concentrations using safety and reachability analysis methods for SHSs. The verification method employs dynamic programming based on a discretisation of the state space and therefore suffers from the curse of dimensionality. To analyse the SCD process, a parallel dynamic programming implementation that can handle large, realistic systems was developed. Although scalability is a limiting factor, this work demonstrates that the proposed method is feasible for realistic biochemical systems.

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

Hartmann, Anja, E-mail: hartmann@ipk-gatersleben.de; Schreiber, Falk; Martin-Luther-University Halle-Wittenberg, Halle

The characterization of biological systems with respect to their behavior and functionality based on versatile biochemical interactions is a major challenge. To understand these complex mechanisms at systems level modeling approaches are investigated. Different modeling formalisms allow metabolic models to be analyzed depending on the question to be solved, the biochemical knowledge and the availability of experimental data. Here, we describe a method for an integrative analysis of the structure and dynamics represented by qualitative and quantitative metabolic models. Using various formalisms, the metabolic model is analyzed from different perspectives. Determined structural and dynamic properties are visualized in the contextmore » of the metabolic model. Interaction techniques allow the exploration and visual analysis thereby leading to a broader understanding of the behavior and functionality of the underlying biological system. The System Biology Metabolic Model Framework (SBM{sup 2} – Framework) implements the developed method and, as an example, is applied for the integrative analysis of the crop plant potato.« less

Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations

NASA Technical Reports Server (NTRS)

Shapiro, Bruce E.; Levchenko, Andre; Meyerowitz, Elliot M.; Wold, Barbara J.; Mjolsness, Eric D.

2003-01-01

Cellerator describes single and multi-cellular signal transduction networks (STN) with a compact, optionally palette-driven, arrow-based notation to represent biochemical reactions and transcriptional activation. Multi-compartment systems are represented as graphs with STNs embedded in each node. Interactions include mass-action, enzymatic, allosteric and connectionist models. Reactions are translated into differential equations and can be solved numerically to generate predictive time courses or output as systems of equations that can be read by other programs. Cellerator simulations are fully extensible and portable to any operating system that supports Mathematica, and can be indefinitely nested within larger data structures to produce highly scaleable models.

Development of a normal tissue complication probability (NTCP) model for radiation-induced hypothyroidism in nasopharyngeal carcinoma patients.

PubMed

Luo, Ren; Wu, Vincent W C; He, Binghui; Gao, Xiaoying; Xu, Zhenxi; Wang, Dandan; Yang, Zhining; Li, Mei; Lin, Zhixiong

2018-05-18

The objectives of this study were to build a normal tissue complication probability (NTCP) model of radiation-induced hypothyroidism (RHT) for nasopharyngeal carcinoma (NPC) patients and to compare it with other four published NTCP models to evaluate its efficacy. Medical notes of 174 NPC patients after radiotherapy were reviewed. Biochemical hypothyroidism was defined as an elevated level of serum thyroid-stimulating hormone (TSH) value with a normal or decreased level of serum free thyroxine (fT4) after radiotherapy. Logistic regression with leave-one-out cross-validation was performed to establish the NTCP model. Model performance was evaluated and compared by the area under the receiver operating characteristic curve (AUC) in our NPC cohort. With a median follow-up of 24 months, 39 (22.4%) patients developed biochemical hypothyroidism. Gender, chemotherapy, the percentage thyroid volume receiving more than 50 Gy (V 50 ), and the maximum dose of the pituitary (P max ) were identified as the most predictive factors for RHT. A NTCP model based on these four parameters were developed. The model comparison was made in our NPC cohort and our NTCP model performed better in RHT prediction than the other four models. This study developed a four-variable NTCP model for biochemical hypothyroidism in NPC patients post-radiotherapy. Our NTCP model for RHT presents a high prediction capability. This is a retrospective study without registration.

Computed Energetics of Nucleotides in Spatial Ribozyme Structures: An Accurate Identification of Functional Regions from Structure

PubMed Central

Torshin, Ivan Y.

2004-01-01

Ribozymes are functionally diverse RNA molecules with intrinsic catalytic activity. Multiple structural and biochemical studies are required to establish which nucleotide bases are involved in the catalysis. The relative energetic properties of the nucleotide bases have been analyzed in a set of the known ribozyme structures. It was found that many of the known catalytic nucleotides can be identified using only the structure without any additional biochemical data. The results of the calculations compare well with the available biochemical data on RNA stability. Extensive in silico mutagenesis suggests that most of the nucleotides in ribozymes stabilize the RNA. The calculations show that relative contribution of the catalytic bases to RNA stability observably differs from contributions of the noncatalytic bases. Distinction between the concepts of “relative stability” and “mutational stability” is suggested. As results of prediction for several models of ribozymes appear to be in agreement with the published data on the potential active site regions, the method can potentially be used for prediction of functional nucleotides from nucleic sequence. PMID:15105962

Prospect redux

NASA Technical Reports Server (NTRS)

Jacquemoud, S.; Ustin, S. L.; Verdebout, J.; Schmuck, G.; Andreoli, G.; Hosgood, B.

1995-01-01

The remote estimation of leaf biochemical content from spaceborne platforms has been the subject of many studies aimed at better understanding of terrestrial ecosystem functioning. The major ecological processes involved in exchange of matter and energy, like photosynthesis, primary production, evaportranspiration, respiration, and decomposition can be related to plant properties e.g., chlorophyll, water, protein, cellulose and lignin contents. As leaves represent the most important plant surfaces interacting with solar energy, a top priority has been to relate optical properties to biochemical constituents. Two different approaches have been considered: first, statistical correlations between the leaf reflectance (or transmittance) and biochemical content, and second, physically based models of leaf scattering and absorption developed using the laws of optics. Recently reviewed by Verdebout et al., the development of models of leaf optical properties has resulted in better understanding of the interaction of light with plant leaves. Present radiative transfer models mainly use chlorophyll and/or water contents as input parameters to calculate leaf reflectance. Inversion of these models allows to retrieve these constituents from spectrophotometric measurements. Conel et al. recently proposed a two-stream Kubelka-Munk model to analyze the influence of protein, cellulose, lignin, and starch on leaf reflectance, but in fact, the estimation of leaf biochemistry from remote sensing is still an open question. In order to clarify it, a laboratory experiment associating visible/infrared spectra of plan leaves both with physical measurements and biochemical analyses was conducted at the Joint Research Center during the summer of 1993. This unique data set has been used to upgrade the PROSPECT model, by including leaf biochemistry.

Efficient rejection-based simulation of biochemical reactions with stochastic noise and delays

NASA Astrophysics Data System (ADS)

Thanh, Vo Hong; Priami, Corrado; Zunino, Roberto

2014-10-01

We propose a new exact stochastic rejection-based simulation algorithm for biochemical reactions and extend it to systems with delays. Our algorithm accelerates the simulation by pre-computing reaction propensity bounds to select the next reaction to perform. Exploiting such bounds, we are able to avoid recomputing propensities every time a (delayed) reaction is initiated or finished, as is typically necessary in standard approaches. Propensity updates in our approach are still performed, but only infrequently and limited for a small number of reactions, saving computation time and without sacrificing exactness. We evaluate the performance improvement of our algorithm by experimenting with concrete biological models.

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data

PubMed Central

Vehtari, Aki; Mäkinen, Ville-Petteri; Soininen, Pasi; Ingman, Petri; Mäkelä, Sanna M; Savolainen, Markku J; Hannuksela, Minna L; Kaski, Kimmo; Ala-Korpela, Mika

2007-01-01

Background A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by 1H NMR spectroscopy of serum. Results A Bayesian methodology, with a biochemical motivation, is presented for a real 1H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the 1H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the 1H NMR spectra. Conclusion The systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics. PMID:17493257

A novel optical fiber biochemical sensor based on long period grating

NASA Astrophysics Data System (ADS)

Mao, Xianhui; Liao, Yanbiao; Zhang, Min; Lai, Shurong; Yin, Haibo

2007-09-01

In this paper, our present work, which aimed at investigating a novel optical fiber biochemical sensor based on long period grating (LPG), is introduced. Biochemical sensor is one of the most attractive fields of sensor research, especially with the development and occurrence of all kinds of novel theory and technology such as LPG. When there is a refraction index periodic perturbation, the guiding mode and cladding mode in LPG couple with each other. This make the LPG is sensitive to the ambient refractive index. This means it can be a novel bio-chemical sensor when it is applied in the fields of biochemistry. After investigating the principle of coupling in LPG, where the formulas of resonance wave length and band width are induced by 3-layer step index model, we developed an optical fiber biochemical sensor. The structure of its probe is designed by coating some function films whose thickness is between several tens and several hundreds nanometers on the cladding of optical fiber. Experiments of monitoring the saline separateness process of Bovine Serum Albumin (BSA) and Mice-Immunoglobulin G (M-IgG) by using the developed LPG sensor have been done. The monitoring indicated that for the BSA, the saline separateness occurs when the saturation is between 50% and 60%, for the M-IgG, the percentage is between 30%-40%. Besides the monitoring, the experiments could also analyze the effects of protein type (different molecule structure), protein consistency and saline saturation to saline separateness. The experimental results show that the optical fiber biochemical sensor based on LPG has many advantages such as simple structure, high sensitivity and miniature. It has a promising future in many research fields and application fields.

Logic-Based Models for the Analysis of Cell Signaling Networks†

PubMed Central

2010-01-01

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks. PMID:20225868

Simulation of dissolved oxygen and biochemical oxygen demand, Plantation Canal, Broward County, Florida with an evaluation of the QUAL-I model for use in south Florida

USGS Publications Warehouse

Russo, Thomas N.; McQuivey, Raul S.

1975-01-01

A mathematical model; QUAL-I, developed by the Texas Water Development Board, was evaluated as a management tool in predicting the spatial and temporal distribution of dissolved oxygen and biochemical oxygen demand in Plantation Canal. Predictions based on the QUAL-I model, which was verified only against midday summer-flow conditions, showed that improvement of quality of inflows from sewage treatment plants and use of at least 130 cubic feet per second of dilution water would improve water quality in the canal significantly. The model was not fully amenable to use on Plantation Canal because: (1) it did not consider photosynthetic production, nitrification, and benthic oxygen demand as sources and sinks of oxygen; (2) the model assumptions of complete mixing, transport, and steady state were not met; and (3) the data base was inadequate because it consisted of only one set of data for each case. However, it was felt that meaningful results could be obtained for some sets of conditions. (Woodard-USGS)

Predicting greenhouse gas emissions from beef cattle feedyard manure

USDA-ARS?s Scientific Manuscript database

Improved predictive models for nitrous oxide and methane are crucial for assessing the greenhouse gas (GHG) footprint of beef cattle production. Biochemical process based models to predict GHG from manure rely on information derived from studies on soil and only limited study has been conducted on m...

Predicting greenhouse gas emissions from beef cattle feedyard manure

USDA-ARS?s Scientific Manuscript database

Improved predictive models for nitrous oxide and methane are crucial for assessing the greenhouse gas (GHG) footprint of beef cattle production. Biochemical process-based models to predict GHG from manure rely on information derived from studies on soil and only limited study has been conducted on m...

Diffusion-controlled interface kinetics-inclusive system-theoretic propagation models for molecular communication systems

NASA Astrophysics Data System (ADS)

Chude-Okonkwo, Uche A. K.; Malekian, Reza; Maharaj, B. T.

2015-12-01

Inspired by biological systems, molecular communication has been proposed as a new communication paradigm that uses biochemical signals to transfer information from one nano device to another over a short distance. The biochemical nature of the information transfer process implies that for molecular communication purposes, the development of molecular channel models should take into consideration diffusion phenomenon as well as the physical/biochemical kinetic possibilities of the process. The physical and biochemical kinetics arise at the interfaces between the diffusion channel and the transmitter/receiver units. These interfaces are herein termed molecular antennas. In this paper, we present the deterministic propagation model of the molecular communication between an immobilized nanotransmitter and nanoreceiver, where the emission and reception kinetics are taken into consideration. Specifically, we derived closed-form system-theoretic models and expressions for configurations that represent different communication systems based on the type of molecular antennas used. The antennas considered are the nanopores at the transmitter and the surface receptor proteins/enzymes at the receiver. The developed models are simulated to show the influence of parameters such as the receiver radius, surface receptor protein/enzyme concentration, and various reaction rate constants. Results show that the effective receiver surface area and the rate constants are important to the system's output performance. Assuming high rate of catalysis, the analysis of the frequency behavior of the developed propagation channels in the form of transfer functions shows significant difference introduce by the inclusion of the molecular antennas into the diffusion-only model. It is also shown that for t > > 0 and with the information molecules' concentration greater than the Michaelis-Menten kinetic constant of the systems, the inclusion of surface receptors proteins and enzymes in the models makes the system act like a band-stop filter over an infinite frequency range.

A systematic petri net approach for multiple-scale modeling and simulation of biochemical processes.

PubMed

Chen, Ming; Hu, Minjie; Hofestädt, Ralf

2011-06-01

A method to exploit hybrid Petri nets for modeling and simulating biochemical processes in a systematic way was introduced. Both molecular biology and biochemical engineering aspects are manipulated. With discrete and continuous elements, the hybrid Petri nets can easily handle biochemical factors such as metabolites concentration and kinetic behaviors. It is possible to translate both molecular biological behavior and biochemical processes workflow into hybrid Petri nets in a natural manner. As an example, penicillin production bioprocess is modeled to illustrate the concepts of the methodology. Results of the dynamic of production parameters in the bioprocess were simulated and observed diagrammatically. Current problems and post-genomic perspectives were also discussed.

Complexity reduction of biochemical rate expressions.

PubMed

Schmidt, Henning; Madsen, Mads F; Danø, Sune; Cedersund, Gunnar

2008-03-15

The current trend in dynamical modelling of biochemical systems is to construct more and more mechanistically detailed and thus complex models. The complexity is reflected in the number of dynamic state variables and parameters, as well as in the complexity of the kinetic rate expressions. However, a greater level of complexity, or level of detail, does not necessarily imply better models, or a better understanding of the underlying processes. Data often does not contain enough information to discriminate between different model hypotheses, and such overparameterization makes it hard to establish the validity of the various parts of the model. Consequently, there is an increasing demand for model reduction methods. We present a new reduction method that reduces complex rational rate expressions, such as those often used to describe enzymatic reactions. The method is a novel term-based identifiability analysis, which is easy to use and allows for user-specified reductions of individual rate expressions in complete models. The method is one of the first methods to meet the classical engineering objective of improved parameter identifiability without losing the systems biology demand of preserved biochemical interpretation. The method has been implemented in the Systems Biology Toolbox 2 for MATLAB, which is freely available from http://www.sbtoolbox2.org. The Supplementary Material contains scripts that show how to use it by applying the method to the example models, discussed in this article.

A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2

USDA-ARS?s Scientific Manuscript database

Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium...

Biochemical Characterization of Prion Strains in Bank Voles

PubMed Central

Pirisinu, Laura; Marcon, Stefano; Di Bari, Michele Angelo; D’Agostino, Claudia; Agrimi, Umberto; Nonno, Romolo

2013-01-01

Prions exist as different strains exhibiting distinct disease phenotypes. Currently, the identification of prion strains is still based on biological strain typing in rodents. However, it has been shown that prion strains may be associated with distinct PrPSc biochemical types. Taking advantage of the availability of several prion strains adapted to a novel rodent model, the bank vole, we investigated if any prion strain was actually associated with distinctive PrPSc biochemical characteristics and if it was possible to univocally identify strains through PrPSc biochemical phenotypes. We selected six different vole-adapted strains (three human-derived and three animal-derived) and analyzed PrPSc from individual voles by epitope mapping of protease resistant core of PrPSc (PrPres) and by conformational stability and solubility assay. Overall, we discriminated five out of six prion strains, while two different scrapie strains showed identical PrPSc types. Our results suggest that the biochemical strain typing approach here proposed was highly discriminative, although by itself it did not allow us to identify all prion strains analyzed. PMID:25437201

Aminoacyl transfer from an adenylate anhydride to polyribonucleotides

NASA Technical Reports Server (NTRS)

Weber, A. L.; Lacey, J. C., Jr.

1975-01-01

Imidazole catalysis of phenylalanyl transfer from phenylalanine adenylate to hydroxyl groups of homopolyribonucleotides is studied as a possible chemical model of biochemical aminoacylation of transfer RNA (tRNA). The effect of pH on imidazole-catalyzed transfer of phenylalanyl residues to poly(U) and poly(A) double helix strands, the number of peptide linkages and their lability to base and neutral hydroxylamine, and the nature of adenylate condensation products are investigated. The chemical model entertained exhibits a constraint by not acylating the hydroxyl groups of polyribonucleotides in a double helix. The constraint is consistent with selective biochemical aminoacylation at the tRNA terminus. Interest in imidazole as a model of histidine residue in protoenzymes participating in prebiotic aminoacyl transfer to polyribonucleotides, and in rendering the tRNA a more efficient adaptor, is indicated.

Exact Hybrid Particle/Population Simulation of Rule-Based Models of Biochemical Systems

PubMed Central

Stover, Lori J.; Nair, Niketh S.; Faeder, James R.

2014-01-01

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This “network-free” approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of “partial network expansion” into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility. PMID:24699269

Exact hybrid particle/population simulation of rule-based models of biochemical systems.

PubMed

Hogg, Justin S; Harris, Leonard A; Stover, Lori J; Nair, Niketh S; Faeder, James R

2014-04-01

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility.

Nursing students learning the pharmacology of diabetes mellitus with complexity-based computerized models: A quasi-experimental study.

PubMed

Dubovi, Ilana; Dagan, Efrat; Sader Mazbar, Ola; Nassar, Laila; Levy, Sharona T

2018-02-01

Pharmacology is a crucial component of medications administration in nursing, yet nursing students generally find it difficult and self-rate their pharmacology skills as low. To evaluate nursing students learning pharmacology with the Pharmacology Inter-Leaved Learning-Cells environment, a novel approach to modeling biochemical interactions using a multiscale, computer-based model with a complexity perspective based on a small set of entities and simple rules. This environment represents molecules, organelles and cells to enhance the understanding of cellular processes, and combines these cells at a higher scale to obtain whole-body interactions. Sophomore nursing students who learned the pharmacology of diabetes mellitus with the Pharmacology Inter-Leaved Learning-Cells environment (experimental group; n=94) or via a lecture-based curriculum (comparison group; n=54). A quasi-experimental pre- and post-test design was conducted. The Pharmacology-Diabetes-Mellitus questionnaire and the course's final exam were used to evaluate students' knowledge of the pharmacology of diabetes mellitus. Conceptual learning was significantly higher for the experimental than for the comparison group for the course final exam scores (unpaired t=-3.8, p<0.001) and for the Pharmacology-Diabetes-Mellitus questionnaire (U=942, p<0.001). The largest effect size for the Pharmacology-Diabetes-Mellitus questionnaire was for the medication action subscale. Analysis of complex-systems component reasoning revealed a significant difference for micro-macro transitions between the levels (F(1, 82)=6.9, p<0.05). Learning with complexity-based computerized models is highly effective and enhances the understanding of moving between micro and macro levels of the biochemical phenomena, this is then related to better understanding of medication actions. Moreover, the Pharmacology Inter-Leaved Learning-Cells approach provides a more general reasoning scheme for biochemical processes, which enhances pharmacology learning beyond the specific topic learned. The present study implies that deeper understanding of pharmacology will support nursing students' clinical decisions and empower their proficiency in medications administration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of renal crystalline size distributions in space using a PBE analytic model. 1. Effect of microgravity-induced biochemical alterations.

PubMed

Kassemi, Mohammad; Thompson, David

2016-09-01

An analytical Population Balance Equation model is developed and used to assess the risk of critical renal stone formation for astronauts during future space missions. The model uses the renal biochemical profile of the subject as input and predicts the steady-state size distribution of the nucleating, growing, and agglomerating calcium oxalate crystals during their transit through the kidney. The model is verified through comparison with published results of several crystallization experiments. Numerical results indicate that the model is successful in clearly distinguishing between 1-G normal and 1-G recurrent stone-former subjects based solely on their published 24-h urine biochemical profiles. Numerical case studies further show that the predicted renal calculi size distribution for a microgravity astronaut is closer to that of a recurrent stone former on Earth rather than to a normal subject in 1 G. This interestingly implies that the increase in renal stone risk level in microgravity is relatively more significant for a normal person than a stone former. However, numerical predictions still underscore that the stone-former subject carries by far the highest absolute risk of critical stone formation during space travel. Copyright © 2016 the American Physiological Society.

Extending rule-based methods to model molecular geometry and 3D model resolution.

PubMed

Hoard, Brittany; Jacobson, Bruna; Manavi, Kasra; Tapia, Lydia

2016-08-01

Computational modeling is an important tool for the study of complex biochemical processes associated with cell signaling networks. However, it is challenging to simulate processes that involve hundreds of large molecules due to the high computational cost of such simulations. Rule-based modeling is a method that can be used to simulate these processes with reasonably low computational cost, but traditional rule-based modeling approaches do not include details of molecular geometry. The incorporation of geometry into biochemical models can more accurately capture details of these processes, and may lead to insights into how geometry affects the products that form. Furthermore, geometric rule-based modeling can be used to complement other computational methods that explicitly represent molecular geometry in order to quantify binding site accessibility and steric effects. We propose a novel implementation of rule-based modeling that encodes details of molecular geometry into the rules and binding rates. We demonstrate how rules are constructed according to the molecular curvature. We then perform a study of antigen-antibody aggregation using our proposed method. We simulate the binding of antibody complexes to binding regions of the shrimp allergen Pen a 1 using a previously developed 3D rigid-body Monte Carlo simulation, and we analyze the aggregate sizes. Then, using our novel approach, we optimize a rule-based model according to the geometry of the Pen a 1 molecule and the data from the Monte Carlo simulation. We use the distances between the binding regions of Pen a 1 to optimize the rules and binding rates. We perform this procedure for multiple conformations of Pen a 1 and analyze the impact of conformation and resolution on the optimal rule-based model. We find that the optimized rule-based models provide information about the average steric hindrance between binding regions and the probability that antibodies will bind to these regions. These optimized models quantify the variation in aggregate size that results from differences in molecular geometry and from model resolution.

Efficient rejection-based simulation of biochemical reactions with stochastic noise and delays

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

Thanh, Vo Hong, E-mail: vo@cosbi.eu; Priami, Corrado, E-mail: priami@cosbi.eu; Department of Mathematics, University of Trento

2014-10-07

We propose a new exact stochastic rejection-based simulation algorithm for biochemical reactions and extend it to systems with delays. Our algorithm accelerates the simulation by pre-computing reaction propensity bounds to select the next reaction to perform. Exploiting such bounds, we are able to avoid recomputing propensities every time a (delayed) reaction is initiated or finished, as is typically necessary in standard approaches. Propensity updates in our approach are still performed, but only infrequently and limited for a small number of reactions, saving computation time and without sacrificing exactness. We evaluate the performance improvement of our algorithm by experimenting with concretemore » biological models.« less

Implementation of a reduced order Kalman filter to assimilate ocean color data into a coupled physical-biochemical model of the North Aegean Sea.

NASA Astrophysics Data System (ADS)

Kalaroni, Sofia; Tsiaras, Kostas; Economou-Amilli, Athena; Petihakis, George; Politikos, Dimitrios; Triantafyllou, George

2013-04-01

Within the framework of the European project OPEC (Operational Ecology), a data assimilation system was implemented to describe chlorophyll-a concentrations of the North Aegean, as well the impact on the European anchovy (Engraulis encrasicolus) biomass distribution provided by a bioenergetics model, related to the density of three low trophic level functional groups of zooplankton (heterotrophic flagellates, microzooplankton and mesozooplankton). The three-dimensional hydrodynamic-biogeochemical model comprises two on-line coupled sub-models: the Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM). The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter and its variant that uses a fixed correction base (SFEK). For the initialization, SEEK filter uses a reduced order error covariance matrix provided by the dominant Empirical Orthogonal Functions (EOF) of model. The assimilation experiments were performed for year 2003 using SeaWiFS chlorophyll-a data during which the physical model uses the atmospheric forcing obtained from the regional climate model HIRHAM5. The assimilation system is validated by assessing the relevance of the system in fitting the data, the impact of the assimilation on non-observed biochemical parameters and the overall quality of the forecasts.

Rapid biochemical methane potential prediction of urban organic waste with near-infrared reflectance spectroscopy.

PubMed

Fitamo, T; Triolo, J M; Boldrin, A; Scheutz, C

2017-08-01

The anaerobic digestibility of various biomass feedstocks in biogas plants is determined with biochemical methane potential (BMP) assays. However, experimental BMP analysis is time-consuming, costly and challenging to optimise stock management and feeding to achieve improved biogas production. The aim of the present study is to develop a fast and reliable model based on near-infrared reflectance spectroscopy (NIRS) for the BMP prediction of urban organic waste (UOW). The model comprised 87 UOW samples. Additionally, 88 plant biomass samples were included, to develop a combined model predicting BMP. The coefficient of determination (R 2 ) and root mean square error in prediction (RMSE P ) of the UOW model were 0.88 and 44 mL CH 4 /g VS, while the combined model was 0.89 and 50 mL CH 4 /g VS. Improved model performance was obtained for the two individual models compared to the combined version. The BMP prediction with NIRS was satisfactory and moderately successful. Copyright © 2017 Elsevier Ltd. All rights reserved.

Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates.

PubMed

Campbell, D A; Chkrebtii, O

2013-12-01

Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

In Vitro Measurements of Metabolism for Application in Pharmacokinetic Modeling

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

Lipscomb, John C.; Poet, Torka S.

2008-04-01

Abstract Human risk and exposure assessments require dosimetry information. Species-specific tissue dose response will be driven by physiological and biochemical processes. While metabolism and pharmacokinetic data are often not available in humans, they are much more available in laboratory animals; metabolic rate constants can be readily derived in vitro. The physiological differences between laboratory animals and humans are known. Biochemical processes, especially metabolism, can be measured in vitro and extrapolated to account for in vivo metabolism through clearance models or when linked to a physiologically based biological (PBPK) model to describe the physiological processes, such as drug delivery to themore » metabolic organ. This review focuses on the different organ, cellular, and subcellular systems that can be used to measure in vitro metabolic rate constants and how that data is extrapolated to be used in biokinetic modeling.« less

A Model Incorporating Some of the Mechanical and Biochemical Factors Underlying Clot Formation and Dissolution in Flowing Blood

DOE PAGES

Anand, M.; Rajagopal, K.; Rajagopal, K. R.

2003-01-01

Multiple interacting mechanisms control the formation and dissolution of clots to maintain blood in a state of delicate balance. In addition to a myriad of biochemical reactions, rheological factors also play a crucial role in modulating the response of blood to external stimuli. To date, a comprehensive model for clot formation and dissolution, that takes into account the biochemical, medical and rheological factors, has not been put into place, the existing models emphasizing either one or the other of the factors. In this paper, after discussing the various biochemical, physiologic and rheological factors at some length, we develop a modelmore » for clot formation and dissolution that incorporates many of the relevant crucial factors that have a bearing on the problem. The model, though just a first step towards understanding a complex phenomenon, goes further than previous models in integrating the biochemical, physiologic and rheological factors that come into play.« less

Design of a biochemical circuit motif for learning linear functions

PubMed Central

Lakin, Matthew R.; Minnich, Amanda; Lane, Terran; Stefanovic, Darko

2014-01-01

Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective. PMID:25401175

Design of a biochemical circuit motif for learning linear functions.

PubMed

Lakin, Matthew R; Minnich, Amanda; Lane, Terran; Stefanovic, Darko

2014-12-06

Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective.

Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks.

PubMed

Adalsteinsson, David; McMillen, David; Elston, Timothy C

2004-03-08

Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. We have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends.

PubMed

Snowden, Thomas J; van der Graaf, Piet H; Tindall, Marcus J

2017-07-01

Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed.

Multiplatform serum metabolic phenotyping combined with pathway mapping to identify biochemical differences in smokers.

PubMed

Kaluarachchi, Manuja R; Boulangé, Claire L; Garcia-Perez, Isabel; Lindon, John C; Minet, Emmanuel F

2016-10-01

Determining perturbed biochemical functions associated with tobacco smoking should be helpful for establishing causal relationships between exposure and adverse events. A multiplatform comparison of serum of smokers (n = 55) and never-smokers (n = 57) using nuclear magnetic resonance spectroscopy, UPLC-MS and statistical modeling revealed clustering of the classes, distinguished by metabolic biomarkers. The identified metabolites were subjected to metabolic pathway enrichment, modeling adverse biological events using available databases. Perturbation of metabolites involved in chronic obstructive pulmonary disease, cardiovascular diseases and cancer were identified and discussed. Combining multiplatform metabolic phenotyping with knowledge-based mapping gives mechanistic insights into disease development, which can be applied to next-generation tobacco and nicotine products for comparative risk assessment.

11th International Conference of Radiation Research

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

NONE

1999-07-18

Topics discussed in the conference included the following: Radiation Physics, Radiation Chemistry and modelling--Radiation physics and dosimetry; Electron transfer in biological media; Radiation chemistry; Biophysical and biochemical modelling; Mechanisms of DNA damage; Assays of DNA damage; Energy deposition in micro volumes; Photo-effects; Special techniques and technologies; Oxidative damage. Molecular and cellular effects-- Photobiology; Cell cycle effects; DNA damage: Strand breaks; DNA damage: Bases; DNA damage Non-targeted; DNA damage: other; Chromosome aberrations: clonal; Chromosomal aberrations: non-clonal; Interactions: Heat/Radiation/Drugs; Biochemical effects; Protein expression; Gene induction; Co-operative effects; ``Bystander'' effects; Oxidative stress effects; Recovery from radiation damage. DNA damage and repair -- DNAmore » repair genes; DNA repair deficient diseases; DNA repair enzymology; Epigenetic effects on repair; and Ataxia and ATM.« less

Accurate atom-mapping computation for biochemical reactions.

PubMed

Latendresse, Mario; Malerich, Jeremiah P; Travers, Mike; Karp, Peter D

2012-11-26

The complete atom mapping of a chemical reaction is a bijection of the reactant atoms to the product atoms that specifies the terminus of each reactant atom. Atom mapping of biochemical reactions is useful for many applications of systems biology, in particular for metabolic engineering where synthesizing new biochemical pathways has to take into account for the number of carbon atoms from a source compound that are conserved in the synthesis of a target compound. Rapid, accurate computation of the atom mapping(s) of a biochemical reaction remains elusive despite significant work on this topic. In particular, past researchers did not validate the accuracy of mapping algorithms. We introduce a new method for computing atom mappings called the minimum weighted edit-distance (MWED) metric. The metric is based on bond propensity to react and computes biochemically valid atom mappings for a large percentage of biochemical reactions. MWED models can be formulated efficiently as Mixed-Integer Linear Programs (MILPs). We have demonstrated this approach on 7501 reactions of the MetaCyc database for which 87% of the models could be solved in less than 10 s. For 2.1% of the reactions, we found multiple optimal atom mappings. We show that the error rate is 0.9% (22 reactions) by comparing these atom mappings to 2446 atom mappings of the manually curated Kyoto Encyclopedia of Genes and Genomes (KEGG) RPAIR database. To our knowledge, our computational atom-mapping approach is the most accurate and among the fastest published to date. The atom-mapping data will be available in the MetaCyc database later in 2012; the atom-mapping software will be available within the Pathway Tools software later in 2012.

Searching whole genome sequences for biochemical identification features of emerging and reemerging pathogenic Corynebacterium species.

PubMed

Santos, André S; Ramos, Rommel T; Silva, Artur; Hirata, Raphael; Mattos-Guaraldi, Ana L; Meyer, Roberto; Azevedo, Vasco; Felicori, Liza; Pacheco, Luis G C

2018-05-11

Biochemical tests are traditionally used for bacterial identification at the species level in clinical microbiology laboratories. While biochemical profiles are generally efficient for the identification of the most important corynebacterial pathogen Corynebacterium diphtheriae, their ability to differentiate between biovars of this bacterium is still controversial. Besides, the unambiguous identification of emerging human pathogenic species of the genus Corynebacterium may be hampered by highly variable biochemical profiles commonly reported for these species, including Corynebacterium striatum, Corynebacterium amycolatum, Corynebacterium minutissimum, and Corynebacterium xerosis. In order to identify the genomic basis contributing for the biochemical variabilities observed in phenotypic identification methods of these bacteria, we combined a comprehensive literature review with a bioinformatics approach based on reconstruction of six specific biochemical reactions/pathways in 33 recently released whole genome sequences. We used data retrieved from curated databases (MetaCyc, PathoSystems Resource Integration Center (PATRIC), The SEED, TransportDB, UniProtKB) associated with homology searches by BLAST and profile Hidden Markov Models (HMMs) to detect enzymes participating in the various pathways and performed ab initio protein structure modeling and molecular docking to confirm specific results. We found a differential distribution among the various strains of genes that code for some important enzymes, such as beta-phosphoglucomutase and fructokinase, and also for individual components of carbohydrate transport systems, including the fructose-specific phosphoenolpyruvate-dependent sugar phosphotransferase (PTS) and the ribose-specific ATP-binging cassette (ABC) transporter. Horizontal gene transfer plays a role in the biochemical variability of the isolates, as some genes needed for sucrose fermentation were seen to be present in genomic islands. Noteworthy, using profile HMMs, we identified an enzyme with putative alpha-1,6-glycosidase activity only in some specific strains of C. diphtheriae and this may aid to understanding of the differential abilities to utilize glycogen and starch between the biovars.

CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis.

PubMed

Inoue, Kentaro; Maeda, Kazuhiro; Miyabe, Takaaki; Matsuoka, Yu; Kurata, Hiroyuki

2014-09-01

Mathematical modeling has become a standard technique to understand the dynamics of complex biochemical systems. To promote the modeling, we had developed the CADLIVE dynamic simulator that automatically converted a biochemical map into its associated mathematical model, simulated its dynamic behaviors and analyzed its robustness. To enhance the feasibility by CADLIVE and extend its functions, we propose the CADLIVE toolbox available for MATLAB, which implements not only the existing functions of the CADLIVE dynamic simulator, but also the latest tools including global parameter search methods with robustness analysis. The seamless, bottom-up processes consisting of biochemical network construction, automatic construction of its dynamic model, simulation, optimization, and S-system analysis greatly facilitate dynamic modeling, contributing to the research of systems biology and synthetic biology. This application can be freely downloaded from http://www.cadlive.jp/CADLIVE_MATLAB/ together with an instruction.

Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models

DTIC Science & Technology

2015-03-16

sensitivity value was the maximum uncertainty in that value estimated by the Sobol method. 2.4. Global Sensitivity Analysis of the Reduced Order Coagulation...sensitivity analysis, using the variance-based method of Sobol , to estimate which parameters controlled the performance of the reduced order model [69]. We...Environment. Comput. Sci. Eng. 2007, 9, 90–95. 69. Sobol , I. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement

PubMed Central

Wu, Alex; Song, Youhong; van Oosterom, Erik J.; Hammer, Graeme L.

2016-01-01

The next advance in field crop productivity will likely need to come from improving crop use efficiency of resources (e.g., light, water, and nitrogen), aspects of which are closely linked with overall crop photosynthetic efficiency. Progress in genetic manipulation of photosynthesis is confounded by uncertainties of consequences at crop level because of difficulties connecting across scales. Crop growth and development simulation models that integrate across biological levels of organization and use a gene-to-phenotype modeling approach may present a way forward. There has been a long history of development of crop models capable of simulating dynamics of crop physiological attributes. Many crop models incorporate canopy photosynthesis (source) as a key driver for crop growth, while others derive crop growth from the balance between source- and sink-limitations. Modeling leaf photosynthesis has progressed from empirical modeling via light response curves to a more mechanistic basis, having clearer links to the underlying biochemical processes of photosynthesis. Cross-scale modeling that connects models at the biochemical and crop levels and utilizes developments in upscaling leaf-level models to canopy models has the potential to bridge the gap between photosynthetic manipulation at the biochemical level and its consequences on crop productivity. Here we review approaches to this emerging cross-scale modeling framework and reinforce the need for connections across levels of modeling. Further, we propose strategies for connecting biochemical models of photosynthesis into the cross-scale modeling framework to support crop improvement through photosynthetic manipulation. PMID:27790232

Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement.

PubMed

Wu, Alex; Song, Youhong; van Oosterom, Erik J; Hammer, Graeme L

2016-01-01

The next advance in field crop productivity will likely need to come from improving crop use efficiency of resources (e.g., light, water, and nitrogen), aspects of which are closely linked with overall crop photosynthetic efficiency. Progress in genetic manipulation of photosynthesis is confounded by uncertainties of consequences at crop level because of difficulties connecting across scales. Crop growth and development simulation models that integrate across biological levels of organization and use a gene-to-phenotype modeling approach may present a way forward. There has been a long history of development of crop models capable of simulating dynamics of crop physiological attributes. Many crop models incorporate canopy photosynthesis (source) as a key driver for crop growth, while others derive crop growth from the balance between source- and sink-limitations. Modeling leaf photosynthesis has progressed from empirical modeling via light response curves to a more mechanistic basis, having clearer links to the underlying biochemical processes of photosynthesis. Cross-scale modeling that connects models at the biochemical and crop levels and utilizes developments in upscaling leaf-level models to canopy models has the potential to bridge the gap between photosynthetic manipulation at the biochemical level and its consequences on crop productivity. Here we review approaches to this emerging cross-scale modeling framework and reinforce the need for connections across levels of modeling. Further, we propose strategies for connecting biochemical models of photosynthesis into the cross-scale modeling framework to support crop improvement through photosynthetic manipulation.

SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool

PubMed Central

Zi, Zhike; Zheng, Yanan; Rundell, Ann E; Klipp, Edda

2008-01-01

Background It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models. Results This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface. Conclusion SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes. PMID:18706080

SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool.

PubMed

Zi, Zhike; Zheng, Yanan; Rundell, Ann E; Klipp, Edda

2008-08-15

It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models. This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface. SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes.

Structure-based Design, Synthesis, Biochemical and Pharmacological Characterization of Novel Salvinorin A Analogues as Active State Probes of the κ-Opioid Receptor

PubMed Central

Yan, Feng; Bikbulatov, Ruslan V.; Mocanu, Viorel; Dicheva, Nedyalka; Parker, Carol E.; Wetsel, William C.; Mosier, Philip D.; Westkaemper, Richard B.; Allen, John A.; Zjawiony, Jordan K.; Roth, Bryan L.

2009-01-01

Salvinorin A, the most potent naturally occurring hallucinogen, has gained increasing attention since the κ-opioid receptor (KOR) was identified as its principal molecular target by us (Roth et al, PNAS, 2002). Here we report the design, synthesis and biochemical characterization of novel, irreversible, salvinorin A-derived ligands suitable as active state probes of the KOR. Based on prior substituted cysteine accessibility and molecular modeling studies, C3157.38 was chosen as a potential anchoring point for covalent labeling of salvinorin A-derived ligands. Automated docking of a series of potential covalently-bound ligands suggested that either a haloacetate moiety or other similar electrophilic groups could irreversibly bind with C3157.38. 22-thiocyanatosalvinorin A (RB-64) and 22-chlorosalvinorin A (RB-48) were both found to be extraordinarily potent and selective KOR agonists in vitro and in vivo. As predicted based on molecular modeling studies, RB-64 induced wash-resistant inhibition of binding with a strict requirement for a free cysteine in or near the binding pocket. Mass spectrometry (MS) studies utilizing synthetic KOR peptides and RB-64 supported the hypothesis that the anchoring residue was C3157.38 and suggested one biochemical mechanism for covalent binding. These studies provide direct evidence for the presence of a free cysteine in the agonist-bound state of KOR and provide novel insights into the mechanism by which salvinorin A binds to and activates KOR. PMID:19555087

Simulation studies in biochemical signaling and enzyme reactions

NASA Astrophysics Data System (ADS)

Nelatury, Sudarshan R.; Vagula, Mary C.

2014-06-01

Biochemical pathways characterize various biochemical reaction schemes that involve a set of species and the manner in which they are connected. Determination of schematics that represent these pathways is an important task in understanding metabolism and signal transduction. Examples of these Pathways are: DNA and protein synthesis, and production of several macro-molecules essential for cell survival. A sustained feedback mechanism arises in gene expression and production of mRNA that lead to protein synthesis if the protein so synthesized serves as a transcription factor and becomes a repressor of the gene expression. The cellular regulations are carried out through biochemical networks consisting of reactions and regulatory proteins. Systems biology is a relatively new area that attempts to describe the biochemical pathways analytically and develop reliable mathematical models for the pathways. A complete understanding of chemical reaction kinetics is prohibitively hard thanks to the nonlinear and highly complex mechanisms that regulate protein formation, but attempting to numerically solve some of the governing differential equations seems to offer significant insight about their biochemical picture. To validate these models, one can perform simple experiments in the lab. This paper introduces fundamental ideas in biochemical signaling and attempts to take first steps into the understanding of biochemical oscillations. Initially, the two-pool model of calcium is used to describe the dynamics behind the oscillations. Later we present some elementary results showing biochemical oscillations arising from solving differential equations of Elowitz and Leibler using MATLAB software.

Zero valent iron significantly enhances methane production from waste activated sludge by improving biochemical methane potential rather than hydrolysis rate.

PubMed

Liu, Yiwen; Wang, Qilin; Zhang, Yaobin; Ni, Bing-Jie

2015-02-05

Anaerobic digestion has been widely applied for waste activated sludge (WAS) treatment. However, methane production from anaerobic digestion of WAS is usually limited by the slow hydrolysis rate and/or poor biochemical methane potential of WAS. This work systematically studied the effects of three different types of zero valent iron (i.e., iron powder, clean scrap and rusty scrap) on methane production from WAS in anaerobic digestion, by using both experimental and mathematical approaches. The results demonstrated that both the clean and the rusty iron scrap were more effective than the iron powder for improving methane production from WAS. Model-based analysis showed that ZVI addition significantly enhanced methane production from WAS through improving the biochemical methane potential of WAS rather than its hydrolysis rate. Economic analysis indicated that the ZVI-based technology for enhancing methane production from WAS is economically attractive, particularly considering that iron scrap can be freely acquired from industrial waste. Based on these results, the ZVI-based anaerobic digestion process of this work could be easily integrated with the conventional chemical phosphorus removal process in wastewater treatment plant to form a cost-effective and environment-friendly approach, enabling maximum resource recovery/reuse while achieving enhanced methane production in wastewater treatment system.

Zero Valent Iron Significantly Enhances Methane Production from Waste Activated Sludge by Improving Biochemical Methane Potential Rather Than Hydrolysis Rate

PubMed Central

Liu, Yiwen; Wang, Qilin; Zhang, Yaobin; Ni, Bing-Jie

2015-01-01

Anaerobic digestion has been widely applied for waste activated sludge (WAS) treatment. However, methane production from anaerobic digestion of WAS is usually limited by the slow hydrolysis rate and/or poor biochemical methane potential of WAS. This work systematically studied the effects of three different types of zero valent iron (i.e., iron powder, clean scrap and rusty scrap) on methane production from WAS in anaerobic digestion, by using both experimental and mathematical approaches. The results demonstrated that both the clean and the rusty iron scrap were more effective than the iron powder for improving methane production from WAS. Model-based analysis showed that ZVI addition significantly enhanced methane production from WAS through improving the biochemical methane potential of WAS rather than its hydrolysis rate. Economic analysis indicated that the ZVI-based technology for enhancing methane production from WAS is economically attractive, particularly considering that iron scrap can be freely acquired from industrial waste. Based on these results, the ZVI-based anaerobic digestion process of this work could be easily integrated with the conventional chemical phosphorus removal process in wastewater treatment plant to form a cost-effective and environment-friendly approach, enabling maximum resource recovery/reuse while achieving enhanced methane production in wastewater treatment system. PMID:25652244

Zero Valent Iron Significantly Enhances Methane Production from Waste Activated Sludge by Improving Biochemical Methane Potential Rather Than Hydrolysis Rate

NASA Astrophysics Data System (ADS)

Liu, Yiwen; Wang, Qilin; Zhang, Yaobin; Ni, Bing-Jie

2015-02-01

Anaerobic digestion has been widely applied for waste activated sludge (WAS) treatment. However, methane production from anaerobic digestion of WAS is usually limited by the slow hydrolysis rate and/or poor biochemical methane potential of WAS. This work systematically studied the effects of three different types of zero valent iron (i.e., iron powder, clean scrap and rusty scrap) on methane production from WAS in anaerobic digestion, by using both experimental and mathematical approaches. The results demonstrated that both the clean and the rusty iron scrap were more effective than the iron powder for improving methane production from WAS. Model-based analysis showed that ZVI addition significantly enhanced methane production from WAS through improving the biochemical methane potential of WAS rather than its hydrolysis rate. Economic analysis indicated that the ZVI-based technology for enhancing methane production from WAS is economically attractive, particularly considering that iron scrap can be freely acquired from industrial waste. Based on these results, the ZVI-based anaerobic digestion process of this work could be easily integrated with the conventional chemical phosphorus removal process in wastewater treatment plant to form a cost-effective and environment-friendly approach, enabling maximum resource recovery/reuse while achieving enhanced methane production in wastewater treatment system.

Growth of Lactobacillus paracasei ATCC334 in a cheese model system: A biochemical approach

USDA-ARS?s Scientific Manuscript database

Growth of Lactobacillus paracasei ATCC 334, in a cheese-ripening model system based upon a medium prepared from ripening Cheddar cheese extract (CCE) was evaluated. Lactobacillus paracasei ATCC 334 grows in CCE made from cheese ripened for 2 (2mCCE), 6 (6mCCE), and 8 (8mCCE) mo, to final cell densit...

Run-length encoding graphic rules, biochemically editable designs and steganographical numeric data embedment for DNA-based cryptographical coding system.

PubMed

Kawano, Tomonori

2013-03-01

There have been a wide variety of approaches for handling the pieces of DNA as the "unplugged" tools for digital information storage and processing, including a series of studies applied to the security-related area, such as DNA-based digital barcodes, water marks and cryptography. In the present article, novel designs of artificial genes as the media for storing the digitally compressed data for images are proposed for bio-computing purpose while natural genes principally encode for proteins. Furthermore, the proposed system allows cryptographical application of DNA through biochemically editable designs with capacity for steganographical numeric data embedment. As a model case of image-coding DNA technique application, numerically and biochemically combined protocols are employed for ciphering the given "passwords" and/or secret numbers using DNA sequences. The "passwords" of interest were decomposed into single letters and translated into the font image coded on the separate DNA chains with both the coding regions in which the images are encoded based on the novel run-length encoding rule, and the non-coding regions designed for biochemical editing and the remodeling processes revealing the hidden orientation of letters composing the original "passwords." The latter processes require the molecular biological tools for digestion and ligation of the fragmented DNA molecules targeting at the polymerase chain reaction-engineered termini of the chains. Lastly, additional protocols for steganographical overwriting of the numeric data of interests over the image-coding DNA are also discussed.

Computational methods for diffusion-influenced biochemical reactions.

PubMed

Dobrzynski, Maciej; Rodríguez, Jordi Vidal; Kaandorp, Jaap A; Blom, Joke G

2007-08-01

We compare stochastic computational methods accounting for space and discrete nature of reactants in biochemical systems. Implementations based on Brownian dynamics (BD) and the reaction-diffusion master equation are applied to a simplified gene expression model and to a signal transduction pathway in Escherichia coli. In the regime where the number of molecules is small and reactions are diffusion-limited predicted fluctuations in the product number vary between the methods, while the average is the same. Computational approaches at the level of the reaction-diffusion master equation compute the same fluctuations as the reference result obtained from the particle-based method if the size of the sub-volumes is comparable to the diameter of reactants. Using numerical simulations of reversible binding of a pair of molecules we argue that the disagreement in predicted fluctuations is due to different modeling of inter-arrival times between reaction events. Simulations for a more complex biological study show that the different approaches lead to different results due to modeling issues. Finally, we present the physical assumptions behind the mesoscopic models for the reaction-diffusion systems. Input files for the simulations and the source code of GMP can be found under the following address: http://www.cwi.nl/projects/sic/bioinformatics2007/

Use of Physiologically Based Pharmacokinetic (PBPK) Models ...

EPA Pesticide Factsheets

EPA announced the availability of the final report, Use of Physiologically Based Pharmacokinetic (PBPK) Models to Quantify the Impact of Human Age and Interindividual Differences in Physiology and Biochemistry Pertinent to Risk Final Report for Cooperative Agreement. This report describes and demonstrates techniques necessary to extrapolate and incorporate in vitro derived metabolic rate constants in PBPK models. It also includes two case study examples designed to demonstrate the applicability of such data for health risk assessment and addresses the quantification, extrapolation and interpretation of advanced biochemical information on human interindividual variability of chemical metabolism for risk assessment application. It comprises five chapters; topics and results covered in the first four chapters have been published in the peer reviewed scientific literature. Topics covered include: Data Quality ObjectivesExperimental FrameworkRequired DataTwo example case studies that develop and incorporate in vitro metabolic rate constants in PBPK models designed to quantify human interindividual variability to better direct the choice of uncertainty factors for health risk assessment. This report is intended to serve as a reference document for risk assors to use when quantifying, extrapolating, and interpretating advanced biochemical information about human interindividual variability of chemical metabolism.

Predicting perturbation patterns from the topology of biological networks.

PubMed

Santolini, Marc; Barabási, Albert-László

2018-06-20

High-throughput technologies, offering an unprecedented wealth of quantitative data underlying the makeup of living systems, are changing biology. Notably, the systematic mapping of the relationships between biochemical entities has fueled the rapid development of network biology, offering a suitable framework to describe disease phenotypes and predict potential drug targets. However, our ability to develop accurate dynamical models remains limited, due in part to the limited knowledge of the kinetic parameters underlying these interactions. Here, we explore the degree to which we can make reasonably accurate predictions in the absence of the kinetic parameters. We find that simple dynamically agnostic models are sufficient to recover the strength and sign of the biochemical perturbation patterns observed in 87 biological models for which the underlying kinetics are known. Surprisingly, a simple distance-based model achieves 65% accuracy. We show that this predictive power is robust to topological and kinetic parameter perturbations, and we identify key network properties that can increase up to 80% the recovery rate of the true perturbation patterns. We validate our approach using experimental data on the chemotactic pathway in bacteria, finding that a network model of perturbation spreading predicts with ∼80% accuracy the directionality of gene expression and phenotype changes in knock-out and overproduction experiments. These findings show that the steady advances in mapping out the topology of biochemical interaction networks opens avenues for accurate perturbation spread modeling, with direct implications for medicine and drug development.

Mitochondrial Based Treatments that Prevent Posttraumatic Osteoarthritis in a Translational Large Animal Intraarticular Fracture Survival Model

DTIC Science & Technology

2012-09-01

enhancing treatments in combination with antioxidants . Seven day biochemical assays demonstrated that proline...with semi- rigid fixation (middle); and after a 2.0 mm stepoff with rigid fixation. The fracture healed the reduced cases. Very little

A Systems Biology Approach to Toxicology Research with Small Fish Models

EPA Science Inventory

Increasing use of mechanistically-based molecular and biochemical endpoints and in vitro assays is being advocated as a more efficient and cost-effective approach for generating chemical hazard data. However, development of effective assays and application of the resulting data i...

There is more than one way to model an elephant. Experiment-driven modeling of the actin cytoskeleton.

PubMed

Ditlev, Jonathon A; Mayer, Bruce J; Loew, Leslie M

2013-02-05

Mathematical modeling has established its value for investigating the interplay of biochemical and mechanical mechanisms underlying actin-based motility. Because of the complex nature of actin dynamics and its regulation, many of these models are phenomenological or conceptual, providing a general understanding of the physics at play. But the wealth of carefully measured kinetic data on the interactions of many of the players in actin biochemistry cries out for the creation of more detailed and accurate models that could permit investigators to dissect interdependent roles of individual molecular components. Moreover, no human mind can assimilate all of the mechanisms underlying complex protein networks; so an additional benefit of a detailed kinetic model is that the numerous binding proteins, signaling mechanisms, and biochemical reactions can be computationally organized in a fully explicit, accessible, visualizable, and reusable structure. In this review, we will focus on how comprehensive and adaptable modeling allows investigators to explain experimental observations and develop testable hypotheses on the intracellular dynamics of the actin cytoskeleton. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

There is More Than One Way to Model an Elephant. Experiment-Driven Modeling of the Actin Cytoskeleton

PubMed Central

Ditlev, Jonathon A.; Mayer, Bruce J.; Loew, Leslie M.

2013-01-01

Mathematical modeling has established its value for investigating the interplay of biochemical and mechanical mechanisms underlying actin-based motility. Because of the complex nature of actin dynamics and its regulation, many of these models are phenomenological or conceptual, providing a general understanding of the physics at play. But the wealth of carefully measured kinetic data on the interactions of many of the players in actin biochemistry cries out for the creation of more detailed and accurate models that could permit investigators to dissect interdependent roles of individual molecular components. Moreover, no human mind can assimilate all of the mechanisms underlying complex protein networks; so an additional benefit of a detailed kinetic model is that the numerous binding proteins, signaling mechanisms, and biochemical reactions can be computationally organized in a fully explicit, accessible, visualizable, and reusable structure. In this review, we will focus on how comprehensive and adaptable modeling allows investigators to explain experimental observations and develop testable hypotheses on the intracellular dynamics of the actin cytoskeleton. PMID:23442903

A MULTILAYER BIOCHEMICAL DRY DEPOSITION MODEL 2. MODEL EVALUATION

EPA Science Inventory

The multilayer biochemical dry deposition model (MLBC) described in the accompanying paper was tested against half-hourly eddy correlation data from six field sites under a wide range of climate conditions with various plant types. Modeled CO₂, O₃, SO_2<...

A BOD monitoring disposable reactor with alginate-entrapped bacteria.

PubMed

Villalobos, Patricio; Acevedo, Cristian A; Albornoz, Fernando; Sánchez, Elizabeth; Valdés, Erika; Galindo, Raúl; Young, Manuel E

2010-10-01

Biochemical oxygen demand (BOD) is a measure of the amount of dissolved oxygen that is required for the biochemical oxidation of the organic compounds in 5 days. New biosensor-based methods have been conducted for a faster determination of BOD. In this study, a mathematical model to evaluate the feasibility of using a BOD sensor, based on disposable alginate-entrapped bacteria, for monitoring BOD in situ was applied. The model considers the influences of alginate bead size and bacterial concentration. The disposable biosensor can be adapted according to specific requirements depending on the organic load contained in the wastewater. Using Klein and Washausen parameter in a Lineweaver-Burk plot, the glucose diffusivity was calculated in 6.4 × 10(-10) (m2/s) for beads of 1 mm in diameter and slight diffusion restrictions were observed (n = 0.85). Experimental results showed a correlation (p < 0.05) between the respirometric peak and the standard BOD test. The biosensor response was representative of BOD.

Conditions for duality between fluxes and concentrations in biochemical networks

PubMed Central

Fleming, Ronan M.T.; Vlassis, Nikos; Thiele, Ines; Saunders, Michael A.

2016-01-01

Mathematical and computational modelling of biochemical networks is often done in terms of either the concentrations of molecular species or the fluxes of biochemical reactions. When is mathematical modelling from either perspective equivalent to the other? Mathematical duality translates concepts, theorems or mathematical structures into other concepts, theorems or structures, in a one-to-one manner. We present a novel stoichiometric condition that is necessary and sufficient for duality between unidirectional fluxes and concentrations. Our numerical experiments, with computational models derived from a range of genome-scale biochemical networks, suggest that this flux-concentration duality is a pervasive property of biochemical networks. We also provide a combinatorial characterisation that is sufficient to ensure flux-concentration duality. The condition prescribes that, for every two disjoint sets of molecular species, there is at least one reaction complex that involves species from only one of the two sets. When unidirectional fluxes and molecular species concentrations are dual vectors, this implies that the behaviour of the corresponding biochemical network can be described entirely in terms of either concentrations or unidirectional fluxes. PMID:27345817

Conditions for duality between fluxes and concentrations in biochemical networks

DOE PAGES

Fleming, Ronan M. T.; Vlassis, Nikos; Thiele, Ines; ...

2016-06-23

Mathematical and computational modelling of biochemical networks is often done in terms of either the concentrations of molecular species or the fluxes of biochemical reactions. When is mathematical modelling from either perspective equivalent to the other? Mathematical duality translates concepts, theorems or mathematical structures into other concepts, theorems or structures, in a one-to-one manner. We present a novel stoichiometric condition that is necessary and sufficient for duality between unidirectional fluxes and concentrations. Our numerical experiments, with computational models derived from a range of genome-scale biochemical networks, suggest that this flux-concentration duality is a pervasive property of biochemical networks. We alsomore » provide a combinatorial characterisation that is sufficient to ensure flux-concentration duality.The condition prescribes that, for every two disjoint sets of molecular species, there is at least one reaction complex that involves species from only one of the two sets. When unidirectional fluxes and molecular species concentrations are dual vectors, this implies that the behaviour of the corresponding biochemical network can be described entirely in terms of either concentrations or unidirectional fluxes« less

Conditions for duality between fluxes and concentrations in biochemical networks

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

Fleming, Ronan M. T.; Vlassis, Nikos; Thiele, Ines

Mathematical and computational modelling of biochemical networks is often done in terms of either the concentrations of molecular species or the fluxes of biochemical reactions. When is mathematical modelling from either perspective equivalent to the other? Mathematical duality translates concepts, theorems or mathematical structures into other concepts, theorems or structures, in a one-to-one manner. We present a novel stoichiometric condition that is necessary and sufficient for duality between unidirectional fluxes and concentrations. Our numerical experiments, with computational models derived from a range of genome-scale biochemical networks, suggest that this flux-concentration duality is a pervasive property of biochemical networks. We alsomore » provide a combinatorial characterisation that is sufficient to ensure flux-concentration duality.The condition prescribes that, for every two disjoint sets of molecular species, there is at least one reaction complex that involves species from only one of the two sets. When unidirectional fluxes and molecular species concentrations are dual vectors, this implies that the behaviour of the corresponding biochemical network can be described entirely in terms of either concentrations or unidirectional fluxes« less

Optimal Cytoplasmic Transport in Viral Infections

PubMed Central

D'Orsogna, Maria R.; Chou, Tom

2009-01-01

For many viruses, the ability to infect eukaryotic cells depends on their transport through the cytoplasm and across the nuclear membrane of the host cell. During this journey, viral contents are biochemically processed into complexes capable of both nuclear penetration and genomic integration. We develop a stochastic model of viral entry that incorporates all relevant aspects of transport, including convection along microtubules, biochemical conversion, degradation, and nuclear entry. Analysis of the nuclear infection probabilities in terms of the transport velocity, degradation, and biochemical conversion rates shows how certain values of key parameters can maximize the nuclear entry probability of the viral material. The existence of such “optimal” infection scenarios depends on the details of the biochemical conversion process and implies potentially counterintuitive effects in viral infection, suggesting new avenues for antiviral treatment. Such optimal parameter values provide a plausible transport-based explanation of the action of restriction factors and of experimentally observed optimal capsid stability. Finally, we propose a new interpretation of how genetic mutations unrelated to the mechanism of drug action may nonetheless confer novel types of overall drug resistance. PMID:20046829

Numerical modeling of gas mixing and bio-chemical transformations during underground hydrogen storage within the project H2STORE

NASA Astrophysics Data System (ADS)

Hagemann, B.; Feldmann, F.; Panfilov, M.; Ganzer, L.

2015-12-01

The change from fossil to renewable energy sources is demanding an increasing amount of storage capacities for electrical energy. A promising technological solution is the storage of hydrogen in the subsurface. Hydrogen can be produced by electrolysis using excessive electrical energy and subsequently converted back into electricity by fuel cells or engine generators. The development of this technology starts with adding small amounts of hydrogen to the high pressure natural gas grid and continues with the creation of pure underground hydrogen storages. The feasibility of hydrogen storage in depleted gas reservoirs is investigated in the lighthouse project H2STORE financed by the German Ministry for Education and Research. The joint research project has project members from the University of Jena, the Clausthal University of Technology, the GFZ Potsdam and the French National Center for Scientic Research in Nancy. The six sub projects are based on laboratory experiments, numerical simulations and analytical work which cover the investigation of mineralogical, geochemical, physio-chemical, sedimentological, microbiological and gas mixing processes in reservoir and cap rocks. The focus in this presentation is on the numerical modeling of underground hydrogen storage. A mathematical model was developed which describes the involved coupled hydrodynamic and microbiological effects. Thereby, the bio-chemical reaction rates depend on the kinetics of microbial growth which is induced by the injection of hydrogen. The model has been numerically implemented on the basis of the open source code DuMuX. A field case study based on a real German gas reservoir was performed to investigate the mixing of hydrogen with residual gases and to discover the consequences of bio-chemical reactions.

Effects of macromolecular crowding on biochemical reaction equilibria: a molecular thermodynamic perspective.

PubMed

Hu, Zhongqiao; Jiang, Jianwen; Rajagopalan, Raj

2007-09-01

A molecular thermodynamic model is developed to investigate the effects of macromolecular crowding on biochemical reactions. Three types of reactions, representing protein folding/conformational isomerization, coagulation/coalescence, and polymerization/association, are considered. The reactants, products, and crowders are modeled as coarse-grained spherical particles or as polymer chains, interacting through hard-sphere interactions with or without nonbonded square-well interactions, and the effects of crowder size and chain length as well as product size are examined. The results predicted by this model are consistent with experimentally observed crowding effects based on preferential binding or preferential exclusion of the crowders. Although simple hard-core excluded-volume arguments do in general predict the qualitative aspects of the crowding effects, the results show that other intermolecular interactions can substantially alter the extent of enhancement or reduction of the equilibrium and can even change the direction of the shift. An advantage of the approach presented here is that competing reactions can be incorporated within the model.

BNDB - the Biochemical Network Database.

PubMed

Küntzer, Jan; Backes, Christina; Blum, Torsten; Gerasch, Andreas; Kaufmann, Michael; Kohlbacher, Oliver; Lenhof, Hans-Peter

2007-10-02

Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a massive amount of life science data. The data is stored in numerous databases that have been established over the last decades and are essential resources for scientists nowadays. However, the diversity of the databases and the underlying data models make it difficult to combine this information for solving complex problems in systems biology. Currently, researchers typically have to browse several, often highly focused, databases to obtain the required information. Hence, there is a pressing need for more efficient systems for integrating, analyzing, and interpreting these data. The standardization and virtual consolidation of the databases is a major challenge resulting in a unified access to a variety of data sources. We present the Biochemical Network Database (BNDB), a powerful relational database platform, allowing a complete semantic integration of an extensive collection of external databases. BNDB is built upon a comprehensive and extensible object model called BioCore, which is powerful enough to model most known biochemical processes and at the same time easily extensible to be adapted to new biological concepts. Besides a web interface for the search and curation of the data, a Java-based viewer (BiNA) provides a powerful platform-independent visualization and navigation of the data. BiNA uses sophisticated graph layout algorithms for an interactive visualization and navigation of BNDB. BNDB allows a simple, unified access to a variety of external data sources. Its tight integration with the biochemical network library BN++ offers the possibility for import, integration, analysis, and visualization of the data. BNDB is freely accessible at http://www.bndb.org.

Untargeted Metabolomic Analysis of Rat Neuroblastoma Cells as a Model System to Study the Biochemical Effects of the Acute Administration of Methamphetamine.

PubMed

Maker, Garth L; Green, Tobias; Mullaney, Ian; Trengove, Robert D

2018-06-07

Methamphetamine is an illicit psychostimulant drug that is linked to a number of diseases of the nervous system. The downstream biochemical effects of its primary mechanisms are not well understood, and the objective of this study was to investigate whether untargeted metabolomic analysis of an in vitro model could generate data relevant to what is already known about this drug. Rat B50 neuroblastoma cells were treated with 1 mM methamphetamine for 48 h, and both intracellular and extracellular metabolites were profiled using gas chromatography⁻mass spectrometry. Principal component analysis of the data identified 35 metabolites that contributed most to the difference in metabolite profiles. Of these metabolites, the most notable changes were in amino acids, with significant increases observed in glutamate, aspartate and methionine, and decreases in phenylalanine and serine. The data demonstrated that glutamate release and, subsequently, excitotoxicity and oxidative stress were important in the response of the neuronal cell to methamphetamine. Following this, the cells appeared to engage amino acid-based mechanisms to reduce glutamate levels. The potential of untargeted metabolomic analysis has been highlighted, as it has generated biochemically relevant data and identified pathways significantly affected by methamphetamine. This combination of technologies has clear uses as a model for the study of neuronal toxicology.

COMPARISON OF PBPK MODELING SOFTWARE FEATURES AND APPROACHES TO MODELING IMPORTNAT PHYSIOLOGICAL AND BIOCHEMICAL BEHAVIORS

EPA Science Inventory

Abstract for 40th Annual Meeting of the Society of Toxicology, March 25-29, 2001

COMPARISON OF PBPK MODELING SOFTWARE FEATURES AND APPROACHES TO MODELING IMPORTANT PHYSIOLOGICAL AND BIOCHEMICAL BEHAVIORS. R S DeWoskin and R W Setzer. USEPA/ORD/NHEERL, RTP, NC, USA.

...

Clinical characteristics of people experiencing biochemical hypoglycaemia during an oral glucose tolerance test: cross-sectional analyses from a UK multi-ethnic population.

PubMed

Parekh, S; Bodicoat, D H; Brady, E; Webb, D; Mani, H; Mostafa, S; Levy, M J; Khunti, K; Davies, M J

2014-06-01

People who experience biochemical hypoglycaemia during an oral glucose tolerance test (OGTT) may be insulin resistant, but this has not been investigated robustly, therefore we examined this in a population-based multi-ethnic UK study. Cross-sectional data from 6478 diabetes-free participants (849 with fasting insulin data available) who had an OGTT in the ADDITION-Leicester screening study (2005-2009) were analysed. People with biochemical hypoglycaemia (2-h glucose <3.3mmol/l) were compared with people with normal glucose tolerance (NGT) or impaired glucose regulation (IGR) using regression methods. 359 participants (5.5%) had biochemical hypoglycaemia, 1079 (16.7%) IGR and 5040 (77.8%) NGT. Biochemical hypoglycaemia was associated with younger age (P<0.01), white European ethnicity (P<0.001), higher HDL cholesterol (P<0.01), higher insulin sensitivity (P<0.05), and lower body mass index (P<0.001), blood pressure (P<0.01), fasting glucose (P<0.001), HbA1C (P<0.01), and triglycerides (P<0.01) compared with NGT and IGR separately in both unadjusted and adjusted (age, sex, ethnicity, body mass index, smoking status) models. Biochemical hypoglycaemia during an OGTT in the absence of diabetes or IGR was not associated with insulin resistance, but instead appeared to be associated with more favourable glycaemic risk profiles than IGR and NGT. Thus, clinicians may not need to intervene due to biochemical hypoglycaemia on a 2-h OGTT. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks.

PubMed

Rumschinski, Philipp; Borchers, Steffen; Bosio, Sandro; Weismantel, Robert; Findeisen, Rolf

2010-05-25

Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand. In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort. The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.

Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks

PubMed Central

2010-01-01

Background Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand. Results In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort. Conclusions The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates. PMID:20500862

Barnacle cement: a polymerization model based on evolutionary concepts

PubMed Central

Dickinson, Gary H.; Vega, Irving E.; Wahl, Kathryn J.; Orihuela, Beatriz; Beyley, Veronica; Rodriguez, Eva N.; Everett, Richard K.; Bonaventura, Joseph; Rittschof, Daniel

2009-01-01

Summary Enzymes and biochemical mechanisms essential to survival are under extreme selective pressure and are highly conserved through evolutionary time. We applied this evolutionary concept to barnacle cement polymerization, a process critical to barnacle fitness that involves aggregation and cross-linking of proteins. The biochemical mechanisms of cement polymerization remain largely unknown. We hypothesized that this process is biochemically similar to blood clotting, a critical physiological response that is also based on aggregation and cross-linking of proteins. Like key elements of vertebrate and invertebrate blood clotting, barnacle cement polymerization was shown to involve proteolytic activation of enzymes and structural precursors, transglutaminase cross-linking and assembly of fibrous proteins. Proteolytic activation of structural proteins maximizes the potential for bonding interactions with other proteins and with the surface. Transglutaminase cross-linking reinforces cement integrity. Remarkably, epitopes and sequences homologous to bovine trypsin and human transglutaminase were identified in barnacle cement with tandem mass spectrometry and/or western blotting. Akin to blood clotting, the peptides generated during proteolytic activation functioned as signal molecules, linking a molecular level event (protein aggregation) to a behavioral response (barnacle larval settlement). Our results draw attention to a highly conserved protein polymerization mechanism and shed light on a long-standing biochemical puzzle. We suggest that barnacle cement polymerization is a specialized form of wound healing. The polymerization mechanism common between barnacle cement and blood may be a theme for many marine animal glues. PMID:19837892

STEPS: efficient simulation of stochastic reaction-diffusion models in realistic morphologies.

PubMed

Hepburn, Iain; Chen, Weiliang; Wils, Stefan; De Schutter, Erik

2012-05-10

Models of cellular molecular systems are built from components such as biochemical reactions (including interactions between ligands and membrane-bound proteins), conformational changes and active and passive transport. A discrete, stochastic description of the kinetics is often essential to capture the behavior of the system accurately. Where spatial effects play a prominent role the complex morphology of cells may have to be represented, along with aspects such as chemical localization and diffusion. This high level of detail makes efficiency a particularly important consideration for software that is designed to simulate such systems. We describe STEPS, a stochastic reaction-diffusion simulator developed with an emphasis on simulating biochemical signaling pathways accurately and efficiently. STEPS supports all the above-mentioned features, and well-validated support for SBML allows many existing biochemical models to be imported reliably. Complex boundaries can be represented accurately in externally generated 3D tetrahedral meshes imported by STEPS. The powerful Python interface facilitates model construction and simulation control. STEPS implements the composition and rejection method, a variation of the Gillespie SSA, supporting diffusion between tetrahedral elements within an efficient search and update engine. Additional support for well-mixed conditions and for deterministic model solution is implemented. Solver accuracy is confirmed with an original and extensive validation set consisting of isolated reaction, diffusion and reaction-diffusion systems. Accuracy imposes upper and lower limits on tetrahedron sizes, which are described in detail. By comparing to Smoldyn, we show how the voxel-based approach in STEPS is often faster than particle-based methods, with increasing advantage in larger systems, and by comparing to MesoRD we show the efficiency of the STEPS implementation. STEPS simulates models of cellular reaction-diffusion systems with complex boundaries with high accuracy and high performance in C/C++, controlled by a powerful and user-friendly Python interface. STEPS is free for use and is available at http://steps.sourceforge.net/

STEPS: efficient simulation of stochastic reaction–diffusion models in realistic morphologies

PubMed Central

2012-01-01

Background Models of cellular molecular systems are built from components such as biochemical reactions (including interactions between ligands and membrane-bound proteins), conformational changes and active and passive transport. A discrete, stochastic description of the kinetics is often essential to capture the behavior of the system accurately. Where spatial effects play a prominent role the complex morphology of cells may have to be represented, along with aspects such as chemical localization and diffusion. This high level of detail makes efficiency a particularly important consideration for software that is designed to simulate such systems. Results We describe STEPS, a stochastic reaction–diffusion simulator developed with an emphasis on simulating biochemical signaling pathways accurately and efficiently. STEPS supports all the above-mentioned features, and well-validated support for SBML allows many existing biochemical models to be imported reliably. Complex boundaries can be represented accurately in externally generated 3D tetrahedral meshes imported by STEPS. The powerful Python interface facilitates model construction and simulation control. STEPS implements the composition and rejection method, a variation of the Gillespie SSA, supporting diffusion between tetrahedral elements within an efficient search and update engine. Additional support for well-mixed conditions and for deterministic model solution is implemented. Solver accuracy is confirmed with an original and extensive validation set consisting of isolated reaction, diffusion and reaction–diffusion systems. Accuracy imposes upper and lower limits on tetrahedron sizes, which are described in detail. By comparing to Smoldyn, we show how the voxel-based approach in STEPS is often faster than particle-based methods, with increasing advantage in larger systems, and by comparing to MesoRD we show the efficiency of the STEPS implementation. Conclusion STEPS simulates models of cellular reaction–diffusion systems with complex boundaries with high accuracy and high performance in C/C++, controlled by a powerful and user-friendly Python interface. STEPS is free for use and is available at http://steps.sourceforge.net/ PMID:22574658

Stochastic hybrid systems for studying biochemical processes.

PubMed

Singh, Abhyudai; Hespanha, João P

2010-11-13

Many protein and mRNA species occur at low molecular counts within cells, and hence are subject to large stochastic fluctuations in copy numbers over time. Development of computationally tractable frameworks for modelling stochastic fluctuations in population counts is essential to understand how noise at the cellular level affects biological function and phenotype. We show that stochastic hybrid systems (SHSs) provide a convenient framework for modelling the time evolution of population counts of different chemical species involved in a set of biochemical reactions. We illustrate recently developed techniques that allow fast computations of the statistical moments of the population count, without having to run computationally expensive Monte Carlo simulations of the biochemical reactions. Finally, we review different examples from the literature that illustrate the benefits of using SHSs for modelling biochemical processes.

Centrifugal multiplexing fixed-volume dispenser on a plastic lab-on-a-disk for parallel biochemical single-end-point assays

PubMed Central

La, Moonwoo; Park, Sang Min; Kim, Dong Sung

2015-01-01

In this study, a multiple sample dispenser for precisely metered fixed volumes was successfully designed, fabricated, and fully characterized on a plastic centrifugal lab-on-a-disk (LOD) for parallel biochemical single-end-point assays. The dispenser, namely, a centrifugal multiplexing fixed-volume dispenser (C-MUFID) was designed with microfluidic structures based on the theoretical modeling about a centrifugal circumferential filling flow. The designed LODs were fabricated with a polystyrene substrate through micromachining and they were thermally bonded with a flat substrate. Furthermore, six parallel metering and dispensing assays were conducted at the same fixed-volume (1.27 μl) with a relative variation of ±0.02 μl. Moreover, the samples were metered and dispensed at different sub-volumes. To visualize the metering and dispensing performances, the C-MUFID was integrated with a serpentine micromixer during parallel centrifugal mixing tests. Parallel biochemical single-end-point assays were successfully conducted on the developed LOD using a standard serum with albumin, glucose, and total protein reagents. The developed LOD could be widely applied to various biochemical single-end-point assays which require different volume ratios of the sample and reagent by controlling the design of the C-MUFID. The proposed LOD is feasible for point-of-care diagnostics because of its mass-producible structures, reliable metering/dispensing performance, and parallel biochemical single-end-point assays, which can identify numerous biochemical. PMID:25610516

Run-length encoding graphic rules, biochemically editable designs and steganographical numeric data embedment for DNA-based cryptographical coding system

PubMed Central

Kawano, Tomonori

2013-01-01

There have been a wide variety of approaches for handling the pieces of DNA as the “unplugged” tools for digital information storage and processing, including a series of studies applied to the security-related area, such as DNA-based digital barcodes, water marks and cryptography. In the present article, novel designs of artificial genes as the media for storing the digitally compressed data for images are proposed for bio-computing purpose while natural genes principally encode for proteins. Furthermore, the proposed system allows cryptographical application of DNA through biochemically editable designs with capacity for steganographical numeric data embedment. As a model case of image-coding DNA technique application, numerically and biochemically combined protocols are employed for ciphering the given “passwords” and/or secret numbers using DNA sequences. The “passwords” of interest were decomposed into single letters and translated into the font image coded on the separate DNA chains with both the coding regions in which the images are encoded based on the novel run-length encoding rule, and the non-coding regions designed for biochemical editing and the remodeling processes revealing the hidden orientation of letters composing the original “passwords.” The latter processes require the molecular biological tools for digestion and ligation of the fragmented DNA molecules targeting at the polymerase chain reaction-engineered termini of the chains. Lastly, additional protocols for steganographical overwriting of the numeric data of interests over the image-coding DNA are also discussed. PMID:23750303

Bio-Spectroscopic Imaging Provides Evidence of Hippocampal Zn Deficiency and Decreased Lipid Unsaturation in an Accelerated Ageing Mouse Model.

PubMed

Fimognari, Nicholas; Hollings, Ashley; Lam, Virginie; Tidy, Rebecca J; Kewish, Cameron M; Albrecht, Matthew A; Takechi, Ryu; Mamo, John C L; Hackett, Mark J

2018-06-14

Western society is facing a health epidemic due to the increasing incidence of dementia in ageing populations, and there are still few effective diagnostic methods, minimal treatment options, and no cure. Ageing is the greatest risk factor for memory loss that occurs during the natural ageing process, as well as being the greatest risk factor for neurodegenerative disease such as Alzheimer's disease. Therefore, greater understanding of the biochemical pathways that drive a healthy ageing brain towards dementia (pathological ageing or Alzheimer's disease), is required to accelerate the development of improved diagnostics and therapies. Unfortunately, many animal models of dementia model chronic amyloid precursor protein over-expression, which although highly relevant to mechanisms of amyloidosis and familial Alzheimer's disease, does not model well dementia during the natural ageing process. A promising animal model reported to model mechanisms of accelerated natural ageing and memory impairments, is the senescence accelerated murine prone strain 8 (SAMP8), which has been adopted by many research group to study the biochemical transitions that occur during brain ageing. A limitation to traditional methods of biochemical characterisation is that many important biochemical and elemental markers (lipid saturation, lactate, transition metals) cannot be imaged at meso- or micro-spatial resolution. Therefore, in this investigation we report the first multi-modal biospectroscopic characterisation of the SAMP8 model, and have identified important biochemical and elemental alterations, and co-localisations, between 4 month old SAMP8 mice and the relevant control (SAMR1) mice. Specifically, we demonstrate direct evidence of altered metabolism and disturbed lipid homeostasis within corpus callosum white matter, in addition to localised hippocampal metal deficiencies, in the accelerated ageing phenotype. Such findings have important implication for future research aimed at elucidating specific biochemical pathways for therapeutic intervention.

Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

PubMed Central

Morris, Melody K.; Saez-Rodriguez, Julio; Clarke, David C.; Sorger, Peter K.; Lauffenburger, Douglas A.

2011-01-01

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone. PMID:21408212

Two Different Template Replicators Coexisting in the Same Protocell: Stochastic Simulation of an Extended Chemoton Model

PubMed Central

Zachar, István; Fedor, Anna; Szathmáry, Eörs

2011-01-01

The simulation of complex biochemical systems, consisting of intertwined subsystems, is a challenging task in computational biology. The complex biochemical organization of the cell is effectively modeled by the minimal cell model called chemoton, proposed by Gánti. Since the chemoton is a system consisting of a large but fixed number of interacting molecular species, it can effectively be implemented in a process algebra-based language such as the BlenX programming language. The stochastic model behaves comparably to previous continuous deterministic models of the chemoton. Additionally to the well-known chemoton, we also implemented an extended version with two competing template cycles. The new insight from our study is that the coupling of reactions in the chemoton ensures that these templates coexist providing an alternative solution to Eigen's paradox. Our technical innovation involves the introduction of a two-state switch to control cell growth and division, thus providing an example for hybrid methods in BlenX. Further developments to the BlenX language are suggested in the Appendix. PMID:21818258

Two different template replicators coexisting in the same protocell: stochastic simulation of an extended chemoton model.

PubMed

Zachar, István; Fedor, Anna; Szathmáry, Eörs

2011-01-01

The simulation of complex biochemical systems, consisting of intertwined subsystems, is a challenging task in computational biology. The complex biochemical organization of the cell is effectively modeled by the minimal cell model called chemoton, proposed by Gánti. Since the chemoton is a system consisting of a large but fixed number of interacting molecular species, it can effectively be implemented in a process algebra-based language such as the BlenX programming language. The stochastic model behaves comparably to previous continuous deterministic models of the chemoton. Additionally to the well-known chemoton, we also implemented an extended version with two competing template cycles. The new insight from our study is that the coupling of reactions in the chemoton ensures that these templates coexist providing an alternative solution to Eigen's paradox. Our technical innovation involves the introduction of a two-state switch to control cell growth and division, thus providing an example for hybrid methods in BlenX. Further developments to the BlenX language are suggested in the Appendix.

Approaching mathematical model of the immune network based DNA Strand Displacement system.

PubMed

Mardian, Rizki; Sekiyama, Kosuke; Fukuda, Toshio

2013-12-01

One biggest obstacle in molecular programming is that there is still no direct method to compile any existed mathematical model into biochemical reaction in order to solve a computational problem. In this paper, the implementation of DNA Strand Displacement system based on nature-inspired computation is observed. By using the Immune Network Theory and Chemical Reaction Network, the compilation of DNA-based operation is defined and the formulation of its mathematical model is derived. Furthermore, the implementation on this system is compared with the conventional implementation by using silicon-based programming. From the obtained results, we can see a positive correlation between both. One possible application from this DNA-based model is for a decision making scheme of intelligent computer or molecular robot. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A Biophysical Model for the Staircase Geometry of Stereocilia

PubMed Central

Orly, Gilad; Manor, Uri; Gov, Nir S.

2015-01-01

Cochlear hair cell bundles, made up of 10s to 100s of individual stereocilia, are essential for hearing, and even relatively minor structural changes, due to mutations or injuries, can result in total deafness. Consistent with its specialized role, the staircase geometry (SCG) of hair cell bundles presents one of the most striking, intricate, and precise organizations of actin-based cellular shapes. Composed of rows of actin-filled stereocilia with increasing lengths, the hair cell’s staircase-shaped bundle is formed from a progenitor field of smaller, thinner, and uniformly spaced microvilli with relatively invariant lengths. While recent genetic studies have provided a significant increase in information on the multitude of stereocilia protein components, there is currently no model that integrates the basic physical forces and biochemical processes necessary to explain the emergence of the SCG. We propose such a model derived from the biophysical and biochemical characteristics of actin-based protrusions. We demonstrate that polarization of the cell’s apical surface, due to the lateral polarization of the entire epithelial layer, plays a key role in promoting SCG formation. Furthermore, our model explains many distinct features of the manifestations of SCG in different species and in the presence of various deafness-associated mutations. PMID:26207893

BioCluster: tool for identification and clustering of Enterobacteriaceae based on biochemical data.

PubMed

Abdullah, Ahmed; Sabbir Alam, S M; Sultana, Munawar; Hossain, M Anwar

2015-06-01

Presumptive identification of different Enterobacteriaceae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochemical property of the unknown sample with known reference samples and inference of its identity based on the maximum similarity pattern with the known samples. This process is labor-intensive, time-consuming, error-prone, and subjective. Therefore, automation of sorting and similarity in calculation would be advantageous. Here we present a MATLAB-based graphical user interface (GUI) tool named BioCluster. This tool was designed for automated clustering and identification of Enterobacteriaceae based on biochemical test results. In this tool, we used two types of algorithms, i.e., traditional hierarchical clustering (HC) and the Improved Hierarchical Clustering (IHC), a modified algorithm that was developed specifically for the clustering and identification of Enterobacteriaceae species. IHC takes into account the variability in result of 1-47 biochemical tests within this Enterobacteriaceae family. This tool also provides different options to optimize the clustering in a user-friendly way. Using computer-generated synthetic data and some real data, we have demonstrated that BioCluster has high accuracy in clustering and identifying enterobacterial species based on biochemical test data. This tool can be freely downloaded at http://microbialgen.du.ac.bd/biocluster/. Copyright © 2015 The Authors. Production and hosting by Elsevier Ltd.. All rights reserved.

Possible incorporation of petroleum-based carbons in biochemicals produced by bioprocess--biomass carbon ratio measured by accelerator mass spectrometry.

PubMed

Kunioka, Masao

2010-06-01

The biomass carbon ratios of biochemicals related to biomass have been reviewed. Commercial products from biomass were explained. The biomass carbon ratios of biochemical compounds were measured by accelerator mass spectrometry (AMS) based on the (14)C concentration of carbons in the compounds. This measuring method uses the mechanism that biomass carbons include a very low level of (14)C and petroleum carbons do not include (14)C similar to the carbon dating measuring method. It was confirmed that there were some biochemicals synthesized from petroleum-based carbons. This AMS method has a high accuracy with a small standard deviation and can be applied to plastic products.

RuleMonkey: software for stochastic simulation of rule-based models

PubMed Central

2010-01-01

Background The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems. Results Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods. Conclusions RuleMonkey enables the simulation of rule-based models for which the underlying reaction networks are large. It is typically faster than DYNSTOC for benchmark problems that we have examined. RuleMonkey is freely available as a stand-alone application http://public.tgen.org/rulemonkey. It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models. PMID:20673321

Evaluation of Hepatoprotective Effect of Curcumin on Liver Cirrhosis Using a Combination of Biochemical Analysis and Magnetic Resonance-Based Electrical Conductivity Imaging

PubMed Central

Kyung, Eun Jung; Kim, Hyun Bum; Hwang, Eun Sang; Lee, Seok; Choi, Bup Kyung; Lim, Sang Moo; Kwon, Oh In

2018-01-01

In oriental medicine, curcumin is used to treat inflammatory diseases, and its anti-inflammatory effect has been reported in recent research. In this feasibility study, the hepatoprotective effect of curcumin was investigated using a rat liver cirrhosis model, which was induced with dimethylnitrosamine (DMN). Together with biochemical analysis, we used a magnetic resonance-based electrical conductivity imaging method to evaluate tissue conditions associated with a protective effect. The effects of curcumin treatment and lactulose treatment on liver cirrhosis were compared. Electrical conductivity images indicated that liver tissues damaged by DMN showed decreased conductivity compared with normal liver tissues. In contrast, cirrhotic liver tissues treated with curcumin or lactulose showed increased conductivity than tissues in the DMN-only group. Specifically, conductivity of cirrhotic liver after curcumin treatment was similar to that of normal liver tissues. Histological staining and immunohistochemical examination showed significant levels of attenuated fibrosis and decreased inflammatory response after both curcumin and lactulose treatments compared with damaged liver tissues by DMN. The conductivity imaging and biochemical examination results indicate that curcumin's anti-inflammatory effect can prevent the progression of irreversible liver dysfunction. PMID:29887757

Generating Systems Biology Markup Language Models from the Synthetic Biology Open Language.

PubMed

Roehner, Nicholas; Zhang, Zhen; Nguyen, Tramy; Myers, Chris J

2015-08-21

In the context of synthetic biology, model generation is the automated process of constructing biochemical models based on genetic designs. This paper discusses the use cases for model generation in genetic design automation (GDA) software tools and introduces the foundational concepts of standards and model annotation that make this process useful. Finally, this paper presents an implementation of model generation in the GDA software tool iBioSim and provides an example of generating a Systems Biology Markup Language (SBML) model from a design of a 4-input AND sensor written in the Synthetic Biology Open Language (SBOL).

Fragment-Based Drug Discovery of Potent Protein Kinase C Iota Inhibitors.

PubMed

Kwiatkowski, Jacek; Liu, Boping; Tee, Doris Hui Ying; Chen, Guoying; Ahmad, Nur Huda Binte; Wong, Yun Xuan; Poh, Zhi Ying; Ang, Shi Hua; Tan, Eldwin Sum Wai; Ong, Esther Hq; Nurul Dinie; Poulsen, Anders; Pendharkar, Vishal; Sangthongpitag, Kanda; Lee, May Ann; Sepramaniam, Sugunavathi; Ho, Soo Yei; Cherian, Joseph; Hill, Jeffrey; Keller, Thomas H; Hung, Alvin W

2018-05-24

Protein kinase C iota (PKC-ι) is an atypical kinase implicated in the promotion of different cancer types. A biochemical screen of a fragment library has identified several hits from which an azaindole-based scaffold was chosen for optimization. Driven by a structure-activity relationship and supported by molecular modeling, a weakly bound fragment was systematically grown into a potent and selective inhibitor against PKC-ι.

Biochemical transport modeling, estimation, and detection in realistic environments

NASA Astrophysics Data System (ADS)

Ortner, Mathias; Nehorai, Arye

2006-05-01

Early detection and estimation of the spread of a biochemical contaminant are major issues for homeland security applications. We present an integrated approach combining the measurements given by an array of biochemical sensors with a physical model of the dispersion and statistical analysis to solve these problems and provide system performance measures. We approximate the dispersion model of the contaminant in a realistic environment through numerical simulations of reflected stochastic diffusions describing the microscopic transport phenomena due to wind and chemical diffusion using the Feynman-Kac formula. We consider arbitrary complex geometries and account for wind turbulence. Localizing the dispersive sources is useful for decontamination purposes and estimation of the cloud evolution. To solve the associated inverse problem, we propose a Bayesian framework based on a random field that is particularly powerful for localizing multiple sources with small amounts of measurements. We also develop a sequential detector using the numerical transport model we propose. Sequential detection allows on-line analysis and detecting wether a change has occurred. We first focus on the formulation of a suitable sequential detector that overcomes the presence of unknown parameters (e.g. release time, intensity and location). We compute a bound on the expected delay before false detection in order to decide the threshold of the test. For a fixed false-alarm rate, we obtain the detection probability of a substance release as a function of its location and initial concentration. Numerical examples are presented for two real-world scenarios: an urban area and an indoor ventilation duct.

Hidden long evolutionary memory in a model biochemical network

NASA Astrophysics Data System (ADS)

Ali, Md. Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-04-01

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

EVALUATION OF ALTERED SENSITIVITY OF OLDER ADULTS TO ENVIRONMENTAL AGENTS USING PHYSIOLOGICALLY BASED PHARMACOKINETIC (PBPK) MODELING

EPA Science Inventory

The population of older Americans is increasing due to the aging of the Baby Boomers as well as an increase in the average life span. A number of physiological and biochemical changes occur during aging that could influence the relationship between exposure, dose, and response to...

Improved biochemical preservation of lung slices during cold storage.

PubMed

Bull, D A; Connors, R C; Reid, B B; Albanil, A; Stringham, J C; Karwande, S V

2000-05-15

Development of lung preservation solutions typically requires whole-organ models which are animal and labor intensive. These models rely on physiologic rather than biochemical endpoints, making accurate comparison of the relative efficacy of individual solution components difficult. We hypothesized that lung slices could be used to assess preservation of biochemical function during cold storage. Whole rat lungs were precision cut into slices with a thickness of 500 microm and preserved at 4 degrees C in the following solutions: University of Wisconsin (UW), Euro-Collins (EC), low-potassium-dextran (LPD), Kyoto (K), normal saline (NS), or a novel lung preservation solution (NPS) developed using this model. Lung biochemical function was assessed by ATP content (etamol ATP/mg wet wt) and capacity for protein synthesis (cpm/mg protein) immediately following slicing (0 h) and at 6, 12, 18, and 24 h of cold storage. Six slices were assayed at each time point for each solution. The data were analyzed using analysis of variance and are presented as means +/- SD. ATP content was significantly higher in the lung slices stored in NPS compared with all other solutions at each time point (P < 0.0001). Protein synthesis was significantly higher in the lung slices stored in NPS compared with all other solutions at 6, 12, and 18 h of preservation (P < 0.05). This lung slice model allows the rapid and efficient screening of lung preservation solutions and their components using quantifiable biochemical endpoints. Using this model, we have developed a novel solution that improves the biochemical preservation of lung slices during cold storage. Copyright 2000 Academic Press.

Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

PubMed Central

Kaltenbacher, Barbara; Hasenauer, Jan

2017-01-01

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351

Scaling up semi-arid grassland biochemical content from the leaf to the canopy level: challenges and opportunities.

PubMed

He, Yuhong; Mui, Amy

2010-01-01

Remote sensing imagery is being used intensively to estimate the biochemical content of vegetation (e.g., chlorophyll, nitrogen, and lignin) at the leaf level. As a result of our need for vegetation biochemical information and our increasing ability to obtain canopy spectral data, a few techniques have been explored to scale leaf-level biochemical content to the canopy level for forests and crops. However, due to the contribution of non-green materials (i.e., standing dead litter, rock, and bare soil) from canopy spectra in semi-arid grasslands, it is difficult to obtain information about grassland biochemical content from remote sensing data at the canopy level. This paper summarizes available methods used to scale biochemical information from the leaf level to the canopy level and groups these methods into three categories: direct extrapolation, canopy-integrated approach, and inversion of physical models. As for semi-arid heterogeneous grasslands, we conclude that all methods are useful, but none are ideal. It is recommended that future research should explore a systematic upscaling framework which combines spatial pattern analysis, canopy-integrated approach, and modeling methods to retrieve vegetation biochemical content at the canopy level.

Discriminating model for skin cancer diagnosis in vivo through Raman spectroscopy

NASA Astrophysics Data System (ADS)

Silveira, Fabrício Luiz; Pacheco, Marcos Tadeu T.; Bodanese, Benito; Zângaro, Renato Amaro; Silveira, Landulfo

2013-03-01

This work aimed the development of a discriminating model, using Raman spectroscopy, based on the estimated concentration of biochemical components presented in skin, for in vivo diagnosis. Raman spectra were collected in patients who underwent excision surgery of suspicious lesions at the lesion site and at a normal circumjacent site. It has been estimated the relative amount of selected biochemical compounds presented in skin. The Raman spectra of normal and malignant (basal cell carcinoma - BCC and squamous cell carcinoma - SCC) skin are quite similar, with some spectral differences in the regions of lipids, nucleic acids, and hemoglobin. Some biochemicals showed statistically significant differences among N, BCC and SCC, such as elastin, ceramide, melanin, nucleid acid, actin and phenylalanine. Elastin and ceramide presented significant differences between N and BCC, melanin, DNA and actin presented significant differences between N and BCC and between N and SCC, being melanin and DNA decreased in neoplasias, in contrast with actin, that increased in neoplasias. Concentration of phenylalanine was significantly increased for SCC compared to N and BCC. The relative concentration of melanin, DNA and phenylalanine showed sensitivity, specificity and accuracy of about 81%, 65% and 60%, respectively, using Mahalanobis distance as a discriminator. This model is being incorporated to a Raman system with automated data collection and processing that could be used for a future in vivo, real time discrimination algorithm.

Metabolomic profiling of urinary changes in mice with monosodium glutamate-induced obesity.

PubMed

Pelantová, Helena; Bártová, Simona; Anýž, Jiří; Holubová, Martina; Železná, Blanka; Maletínská, Lenka; Novák, Daniel; Lacinová, Zdena; Šulc, Miroslav; Haluzík, Martin; Kuzma, Marek

2016-01-01

Obesity with related complications represents a widespread health problem. The etiopathogenesis of obesity is often studied using numerous rodent models. The mouse model of monosodium glutamate (MSG)-induced obesity was exploited as a model of obesity combined with insulin resistance. The aim of this work was to characterize the metabolic status of MSG mice by NMR-based metabolomics in combination with relevant biochemical and hormonal parameters. NMR analysis of urine at 2, 6, and 9 months revealed altered metabolism of nicotinamide and polyamines, attenuated excretion of major urinary proteins, increased levels of phenylacetylglycine and allantoin, and decreased concentrations of methylamine in urine of MSG-treated mice. Altered levels of creatine, citrate, succinate, and acetate were observed at 2 months of age and approached the values of control mice with aging. The development of obesity and insulin resistance in 6-month-old MSG mice was also accompanied by decreased mRNA expressions of adiponectin, lipogenetic and lipolytic enzymes and peroxisome proliferator-activated receptor-gamma in fat while mRNA expressions of lipogenetic enzymes in the liver were enhanced. At the age of 9 months, biochemical parameters of MSG mice were normalized to the values of the controls. This fact pointed to a limited predictive value of biochemical data up to age of 6 months as NMR metabolomics confirmed altered urine metabolic composition even at 9 months.

MreB: pilot or passenger of cell wall synthesis?

PubMed

White, Courtney L; Gober, James W

2012-02-01

The discovery that the bacterial cell shape determinant MreB is related to actin spurred new insights into bacterial morphogenesis and development. The trafficking and mechanical roles of the eukaryotic cytoskeleton were hypothesized to have a functional ancestor in MreB based on evidence implicating MreB as an organizer of cell wall synthesis. Genetic, biochemical and cytological studies implicate MreB as a coordinator of a large multi-protein peptidoglycan (PG) synthesizing holoenzyme. Recent advances in microscopy and new biochemical evidence, however, suggest that MreB may function differently than previously envisioned. This review summarizes our evolving knowledge of MreB and attempts to refine the generalized model of the proteins organizing PG synthesis in bacteria. This is generally thought to be conserved among eubacteria and the majority of the discussion will focus on studies from a few well-studied model organisms. Copyright © 2011 Elsevier Ltd. All rights reserved.

GillesPy: A Python Package for Stochastic Model Building and Simulation.

PubMed

Abel, John H; Drawert, Brian; Hellander, Andreas; Petzold, Linda R

2016-09-01

GillesPy is an open-source Python package for model construction and simulation of stochastic biochemical systems. GillesPy consists of a Python framework for model building and an interface to the StochKit2 suite of efficient simulation algorithms based on the Gillespie stochastic simulation algorithms (SSA). To enable intuitive model construction and seamless integration into the scientific Python stack, we present an easy to understand, action-oriented programming interface. Here, we describe the components of this package and provide a detailed example relevant to the computational biology community.

GillesPy: A Python Package for Stochastic Model Building and Simulation

PubMed Central

Abel, John H.; Drawert, Brian; Hellander, Andreas; Petzold, Linda R.

2017-01-01

GillesPy is an open-source Python package for model construction and simulation of stochastic biochemical systems. GillesPy consists of a Python framework for model building and an interface to the StochKit2 suite of efficient simulation algorithms based on the Gillespie stochastic simulation algorithms (SSA). To enable intuitive model construction and seamless integration into the scientific Python stack, we present an easy to understand, action-oriented programming interface. Here, we describe the components of this package and provide a detailed example relevant to the computational biology community. PMID:28630888

Reduced linear noise approximation for biochemical reaction networks with time-scale separation: The stochastic tQSSA+

NASA Astrophysics Data System (ADS)

Herath, Narmada; Del Vecchio, Domitilla

2018-03-01

Biochemical reaction networks often involve reactions that take place on different time scales, giving rise to "slow" and "fast" system variables. This property is widely used in the analysis of systems to obtain dynamical models with reduced dimensions. In this paper, we consider stochastic dynamics of biochemical reaction networks modeled using the Linear Noise Approximation (LNA). Under time-scale separation conditions, we obtain a reduced-order LNA that approximates both the slow and fast variables in the system. We mathematically prove that the first and second moments of this reduced-order model converge to those of the full system as the time-scale separation becomes large. These mathematical results, in particular, provide a rigorous justification to the accuracy of LNA models derived using the stochastic total quasi-steady state approximation (tQSSA). Since, in contrast to the stochastic tQSSA, our reduced-order model also provides approximations for the fast variable stochastic properties, we term our method the "stochastic tQSSA+". Finally, we demonstrate the application of our approach on two biochemical network motifs found in gene-regulatory and signal transduction networks.

Biophysical stimulation for in vitro engineering of functional cardiac tissues.

PubMed

Korolj, Anastasia; Wang, Erika Yan; Civitarese, Robert A; Radisic, Milica

2017-07-01

Engineering functional cardiac tissues remains an ongoing significant challenge due to the complexity of the native environment. However, our growing understanding of key parameters of the in vivo cardiac microenvironment and our ability to replicate those parameters in vitro are resulting in the development of increasingly sophisticated models of engineered cardiac tissues (ECT). This review examines some of the most relevant parameters that may be applied in culture leading to higher fidelity cardiac tissue models. These include the biochemical composition of culture media and cardiac lineage specification, co-culture conditions, electrical and mechanical stimulation, and the application of hydrogels, various biomaterials, and scaffolds. The review will also summarize some of the recent functional human tissue models that have been developed for in vivo and in vitro applications. Ultimately, the creation of sophisticated ECT that replicate native structure and function will be instrumental in advancing cell-based therapeutics and in providing advanced models for drug discovery and testing. © 2017 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.

A novel method for multifactorial bio-chemical experiments design based on combinational design theory.

PubMed

Wang, Xun; Sun, Beibei; Liu, Boyang; Fu, Yaping; Zheng, Pan

2017-01-01

Experimental design focuses on describing or explaining the multifactorial interactions that are hypothesized to reflect the variation. The design introduces conditions that may directly affect the variation, where particular conditions are purposely selected for observation. Combinatorial design theory deals with the existence, construction and properties of systems of finite sets whose arrangements satisfy generalized concepts of balance and/or symmetry. In this work, borrowing the concept of "balance" in combinatorial design theory, a novel method for multifactorial bio-chemical experiments design is proposed, where balanced templates in combinational design are used to select the conditions for observation. Balanced experimental data that covers all the influencing factors of experiments can be obtianed for further processing, such as training set for machine learning models. Finally, a software based on the proposed method is developed for designing experiments with covering influencing factors a certain number of times.

Subunit Organisation of In Vitro Reconstituted HOPS and CORVET Multisubunit Membrane Tethering Complexes

PubMed Central

Guo, Zhong; Johnston, Wayne; Kovtun, Oleksiy; Mureev, Sergey; Bröcker, Cornelia; Ungermann, Christian; Alexandrov, Kirill

2013-01-01

Biochemical and structural analysis of macromolecular protein assemblies remains challenging due to technical difficulties in recombinant expression, engineering and reconstitution of multisubunit complexes. Here we use a recently developed cell-free protein expression system based on the protozoan Leishmania tarentolae to produce in vitro all six subunits of the 600 kDa HOPS and CORVET membrane tethering complexes. We demonstrate that both subcomplexes and the entire HOPS complex can be reconstituted in vitro resulting in a comprehensive subunit interaction map. To our knowledge this is the largest eukaryotic protein complex in vitro reconstituted to date. Using the truncation and interaction analysis, we demonstrate that the complex is assembled through short hydrophobic sequences located in the C-terminus of the individual Vps subunits. Based on this data we propose a model of the HOPS and CORVET complex assembly that reconciles the available biochemical and structural data. PMID:24312556

Influence of the feedback loops in the trp operon of B. subtilis on the system dynamic response and noise amplitude.

PubMed

Zamora-Chimal, Criseida; Santillán, Moisés; Rodríguez-González, Jesús

2012-10-07

In this paper we introduce a mathematical model for the tryptophan operon regulatory pathway in Bacillus subtilis. This model considers the transcription-attenuation, and the enzyme-inhibition regulatory mechanisms. Special attention is paid to the estimation of all the model parameters from reported experimental data. With the aid of this model we investigate, from a mathematical-modeling point of view, whether the existing multiplicity of regulatory feedback loops is advantageous in some sense, regarding the dynamic response and the biochemical noise in the system. The tryptophan operon dynamic behavior is studied by means of deterministic numeric simulations, while the biochemical noise is analyzed with the aid of stochastic simulations. The model feasibility is tested comparing its stochastic and deterministic results with experimental reports. Our results for the wildtype and for a couple of mutant bacterial strains suggest that the enzyme-inhibition feedback loop, dynamically accelerates the operon response, and plays a major role in the reduction of biochemical noise. Also, the transcription-attenuation feedback loop makes the trp operon sensitive to changes in the endogenous tryptophan level, and increases the amplitude of the biochemical noise. Copyright © 2012 Elsevier Ltd. All rights reserved.

Thermodynamically consistent model calibration in chemical kinetics

PubMed Central

2011-01-01

Background The dynamics of biochemical reaction systems are constrained by the fundamental laws of thermodynamics, which impose well-defined relationships among the reaction rate constants characterizing these systems. Constructing biochemical reaction systems from experimental observations often leads to parameter values that do not satisfy the necessary thermodynamic constraints. This can result in models that are not physically realizable and may lead to inaccurate, or even erroneous, descriptions of cellular function. Results We introduce a thermodynamically consistent model calibration (TCMC) method that can be effectively used to provide thermodynamically feasible values for the parameters of an open biochemical reaction system. The proposed method formulates the model calibration problem as a constrained optimization problem that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account. By calculating thermodynamically feasible values for the kinetic parameters of a well-known model of the EGF/ERK signaling cascade, we demonstrate the qualitative and quantitative significance of imposing thermodynamic constraints on these parameters and the effectiveness of our method for accomplishing this important task. MATLAB software, using the Systems Biology Toolbox 2.1, can be accessed from http://www.cis.jhu.edu/~goutsias/CSS lab/software.html. An SBML file containing the thermodynamically feasible EGF/ERK signaling cascade model can be found in the BioModels database. Conclusions TCMC is a simple and flexible method for obtaining physically plausible values for the kinetic parameters of open biochemical reaction systems. It can be effectively used to recalculate a thermodynamically consistent set of parameter values for existing thermodynamically infeasible biochemical reaction models of cellular function as well as to estimate thermodynamically feasible values for the parameters of new models. Furthermore, TCMC can provide dimensionality reduction, better estimation performance, and lower computational complexity, and can help to alleviate the problem of data overfitting. PMID:21548948

Improved biochemical preservation of heart slices during cold storage.

PubMed

Bull, D A; Reid, B B; Connors, R C; Albanil, A; Stringham, J C; Karwande, S V

2000-01-01

Development of myocardial preservation solutions requires the use of whole organ models which are animal and labor intensive. These models rely on physiologic rather than biochemical endpoints, making accurate comparison of the relative efficacy of individual solution components difficult. We hypothesized that myocardial slices could be used to assess preservation of biochemical function during cold storage. Whole rat hearts were precision cut into slices with a thickness of 200 microm and preserved at 4 degrees C in one of the following solutions: Columbia University (CU), University of Wisconsin (UW), D5 0.2% normal saline with 20 meq/l KCL (QNS), normal saline (NS), or a novel cardiac preservation solution (NPS) developed using this model. Myocardial biochemical function was assessed by ATP content (etamoles ATP/mg wet weight) and capacity for protein synthesis (counts per minute (cpm)/mg protein) immediately following slicing (0 hours), and at 6, 12, 18, and 24 hours of cold storage. Six slices were assayed at each time point for each solution. The data were analyzed using analysis of variance and are presented as the mean +/- standard deviation. ATP content was higher in the heart slices stored in the NPS compared to all other solutions at 6, 12, 18 and 24 hours of cold storage (p < 0.05). Capacity for protein synthesis was higher in the heart slices stored in the NPS compared to all other solutions at 6, 12, and 18 hours of cold storage (p < 0.05). CONCLUSIONS This myocardial slice model allows the rapid and efficient screening of cardiac preservation solutions and their components using quantifiable biochemical endpoints. Using this model, we have developed a novel preservation solution which improves the biochemical function of myocardial slices during cold storage.

Overcoming Student Misconceptions about Photosynthesis: A Model- and Inquiry-Based Approach Using Aquatic Plants

ERIC Educational Resources Information Center

Ray, Andrew M.; Beardsley, Paul M.

2008-01-01

Even though photosynthesis is an obligatory part of the science curriculum, research has shown that students often have a poor understanding of it. The authors advocate that classroom coverage of the topic of photosynthesis should include not only its biochemical properties but also the role of photosynthesis or photosynthetic organisms in matter…

Developing Reverse Toxicokinetic Models to Correlate In Vitro and In Vivo Activity (ICCVAM Communities of Practice Webinar 2015)

EPA Science Inventory

Many commercial and environmental chemicals lack toxicity data necessary for users and risk assessors to make fully informed decisions about potential health effects. Generating these data using high throughput in vitro cell- or biochemical-based tests would be faster and less e...

Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering.

PubMed

Menolascina, Filippo; Bellomo, Domenico; Maiwald, Thomas; Bevilacqua, Vitoantonio; Ciminelli, Caterina; Paradiso, Angelo; Tommasi, Stefania

2009-10-15

Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments. We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification. We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich--data poor' paradox in Systems Biology.

Biochemical interpretation of quantitative structure-activity relationships (QSAR) for biodegradation of N-heterocycles: a complementary approach to predict biodegradability.

PubMed

Philipp, Bodo; Hoff, Malte; Germa, Florence; Schink, Bernhard; Beimborn, Dieter; Mersch-Sundermann, Volker

2007-02-15

Prediction of the biodegradability of organic compounds is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. We combined quantitative structure-activity relationships (QSAR) with the systematic collection of biochemical knowledge to establish rules for the prediction of aerobic biodegradation of N-heterocycles. Validated biodegradation data of 194 N-heterocyclic compounds were analyzed using the MULTICASE-method which delivered two QSAR models based on 17 activating (OSAR 1) and on 16 inactivating molecular fragments (GSAR 2), which were statistically significantly linked to efficient or poor biodegradability, respectively. The percentages of correct classifications were over 99% for both models, and cross-validation resulted in 67.9% (GSAR 1) and 70.4% (OSAR 2) correct predictions. Biochemical interpretation of the activating and inactivating characteristics of the molecular fragments delivered plausible mechanistic interpretations and enabled us to establish the following biodegradation rules: (1) Target sites for amidohydrolases and for cytochrome P450 monooxygenases enhance biodegradation of nonaromatic N-heterocycles. (2) Target sites for molybdenum hydroxylases enhance biodegradation of aromatic N-heterocycles. (3) Target sites for hydratation by an urocanase-like mechanism enhance biodegradation of imidazoles. Our complementary approach represents a feasible strategy for generating concrete rules for the prediction of biodegradability of organic compounds.

Simulated maximum likelihood method for estimating kinetic rates in gene expression.

PubMed

Tian, Tianhai; Xu, Songlin; Gao, Junbin; Burrage, Kevin

2007-01-01

Kinetic rate in gene expression is a key measurement of the stability of gene products and gives important information for the reconstruction of genetic regulatory networks. Recent developments in experimental technologies have made it possible to measure the numbers of transcripts and protein molecules in single cells. Although estimation methods based on deterministic models have been proposed aimed at evaluating kinetic rates from experimental observations, these methods cannot tackle noise in gene expression that may arise from discrete processes of gene expression, small numbers of mRNA transcript, fluctuations in the activity of transcriptional factors and variability in the experimental environment. In this paper, we develop effective methods for estimating kinetic rates in genetic regulatory networks. The simulated maximum likelihood method is used to evaluate parameters in stochastic models described by either stochastic differential equations or discrete biochemical reactions. Different types of non-parametric density functions are used to measure the transitional probability of experimental observations. For stochastic models described by biochemical reactions, we propose to use the simulated frequency distribution to evaluate the transitional density based on the discrete nature of stochastic simulations. The genetic optimization algorithm is used as an efficient tool to search for optimal reaction rates. Numerical results indicate that the proposed methods can give robust estimations of kinetic rates with good accuracy.

Kinetics of DSB rejoining and formation of simple chromosome exchange aberrations

NASA Technical Reports Server (NTRS)

Cucinotta, F. A.; Nikjoo, H.; O'Neill, P.; Goodhead, D. T.

2000-01-01

PURPOSE: To investigate the role of kinetics in the processing of DNA double strand breaks (DSB), and the formation of simple chromosome exchange aberrations following X-ray exposures to mammalian cells based on an enzymatic approach. METHODS: Using computer simulations based on a biochemical approach, rate-equations that describe the processing of DSB through the formation of a DNA-enzyme complex were formulated. A second model that allows for competition between two processing pathways was also formulated. The formation of simple exchange aberrations was modelled as misrepair during the recombination of single DSB with undamaged DNA. Non-linear coupled differential equations corresponding to biochemical pathways were solved numerically by fitting to experimental data. RESULTS: When mediated by a DSB repair enzyme complex, the processing of single DSB showed a complex behaviour that gives the appearance of fast and slow components of rejoining. This is due to the time-delay caused by the action time of enzymes in biomolecular reactions. It is shown that the kinetic- and dose-responses of simple chromosome exchange aberrations are well described by a recombination model of DSB interacting with undamaged DNA when aberration formation increases with linear dose-dependence. Competition between two or more recombination processes is shown to lead to the formation of simple exchange aberrations with a dose-dependence similar to that of a linear quadratic model. CONCLUSIONS: Using a minimal number of assumptions, the kinetics and dose response observed experimentally for DSB rejoining and the formation of simple chromosome exchange aberrations are shown to be consistent with kinetic models based on enzymatic reaction approaches. A non-linear dose response for simple exchange aberrations is possible in a model of recombination of DNA containing a DSB with undamaged DNA when two or more pathways compete for DSB repair.

A catalytic metal ion interacts with the cleavage site G•U wobble in the HDV ribozyme†

PubMed Central

Chen, Jui-Hui; Gong, Bo; Bevilacqua, Philip C.; Carey, Paul R.; Golden, Barbara L.

2009-01-01

The HDV ribozyme self-cleaves by a chemical mechanism involving general acid-base catalysis to generate a 2′,3′-cyclic phosphate and a 5′-hydroxyl termini. Biochemical studies from several laboratories have implicated C75 as the general acid and hydrated magnesium as the general base. We have previously shown that C75 has a pKa shifted > 2 pH units toward neutrality [Gong, B., Chen, J. H., Chase, E., Chadalavada, D. M., Yajima, R., Golden, B. L., Bevilacqua, P. C., and Carey, P. R. (2007) J. Am. Chem. Soc. 129, 13335–13342.], while in crystal structures, it is well-positioned for proton transfer. However no crystallographic evidence for a hydrated magnesium poised to serve as a general base in the reaction has been observed in high-resolution crystal structures of various reaction states and mutants. Herein, we use solution kinetic experiments and parallel Raman crystallographic studies to examine the effects of pH on rate and Mg2+-binding properties of wild-type and 7-deazaguanosine mutants of the HDV ribozyme. These data suggest that a previously-unobserved hydrated magnesium ion interacts with the N7 of the cleavage site G•U wobble base pair. Integrating this metal ion binding site with the available crystal structures provides a new three-dimensional model for the active site of the ribozyme that accommodates all available biochemical data and appears competent for catalysis. The position of this metal is consistent with a role of a magnesium-bound hydroxide as a general base as dictated by biochemical data. PMID:19178151

A catalytic metal ion interacts with the cleavage Site G.U wobble in the HDV ribozyme.

PubMed

Chen, Jui-Hui; Gong, Bo; Bevilacqua, Philip C; Carey, Paul R; Golden, Barbara L

2009-02-24

The HDV ribozyme self-cleaves by a chemical mechanism involving general acid-base catalysis to generate 2',3'-cyclic phosphate and 5'-hydroxyl termini. Biochemical studies from several laboratories have implicated C75 as the general acid and hydrated magnesium as the general base. We have previously shown that C75 has a pK(a) shifted >2 pH units toward neutrality [Gong, B., Chen, J. H., Chase, E., Chadalavada, D. M., Yajima, R., Golden, B. L., Bevilacqua, P. C., and Carey, P. R. (2007) J. Am. Chem. Soc. 129, 13335-13342], while in crystal structures, it is well-positioned for proton transfer. However, no evidence for a hydrated magnesium poised to serve as a general base in the reaction has been observed in high-resolution crystal structures of various reaction states and mutants. Herein, we use solution kinetic experiments and parallel Raman crystallographic studies to examine the effects of pH on the rate and Mg(2+) binding properties of wild-type and 7-deazaguanosine mutants of the HDV ribozyme. These data suggest that a previously unobserved hydrated magnesium ion interacts with N7 of the cleavage site G.U wobble base pair. Integrating this metal ion binding site with the available crystal structures provides a new three-dimensional model for the active site of the ribozyme that accommodates all available biochemical data and appears competent for catalysis. The position of this metal is consistent with a role of a magnesium-bound hydroxide as a general base as dictated by biochemical data.

Model-Based Design of Biochemical Microreactors

PubMed Central

Elbinger, Tobias; Gahn, Markus; Neuss-Radu, Maria; Hante, Falk M.; Voll, Lars M.; Leugering, Günter; Knabner, Peter

2016-01-01

Mathematical modeling of biochemical pathways is an important resource in Synthetic Biology, as the predictive power of simulating synthetic pathways represents an important step in the design of synthetic metabolons. In this paper, we are concerned with the mathematical modeling, simulation, and optimization of metabolic processes in biochemical microreactors able to carry out enzymatic reactions and to exchange metabolites with their surrounding medium. The results of the reported modeling approach are incorporated in the design of the first microreactor prototypes that are under construction. These microreactors consist of compartments separated by membranes carrying specific transporters for the input of substrates and export of products. Inside the compartments of the reactor multienzyme complexes assembled on nano-beads by peptide adapters are used to carry out metabolic reactions. The spatially resolved mathematical model describing the ongoing processes consists of a system of diffusion equations together with boundary and initial conditions. The boundary conditions model the exchange of metabolites with the neighboring compartments and the reactions at the surface of the nano-beads carrying the multienzyme complexes. Efficient and accurate approaches for numerical simulation of the mathematical model and for optimal design of the microreactor are developed. As a proof-of-concept scenario, a synthetic pathway for the conversion of sucrose to glucose-6-phosphate (G6P) was chosen. In this context, the mathematical model is employed to compute the spatio-temporal distributions of the metabolite concentrations, as well as application relevant quantities like the outflow rate of G6P. These computations are performed for different scenarios, where the number of beads as well as their loading capacity are varied. The computed metabolite distributions show spatial patterns, which differ for different experimental arrangements. Furthermore, the total output of G6P increases for scenarios where microcompartimentation of enzymes occurs. These results show that spatially resolved models are needed in the description of the conversion processes. Finally, the enzyme stoichiometry on the nano-beads is determined, which maximizes the production of glucose-6-phosphate. PMID:26913283

Self-organizing ontology of biochemically relevant small molecules

PubMed Central

2012-01-01

Background The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest. Results To address this, we present a formal framework based on Semantic Web technologies for the automatic design of chemical ontology which can be used for automated classification of novel entities. We demonstrate the automatic self-assembly of a structure-based chemical ontology based on 60 MeSH and 40 ChEBI chemical classes. This ontology is then used to classify 200 compounds with an accuracy of 92.7%. We extend these structure-based classes with molecular feature information and demonstrate the utility of our framework for classification of functionally relevant chemicals. Finally, we discuss an iterative approach that we envision for future biochemical ontology development. Conclusions We conclude that the proposed methodology can ease the burden of chemical data annotators and dramatically increase their productivity. We anticipate that the use of formal logic in our proposed framework will make chemical classification criteria more transparent to humans and machines alike and will thus facilitate predictive and integrative bioactivity model development. PMID:22221313

Does glimepiride alter the pharmacokinetics of sildenafil citrate in diabetic nephropathy animals: investigating mechanism of interaction by molecular modeling studies.

PubMed

Tripathi, Alok Shiomurti; Timiri, Ajay Kumar; Mazumder, Papiya Mitra; Chandewar, Anil

2015-10-01

The present study evaluates possible drug interactions between glimepiride (GLIM) and sildenafil citrate (SIL) in streptozotocin (STZ)-induced diabetic nephropathic (DN) animals and also postulates the possible mechanism of interaction based on molecular modeling studies. Diabetic nephropathy was induced by single dose of STZ (60 mg kg(-1), i.p.) and was confirmed by assessing blood and urine biochemical parameters 28 days after induction. Selected DN animals were used to explore the drug interaction between GLIM (0.5 mg kg(-1), p.o.) and SIL (2.5 mg kg(-1), p.o.) on the 29th and 70th day of the protocol. Possible drug interaction was assessed by evaluating the plasma drug concentration using HPLC-UV and changes in biochemical parameters in blood and urine were also determined. The mechanism of the interaction was postulated from the results of a molecular modeling study using the Maestro module of Schrodinger software. DN was confirmed as there was significant alteration in blood and urine biochemical parameters in STZ-treated groups. The concentration of SIL increased significantly (P < 0.001) in rat plasma when co-administered with GLIM on the 70th day of the protocol. Molecular modeling revealed important interactions with rat serum albumin and CYP2C9. GLIM has a strong hydrophobic interaction with binding site residues of rat serum albumin compared to SIL, whereas for CYP2C9, GLIM forms a stronger hydrogen bond than SIL with polar contacts and hydrophobic interactions. The present study concludes that bioavailability of SIL increases when co-administered chronically with GLIM in the management of DN animals, and the mechanism is supported by molecular modeling studies.

Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

PubMed Central

Wang, Yifan; Liu, Aiyi; Mills, James L.; Boehnke, Michael; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Xiong, Momiao; Wu, Colin O.; Fan, Ruzong

2015-01-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

PubMed

Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

2015-05-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

Haralick textural features on T2 -weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer.

PubMed

Gnep, Khémara; Fargeas, Auréline; Gutiérrez-Carvajal, Ricardo E; Commandeur, Frédéric; Mathieu, Romain; Ospina, Juan D; Rolland, Yan; Rohou, Tanguy; Vincendeau, Sébastien; Hatt, Mathieu; Acosta, Oscar; de Crevoisier, Renaud

2017-01-01

To explore the association between magnetic resonance imaging (MRI), including Haralick textural features, and biochemical recurrence following prostate cancer radiotherapy. In all, 74 patients with peripheral zone localized prostate adenocarcinoma underwent pretreatment 3.0T MRI before external beam radiotherapy. Median follow-up of 47 months revealed 11 patients with biochemical recurrence. Prostate tumors were segmented on T 2 -weighted sequences (T 2 -w) and contours were propagated onto the coregistered apparent diffusion coefficient (ADC) images. We extracted 140 image features from normalized T 2 -w and ADC images corresponding to first-order (n = 6), gradient-based (n = 4), and second-order Haralick textural features (n = 130). Four geometrical features (tumor diameter, perimeter, area, and volume) were also computed. Correlations between Gleason score and MRI features were assessed. Cox regression analysis and random survival forests (RSF) were performed to assess the association between MRI features and biochemical recurrence. Three T 2 -w and one ADC Haralick textural features were significantly correlated with Gleason score (P < 0.05). Twenty-eight T 2 -w Haralick features and all four geometrical features were significantly associated with biochemical recurrence (P < 0.05). The most relevant features were Haralick features T 2 -w contrast, T 2 -w difference variance, ADC median, along with tumor volume and tumor area (C-index from 0.76 to 0.82; P < 0.05). By combining these most powerful features in an RSF model, the obtained C-index was 0.90. T 2 -w Haralick features appear to be strongly associated with biochemical recurrence following prostate cancer radiotherapy. 3 J. Magn. Reson. Imaging 2017;45:103-117. © 2016 International Society for Magnetic Resonance in Medicine.

Compartmentalized Culture of Perivascular Stroma and Endothelial Cells in a Microfluidic Model of the Human Endometrium.

PubMed

Gnecco, Juan S; Pensabene, Virginia; Li, David J; Ding, Tianbing; Hui, Elliot E; Bruner-Tran, Kaylon L; Osteen, Kevin G

2017-07-01

The endometrium is the inner lining of the uterus. Following specific cyclic hormonal stimulation, endometrial stromal fibroblasts (stroma) and vascular endothelial cells exhibit morphological and biochemical changes to support embryo implantation and regulate vascular function, respectively. Herein, we integrated a resin-based porous membrane in a dual chamber microfluidic device in polydimethylsiloxane that allows long term in vitro co-culture of human endometrial stromal and endothelial cells. This transparent, 2-μm porous membrane separates the two chambers, allows for the diffusion of small molecules and enables high resolution bright field and fluorescent imaging. Within our primary human co-culture model of stromal and endothelial cells, we simulated the temporal hormone changes occurring during an idealized 28-day menstrual cycle. We observed the successful differentiation of stroma into functional decidual cells, determined by morphology as well as biochemically as measured by increased production of prolactin. By controlling the microfluidic properties of the device, we additionally found that shear stress forces promoted cytoskeleton alignment and tight junction formation in the endothelial layer. Finally, we demonstrated that the endometrial perivascular stroma model was sustainable for up to 4 weeks, remained sensitive to steroids and is suitable for quantitative biochemical analysis. Future utilization of this device will allow the direct evaluation of paracrine and endocrine crosstalk between these two cell types as well as studies of immunological events associated with normal vs. disease-related endometrial microenvironments.

Real-time biochemical sensor based on Raman scattering with CMOS contact imaging.

PubMed

Muyun Cao; Yuhua Li; Yadid-Pecht, Orly

2015-08-01

This work presents a biochemical sensor based on Raman scattering with Complementary metal-oxide-semiconductor (CMOS) contact imaging. This biochemical optical sensor is designed for detecting the concentration of solutions. The system is built with a laser diode, an optical filter, a sample holder and a commercial CMOS sensor. The output of the system is analyzed by an image processing program. The system provides instant measurements with a resolution of 0.2 to 0.4 Mol. This low cost and easy-operated small scale system is useful in chemical, biomedical and environmental labs for quantitative bio-chemical concentration detection with results reported comparable to a highly cost commercial spectrometer.

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

PubMed Central

Trame, MN; Lesko, LJ

2015-01-01

A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge. CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69–79; doi:10.1002/psp4.6; published online 25 February 2015 PMID:27548289

Exposure of engineered nanomaterials to plants: Insights into the physiological and biochemical responses-A review.

PubMed

Zuverza-Mena, Nubia; Martínez-Fernández, Domingo; Du, Wenchao; Hernandez-Viezcas, Jose A; Bonilla-Bird, Nestor; López-Moreno, Martha L; Komárek, Michael; Peralta-Videa, Jose R; Gardea-Torresdey, Jorge L

2017-01-01

Recent investigations show that carbon-based and metal-based engineered nanomaterials (ENMs), components of consumer goods and agricultural products, have the potential to build up in sediments and biosolid-amended agricultural soils. In addition, reports indicate that both carbon-based and metal-based ENMs affect plants differently at the physiological, biochemical, nutritional, and genetic levels. The toxicity threshold is species-dependent and responses to ENMs are driven by a series of factors including the nanomaterial characteristics and environmental conditions. Effects on the growth, physiological and biochemical traits, production and food quality, among others, have been reported. However, a complete understanding of the dynamics of interactions between plants and ENMs is not clear enough yet. This review presents recent publications on the physiological and biochemical effects that commercial carbon-based and metal-based ENMs have in terrestrial plants. This document focuses on crop plants because of their relevance in human nutrition and health. We have summarized the mechanisms of interaction between plants and ENMs as well as identified gaps in knowledge for future investigations. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions

PubMed Central

2010-01-01

Background Genome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use. Description We describe BiGG, a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest. Conclusions BiGG addresses a need in the systems biology community to have access to high quality curated metabolic models and reconstructions. It is freely available for academic use at http://bigg.ucsd.edu. PMID:20426874

Novel approach for differentiating Shigella species and Escherichia coli by matrix-assisted laser desorption ionization-time of flight mass spectrometry.

PubMed

Khot, Prasanna D; Fisher, Mark A

2013-11-01

Shigella species are so closely related to Escherichia coli that routine matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) cannot reliably differentiate them. Biochemical and serological methods are typically used to distinguish these species; however, "inactive" isolates of E. coli are biochemically very similar to Shigella species and thus pose a greater diagnostic challenge. We used ClinProTools (Bruker Daltonics) software to discover MALDI-TOF MS biomarker peaks and to generate classification models based on the genetic algorithm to differentiate between Shigella species and E. coli. Sixty-six Shigella spp. and 72 E. coli isolates were used to generate and test classification models, and the optimal models contained 15 biomarker peaks for genus-level classification and 12 peaks for species-level classification. We were able to identify 90% of E. coli and Shigella clinical isolates correctly to the species level. Only 3% of tested isolates were misidentified. This novel MALDI-TOF MS approach allows laboratories to streamline the identification of E. coli and Shigella species.

Ex vivo study of the home-use TriPollar RF device using an experimental human skin model.

PubMed

Boisnic, Sylvie; Branchet, Marie Christine

2010-09-01

A wide variety of professional radio frequency (RF) aesthetic treatments for anti-aging are available aiming at skin tightening. A new home-use RF device for facial treatments has recently been developed based on TriPollar technology. To evaluate the mechanism of the new home-use device, in the process of collagen remodeling, using an ex vivo skin model. Human skin samples were collected in order to evaluate the anti-aging effect of a home-use device for facial treatments on an ex vivo human skin model. Skin tightening was evaluated by dermal histology, quantitative analysis of collagen fibers and dosage of collagen synthesis. Significant collagen remodeling following RF treatment with the device was found in the superficial and mid-deep dermis. Biochemical measurement of newly synthesized collagen showed an increase of 41% in the treated samples as compared to UV-aged control samples. The new home-use device has been demonstrated to affect significant collagen remodeling, in terms of the structural and biochemical improvement of dermal collagen on treated skin samples.

Model-Based Analysis for Qualitative Data: An Application in Drosophila Germline Stem Cell Regulation

PubMed Central

Pargett, Michael; Rundell, Ann E.; Buzzard, Gregery T.; Umulis, David M.

2014-01-01

Discovery in developmental biology is often driven by intuition that relies on the integration of multiple types of data such as fluorescent images, phenotypes, and the outcomes of biochemical assays. Mathematical modeling helps elucidate the biological mechanisms at play as the networks become increasingly large and complex. However, the available data is frequently under-utilized due to incompatibility with quantitative model tuning techniques. This is the case for stem cell regulation mechanisms explored in the Drosophila germarium through fluorescent immunohistochemistry. To enable better integration of biological data with modeling in this and similar situations, we have developed a general parameter estimation process to quantitatively optimize models with qualitative data. The process employs a modified version of the Optimal Scaling method from social and behavioral sciences, and multi-objective optimization to evaluate the trade-off between fitting different datasets (e.g. wild type vs. mutant). Using only published imaging data in the germarium, we first evaluated support for a published intracellular regulatory network by considering alternative connections of the same regulatory players. Simply screening networks against wild type data identified hundreds of feasible alternatives. Of these, five parsimonious variants were found and compared by multi-objective analysis including mutant data and dynamic constraints. With these data, the current model is supported over the alternatives, but support for a biochemically observed feedback element is weak (i.e. these data do not measure the feedback effect well). When also comparing new hypothetical models, the available data do not discriminate. To begin addressing the limitations in data, we performed a model-based experiment design and provide recommendations for experiments to refine model parameters and discriminate increasingly complex hypotheses. PMID:24626201

Physiologically Based Pharmacokinetic Modeling of the Lactating Rat and Nursing Pup: a Multiroute Exposure Model for Trichloroethylene and its Metabolite, Trichloroacetic Acid

DTIC Science & Technology

1990-01-01

cumulated during pregnancy was described as a linear animal (allometrically scaled), was estimated by comput- process changing from 12.0% of body weight of...biochemical effects of TCE in neonatal pregnancy (Fisher et al., 1989) were used for repeated- rats born to dams exposed to TCE via drink- exposure...studies during lactation. Female cesarean-de- rived Fischer-344 rats, obtained from Charles Rivering water during pregnancy and lactation. Breeding

Integrating 'omic' data and biogeochemical modeling: the key to understanding the microbial regulation of matter cycling in soil

NASA Astrophysics Data System (ADS)

Pagel, Holger; Kandeler, Ellen; Seifert, Jana; Camarinha-Silva, Amélia; Kügler, Philipp; Rennert, Thilo; Poll, Christian; Streck, Thilo

2016-04-01

Matter cycling in soils and associated soil functions are intrinsically controlled by microbial dynamics. It is therefore crucial to consider functional traits of microorganisms in biogeochemical models. Tremendous advances in 'omic' methods provide a plethora of data on physiology, metabolic capabilities and ecological life strategies of microorganisms in soil. Combined with isotopic techniques, biochemical pathways and transformations can be identified and quantified. Such data have been, however, rarely used to improve the mechanistic representation of microbial dynamics in soil organic matter models. It is the goal of the Young Investigator Group SoilReg to address this challenge. Our general approach is to tightly integrate experiments and biochemical modeling. NextGen sequencing will be applied to identify key functional groups. Active microbial groups will be quantified by measurements of functional genes and by stable isotope probing methods of DNA and proteins. Based on this information a biogeochemical model that couples a mechanistic representation of microbial dynamics with physicochemical processes will be set up and calibrated. Sensitivity and stability analyses of the model as well as scenario simulations will reveal the importance of intrinsic and extrinsic controls of organic matter turnover. We will demonstrate our concept and present first results of two case studies on pesticide degradation and methane oxidation.

pH measurements of FET-based (bio)chemical sensors using portable measurement system.

PubMed

Voitsekhivska, T; Zorgiebel, F; Suthau, E; Wolter, K-J; Bock, K; Cuniberti, G

2015-01-01

In this study we demonstrate the sensing capabilities of a portable multiplex measurement system for FET-based (bio)chemical sensors with an integrated microfluidic interface. We therefore conducted pH measurements with Silicon Nanoribbon FET-based Sensors using different measurement procedures that are suitable for various applications. We have shown multiplexed measurements in aqueous medium for three different modes that are mutually specialized in fast data acquisition (constant drain current), calibration-less sensing (constant gate voltage) and in providing full information content (sweeping mode). Our system therefore allows surface charge sensing for a wide range of applications and is easily adaptable for multiplexed sensing with novel FET-based (bio)chemical sensors.

Feasibility of biochemical verification in a web-based smoking cessation study.

PubMed

Cha, Sarah; Ganz, Ollie; Cohn, Amy M; Ehlke, Sarah J; Graham, Amanda L

2017-10-01

Cogent arguments have been made against the need for biochemical verification in population-based studies with low-demand characteristics. Despite this fact, studies involving digital interventions (low-demand) are often required in peer review to report biochemically verified abstinence. To address this discrepancy, we examined the feasibility and costs of biochemical verification in a web-based study conducted with a national sample. Participants were 600U.S. adult current smokers who registered on a web-based smoking cessation program and completed surveys at baseline and 3months. Saliva sampling kits were sent to participants who reported 7-day abstinence at 3months, and analyzed for cotinine. The response rate at 3-months was 41.2% (n=247): 93 participants reported 7-day abstinence (38%) and were mailed a saliva kit (71% returned). The discordance rate was 36.4%. Participants with discordant responses were more likely to report 3-month use of nicotine replacement therapy or e-cigarettes than those with concordant responses (79.2% vs. 45.2%, p=0.007). The total cost of saliva sampling was $8280 ($125/sample). Biochemical verification was both time- and cost-intensive, and yielded a relatively small number of samples due to low response rates and use of other nicotine products during the follow-up period. There was a high rate of discordance of self-reported abstinence and saliva testing. Costs for data collection may be prohibitive for studies with large sample sizes or limited budgets. Our findings echo previous statements that biochemical verification is not necessary in population-based studies, and add evidence specific to technology-based studies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mechanism of Actin-Based Motility

NASA Astrophysics Data System (ADS)

Pantaloni, Dominique; Le Clainche, Christophe; Carlier, Marie-France

2001-05-01

Spatially controlled polymerization of actin is at the origin of cell motility and is responsible for the formation of cellular protrusions like lamellipodia. The pathogens Listeria monocytogenes and Shigella flexneri, which undergo actin-based propulsion, are acknowledged models of the leading edge of lamellipodia. Actin-based motility of the bacteria or of functionalized microspheres can be reconstituted in vitro from only five pure proteins. Movement results from the regulated site-directed treadmilling of actin filaments, consistent with observations of actin dynamics in living motile cells and with the biochemical properties of the components of the synthetic motility medium.

Near infrared spectroscopy for determination of various physical, chemical and biochemical properties in Mediterranean soils.

PubMed

Zornoza, R; Guerrero, C; Mataix-Solera, J; Scow, K M; Arcenegui, V; Mataix-Beneyto, J

2008-07-01

The potential of near infrared (NIR) reflectance spectroscopy to predict various physical, chemical and biochemical properties in Mediterranean soils from SE Spain was evaluated. Soil samples (n=393) were obtained by sampling thirteen locations during three years (2003-2005 period). These samples had a wide range of soil characteristics due to variations in land use, vegetation cover and specific climatic conditions. Biochemical properties also included microbial biomarkers based on phospholipid fatty acids (PLFA). Partial least squares (PLS) regression with cross validation was used to establish relationships between the NIR spectra and the reference data from physical, chemical and biochemical analyses. Based on the values of coefficient of determination (r(2)) and the ratio of standard deviation of validation set to root mean square error of cross validation (RPD), predicted results were evaluated as excellent (r(2)>0.90 and RPD>3) for soil organic carbon, Kjeldahl nitrogen, soil moisture, cation exchange capacity, microbial biomass carbon, basal soil respiration, acid phosphatase activity, β-glucosidase activity and PLFA biomarkers for total bacteria, Gram positive bacteria, actinomycetes, vesicular-arbuscular mycorrhizal fungi and total PLFA biomass. Good predictions (0.81

Hierarchical graphs for rule-based modeling of biochemical systems

PubMed Central

2011-01-01

Background In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal) of an edge represents a class of association (dissociation) reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Results For purposes of model annotation, we propose the use of hierarchical graphs to represent structural relationships among components and subcomponents of molecules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR) complex. We also show that computational methods developed for regular graphs can be applied to hierarchical graphs. In particular, we describe a generalization of Nauty, a graph isomorphism and canonical labeling algorithm. The generalized version of the Nauty procedure, which we call HNauty, can be used to assign canonical labels to hierarchical graphs or more generally to graphs with multiple edge types. The difference between the Nauty and HNauty procedures is minor, but for completeness, we provide an explanation of the entire HNauty algorithm. Conclusions Hierarchical graphs provide more intuitive formal representations of proteins and other structured molecules with multiple functional components than do the regular graphs of current languages for specifying rule-based models, such as the BioNetGen language (BNGL). Thus, the proposed use of hierarchical graphs should promote clarity and better understanding of rule-based models. PMID:21288338

Complexity and performance of on-chip biochemical assays

NASA Astrophysics Data System (ADS)

Kopf-Sill, Anne R.; Nikiforov, Theo; Bousse, Luc J.; Nagle, Rob; Parce, J. W.

1997-03-01

The use of microchips for performing biochemical processes has the potential to reduce reagent use and thus assay costs, increase throughput, and automate complex processes. We are building a multifunctional platform that provides sensing and actuation functions for a variety of microchip- based biochemical and analytical processes. Here we describe recent experiments that include on-chip dilution, reagent mixing, reaction, separation, and detection for important classes of biochemical assays. Issues in chip design and control are discussed.

Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach

PubMed Central

2014-01-01

Background Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. Results We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. Conclusions A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission. PMID:24507381

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

NASA Astrophysics Data System (ADS)

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

2017-12-01

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, ELR yields promising results compared to PLSR and SVR in this study. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment.

A novel quantitative model of cell cycle progression based on cyclin-dependent kinases activity and population balances.

PubMed

Pisu, Massimo; Concas, Alessandro; Cao, Giacomo

2015-04-01

Cell cycle regulates proliferative cell capacity under normal or pathologic conditions, and in general it governs all in vivo/in vitro cell growth and proliferation processes. Mathematical simulation by means of reliable and predictive models represents an important tool to interpret experiment results, to facilitate the definition of the optimal operating conditions for in vitro cultivation, or to predict the effect of a specific drug in normal/pathologic mammalian cells. Along these lines, a novel model of cell cycle progression is proposed in this work. Specifically, it is based on a population balance (PB) approach that allows one to quantitatively describe cell cycle progression through the different phases experienced by each cell of the entire population during its own life. The transition between two consecutive cell cycle phases is simulated by taking advantage of the biochemical kinetic model developed by Gérard and Goldbeter (2009) which involves cyclin-dependent kinases (CDKs) whose regulation is achieved through a variety of mechanisms that include association with cyclins and protein inhibitors, phosphorylation-dephosphorylation, and cyclin synthesis or degradation. This biochemical model properly describes the entire cell cycle of mammalian cells by maintaining a sufficient level of detail useful to identify check point for transition and to estimate phase duration required by PB. Specific examples are discussed to illustrate the ability of the proposed model to simulate the effect of drugs for in vitro trials of interest in oncology, regenerative medicine and tissue engineering. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modularization of biochemical networks based on classification of Petri net t-invariants.

PubMed

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-02-08

Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.

Modularization of biochemical networks based on classification of Petri net t-invariants

PubMed Central

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-01-01

Background Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior. With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Methods Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. Results We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. Conclusion We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis. PMID:18257938

Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories

PubMed Central

Donovan, Rory M.; Tapia, Jose-Juan; Sullivan, Devin P.; Faeder, James R.; Murphy, Robert F.; Dittrich, Markus; Zuckerman, Daniel M.

2016-01-01

The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables. PMID:26845334

Bootstrapping Least Squares Estimates in Biochemical Reaction Networks

PubMed Central

Linder, Daniel F.

2015-01-01

The paper proposes new computational methods of computing confidence bounds for the least squares estimates (LSEs) of rate constants in mass-action biochemical reaction network and stochastic epidemic models. Such LSEs are obtained by fitting the set of deterministic ordinary differential equations (ODEs), corresponding to the large volume limit of a reaction network, to network’s partially observed trajectory treated as a continuous-time, pure jump Markov process. In the large volume limit the LSEs are asymptotically Gaussian, but their limiting covariance structure is complicated since it is described by a set of nonlinear ODEs which are often ill-conditioned and numerically unstable. The current paper considers two bootstrap Monte-Carlo procedures, based on the diffusion and linear noise approximations for pure jump processes, which allow one to avoid solving the limiting covariance ODEs. The results are illustrated with both in-silico and real data examples from the LINE 1 gene retrotranscription model and compared with those obtained using other methods. PMID:25898769

Non-linear corrections to the time-covariance function derived from a multi-state chemical master equation.

PubMed

Scott, M

2012-08-01

The time-covariance function captures the dynamics of biochemical fluctuations and contains important information about the underlying kinetic rate parameters. Intrinsic fluctuations in biochemical reaction networks are typically modelled using a master equation formalism. In general, the equation cannot be solved exactly and approximation methods are required. For small fluctuations close to equilibrium, a linearisation of the dynamics provides a very good description of the relaxation of the time-covariance function. As the number of molecules in the system decrease, deviations from the linear theory appear. Carrying out a systematic perturbation expansion of the master equation to capture these effects results in formidable algebra; however, symbolic mathematics packages considerably expedite the computation. The authors demonstrate that non-linear effects can reveal features of the underlying dynamics, such as reaction stoichiometry, not available in linearised theory. Furthermore, in models that exhibit noise-induced oscillations, non-linear corrections result in a shift in the base frequency along with the appearance of a secondary harmonic.

Wound healing potential of Pterocarpus santalinus linn: a pharmacological evaluation.

PubMed

Biswas, Tuhin Kanti; Maity, Lakshmi Narayan; Mukherjee, Biswapati

2004-09-01

The need for new therapeutics for wound healing has encouraged the drive to examine the nature and value of plant products. Ayurveda, the Indian traditional system of medicine, mentions the values of medicinal plants for wound healing. One of these is Pterocarpus santalinus. This article describes a pharmacological study to evaluate its toxicity as well as wound-healing potential in animal studies. Powder made from the wood of the P. santalinus tree was used to make up an ointment in a petroleum jelly base. No toxic effects were observed in 72 hours. Studies were done on punch and burn wound models on normal and diabetic rats using the test ointment, untreated and vehicle controls, and standard therapy. Physical and biochemical measurements were made. The test ointment-treated wounds healed significantly faster. On healing, collagenesis and biochemical measurements yielded supportive data. These studies permit the conclusion that the P. santalinus ointment is safe and effective in treating acute wounds in animal models.

Topological analysis of metabolic networks based on petri net theory.

PubMed

Zevedei-Oancea, Ionela; Schuster, Stefan

2011-01-01

Petri net concepts provide additional tools for the modelling of metabolic networks. Here, the similarities between the counterparts in traditional biochemical modelling and Petri net theory are discussed. For example the stoichiometry matrix of a metabolic network corresponds to the incidence matrix of the Petri net. The flux modes and conservation relations have the T-invariants, respectively, P-invariants as counterparts. We reveal the biological meaning of some notions specific to the Petri net framework (traps, siphons, deadlocks, liveness). We focus on the topological analysis rather than on the analysis of the dynamic behaviour. The treatment of external metabolites is discussed. Some simple theoretical examples are presented for illustration. Also the Petri nets corresponding to some biochemical networks are built to support our results. For example, the role of triose phosphate isomerase (TPI) in Trypanosoma brucei metabolism is evaluated by detecting siphons and traps. All Petri net properties treated in this contribution are exemplified on a system extracted from nucleotide metabolism.

Topological analysis of metabolic networks based on Petri net theory.

PubMed

Zevedei-Oancea, Ionela; Schuster, Stefan

2003-01-01

Petri net concepts provide additional tools for the modelling of metabolic networks. Here, the similarities between the counterparts in traditional biochemical modelling and Petri net theory are discussed. For example the stoichiometry matrix of a metabolic network corresponds to the incidence matrix of the Petri net. The flux modes and conservation relations have the T-invariants, respectively, P-invariants as counterparts. We reveal the biological meaning of some notions specific to the Petri net framework (traps, siphons, deadlocks, liveness). We focus on the topological analysis rather than on the analysis of the dynamic behaviour. The treatment of external metabolites is discussed. Some simple theoretical examples are presented for illustration. Also the Petri nets corresponding to some biochemical networks are built to support our results. For example, the role of triose phosphate isomerase (TPI) in Trypanosoma brucei metabolism is evaluated by detecting siphons and traps. All Petri net properties treated in this contribution are exemplified on a system extracted from nucleotide metabolism.

Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection.

PubMed

Yoon, Jeongah; Si, Yaguang; Nolan, Ryan; Lee, Kyongbum

2007-09-15

The rational decomposition of biochemical networks into sub-structures has emerged as a useful approach to study the design of these complex systems. A biochemical network is characterized by an inhomogeneous connectivity distribution, which gives rise to several organizational features, including modularity. To what extent the connectivity-based modules reflect the functional organization of the network remains to be further explored. In this work, we examine the influence of physiological perturbations on the modular organization of cellular metabolism. Modules were characterized for two model systems, liver and adipocyte primary metabolism, by applying an algorithm for top-down partition of directed graphs with non-uniform edge weights. The weights were set by the engagement of the corresponding reactions as expressed by the flux distribution. For the base case of the fasted rat liver, three modules were found, carrying out the following biochemical transformations: ketone body production, glucose synthesis and transamination. This basic organization was further modified when different flux distributions were applied that describe the liver's metabolic response to whole body inflammation. For the fully mature adipocyte, only a single module was observed, integrating all of the major pathways needed for lipid storage. Weaker levels of integration between the pathways were found for the early stages of adipocyte differentiation. Our results underscore the inhomogeneous distribution of both connectivity and connection strengths, and suggest that global activity data such as the flux distribution can be used to study the organizational flexibility of cellular metabolism. Supplementary data are available at Bioinformatics online.

AVIRIS spectra correlated with the chlorophyll concentration of a forest canopy

NASA Technical Reports Server (NTRS)

Kupiec, John; Smith, Geoffrey M.; Curran, Paul J.

1993-01-01

Imaging spectrometers have many potential applications in the environmental sciences. One of the more promising applications is that of estimating the biochemical concentrations of key foliar biochemicals in forest canopies. These estimates are based on spectroscopic theory developed in agriculture and could be used to provide the spatial inputs necessary for the modeling of forest ecosystem dynamics and productivity. Several foliar biochemicals are currently under investigation ranging from those with primary absorption features in visible to middle infrared wavelengths (e.g., water, chlorophyll) to those with secondary to tertiary absorption features in this part of the spectrum (e.g., nitrogen, lignin). The foliar chemical of interest in this paper is chlorophyll; this is a photoreceptor and catalyst for the conversion of sunlight into chemical energy and as such plays a vital role in the photochemical synthesis of carbohydrates in plants. The aim of the research reported here was to determine if the chlorophyll concentration of a forest canopy could be correlated with the reflectance spectra recorded by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS).

STEPP--Search Tool for Exploration of Petri net Paths: a new tool for Petri net-based path analysis in biochemical networks.

PubMed

Koch, Ina; Schueler, Markus; Heiner, Monika

2005-01-01

To understand biochemical processes caused by, e. g., mutations or deletions in the genome, the knowledge of possible alternative paths between two arbitrary chemical compounds is of increasing interest for biotechnology, pharmacology, medicine, and drug design. With the steadily increasing amount of data from high-throughput experiments new biochemical networks can be constructed and existing ones can be extended, which results in many large metabolic, signal transduction, and gene regulatory networks. The search for alternative paths within these complex and large networks can provide a huge amount of solutions, which can not be handled manually. Moreover, not all of the alternative paths are generally of interest. Therefore, we have developed and implemented a method, which allows us to define constraints to reduce the set of all structurally possible paths to the truly interesting path set. The paper describes the search algorithm and the constraints definition language. We give examples for path searches using this dedicated special language for a Petri net model of the sucrose-to-starch breakdown in the potato tuber.

STEPP - Search Tool for Exploration of Petri net Paths: A New Tool for Petri Net-Based Path Analysis in Biochemical Networks.

PubMed

Koch, Ina; Schüler, Markus; Heiner, Monika

2011-01-01

To understand biochemical processes caused by, e.g., mutations or deletions in the genome, the knowledge of possible alternative paths between two arbitrary chemical compounds is of increasing interest for biotechnology, pharmacology, medicine, and drug design. With the steadily increasing amount of data from high-throughput experiments new biochemical networks can be constructed and existing ones can be extended, which results in many large metabolic, signal transduction, and gene regulatory networks. The search for alternative paths within these complex and large networks can provide a huge amount of solutions, which can not be handled manually. Moreover, not all of the alternative paths are generally of interest. Therefore, we have developed and implemented a method, which allows us to define constraints to reduce the set of all structurally possible paths to the truly interesting path set. The paper describes the search algorithm and the constraints definition language. We give examples for path searches using this dedicated special language for a Petri net model of the sucrose-to-starch breakdown in the potato tuber. http://sanaga.tfh-berlin.de/~stepp/

Three-dimensional modeling of oxidized-LDL accumulation and HDL mass transport in a coronary artery: a proof-of-concept study for predicting the region of atherosclerotic plaque development.

PubMed

Sakellarios, Antonis I; Siogkas, Panagiotis K; Athanasiou, Lambros S; Exarchos, Themis P; Papafaklis, Michail I; Bourantas, Christos V; Naka, Katerina K; Michalis, Lampros K; Filipovic, Nenad; Parodi, Oberdan; Fotiadis, Dimitrios I

2013-01-01

Low density lipoprotein (LDL) has a significant role on the atherosclerotic plaque development, while the concentration of high density lipoproteins (HDL) is considered to play an atheroprotective role according to several biochemical mechanisms. In this work, it is the first time that both LDL and HDL concentrations are taken into account in order to predict the regions prone for plaque development. Our modeling approach is based on the use of a realistic three-dimensional reconstructed pig coronary artery in two time points. Biochemical data measured in the pig were also included in order to develop a more customized model. We modeled coronary blood flow by solving the Navier-Stokes equations in the arterial lumen and plasma filtration in the arterial wall using Darcy's Law. HDL transport was modeled only in the arterial lumen using the convection-diffusion equation, while LDL transport was modeled both in the lumen and the arterial wall. An additional novelty of this work is that we model the oxidation of LDL taking into account the atheroprotective role of HDL. The results of our model were in good agreement with histological findings demonstrating that increased oxidized LDL is found near regions of advanced plaques, while non-oxidized LDL is found in regions of early plaque types.

Modeling the dispersion effects of contractile fibers in smooth muscles

NASA Astrophysics Data System (ADS)

Murtada, Sae-Il; Kroon, Martin; Holzapfel, Gerhard A.

2010-12-01

Micro-structurally based models for smooth muscle contraction are crucial for a better understanding of pathological conditions such as atherosclerosis, incontinence and asthma. It is meaningful that models consider the underlying mechanical structure and the biochemical activation. Hence, a simple mechanochemical model is proposed that includes the dispersion of the orientation of smooth muscle myofilaments and that is capable to capture available experimental data on smooth muscle contraction. This allows a refined study of the effects of myofilament dispersion on the smooth muscle contraction. A classical biochemical model is used to describe the cross-bridge interactions with the thin filament in smooth muscles in which calcium-dependent myosin phosphorylation is the only regulatory mechanism. A novel mechanical model considers the dispersion of the contractile fiber orientations in smooth muscle cells by means of a strain-energy function in terms of one dispersion parameter. All model parameters have a biophysical meaning and may be estimated through comparisons with experimental data. The contraction of the middle layer of a carotid artery is studied numerically. Using a tube the relationships between the internal pressure and the stretches are investigated as functions of the dispersion parameter, which implies a strong influence of the orientation of smooth muscle myofilaments on the contraction response. It is straightforward to implement this model in a finite element code to better analyze more complex boundary-value problems.

Simulating carbon dioxide exchange rates of deciduous tree species: evidence for a general pattern in biochemical changes and water stress response.

PubMed

Reynolds, Robert F; Bauerle, William L; Wang, Ying

2009-09-01

Deciduous trees have a seasonal carbon dioxide exchange pattern that is attributed to changes in leaf biochemical properties. However, it is not known if the pattern in leaf biochemical properties - maximum Rubisco carboxylation (V(cmax)) and electron transport (J(max)) - differ between species. This study explored whether a general pattern of changes in V(cmax), J(max), and a standardized soil moisture response accounted for carbon dioxide exchange of deciduous trees throughout the growing season. The model MAESTRA was used to examine V(cmax) and J(max) of leaves of five deciduous trees, Acer rubrum 'Summer Red', Betula nigra, Quercus nuttallii, Quercus phellos and Paulownia elongata, and their response to soil moisture. MAESTRA was parameterized using data from in situ measurements on organs. Linking the changes in biochemical properties of leaves to the whole tree, MAESTRA integrated the general pattern in V(cmax) and J(max) from gas exchange parameters of leaves with a standardized soil moisture response to describe carbon dioxide exchange throughout the growing season. The model estimates were tested against measurements made on the five species under both irrigated and water-stressed conditions. Measurements and modelling demonstrate that the seasonal pattern of biochemical activity in leaves and soil moisture response can be parameterized with straightforward general relationships. Over the course of the season, differences in carbon exchange between measured and modelled values were within 6-12 % under well-watered conditions and 2-25 % under water stress conditions. Hence, a generalized seasonal pattern in the leaf-level physiological change of V(cmax) and J(max), and a standardized response to soil moisture was sufficient to parameterize carbon dioxide exchange for large-scale evaluations. Simplification in parameterization of the seasonal pattern of leaf biochemical activity and soil moisture response of deciduous forest species is demonstrated. This allows reliable modelling of carbon exchange for deciduous trees, thus circumventing the need for extensive gas exchange experiments on different species.

A Randomized Phase 2 Trial of 177Lu Radiolabeled Anti-PSMA Biochemically Monoclonal Antibody J591 in Patients with High-Risk Castrate, Biochemically Relapsed Prostate Cancer

DTIC Science & Technology

2010-09-01

relapsed prostate cancer (PC) after local therapy. J Clin Oncol 28: 15s, 2010 (suppl; Abstr TPS248) Presentation: Poster presentation, 2010 ASCO...Annual Meeting V. Conclusions Biochemical relapse is common after local therapy for prostate cancer. Based on the physical properties of 177Lu...ketoconazole in patients (pts) with high-risk castrate biochemically relapsed prostate cancer (PC) after local therapy. S. T. Tagawa, J. Osborne, P. J

Modelling biochemical reaction systems by stochastic differential equations with reflection.

PubMed

Niu, Yuanling; Burrage, Kevin; Chen, Luonan

2016-05-07

In this paper, we gave a new framework for modelling and simulating biochemical reaction systems by stochastic differential equations with reflection not in a heuristic way but in a mathematical way. The model is computationally efficient compared with the discrete-state Markov chain approach, and it ensures that both analytic and numerical solutions remain in a biologically plausible region. Specifically, our model mathematically ensures that species numbers lie in the domain D, which is a physical constraint for biochemical reactions, in contrast to the previous models. The domain D is actually obtained according to the structure of the corresponding chemical Langevin equations, i.e., the boundary is inherent in the biochemical reaction system. A variant of projection method was employed to solve the reflected stochastic differential equation model, and it includes three simple steps, i.e., Euler-Maruyama method was applied to the equations first, and then check whether or not the point lies within the domain D, and if not perform an orthogonal projection. It is found that the projection onto the closure D¯ is the solution to a convex quadratic programming problem. Thus, existing methods for the convex quadratic programming problem can be employed for the orthogonal projection map. Numerical tests on several important problems in biological systems confirmed the efficiency and accuracy of this approach. Copyright © 2016 Elsevier Ltd. All rights reserved.

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

PubMed

Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J

2014-01-01

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

Examining the effect of down regulation under high [CO2] on the growth of soybean assimilating a semi process-based model and FACE data

NASA Astrophysics Data System (ADS)

Sakurai, G.; Iizumi, T.; Yokozawa, M.

2011-12-01

The actual impact of elevated [CO2] with the interaction of the other climatic factors on the crop growth is still debated. In many process-based crop models, the response of photosynthesis per single leaf to environmental factors is basically described using the biochemical model of Farquhar et al. (1980). However, the decline in photosynthetic enhancement known as down regulation has not been taken into account. On the other hand, the mechanisms causing photosynthetic down regulation is still unknown, which makes it difficult to include the effect of down regulation into process-based crop models. The current results of Free-air CO2 enrichment (FACE) experiments have reported the effect of down regulation under actual environments. One of the effective approaches to involve these results into future crop yield prediction is developing a semi process-based crop growth model, which includes the effect of photosynthetic down regulation as a statistical model, and assimilating the data obtained in FACE experiments. In this study, we statistically estimated the parameters of a semi process-based model for soybean growth ('SPM-soybean') using a hierarchical Baysian method with the FACE data on soybeans (Morgan et al. 2005). We also evaluated the effect of down regulation on the soybean yield in future climatic conditions. The model selection analysis showed that the effective correction to the overestimation of the Farquhar's biochemical C3 model was to reduce the maximum rate of carboxylation (Vcmax) under elevated [CO2]. However, interestingly, the difference in the estimated final crop yields between the corrected model and the non-corrected model was very slight (Fig.1a) for future projection under climate change scenario (Miroc-ESM). This was due to that the reduction in Vcmax also brought about the reduction of the base dark respiration rate of leaves. Because the dark respiration rate exponentially increases with temperature, the slight difference in base respiration rate becomes a large difference under high temperature under the future climate scenarios. In other words, if the temperature rise is very small or zero under elevated [CO2] condition, the effect of down regulation significantly appears (Fig.1b). This result suggest that further experimental data that considering high CO2 effect and high temperature effect in field conditions should be important and elaborate the model projection of the future crop yield through data assimilation method.

Evaluation of Stem Cell-Derived Red Blood Cells as a Transfusion Product Using a Novel Animal Model.

PubMed

Shah, Sandeep N; Gelderman, Monique P; Lewis, Emily M A; Farrel, John; Wood, Francine; Strader, Michael Brad; Alayash, Abdu I; Vostal, Jaroslav G

2016-01-01

Reliance on volunteer blood donors can lead to transfusion product shortages, and current liquid storage of red blood cells (RBCs) is associated with biochemical changes over time, known as 'the storage lesion'. Thus, there is a need for alternative sources of transfusable RBCs to supplement conventional blood donations. Extracorporeal production of stem cell-derived RBCs (stemRBCs) is a potential and yet untapped source of fresh, transfusable RBCs. A number of groups have attempted RBC differentiation from CD34+ cells. However, it is still unclear whether these stemRBCs could eventually be effective substitutes for traditional RBCs due to potential differences in oxygen carrying capacity, viability, deformability, and other critical parameters. We have generated ex vivo stemRBCs from primary human cord blood CD34+ cells and compared them to donor-derived RBCs based on a number of in vitro parameters. In vivo, we assessed stemRBC circulation kinetics in an animal model of transfusion and oxygen delivery in a mouse model of exercise performance. Our novel, chronically anemic, SCID mouse model can evaluate the potential of stemRBCs to deliver oxygen to tissues (muscle) under resting and exercise-induced hypoxic conditions. Based on our data, stem cell-derived RBCs have a similar biochemical profile compared to donor-derived RBCs. While certain key differences remain between donor-derived RBCs and stemRBCs, the ability of stemRBCs to deliver oxygen in a living organism provides support for further development as a transfusion product.

Molecular characterization of human ABHD2 as TAG lipase and ester hydrolase

PubMed Central

M., Naresh Kumar; V.B.S.C., Thunuguntla; G.K., Veeramachaneni; B., Chandra Sekhar; Guntupalli, Swapna; J.S., Bondili

2016-01-01

Alterations in lipid metabolism have been progressively documented as a characteristic property of cancer cells. Though, human ABHD2 gene was found to be highly expressed in breast and lung cancers, its biochemical functionality is yet uncharacterized. In the present study we report, human ABHD2 as triacylglycerol (TAG) lipase along with ester hydrolysing capacity. Sequence analysis of ABHD2 revealed the presence of conserved motifs G205XS207XG209 and H120XXXXD125. Phylogenetic analysis showed homology to known lipases, Drosophila melanogaster CG3488. To evaluate the biochemical role, recombinant ABHD2 was expressed in Saccharomyces cerevisiae using pYES2/CT vector and His-tag purified protein showed TAG lipase activity. Ester hydrolase activity was confirmed with pNP acetate, butyrate and palmitate substrates respectively. Further, the ABHD2 homology model was built and the modelled protein was analysed based on the RMSD and root mean square fluctuation (RMSF) of the 100 ns simulation trajectory. Docking the acetate, butyrate and palmitate ligands with the model confirmed covalent binding of ligands with the Ser207 of the GXSXG motif. The model was validated with a mutant ABHD2 developed with alanine in place of Ser207 and the docking studies revealed loss of interaction between selected ligands and the mutant protein active site. Based on the above results, human ABHD2 was identified as a novel TAG lipase and ester hydrolase. PMID:27247428

Molecular characterization of human ABHD2 as TAG lipase and ester hydrolase.

PubMed

M, Naresh Kumar; V B S C, Thunuguntla; G K, Veeramachaneni; B, Chandra Sekhar; Guntupalli, Swapna; J S, Bondili

2016-08-01

Alterations in lipid metabolism have been progressively documented as a characteristic property of cancer cells. Though, human ABHD2 gene was found to be highly expressed in breast and lung cancers, its biochemical functionality is yet uncharacterized. In the present study we report, human ABHD2 as triacylglycerol (TAG) lipase along with ester hydrolysing capacity. Sequence analysis of ABHD2 revealed the presence of conserved motifs G(205)XS(207)XG(209) and H(120)XXXXD(125) Phylogenetic analysis showed homology to known lipases, Drosophila melanogaster CG3488. To evaluate the biochemical role, recombinant ABHD2 was expressed in Saccharomyces cerevisiae using pYES2/CT vector and His-tag purified protein showed TAG lipase activity. Ester hydrolase activity was confirmed with pNP acetate, butyrate and palmitate substrates respectively. Further, the ABHD2 homology model was built and the modelled protein was analysed based on the RMSD and root mean square fluctuation (RMSF) of the 100 ns simulation trajectory. Docking the acetate, butyrate and palmitate ligands with the model confirmed covalent binding of ligands with the Ser(207) of the GXSXG motif. The model was validated with a mutant ABHD2 developed with alanine in place of Ser(207) and the docking studies revealed loss of interaction between selected ligands and the mutant protein active site. Based on the above results, human ABHD2 was identified as a novel TAG lipase and ester hydrolase. © 2016 The Author(s).

Using NMR-based metabolomics to monitor the biochemical composition of agricultural soils: a pilot study

USDA-ARS?s Scientific Manuscript database

NMR-based metabolomics plays a major role studying complex living systems. However, very few studies describe the application of this technique to the evaluation of soil metabolome. Here, we introduce a protocol for analyzing the biochemical compounds from agricultural soils where the microbial comm...

An extension of ASM2d including pH calculation.

PubMed

Serralta, J; Ferrer, J; Borrás, L; Seco, A

2004-11-01

This paper presents an extension of the Activated Sludge Model No. 2d (ASM2d) including a chemical model able to calculate the pH value in biological processes. The developed chemical model incorporates the complete set of chemical species affecting the pH value to ASM2d describing non-equilibrium biochemical processes. It considers the system formed by one aqueous phase, in which biochemical processes take place, and one gaseous phase, and is based on the assumptions of instantaneous chemical equilibrium under liquid phase and kinetically governed mass transport between the liquid and gas phase. The ASM2d enlargement comprises the addition of every component affecting the pH value and an ion-balance for the calculation of the pH value and the dissociation species. The significant pH variations observed in a sequencing batch reactor operated for enhanced biological phosphorus removal were used to verify the capability of the extended model for predicting the dynamics of pH jointly with concentrations of acetic acid and phosphate. A pH inhibition function for polyphosphate accumulating bacteria has also been included in the model to simulate the behaviour observed. Experimental data obtained in four different experiments (with different sludge retention time and influent phosphorus concentrations) were accurately reproduced.

Laboratory-Based Biomarkers and Liver Metastases in Metastatic Castration-Resistant Prostate Cancer.

PubMed

Cotogno, Patrick M; Ranasinghe, Lahiru K; Ledet, Elisa M; Lewis, Brian E; Sartor, Oliver

2018-04-26

Metastatic castrate-resistant prostate cancer (mCRPC) patients with liver metastases have a poor prognosis. No large studies have investigated the clinical and biochemical parameters associated with liver metastases in this population. Patient data made available via Project Data Sphere were collected from 1,281 men with mCRPC who were enrolled on to three phase III clinical trials for the treatment of their disease. Multiple logistic regression was performed on eight clinical and biochemical baseline variables to test their association with the presence of liver metastases on baseline radiographic imaging. Variables of interest included prior docetaxel exposure, Eastern Cooperative Oncology Group performance status, albumin, alkaline phosphatase, alanine transaminase, aspartate transaminase (AST), hemoglobin (HGB), lactate dehydrogenase (LDH), prostate-specific antigen, and total bilirubin. Final models were compared when treating the variables as either continuous or categorized. Multiple variable analysis demonstrated that an increasing serum AST or LDH or a decreasing HGB was associated with an increased probability of having documented radiographic liver metastases ( p  < .0001). The area under the curve for the continuous model was 0.6842 and 0.6890 for the categorical one, with the latter model containing a dichotomized AST and LDH based on the upper limit of normal and tertile ranges of HGB based on the distribution of the outcome. Our analysis demonstrated a significant association between the presence of liver metastases and laboratory levels of AST, LDH, and HGB. These have implications for patient management. More research is needed to validate these biomarkers and prospectively determine their application in the clinical setting. The purpose of this study was to evaluate biochemical and clinical biomarkers associated with the presence of liver metastases in men diagnosed with metastatic castrate-resistant prostate cancer. The results indicate that quantitative assessments of aspartate transaminase, lactate dehydrogenase, and hemoglobin are significantly associated with an increased probability of having documented radiographic liver metastases. Analysis of these simple variables can alert clinicians to those at high risk for prostate cancer that has spread to the liver, a finding of clear importance for clinical management. © AlphaMed Press 2018.

A Graphical User Interface for Parameterizing Biochemical Models of Photosynthesis and Chlorophyll Fluorescence

NASA Astrophysics Data System (ADS)

Kornfeld, A.; Van der Tol, C.; Berry, J. A.

2015-12-01

Recent advances in optical remote sensing of photosynthesis offer great promise for estimating gross primary productivity (GPP) at leaf, canopy and even global scale. These methods -including solar-induced chlorophyll fluorescence (SIF) emission, fluorescence spectra, and hyperspectral features such as the red edge and the photochemical reflectance index (PRI) - can be used to greatly enhance the predictive power of global circulation models (GCMs) by providing better constraints on GPP. The way to use measured optical data to parameterize existing models such as SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) is not trivial, however. We have therefore extended a biochemical model to include fluorescence and other parameters in a coupled treatment. To help parameterize the model, we then use nonlinear curve-fitting routines to determine the parameter set that enables model results to best fit leaf-level gas exchange and optical data measurements. To make the tool more accessible to all practitioners, we have further designed a graphical user interface (GUI) based front-end to allow researchers to analyze data with a minimum of effort while, at the same time, allowing them to change parameters interactively to visualize how variation in model parameters affect predicted outcomes such as photosynthetic rates, electron transport, and chlorophyll fluorescence. Here we discuss the tool and its effectiveness, using recently-gathered leaf-level data.

Techniques for estimating the quantity and quality of storm runoff from urban watersheds of Jefferson County, Kentucky

USGS Publications Warehouse

Evaldi, R.D.; Moore, B.L.

1994-01-01

Linear regression models are presented for estimating storm-runoff volumes, and mean con- centrations and loads of selected constituents in storm runoff from urban watersheds of Jefferson County, Kentucky. Constituents modeled include dissolved oxygen, biochemical and chemical oxygen demand, total and suspended solids, volatile residue, nitrogen, phosphorus and phosphate, calcium, magnesium, barium, copper, iron, lead, and zinc. Model estimations are a function of drainage area, percentage of impervious area, climatological data, and land uses. Estimation models are based on runoff volumes, and concen- trations and loads of constituents in runoff measured at 6 stormwater outfalls and 25 streams in Jefferson County.

Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection

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

Zhao, Kaiguang; Valle, Denis; Popescu, Sorin

2013-05-15

Model specification remains challenging in spectroscopy of plant biochemistry, as exemplified by the availability of various spectral indices or band combinations for estimating the same biochemical. This lack of consensus in model choice across applications argues for a paradigm shift in hyperspectral methods to address model uncertainty and misspecification. We demonstrated one such method using Bayesian model averaging (BMA), which performs variable/band selection and quantifies the relative merits of many candidate models to synthesize a weighted average model with improved predictive performances. The utility of BMA was examined using a portfolio of 27 foliage spectral–chemical datasets representing over 80 speciesmore » across the globe to estimate multiple biochemical properties, including nitrogen, hydrogen, carbon, cellulose, lignin, chlorophyll (a or b), carotenoid, polar and nonpolar extractives, leaf mass per area, and equivalent water thickness. We also compared BMA with partial least squares (PLS) and stepwise multiple regression (SMR). Results showed that all the biochemicals except carotenoid were accurately estimated from hyerspectral data with R2 values > 0.80.« less

Biogas production from Pongamia biomass wastes and a model to estimate biodegradability from their composition.

PubMed

Gunaseelan, Victor Nallathambi

2014-02-01

In this study, I investigated the chemical characteristics, biochemical methane potential, conversion kinetics and biodegradability of untreated and NaOH-treated Pongamia plant parts, and pod husk and press cake from the biodiesel industry to evaluate their suitability as an alternative feedstock for biogas production. The untreated Pongamia seeds exhibited the maximum CH4 yield of 473 ml g (-1) volatile solid (VS) added. Yellow, withered leaves gave a yield as low as 122 ml CH4 g (-1) VS added. There were significant variations in the CH4 production rate constants, which ranged from 0.02 to 0.15 d (-1), and biodegradability, which ranged from 0.25 to 0.98. NaOH treatment of leaf and pod husk, which were highly rich in fibers, increased the yields by 15-22% and CH4 production rate constants by 20-75%. Utilization of Pongamia wastes in biogas digesters not only influences the economics of biodiesel production but also yields CH4 fuel and protects the environment. The experimental data from this study were used to develop a multiple regression model, which could estimate biodegradability based on biochemical characteristics. The model predicted the biodegradability of previously published biomass wastes (r(2) = 0.88) from their biochemical composition. The theoretical CH4 yields estimated as 350 ml g(-1) chemical oxygen demand destroyed are much higher than the experimental yields as 100% biodegradability is assumed for each substrate. Upon correcting the theoretical CH4 yields with biodegradability data obtained from chemical analyses of substrates, their ultimate CH4 yields could be predicted rapidly.

Could the differences in the biochemistry of prostate carcinoma compared to benign prostate tissue biopsy fragments be evaluated through Raman spectroscopy?

NASA Astrophysics Data System (ADS)

Silveira, Landulfo; Leite, Kátia Ramos M.; Srougi, Miguel; Silveira, Fabrício L.; Pacheco, Marcos Tadeu T.; Zângaro, Renato A.; Pasqualucci, Carlos A.

2013-03-01

It has been proposed a spectral model to evaluate the biochemical differences between prostate carcinoma and benign fragments using dispersive Raman spectroscopy. We have examined 51 prostate fragments from surgically removed PrCa; each fragment was snap-frozen and stored (-80°C) prior spectral analysis. Raman spectrum was measured using a Raman spectrometer (830 nm excitation) coupled to a fiber-optic probe. Integration time and laser power were set to 50 s and 300 mW, respectively. It has been collected triplicate spectra from each fragment (total 153 spectra). Some samples exhibited a strong fluorescence, which was removed by a 7th order polynomial fitting. It has been developed a spectral model based on the least-squares fitting of the spectra of pure biochemicals (actin, collagen, elastin, carotene, glycogen, phosphatidylcholine, hemoglobin, and water) with the spectra of tissues, where the fitting parameters are the relative contribution of the compounds to the tissue spectrum. The spectra (600-1800 cm-1 range) are dominated by bands of proteins; it has been found a small difference in the mean spectra of PrCa compared to the benign tissue, mainly in the 1000-1400 cm-1 region, indicating similar biochemical constitution. The spectral fitting model revealed that elastin and phosphatidylcholine were increased in PrCa, whereas blood and water were reduced in malignant lesions (p < 0.05). A discrimination of PrCa from benign tissue using Mahalanobis distance applied to the contribution of elastin, hemoglobin and phosphatidylcholine resulted in sensitivity of 72% and specificity of 70%.

Predicting hepatic steatosis and liver fat content in obese children based on biochemical parameters and anthropometry.

PubMed

Zhang, H-X; Xu, X-Q; Fu, J-F; Lai, C; Chen, X-F

2015-04-01

Predictors of quantitative evaluation of hepatic steatosis and liver fat content (LFC) using clinical and laboratory variables available in the general practice in the obese children are poorly identified. To build predictive models of hepatic steatosis and LFC in obese children based on biochemical parameters and anthropometry. Hepatic steatosis and LFC were determined using proton magnetic resonance spectroscopy in 171 obese children aged 5.5-18.0 years. Routine clinical and laboratory parameters were also measured in all subjects. Group analysis, univariable correlation analysis, and multivariate logistic and linear regression analysis were used to develop a liver fat score to identify hepatic steatosis and a liver fat equation to predict LFC in each subject. The predictive model of hepatic steatosis in our participants based on waist circumference and alanine aminotransferase had an area under the receiver operating characteristic curve of 0.959 (95% confidence interval: 0.927-0.990). The optimal cut-off value of 0.525 for determining hepatic steatosis had sensitivity of 93% and specificity of 90%. A liver fat equation was also developed based on the same parameters of hepatic steatosis liver fat score, which would be used to calculate the LFC in each individual. The liver fat score and liver fat equation, consisting of routinely available variables, may help paediatricians to accurately determine hepatic steatosis and LFC in clinical practice, but external validation is needed before it can be employed for this purpose. © 2014 The Authors. Pediatric Obesity © 2014 World Obesity.

Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo

PubMed Central

Golightly, Andrew; Wilkinson, Darren J.

2011-01-01

Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but we find here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem. Approximations to the inferential model based on stochastic differential equations (SDEs) are considered, as well as improvements to the inference scheme that exploit the SDE structure. We apply the methodology to a Lotka–Volterra system and a prokaryotic auto-regulatory network. PMID:23226583

Mathematical Modeling Approaches in Plant Metabolomics.

PubMed

Fürtauer, Lisa; Weiszmann, Jakob; Weckwerth, Wolfram; Nägele, Thomas

2018-01-01

The experimental analysis of a plant metabolome typically results in a comprehensive and multidimensional data set. To interpret metabolomics data in the context of biochemical regulation and environmental fluctuation, various approaches of mathematical modeling have been developed and have proven useful. In this chapter, a general introduction to mathematical modeling is presented and discussed in context of plant metabolism. A particular focus is laid on the suitability of mathematical approaches to functionally integrate plant metabolomics data in a metabolic network and combine it with other biochemical or physiological parameters.

Java Web Simulation (JWS); a web based database of kinetic models.

PubMed

Snoep, J L; Olivier, B G

2002-01-01

Software to make a database of kinetic models accessible via the internet has been developed and a core database has been set up at http://jjj.biochem.sun.ac.za/. This repository of models, available to everyone with internet access, opens a whole new way in which we can make our models public. Via the database, a user can change enzyme parameters and run time simulations or steady state analyses. The interface is user friendly and no additional software is necessary. The database currently contains 10 models, but since the generation of the program code to include new models has largely been automated the addition of new models is straightforward and people are invited to submit their models to be included in the database.

Biochemical correlates in an animal model of depression

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

Johnson, J.O.

1986-01-01

A valid animal model of depression was used to explore specific adrenergic receptor differences between rats exhibiting aberrant behavior and control groups. Preliminary experiments revealed a distinct upregulation of hippocampal beta-receptors (as compared to other brain regions) in those animals acquiring a response deficit as a result of exposure to inescapable footshock. Concurrent studies using standard receptor binding techniques showed no large changes in the density of alpha-adrenergic, serotonergic, or dopaminergic receptor densities. This led to the hypothesis that the hippocampal beta-receptor in responses deficient animals could be correlated with the behavioral changes seen after exposure to the aversive stimulus.more » Normalization of the behavior through the administration of antidepressants could be expected to reverse the biochemical changes if these are related to the mechanism of action of antidepressant drugs. This study makes three important points: (1) there is a relevant biochemical change in the hippocampus of response deficient rats which occurs in parallel to a well-defined behavior, (2) the biochemical and behavioral changes are normalized by antidepressant treatments exhibiting both serotonergic and adrenergic mechanisms of action, and (3) the mode of action of antidepressants in this model is probably a combination of serotonergic and adrenergic influences modulating the hippocampal beta-receptor. These results are discussed in relation to anatomical and biochemical aspects of antidepressant action.« less

CELLULAR, BIOCHEMICAL, AND MOLECULAR TECHNIQUES IN DEVELOPMENTAL TOXICOLOGY

EPA Science Inventory

Cellular, molecular and biochemical approaches vastly expand the possibilities for revealing the underlying mechanisms of developmental toxicity. The increasing interest in embryonic development as a model system for the study of gene expression has resulted in a cornucopia of i...

Logic-based models in systems biology: a predictive and parameter-free network analysis method†

PubMed Central

Wynn, Michelle L.; Consul, Nikita; Merajver, Sofia D.

2012-01-01

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples. PMID:23072820

Mass balances for a biological life support system simulation model

NASA Technical Reports Server (NTRS)

Volk, Tyler; Rumel, John D.

1987-01-01

Design decisions to aid the development of future space-based biological life support systems (BLSS) can be made with simulation models. Here the biochemical stoichiometry is developed for: (1) protein, carbohydrate, fat, fiber, and lignin production in the edible and inedible parts of plants; (2) food consumption and production of organic solids in urine, feces, and wash water by the humans; and (3) operation of the waste processor. Flux values for all components are derived for a steady-state system with wheat as the sole food source.

'Enzyme Test Bench': A biochemical application of the multi-rate modeling

NASA Astrophysics Data System (ADS)

Rachinskiy, K.; Schultze, H.; Boy, M.; Büchs, J.

2008-11-01

In the expanding field of 'white biotechnology' enzymes are frequently applied to catalyze the biochemical reaction from a resource material to a valuable product. Evolutionary designed to catalyze the metabolism in any life form, they selectively accelerate complex reactions under physiological conditions. Modern techniques, such as directed evolution, have been developed to satisfy the increasing demand on enzymes. Applying these techniques together with rational protein design, we aim at improving of enzymes' activity, selectivity and stability. To tap the full potential of these techniques, it is essential to combine them with adequate screening methods. Nowadays a great number of high throughput colorimetric and fluorescent enzyme assays are applied to measure the initial enzyme activity with high throughput. However, the prediction of enzyme long term stability within short experiments is still a challenge. A new high throughput technique for enzyme characterization with specific attention to the long term stability, called 'Enzyme Test Bench', is presented. The concept of the Enzyme Test Bench consists of short term enzyme tests conducted under partly extreme conditions to predict the enzyme long term stability under moderate conditions. The technique is based on the mathematical modeling of temperature dependent enzyme activation and deactivation. Adapting the temperature profiles in sequential experiments by optimum non-linear experimental design, the long term deactivation effects can be purposefully accelerated and detected within hours. During the experiment the enzyme activity is measured online to estimate the model parameters from the obtained data. Thus, the enzyme activity and long term stability can be calculated as a function of temperature. The results of the characterization, based on micro liter format experiments of hours, are in good agreement with the results of long term experiments in 1L format. Thus, the new technique allows for both: the enzyme screening with regard to the long term stability and the choice of the optimal process temperature. The presented article gives a successful example for the application of multi-rate modeling, experimental design and parameter estimation within biochemical engineering. At the same time, it shows the limitations of the methods at the state of the art and addresses the current problems to the applied mathematics community.

Hierarchical graphs for better annotations of rule-based models of biochemical systems

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

Hu, Bin; Hlavacek, William

2009-01-01

In the graph-based formalism of the BioNetGen language (BNGL), graphs are used to represent molecules, with a colored vertex representing a component of a molecule, a vertex label representing the internal state of a component, and an edge representing a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions, with a rule that specifies addition (removal) of an edge representing a class of association (dissociation) reactions and with a rule that specifies a change of vertex label representing a class of reactions that affect the internal state of amore » molecular component. A set of rules comprises a mathematical/computational model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Here, for purposes of model annotation, we propose an extension of BNGL that involves the use of hierarchical graphs to represent (1) relationships among components and subcomponents of molecules and (2) relationships among classes of reactions defined by rules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR)/CD3 complex. Likewise, we illustrate how hierarchical graphs can be used to document the similarity of two related rules for kinase-catalyzed phosphorylation of a protein substrate. We also demonstrate how a hierarchical graph representing a protein can be encoded in an XML-based format.« less

Modeling the relationships between quality and biochemical composition of fatty liver in mule ducks.

PubMed

Theron, L; Cullere, M; Bouillier-Oudot, M; Manse, H; Dalle Zotte, A; Molette, C; Fernandez, X; Vitezica, Z G

2012-09-01

The fatty liver of mule ducks (i.e., French "foie gras") is the most valuable product in duck production systems. Its quality is measured by the technological yield, which is the opposite of the fat loss during cooking. The purpose of this study was to determine whether biochemical measures of fatty liver could be used to accurately predict the technological yield (TY). Ninety-one male mule ducks were bred, overfed, and slaughtered under commercial conditions. Fatty liver weight (FLW) and biochemical variables, such as DM, lipid (LIP), and protein content (PROT), were collected. To evaluate evidence for nonlinear fat loss during cooking, we compared regression models describing linear and nonlinear relations between biochemical measures and TY. We detected significantly greater (P = 0.02) linear relation between DM and TY. Our results indicate that LIP and PROT follow a different pattern (linear) than DM and showed that LIP and PROT are nonexclusive contributing factors to TY. Other components, such as carbohydrates, other than those measured in this study, could contribute to DM. Stepwise regression for TY was performed. The traditional model with FLW was tested. The results showed that the weight of the liver is of limited value in the determination of fat loss during cooking (R(2) = 0.14). The most accurate TY prediction equation included DM (in linear and quadratic terms), FLW, and PROT (R(2) = 0.43). Biochemical measures in the fatty liver were more accurate predictors of TY than FLW. The model is useful in commercial conditions because DM, PROT, and FLW are noninvasive measures.

A mathematical model for regulating monomer composition of the microbially synthesized polyhydroxyalkanoate copolymers.

PubMed

Xu, Jun; Guo, Baohua; Zhang, Zengmin; Wu, Qiong; Zhou, Quan; Chen, Jinchun; Chen, Guoqiang; Li, Guodong

2005-06-30

A mathematical model is proposed for predicting the copolymer composition of the microbially synthesized polyhydroxyalkanoate (PHA) copolymers. Based on the biochemical reactions involved in the precursor formation and polymerization pathways, the model correlates the copolymer composition with the cultivation conditions, the enzyme levels and selectivity, and the metabolic pathways. It suggests the following points: (1) in the case of a sole carbon source, the copolymer composition depends mainly on the topology of the metabolic pathways and the selectivity of both the enzymes involved in the precursor formation and the polymerization route; (2) the copolymer composition can be varied in a wide range via alteration of the flux ratio of different types of monomers channeled from two or more independent and simultaneous pathways; (3) the enzymes which should be over-expressed or inhibited to obtain the desired copolymer composition can be predicted. For example, inhibition of the beta-oxidation pathway will increase the content of the monomer units with longer chain length. To test the model, various experiments were envisaged by varying cultivation time, concentration and chain length of the sole carbon source, and molar ratio of the cosubstrates. The predictions from the model agree well with the experimental results. Therefore, the proposed model will be useful in predicting the PHA copolymer composition under different biochemical reaction conditions. In other words, it can provide a guide for the synthesis of desired PHA copolymers.

Correcting the record of structural publications requires joint effort of the community and journal editors.

PubMed

Rupp, Bernhard; Wlodawer, Alexander; Minor, Wladek; Helliwell, John R; Jaskolski, Mariusz

2016-12-01

Seriously flawed and even fictional models of biomolecular crystal structures, although rare, still persist in the record of structural repositories and databases. The ensuing problems of database contamination and persistence of publications based on incorrect structure models must be effectively addressed. The burden cannot be simply left to the critical voices who take the effort to contribute dissenting comments that are mostly ignored. The entire structural biology community, and particularly the journal editors who exercise significant power in this respect, must engage in a constructive dialog lest structural biology lose its credibility as an evidence-based empirical science. © 2016 Federation of European Biochemical Societies.

Characterization of postmortem biochemical changes in rabbit plasma using ATR-FTIR combined with chemometrics: A preliminary study

NASA Astrophysics Data System (ADS)

Zhang, Ji; Li, Bing; Wang, Qi; Li, Chengzhi; Zhang, Yinming; Lin, Hancheng; Wang, Zhenyuan

2017-02-01

Postmortem interval (PMI) determination is one of the most challenging tasks in forensic medicine due to a lack of accurate and reliable methods. It is especially difficult for late PMI determination. Although many attempts with various types of body fluids based on chemical methods have been made to solve this problem, few investigations are focused on blood samples. In this study, we employed an attenuated total reflection (ATR)-Fourier transform infrared (FTIR) technique coupled with principle component analysis (PCA) to monitor biochemical changes in rabbit plasma with increasing PMI. Partial least square (PLS) model was used based on the spectral data for PMI prediction in an independent sample set. Our results revealed that postmortem chemical changes in compositions of the plasma were time-dependent, and various components including proteins, lipids and nucleic acids contributed to the discrimination of the samples at different time points. A satisfactory prediction within 48 h postmortem was performed by the combined PLS model with a good fitting between actual and predicted PMI of 0.984 and with an error of ± 1.92 h. In consideration of the simplicity and portability of ATR-FTIR, our preliminary study provides an experimental and theoretical basis for application of this technique in forensic practice.

How well can morphology assess cell death modality? A proteomics study

PubMed Central

Chernobrovkin, Alexey L; Zubarev, Roman A

2016-01-01

While the focus of attempts to classify cell death programs has finally shifted in 2010s from microscopy-based morphological characteristics to biochemical assays, more recent discoveries have put the underlying assumptions of many such assays under severe stress, mostly because of the limited specificity of the assays. On the other hand, proteomics can quantitatively measure the abundances of thousands of proteins in a single experiment. Thus proteomics could develop a modern alternative to both semiquantitative morphology assessment as well as single-molecule biochemical assays. Here we tested this hypothesis by analyzing the proteomes of cells dying after been treated with various chemical agents. The most striking finding is that, for a multivariate model based on the proteome changes in three cells lines, the regulation patterns of the 200–500 most abundant proteins typically attributed to household type more accurately reflect that of the proteins directly interacting with the drug than any other protein subset grouped by common function or biological process, including cell death. This is in broad agreement with the 'rigid cell death mechanics' model where drug action mechanism and morphological changes caused by it are bijectively linked. This finding, if confirmed, will open way for a broad use of proteomics in death modality assessment. PMID:27752363

Raman spectroscopic investigation of spinal cord injury in a rat model

NASA Astrophysics Data System (ADS)

Saxena, Tarun; Deng, Bin; Stelzner, Dennis; Hasenwinkel, Julie; Chaiken, Joseph

2011-02-01

Raman spectroscopy was used to study temporal molecular changes associated with spinal cord injury (SCI) in a rat model. Raman spectra of saline-perfused, injured, and healthy rat spinal cords were obtained and compared. Two injury models, a lateral hemisection and a moderate contusion were investigated. The net fluorescence and the Raman spectra showed clear differences between the injured and healthy spinal cords. Based on extensive histological and biochemical characterization of SCI available in the literature, these differences were hypothesized to be due to cell death, demyelination, and changes in the extracellular matrix composition, such as increased expression of proteoglycans and hyaluronic acid, at the site of injury where the glial scar forms. Further, analysis of difference spectra indicated the presence of carbonyl containing compounds, hypothesized to be products of lipid peroxidation and acid catalyzed hydrolysis of glycosaminoglycan moieties. These results compared well with in vitro experiments conducted on chondroitin sulfate sugars. Since the glial scar is thought to be a potent biochemical barrier to nerve regeneration, this observation suggests the possibility of using near infrared Raman spectroscopy to study injury progression and explore potential treatments ex vivo, and ultimately monitor potential remedial treatments within the spinal cord in vivo.

Complexity of generic biochemical circuits: topology versus strength of interactions.

PubMed

Tikhonov, Mikhail; Bialek, William

2016-12-06

The historical focus on network topology as a determinant of biological function is still largely maintained today, illustrated by the rise of structure-only approaches to network analysis. However, biochemical circuits and genetic regulatory networks are defined both by their topology and by a multitude of continuously adjustable parameters, such as the strength of interactions between nodes, also recognized as important. Here we present a class of simple perceptron-based Boolean models within which comparing the relative importance of topology versus interaction strengths becomes a quantitatively well-posed problem. We quantify the intuition that for generic networks, optimization of interaction strengths is a crucial ingredient of achieving high complexity, defined here as the number of fixed points the network can accommodate. We propose a new methodology for characterizing the relative role of parameter optimization for topologies of a given class.

Data quality in drug discovery: the role of analytical performance in ligand binding assays

NASA Astrophysics Data System (ADS)

Wätzig, Hermann; Oltmann-Norden, Imke; Steinicke, Franziska; Alhazmi, Hassan A.; Nachbar, Markus; El-Hady, Deia Abd; Albishri, Hassan M.; Baumann, Knut; Exner, Thomas; Böckler, Frank M.; El Deeb, Sami

2015-09-01

Despite its importance and all the considerable efforts made, the progress in drug discovery is limited. One main reason for this is the partly questionable data quality. Models relating biological activity and structures and in silico predictions rely on precisely and accurately measured binding data. However, these data vary so strongly, such that only variations by orders of magnitude are considered as unreliable. This can certainly be improved considering the high analytical performance in pharmaceutical quality control. Thus the principles, properties and performances of biochemical and cell-based assays are revisited and evaluated. In the part of biochemical assays immunoassays, fluorescence assays, surface plasmon resonance, isothermal calorimetry, nuclear magnetic resonance and affinity capillary electrophoresis are discussed in details, in addition radiation-based ligand binding assays, mass spectrometry, atomic force microscopy and microscale thermophoresis are briefly evaluated. In addition, general sources of error, such as solvent, dilution, sample pretreatment and the quality of reagents and reference materials are discussed. Biochemical assays can be optimized to provide good accuracy and precision (e.g. percental relative standard deviation <10 %). Cell-based assays are often considered superior related to the biological significance, however, typically they cannot still be considered as really quantitative, in particular when results are compared over longer periods of time or between laboratories. A very careful choice of assays is therefore recommended. Strategies to further optimize assays are outlined, considering the evaluation and the decrease of the relevant error sources. Analytical performance and data quality are still advancing and will further advance the progress in drug development.

Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

PubMed

Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola

2016-01-01

Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

Bioelectrical Signals and Ion Channels in the Modeling of Multicellular Patterns and Cancer Biophysics.

PubMed

Cervera, Javier; Alcaraz, Antonio; Mafe, Salvador

2016-02-04

Bioelectrical signals and ion channels are central to spatial patterns in cell ensembles, a problem of fundamental interest in positional information and cancer processes. We propose a model for electrically connected cells based on simple biological concepts: i) the membrane potential of a single cell characterizes its electrical state; ii) the long-range electrical coupling of the multicellular ensemble is realized by a network of gap junction channels between neighboring cells; and iii) the spatial distribution of an external biochemical agent can modify the conductances of the ion channels in a cell membrane and the multicellular electrical state. We focus on electrical effects in small multicellular ensembles, ignoring slow diffusional processes. The spatio-temporal patterns obtained for the local map of cell electric potentials illustrate the normalization of regions with abnormal cell electrical states. The effects of intercellular coupling and blocking of specific channels on the electrical patterns are described. These patterns can regulate the electrically-induced redistribution of charged nanoparticles over small regions of a model tissue. The inclusion of bioelectrical signals provides new insights for the modeling of cancer biophysics because collective multicellular states show electrical coupling mechanisms that are not readily deduced from biochemical descriptions at the individual cell level.

Bioelectrical Signals and Ion Channels in the Modeling of Multicellular Patterns and Cancer Biophysics

PubMed Central

Cervera, Javier; Alcaraz, Antonio; Mafe, Salvador

2016-01-01

Bioelectrical signals and ion channels are central to spatial patterns in cell ensembles, a problem of fundamental interest in positional information and cancer processes. We propose a model for electrically connected cells based on simple biological concepts: i) the membrane potential of a single cell characterizes its electrical state; ii) the long-range electrical coupling of the multicellular ensemble is realized by a network of gap junction channels between neighboring cells; and iii) the spatial distribution of an external biochemical agent can modify the conductances of the ion channels in a cell membrane and the multicellular electrical state. We focus on electrical effects in small multicellular ensembles, ignoring slow diffusional processes. The spatio-temporal patterns obtained for the local map of cell electric potentials illustrate the normalization of regions with abnormal cell electrical states. The effects of intercellular coupling and blocking of specific channels on the electrical patterns are described. These patterns can regulate the electrically-induced redistribution of charged nanoparticles over small regions of a model tissue. The inclusion of bioelectrical signals provides new insights for the modeling of cancer biophysics because collective multicellular states show electrical coupling mechanisms that are not readily deduced from biochemical descriptions at the individual cell level. PMID:26841954

Reconstruction of palaeoatmospheric carbon dioxide using stomatal densities of various beech plants (Fagaceae): testing and application of a mechanistic model

NASA Astrophysics Data System (ADS)

Grein, M.; Roth-Nebelsick, A.; Konrad, W.

2006-12-01

A mechanistic model (Konrad &Roth-Nebelsick a, in prep.) was applied for the reconstruction of atmospheric carbon dioxide using stomatal densities and photosynthesis parameters of extant and fossil Fagaceae. The model is based on an approach which couples diffusion and the biochemical process of photosynthesis. Atmospheric CO2 is calculated on the basis of stomatal diffusion and photosynthesis parameters of the considered taxa. The considered species include the castanoid Castanea sativa, two quercoids Quercus petraea and Quercus rhenana and an intermediate species Eotrigonobalanus furcinervis. In the case of Quercus petraea literature data were used. Stomatal data of Eotrigonobalanus furcinervis, Quercus rhenana and Castanea sativa were determined by the authors. Data of the extant Castanea sativa were collected by applying a peeling method and by counting of stomatal densities on the digitalized images of the peels. Additionally, isotope data of leaf samples of Castanea sativa were determined to estimate the ratio of intercellular to ambient carbon dioxide. The CO2 values calculated by the model (on the basis of stomatal data and measured or estimated biochemical parameters) are in good agreement with literature data, with the exception of the Late Eocene. The results thus demonstrate that the applied approach is principally suitable for reconstructing palaeoatmospheric CO2.

Bioelectrical Signals and Ion Channels in the Modeling of Multicellular Patterns and Cancer Biophysics

NASA Astrophysics Data System (ADS)

Cervera, Javier; Alcaraz, Antonio; Mafe, Salvador

2016-02-01

Bioelectrical signals and ion channels are central to spatial patterns in cell ensembles, a problem of fundamental interest in positional information and cancer processes. We propose a model for electrically connected cells based on simple biological concepts: i) the membrane potential of a single cell characterizes its electrical state; ii) the long-range electrical coupling of the multicellular ensemble is realized by a network of gap junction channels between neighboring cells; and iii) the spatial distribution of an external biochemical agent can modify the conductances of the ion channels in a cell membrane and the multicellular electrical state. We focus on electrical effects in small multicellular ensembles, ignoring slow diffusional processes. The spatio-temporal patterns obtained for the local map of cell electric potentials illustrate the normalization of regions with abnormal cell electrical states. The effects of intercellular coupling and blocking of specific channels on the electrical patterns are described. These patterns can regulate the electrically-induced redistribution of charged nanoparticles over small regions of a model tissue. The inclusion of bioelectrical signals provides new insights for the modeling of cancer biophysics because collective multicellular states show electrical coupling mechanisms that are not readily deduced from biochemical descriptions at the individual cell level.

Reconstructing the regulatory network controlling commitment and sporulation in Physarum polycephalum based on hierarchical Petri Net modelling and simulation.

PubMed

Marwan, Wolfgang; Sujatha, Arumugam; Starostzik, Christine

2005-10-21

We reconstruct the regulatory network controlling commitment and sporulation of Physarum polycephalum from experimental results using a hierarchical Petri Net-based modelling and simulation framework. The stochastic Petri Net consistently describes the structure and simulates the dynamics of the molecular network as analysed by genetic, biochemical and physiological experiments within a single coherent model. The Petri Net then is extended to simulate time-resolved somatic complementation experiments performed by mixing the cytoplasms of mutants altered in the sporulation response, to systematically explore the network structure and to probe its dynamics. This reverse engineering approach presumably can be employed to explore other molecular or genetic signalling systems where the activity of genes or their products can be experimentally controlled in a time-resolved manner.

Numerical simulations of a reduced model for blood coagulation

NASA Astrophysics Data System (ADS)

Pavlova, Jevgenija; Fasano, Antonio; Sequeira, Adélia

2016-04-01

In this work, the three-dimensional numerical resolution of a complex mathematical model for the blood coagulation process is presented. The model was illustrated in Fasano et al. (Clin Hemorheol Microcirc 51:1-14, 2012), Pavlova et al. (Theor Biol 380:367-379, 2015). It incorporates the action of the biochemical and cellular components of blood as well as the effects of the flow. The model is characterized by a reduction in the biochemical network and considers the impact of the blood slip at the vessel wall. Numerical results showing the capacity of the model to predict different perturbations in the hemostatic system are discussed.

Text-based phenotypic profiles incorporating biochemical phenotypes of inborn errors of metabolism improve phenomics-based diagnosis.

PubMed

Lee, Jessica J Y; Gottlieb, Michael M; Lever, Jake; Jones, Steven J M; Blau, Nenad; van Karnebeek, Clara D M; Wasserman, Wyeth W

2018-05-01

Phenomics is the comprehensive study of phenotypes at every level of biology: from metabolites to organisms. With high throughput technologies increasing the scope of biological discoveries, the field of phenomics has been developing rapid and precise methods to collect, catalog, and analyze phenotypes. Such methods have allowed phenotypic data to be widely used in medical applications, from assisting clinical diagnoses to prioritizing genomic diagnoses. To channel the benefits of phenomics into the field of inborn errors of metabolism (IEM), we have recently launched IEMbase, an expert-curated knowledgebase of IEM and their disease-characterizing phenotypes. While our efforts with IEMbase have realized benefits, taking full advantage of phenomics requires a comprehensive curation of IEM phenotypes in core phenomics projects, which is dependent upon contributions from the IEM clinical and research community. Here, we assess the inclusion of IEM biochemical phenotypes in a core phenomics project, the Human Phenotype Ontology. We then demonstrate the utility of biochemical phenotypes using a text-based phenomics method to predict gene-disease relationships, showing that the prediction of IEM genes is significantly better using biochemical rather than clinical profiles. The findings herein provide a motivating goal for the IEM community to expand the computationally accessible descriptions of biochemical phenotypes associated with IEM in phenomics resources.

Cause and cure of sloppiness in ordinary differential equation models.

PubMed

Tönsing, Christian; Timmer, Jens; Kreutz, Clemens

2014-08-01

Data-based mathematical modeling of biochemical reaction networks, e.g., by nonlinear ordinary differential equation (ODE) models, has been successfully applied. In this context, parameter estimation and uncertainty analysis is a major task in order to assess the quality of the description of the system by the model. Recently, a broadened eigenvalue spectrum of the Hessian matrix of the objective function covering orders of magnitudes was observed and has been termed as sloppiness. In this work, we investigate the origin of sloppiness from structures in the sensitivity matrix arising from the properties of the model topology and the experimental design. Furthermore, we present strategies using optimal experimental design methods in order to circumvent the sloppiness issue and present nonsloppy designs for a benchmark model.

Cause and cure of sloppiness in ordinary differential equation models

NASA Astrophysics Data System (ADS)

Tönsing, Christian; Timmer, Jens; Kreutz, Clemens

2014-08-01

Data-based mathematical modeling of biochemical reaction networks, e.g., by nonlinear ordinary differential equation (ODE) models, has been successfully applied. In this context, parameter estimation and uncertainty analysis is a major task in order to assess the quality of the description of the system by the model. Recently, a broadened eigenvalue spectrum of the Hessian matrix of the objective function covering orders of magnitudes was observed and has been termed as sloppiness. In this work, we investigate the origin of sloppiness from structures in the sensitivity matrix arising from the properties of the model topology and the experimental design. Furthermore, we present strategies using optimal experimental design methods in order to circumvent the sloppiness issue and present nonsloppy designs for a benchmark model.

Modeling of uncertainties in biochemical reactions.

PubMed

Mišković, Ljubiša; Hatzimanikatis, Vassily

2011-02-01

Mathematical modeling is an indispensable tool for research and development in biotechnology and bioengineering. The formulation of kinetic models of biochemical networks depends on knowledge of the kinetic properties of the enzymes of the individual reactions. However, kinetic data acquired from experimental observations bring along uncertainties due to various experimental conditions and measurement methods. In this contribution, we propose a novel way to model the uncertainty in the enzyme kinetics and to predict quantitatively the responses of metabolic reactions to the changes in enzyme activities under uncertainty. The proposed methodology accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints, and is based on formalism from systems theory and metabolic control analysis. We achieve this by observing that kinetic responses of metabolic reactions depend: (i) on the distribution of the enzymes among their free form and all reactive states; (ii) on the equilibrium displacements of the overall reaction and that of the individual enzymatic steps; and (iii) on the net fluxes through the enzyme. Relying on this observation, we develop a novel, efficient Monte Carlo sampling procedure to generate all states within a metabolic reaction that satisfy imposed constrains. Thus, we derive the statistics of the expected responses of the metabolic reactions to changes in enzyme levels and activities, in the levels of metabolites, and in the values of the kinetic parameters. We present aspects of the proposed framework through an example of the fundamental three-step reversible enzymatic reaction mechanism. We demonstrate that the equilibrium displacements of the individual enzymatic steps have an important influence on kinetic responses of the enzyme. Furthermore, we derive the conditions that must be satisfied by a reversible three-step enzymatic reaction operating far away from the equilibrium in order to respond to changes in metabolite levels according to the irreversible Michelis-Menten kinetics. The efficient sampling procedure allows easy, scalable, implementation of this methodology to modeling of large-scale biochemical networks. © 2010 Wiley Periodicals, Inc.

A grid layout algorithm for automatic drawing of biochemical networks.

PubMed

Li, Weijiang; Kurata, Hiroyuki

2005-05-01

Visualization is indispensable in the research of complex biochemical networks. Available graph layout algorithms are not adequate for satisfactorily drawing such networks. New methods are required to visualize automatically the topological architectures and facilitate the understanding of the functions of the networks. We propose a novel layout algorithm to draw complex biochemical networks. A network is modeled as a system of interacting nodes on squared grids. A discrete cost function between each node pair is designed based on the topological relation and the geometric positions of the two nodes. The layouts are produced by minimizing the total cost. We design a fast algorithm to minimize the discrete cost function, by which candidate layouts can be produced efficiently. A simulated annealing procedure is used to choose better candidates. Our algorithm demonstrates its ability to exhibit cluster structures clearly in relatively compact layout areas without any prior knowledge. We developed Windows software to implement the algorithm for CADLIVE. All materials can be freely downloaded from http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/ http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/

Modeling oscillations and spiral waves in Dictyostelium populations

NASA Astrophysics Data System (ADS)

Noorbakhsh, Javad; Schwab, David J.; Sgro, Allyson E.; Gregor, Thomas; Mehta, Pankaj

2015-06-01

Unicellular organisms exhibit elaborate collective behaviors in response to environmental cues. These behaviors are controlled by complex biochemical networks within individual cells and coordinated through cell-to-cell communication. Describing these behaviors requires new mathematical models that can bridge scales—from biochemical networks within individual cells to spatially structured cellular populations. Here we present a family of "multiscale" models for the emergence of spiral waves in the social amoeba Dictyostelium discoideum. Our models exploit new experimental advances that allow for the direct measurement and manipulation of the small signaling molecule cyclic adenosine monophosphate (cAMP) used by Dictyostelium cells to coordinate behavior in cellular populations. Inspired by recent experiments, we model the Dictyostelium signaling network as an excitable system coupled to various preprocessing modules. We use this family of models to study spatially unstructured populations of "fixed" cells by constructing phase diagrams that relate the properties of population-level oscillations to parameters in the underlying biochemical network. We then briefly discuss an extension of our model that includes spatial structure and show how this naturally gives rise to spiral waves. Our models exhibit a wide range of novel phenomena. including a density-dependent frequency change, bistability, and dynamic death due to slow cAMP dynamics. Our modeling approach provides a powerful tool for bridging scales in modeling of Dictyostelium populations.

Genome-scale reconstruction of the metabolic network in Yersinia pestis CO92

NASA Astrophysics Data System (ADS)

Navid, Ali; Almaas, Eivind

2007-03-01

The gram-negative bacterium Yersinia pestis is the causative agent of bubonic plague. Using publicly available genomic, biochemical and physiological data, we have developed a constraint-based flux balance model of metabolism in the CO92 strain (biovar Orientalis) of this organism. The metabolic reactions were appropriately compartmentalized, and the model accounts for the exchange of metabolites, as well as the import of nutrients and export of waste products. We have characterized the metabolic capabilities and phenotypes of this organism, after comparing the model predictions with available experimental observations to evaluate accuracy and completeness. We have also begun preliminary studies into how cellular metabolism affects virulence.

BioNetSim: a Petri net-based modeling tool for simulations of biochemical processes.

PubMed

Gao, Junhui; Li, Li; Wu, Xiaolin; Wei, Dong-Qing

2012-03-01

BioNetSim, a Petri net-based software for modeling and simulating biochemistry processes, is developed, whose design and implement are presented in this paper, including logic construction, real-time access to KEGG (Kyoto Encyclopedia of Genes and Genomes), and BioModel database. Furthermore, glycolysis is simulated as an example of its application. BioNetSim is a helpful tool for researchers to download data, model biological network, and simulate complicated biochemistry processes. Gene regulatory networks, metabolic pathways, signaling pathways, and kinetics of cell interaction are all available in BioNetSim, which makes modeling more efficient and effective. Similar to other Petri net-based softwares, BioNetSim does well in graphic application and mathematic construction. Moreover, it shows several powerful predominances. (1) It creates models in database. (2) It realizes the real-time access to KEGG and BioModel and transfers data to Petri net. (3) It provides qualitative analysis, such as computation of constants. (4) It generates graphs for tracing the concentration of every molecule during the simulation processes.

Prediction of microalgae hydrothermal liquefaction products from feedstock biochemical composition

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

Leow, Shijie; Witter, John R.; Vardon, Derek R.

Hydrothermal liquefaction (HTL) uses water under elevated temperatures and pressures (200–350 °C, 5–20 MPa) to convert biomass into liquid “biocrude” oil. Despite extensive reports on factors influencing microalgae cell composition during cultivation and separate reports on HTL products linked to cell composition, the field still lacks a quantitative model to predict HTL conversion product yield and qualities from feedstock biochemical composition; the tailoring of microalgae feedstock for downstream conversion is a unique and critical aspect of microalgae biofuels that must be leveraged upon for optimization of the whole process. This study developed predictive relationships for HTL biocrude yield and othermore » conversion product characteristics based on HTL of Nannochloropsis oculata batches harvested with a wide range of compositions (23–59% dw lipids, 58–17% dw proteins, 12–22% dw carbohydrates) and a defatted batch (0% dw lipids, 75% dw proteins, 19% dw carbohydrates). HTL biocrude yield (33–68% dw) and carbon distribution (49–83%) increased in proportion to the fatty acid (FA) content. A component additivity model (predicting biocrude yield from lipid, protein, and carbohydrates) was more accurate predicting literature yields for diverse microalgae species than previous additivity models derived from model compounds. FA profiling of the biocrude product showed strong links to the initial feedstock FA profile of the lipid component, demonstrating that HTL acts as a water-based extraction process for FAs; the remainder non-FA structural components could be represented using the defatted batch. These findings were used to introduce a new FA-based model that predicts biocrude oil yields along with other critical parameters, and is capable of adjusting for the wide variations in HTL methodology and microalgae species through the defatted batch. Lastly, the FA model was linked to an upstream cultivation model (Phototrophic Process Model), providing for the first time an integrated modeling framework to overcome a critical barrier to microalgae-derived HTL biofuels and enable predictive analysis of the overall microalgal-to-biofuel process.« less

Prediction of microalgae hydrothermal liquefaction products from feedstock biochemical composition

DOE PAGES

Leow, Shijie; Witter, John R.; Vardon, Derek R.; ...

2015-05-11

Hydrothermal liquefaction (HTL) uses water under elevated temperatures and pressures (200–350 °C, 5–20 MPa) to convert biomass into liquid “biocrude” oil. Despite extensive reports on factors influencing microalgae cell composition during cultivation and separate reports on HTL products linked to cell composition, the field still lacks a quantitative model to predict HTL conversion product yield and qualities from feedstock biochemical composition; the tailoring of microalgae feedstock for downstream conversion is a unique and critical aspect of microalgae biofuels that must be leveraged upon for optimization of the whole process. This study developed predictive relationships for HTL biocrude yield and othermore » conversion product characteristics based on HTL of Nannochloropsis oculata batches harvested with a wide range of compositions (23–59% dw lipids, 58–17% dw proteins, 12–22% dw carbohydrates) and a defatted batch (0% dw lipids, 75% dw proteins, 19% dw carbohydrates). HTL biocrude yield (33–68% dw) and carbon distribution (49–83%) increased in proportion to the fatty acid (FA) content. A component additivity model (predicting biocrude yield from lipid, protein, and carbohydrates) was more accurate predicting literature yields for diverse microalgae species than previous additivity models derived from model compounds. FA profiling of the biocrude product showed strong links to the initial feedstock FA profile of the lipid component, demonstrating that HTL acts as a water-based extraction process for FAs; the remainder non-FA structural components could be represented using the defatted batch. These findings were used to introduce a new FA-based model that predicts biocrude oil yields along with other critical parameters, and is capable of adjusting for the wide variations in HTL methodology and microalgae species through the defatted batch. Lastly, the FA model was linked to an upstream cultivation model (Phototrophic Process Model), providing for the first time an integrated modeling framework to overcome a critical barrier to microalgae-derived HTL biofuels and enable predictive analysis of the overall microalgal-to-biofuel process.« less

Payao: a community platform for SBML pathway model curation

PubMed Central

Matsuoka, Yukiko; Ghosh, Samik; Kikuchi, Norihiro; Kitano, Hiroaki

2010-01-01

Summary: Payao is a community-based, collaborative web service platform for gene-regulatory and biochemical pathway model curation. The system combines Web 2.0 technologies and online model visualization functions to enable a collaborative community to annotate and curate biological models. Payao reads the models in Systems Biology Markup Language format, displays them with CellDesigner, a process diagram editor, which complies with the Systems Biology Graphical Notation, and provides an interface for model enrichment (adding tags and comments to the models) for the access-controlled community members. Availability and implementation: Freely available for model curation service at http://www.payaologue.org. Web site implemented in Seaser Framework 2.0 with S2Flex2, MySQL 5.0 and Tomcat 5.5, with all major browsers supported. Contact: kitano@sbi.jp PMID:20371497

Metabolomics and cytotoxicity of monomethylhydrazine (MMH) and (E)-1,1,4,4-tetramethyl-2-tetrazene (TMTZ), two liquid propellants.

PubMed

Guyot, Laetitia; Machon, Christelle; Honorat, Myléne; Manship, Brigitte; Bouard, Charlotte; Vigneron, Arnaud; Puisieux, Alain; Labarthe, Emilie; Jacob, Guy; Dhenain, Anne; Guitton, Jérôme; Payen, Léa

2018-06-07

Hydrazine-based liquid propellants are routinely used for space rocket propulsion, in particular monomethylhydrazine (MMH), although such compounds are highly hazardous. For several years, great efforts were devoted to developing a less hazardous molecule. To explore the toxicological effects of an alternative compound, namely (E)-1,1,4,4-tetramethyl-2-tetrazene (TMTZ), we exposed various cellular animal and human models to this compound and to the reference compound MMH. We observed no cytotoxic effects following exposure to TMTZ in animal, as well as human models. However, although the three animal models were unaffected by MMH, exposure of the human hepatic HepaRG cell model revealed that apoptotic cytotoxic effects were only detectable in proliferative human hepatic HepaRG cells and not in differentiated cells, although major biochemical modifications were uncovered in the latter. The present findings indicate that the metabolic mechanisms of MMH toxicity is close to those described for hydrazine with numerous biochemical alterations induced by mitochondrial disruption, production of radical species, and aminotransferase inhibition. The alternative TMTZ molecule had little impact on cellular viability and proliferation of rodent and human dermic and hepatic cell models. TMTZ did not produce any metabolomic effects and appears to be a promising putative industrial alternative to MMH. Copyright © 2018. Published by Elsevier Ltd.

Plasma biochemical reference values in clinically healthy captive bearded dragons (Pogona vitticeps) and the effects of sex and season.

PubMed

Tamukai, Kenichi; Takami, Yoshinori; Akabane, Yoshihito; Kanazawa, Yuko; Une, Yumi

2011-09-01

Bearded dragons are one of the most popular pet lizard species, and biochemical reference values are useful for health management of these reptiles. The objectives of this study were to measure plasma biochemical values in healthy captive bearded dragons, determine reference values, and evaluate the effects of sex and season on the results. Blood samples were collected from 100 captive healthy bearded dragons in Tokyo during the summer and winter. Plasma biochemical measurements were performed using a dry-slide automated biochemical analyzer. The data were then compared based on sex and season using 2-way ANOVA. Globulin, cholesterol, and calcium concentrations of females were higher in both summer and winter compared with the values obtained for males. Both males and females had higher uric acid concentrations in winter than in summer. When compared with males, females had a higher chloride concentration in summer and a higher total protein concentration and aspartate aminotransferase activity in winter. Potassium concentration in males was lower in winter than in summer, whereas in females cholesterol concentration was lower in winter than in summer. Biochemical values that differed based on sex and season in bearded dragons were similar to those in other lizards. These differences reflect physiologic differences in reproductive status in females and seasonal changes in temperature and hydration status. Plasma biochemical values established for bearded dragons in this study will be useful in the diagnostic assessment of captive animals. ©2011 American Society for Veterinary Clinical Pathology.

Biochemical mechanisms of cisplatin cytotoxicity.

PubMed

Cepeda, Victoria; Fuertes, Miguel A; Castilla, Josefina; Alonso, Carlos; Quevedo, Celia; Pérez, Jose M

2007-01-01

Since the discovery by Rosenberg and collaborators of the antitumor activity of cisplatin 35 years ago, three platinum antitumor drugs (cisplatin, carboplatin and oxaliplatin) have enjoyed a huge clinical and commercial hit. Ever since the initial discovery of the anticancer activity of cisplatin, major efforts have been devoted to elucidate the biochemical mechanisms of antitumor activity of cisplatin in order to be able to rationally design novel platinum based drugs with superior pharmacological profiles. In this report we attempt to provide a current picture of the known facts pertaining to the mechanism of action of the drug, including those involved in drug uptake, DNA damage signals transduction, and cell death through apoptosis or necrosis. A deep knowledge of the biochemical mechanisms, which are triggered in the tumor cell in response to cisplatin injury not only may lead to the design of more efficient platinum antitumor drugs but also may provide new therapeutic strategies based on the biochemical modulation of cisplatin activity.

Partially reduced graphene oxide based FRET on fiber optic interferometer for biochemical detection

NASA Astrophysics Data System (ADS)

Yao, B. C.; Wu, Y.; Yu, C. B.; He, J. R.; Rao, Y. J.; Gong, Y.; Chen, Y. F.; Li, Y. R.

2017-04-01

An all-fiber graphene oxide (GO) based 'FRET on Fiber' concept is proposed and applied in biochemical detections. This method is of both good selectivity and high sensitivity, with detection limits of 1.2 nM, 1.3 μM and 1 pM, for metal ion, dopamine and single-stranded DNA (ssDNA), respectively.

Comparative life cycle assessment of lignocellulosic ethanol production: biochemical versus thermochemical conversion.

PubMed

Mu, Dongyan; Seager, Thomas; Rao, P Suresh; Zhao, Fu

2010-10-01

Lignocellulosic biomass can be converted into ethanol through either biochemical or thermochemical conversion processes. Biochemical conversion involves hydrolysis and fermentation while thermochemical conversion involves gasification and catalytic synthesis. Even though these routes produce comparable amounts of ethanol and have similar energy efficiency at the plant level, little is known about their relative environmental performance from a life cycle perspective. Especially, the indirect impacts, i.e. emissions and resource consumption associated with the production of various process inputs, are largely neglected in previous studies. This article compiles material and energy flow data from process simulation models to develop life cycle inventory and compares the fossil fuel consumption, greenhouse gas emissions, and water consumption of both biomass-to-ethanol production processes. The results are presented in terms of contributions from feedstock, direct, indirect, and co-product credits for four representative biomass feedstocks i.e., wood chips, corn stover, waste paper, and wheat straw. To explore the potentials of the two conversion pathways, different technological scenarios are modeled, including current, 2012 and 2020 technology targets, as well as different production/co-production configurations. The modeling results suggest that biochemical conversion has slightly better performance on greenhouse gas emission and fossil fuel consumption, but that thermochemical conversion has significantly less direct, indirect, and life cycle water consumption. Also, if the thermochemical plant operates as a biorefinery with mixed alcohol co-products separated for chemicals, it has the potential to achieve better performance than biochemical pathway across all environmental impact categories considered due to higher co-product credits associated with chemicals being displaced. The results from this work serve as a starting point for developing full life cycle assessment model that facilitates effective decision-making regarding lignocellulosic ethanol production.

Comparative Life Cycle Assessment of Lignocellulosic Ethanol Production: Biochemical Versus Thermochemical Conversion

NASA Astrophysics Data System (ADS)

Mu, Dongyan; Seager, Thomas; Rao, P. Suresh; Zhao, Fu

2010-10-01

Lignocellulosic biomass can be converted into ethanol through either biochemical or thermochemical conversion processes. Biochemical conversion involves hydrolysis and fermentation while thermochemical conversion involves gasification and catalytic synthesis. Even though these routes produce comparable amounts of ethanol and have similar energy efficiency at the plant level, little is known about their relative environmental performance from a life cycle perspective. Especially, the indirect impacts, i.e. emissions and resource consumption associated with the production of various process inputs, are largely neglected in previous studies. This article compiles material and energy flow data from process simulation models to develop life cycle inventory and compares the fossil fuel consumption, greenhouse gas emissions, and water consumption of both biomass-to-ethanol production processes. The results are presented in terms of contributions from feedstock, direct, indirect, and co-product credits for four representative biomass feedstocks i.e., wood chips, corn stover, waste paper, and wheat straw. To explore the potentials of the two conversion pathways, different technological scenarios are modeled, including current, 2012 and 2020 technology targets, as well as different production/co-production configurations. The modeling results suggest that biochemical conversion has slightly better performance on greenhouse gas emission and fossil fuel consumption, but that thermochemical conversion has significantly less direct, indirect, and life cycle water consumption. Also, if the thermochemical plant operates as a biorefinery with mixed alcohol co-products separated for chemicals, it has the potential to achieve better performance than biochemical pathway across all environmental impact categories considered due to higher co-product credits associated with chemicals being displaced. The results from this work serve as a starting point for developing full life cycle assessment model that facilitates effective decision-making regarding lignocellulosic ethanol production.

A scalable moment-closure approximation for large-scale biochemical reaction networks

PubMed Central

Kazeroonian, Atefeh; Theis, Fabian J.; Hasenauer, Jan

2017-01-01

Abstract Motivation: Stochastic molecular processes are a leading cause of cell-to-cell variability. Their dynamics are often described by continuous-time discrete-state Markov chains and simulated using stochastic simulation algorithms. As these stochastic simulations are computationally demanding, ordinary differential equation models for the dynamics of the statistical moments have been developed. The number of state variables of these approximating models, however, grows at least quadratically with the number of biochemical species. This limits their application to small- and medium-sized processes. Results: In this article, we present a scalable moment-closure approximation (sMA) for the simulation of statistical moments of large-scale stochastic processes. The sMA exploits the structure of the biochemical reaction network to reduce the covariance matrix. We prove that sMA yields approximating models whose number of state variables depends predominantly on local properties, i.e. the average node degree of the reaction network, instead of the overall network size. The resulting complexity reduction is assessed by studying a range of medium- and large-scale biochemical reaction networks. To evaluate the approximation accuracy and the improvement in computational efficiency, we study models for JAK2/STAT5 signalling and NFκB signalling. Our method is applicable to generic biochemical reaction networks and we provide an implementation, including an SBML interface, which renders the sMA easily accessible. Availability and implementation: The sMA is implemented in the open-source MATLAB toolbox CERENA and is available from https://github.com/CERENADevelopers/CERENA. Contact: jan.hasenauer@helmholtz-muenchen.de or atefeh.kazeroonian@tum.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881983

Characterization of Rubia cordifolia L. root extract and its evaluation of cardioprotective effect in Wistar rat model

PubMed Central

Chandrashekar, BS; Prabhakara, S; Mohan, T; Shabeer, D; Bhandare, Basavaraj; Nalini, M; Sharmila, PS; Meghana, DL; Reddy, Basanth Kumar; Hanumantha Rao, HM; Sahajananda, H; Anbazhagan, K

2018-01-01

OBJECTIVES: Rubia cordifolia L. (RC) is a well-known and highly valuable medicinal plant in the Ayurvedic system. The present study involves evaluating antioxidant and cardioprotective property of RC root extract. MATERIALS AND METHODS: The characterization of RC root extract was carried out using standard phytochemical and biochemical analysis. The functional groups were analyzed by Fourier transform infrared (FTIR) spectroscopy and phytotherapeutic compounds were identified using high-resolution mass spectrometry (HR-MS). Cardioprotective activity of RC root extract was investigated against cyclophosphamide (CP; 100 mg/kg, i.p)-induced cardiotoxicity in male albino Wistar rats. RC (100, 200, and 400 mg/kg, p.o) or silymarin (100 mg/kg, p.o) was administered immediately after CP on the 1st day and the next consecutive 10 days. Biochemical and histopathological analysis was performed to observe the cardioprotective effects of RC root extract. RESULTS: Phytochemical analysis revealed the presence of secondary metabolites that include alkaloids, flavonoids, saponins, and anthraquinones in RC root extract. FTIR analysis revealed the presence of several functional groups. Based on HR-MS analysis, eight major phytotherapeutic compounds were identified in methanol root extract of RC. Biochemical analysis in CP-induced rat model administered with RC extract revealed significantly enhanced levels of antioxidant markers such as superoxide dismutase, catalase, and glutathione S-transferase. Histopathological study showed that the rat model treated with the root extract had reduced the cardiac injury. CONCLUSION: Our results have shown that the RC extract contains various antioxidant compounds with cardioprotective effect. Treatment with RC root extract could significantly protect CP-induced rats from cardiac tissue injury by restoring the antioxidant markers. PMID:29861523

Analyzing key constraints to biogas production from crop residues and manure in the EU—A spatially explicit model

PubMed Central

Persson, U. Martin

2017-01-01

This paper presents a spatially explicit method for making regional estimates of the potential for biogas production from crop residues and manure, accounting for key technical, biochemical, environmental and economic constraints. Methods for making such estimates are important as biofuels from agricultural residues are receiving increasing policy support from the EU and major biogas producers, such as Germany and Italy, in response to concerns over unintended negative environmental and social impacts of conventional biofuels. This analysis comprises a spatially explicit estimate of crop residue and manure production for the EU at 250 m resolution, and a biogas production model accounting for local constraints such as the sustainable removal of residues, transportation of substrates, and the substrates’ biochemical suitability for anaerobic digestion. In our base scenario, the EU biogas production potential from crop residues and manure is about 0.7 EJ/year, nearly double the current EU production of biogas from agricultural substrates, most of which does not come from residues or manure. An extensive sensitivity analysis of the model shows that the potential could easily be 50% higher or lower, depending on the stringency of economic, technical and biochemical constraints. We find that the potential is particularly sensitive to constraints on the substrate mixtures’ carbon-to-nitrogen ratio and dry matter concentration. Hence, the potential to produce biogas from crop residues and manure in the EU depends to large extent on the possibility to overcome the challenges associated with these substrates, either by complementing them with suitable co-substrates (e.g. household waste and energy crops), or through further development of biogas technology (e.g. pretreatment of substrates and recirculation of effluent). PMID:28141827

Analyzing key constraints to biogas production from crop residues and manure in the EU-A spatially explicit model.

PubMed

Einarsson, Rasmus; Persson, U Martin

2017-01-01

This paper presents a spatially explicit method for making regional estimates of the potential for biogas production from crop residues and manure, accounting for key technical, biochemical, environmental and economic constraints. Methods for making such estimates are important as biofuels from agricultural residues are receiving increasing policy support from the EU and major biogas producers, such as Germany and Italy, in response to concerns over unintended negative environmental and social impacts of conventional biofuels. This analysis comprises a spatially explicit estimate of crop residue and manure production for the EU at 250 m resolution, and a biogas production model accounting for local constraints such as the sustainable removal of residues, transportation of substrates, and the substrates' biochemical suitability for anaerobic digestion. In our base scenario, the EU biogas production potential from crop residues and manure is about 0.7 EJ/year, nearly double the current EU production of biogas from agricultural substrates, most of which does not come from residues or manure. An extensive sensitivity analysis of the model shows that the potential could easily be 50% higher or lower, depending on the stringency of economic, technical and biochemical constraints. We find that the potential is particularly sensitive to constraints on the substrate mixtures' carbon-to-nitrogen ratio and dry matter concentration. Hence, the potential to produce biogas from crop residues and manure in the EU depends to large extent on the possibility to overcome the challenges associated with these substrates, either by complementing them with suitable co-substrates (e.g. household waste and energy crops), or through further development of biogas technology (e.g. pretreatment of substrates and recirculation of effluent).

Cyto-Sim: a formal language model and stochastic simulator of membrane-enclosed biochemical processes.

PubMed

Sedwards, Sean; Mazza, Tommaso

2007-10-15

Compartments and membranes are the basis of cell topology and more than 30% of the human genome codes for membrane proteins. While it is possible to represent compartments and membrane proteins in a nominal way with many mathematical formalisms used in systems biology, few, if any, explicitly model the topology of the membranes themselves. Discrete stochastic simulation potentially offers the most accurate representation of cell dynamics. Since the details of every molecular interaction in a pathway are often not known, the relationship between chemical species in not necessarily best described at the lowest level, i.e. by mass action. Simulation is a form of computer-aided analysis, relying on human interpretation to derive meaning. To improve efficiency and gain meaning in an automatic way, it is necessary to have a formalism based on a model which has decidable properties. We present Cyto-Sim, a stochastic simulator of membrane-enclosed hierarchies of biochemical processes, where the membranes comprise an inner, outer and integral layer. The underlying model is based on formal language theory and has been shown to have decidable properties (Cavaliere and Sedwards, 2006), allowing formal analysis in addition to simulation. The simulator provides variable levels of abstraction via arbitrary chemical kinetics which link to ordinary differential equations. In addition to its compact native syntax, Cyto-Sim currently supports models described as Petri nets, can import all versions of SBML and can export SBML and MATLAB m-files. Cyto-Sim is available free, either as an applet or a stand-alone Java program via the web page (http://www.cosbi.eu/Rpty_Soft_CytoSim.php). Other versions can be made available upon request.

The polygonal model: A simple representation of biomolecules as a tool for teaching metabolism.

PubMed

Bonafe, Carlos Francisco Sampaio; Bispo, Jose Ailton Conceição; de Jesus, Marcelo Bispo

2018-01-01

Metabolism involves numerous reactions and organic compounds that the student must master to understand adequately the processes involved. Part of biochemical learning should include some knowledge of the structure of biomolecules, although the acquisition of such knowledge can be time-consuming and may require significant effort from the student. In this report, we describe the "polygonal model" as a new means of graphically representing biomolecules. This model is based on the use of geometric figures such as open triangles, squares, and circles to represent hydroxyl, carbonyl, and carboxyl groups, respectively. The usefulness of the polygonal model was assessed by undergraduate students in a classroom activity that consisted of "transforming" molecules from Fischer models to polygonal models and vice and versa. The survey was applied to 135 undergraduate Biology and Nursing students. Students found the model easy to use and we noted that it allowed identification of students' misconceptions in basic concepts of organic chemistry, such as in stereochemistry and organic groups that could then be corrected. The students considered the polygonal model easier and faster for representing molecules than Fischer representations, without loss of information. These findings indicate that the polygonal model can facilitate the teaching of metabolism when the structures of biomolecules are discussed. Overall, the polygonal model promoted contact with chemical structures, e.g. through drawing activities, and encouraged student-student dialog, thereby facilitating biochemical learning. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(1):66-75, 2018. © 2017 The International Union of Biochemistry and Molecular Biology.

An unconventional family 1 uracil DNA glycosylase in Nitratifractor salsuginis.

PubMed

Li, Jing; Chen, Ran; Yang, Ye; Zhang, Zhemin; Fang, Guang-Chen; Xie, Wei; Cao, Weiguo

2017-12-01

The uracil DNA glycosylase superfamily consists of at least six families with a diverse specificity toward DNA base damage. Family 1 uracil N-glycosylase (UNG) exhibits exclusive specificity on uracil-containing DNA. Here, we report a family 1 UNG homolog from Nitratifractor salsuginis with distinct biochemical features that differentiate it from conventional family 1 UNGs. Globally, the crystal structure of N. salsuginisUNG shows a few additional secondary structural elements. Biochemical and enzyme kinetic analysis, coupled with structural determination, molecular modeling, and molecular dynamics simulations, shows that N. salsuginisUNG contains a salt bridge network that plays an important role in DNA backbone interactions. Disruption of the amino acid residues involved in the salt bridges greatly impedes the enzymatic activity. A tyrosine residue in motif 1 (GQDPY) is one of the distinct sequence features setting family 1 UNG apart from other families. The crystal structure of Y81G mutant indicates that several subtle changes may account for its inactivity. Unlike the conventional family 1 UNG enzymes, N. salsuginisUNG is not inhibited by Ugi, a potent inhibitor specific for family 1 UNG. This study underscores the diversity of paths that a uracil DNA glycosylase may take to acquire its unique structural and biochemical properties during evolution. Structure data are available in the PDB under accession numbers 5X3G and 5X3H. © 2017 Federation of European Biochemical Societies.

Near-infrared Raman spectroscopy for assessing biochemical changes of cervical tissue associated with precarcinogenic transformation.

PubMed

Duraipandian, Shiyamala; Mo, Jianhua; Zheng, Wei; Huang, Zhiwei

2014-11-07

Raman spectroscopy measures the inelastically scattered light from tissue that is capable of identifying native tissue biochemical constituents and their changes associated with disease transformation. This study aims to characterize the Raman spectroscopic properties of cervical tissue associated with the multi-stage progression of cervical precarcinogenic sequence. A rapid-acquisition fiber-optic near-infrared (NIR) Raman diagnostic system was employed for tissue Raman spectral measurements at 785 nm excitation. A total of 68 Raman spectra (23 benign, 29 low-grade squamous intraepithelial lesions (LSIL) and 16 high grade squamous intraepithelial lesions (HSIL)) were measured from 25 cervical tissue biopsy specimens, as confirmed by colposcopy-histopathology. The semi-quantitative biochemical modeling based on the major biochemicals (i.e., DNA, proteins (histone, collagen), lipid (triolein) and carbohydrates (glycogen)) in cervical tissue uncovers the stepwise accumulation of biomolecular changes associated with progressive cervical precarcinogenesis. Multi-class partial least squares-discriminant analysis (PLS-DA) together with leave-one tissue site-out, cross-validation yielded the diagnostic sensitivities of 95.7%, 82.8% and 81.3%; specificities of 100.0%, 92.3% and 88.5%,for discrimination among benign, LSIL and HSIL cervical tissues, respectively. This work suggests that the Raman spectral biomarkers have identified the potential to be used for monitoring the multi-stage cervical precarcinogenesis, forming the foundation of applying NIR Raman spectroscopy for the early diagnosis of cervical precancer in vivo at the molecular level.

Development of constraint-based system-level models of microbial metabolism.

PubMed

Navid, Ali

2012-01-01

Genome-scale models of metabolism are valuable tools for using genomic information to predict microbial phenotypes. System-level mathematical models of metabolic networks have been developed for a number of microbes and have been used to gain new insights into the biochemical conversions that occur within organisms and permit their survival and proliferation. Utilizing these models, computational biologists can (1) examine network structures, (2) predict metabolic capabilities and resolve unexplained experimental observations, (3) generate and test new hypotheses, (4) assess the nutritional requirements of the organism and approximate its environmental niche, (5) identify missing enzymatic functions in the annotated genome, and (6) engineer desired metabolic capabilities in model organisms. This chapter details the protocol for developing genome-scale models of metabolism in microbes as well as tips for accelerating the model building process.

Mathematical modelling of decline in follicle pool during female reproductive ageing.

PubMed

Thilagam, Alagu

2016-03-01

The factors which govern the subtle links between follicle loss and mammalian female reproductive ageing remain unclear despite extensive studies undertaken to understand the critical physiological and biochemical mechanisms that underly the accelerated decline in follicle numbers in women older than 37 years. It is not certain whether there is a sole control by the ovary or whether other factors which affect ageing also intersect with the ovarian effect. There is convincing experimental evidence for an interplay of several processes that seem to influence the follicle loss-female reproductive ageing links, with specific hormones (follicle-stimulating hormone, anti-Müllerian hormone, dehydroepiandrosterone) noted to play important roles in follicular dynamics and ovarian ageing. In this work, we examine the subtle links between the rate of follicular decline with ageing and the role of hormones via a series of non-autonomous equations. Simulation results based on the time evolution of the number of ovarian follicles and biochemical changes in the ovarian environment influenced by hormone levels is compared with empirical data based on follicle loss-reproductive ageing correlation studies. © Crown copyright 2015.

Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks.

PubMed

Meng, X Flora; Baetica, Ania-Ariadna; Singhal, Vipul; Murray, Richard M

2017-05-01

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterize state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing the stationary behaviour of stochastic biochemical reaction networks. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and biochemical reactions with two-dimensional state spaces. © 2017 The Author(s).

Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks

PubMed Central

Baetica, Ania-Ariadna; Singhal, Vipul; Murray, Richard M.

2017-01-01

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterize state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing the stationary behaviour of stochastic biochemical reaction networks. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and biochemical reactions with two-dimensional state spaces. PMID:28566513

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

DOE PAGES

King, Zachary A.; Lu, Justin; Drager, Andreas; ...

2015-10-17

In this study, genome-scale metabolic models are mathematically structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scalemore » metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.« less

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

PubMed Central

King, Zachary A.; Lu, Justin; Dräger, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A.; Ebrahim, Ali; Palsson, Bernhard O.; Lewis, Nathan E.

2016-01-01

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data. PMID:26476456

Deterministic modelling and stochastic simulation of biochemical pathways using MATLAB.

PubMed

Ullah, M; Schmidt, H; Cho, K H; Wolkenhauer, O

2006-03-01

The analysis of complex biochemical networks is conducted in two popular conceptual frameworks for modelling. The deterministic approach requires the solution of ordinary differential equations (ODEs, reaction rate equations) with concentrations as continuous state variables. The stochastic approach involves the simulation of differential-difference equations (chemical master equations, CMEs) with probabilities as variables. This is to generate counts of molecules for chemical species as realisations of random variables drawn from the probability distribution described by the CMEs. Although there are numerous tools available, many of them free, the modelling and simulation environment MATLAB is widely used in the physical and engineering sciences. We describe a collection of MATLAB functions to construct and solve ODEs for deterministic simulation and to implement realisations of CMEs for stochastic simulation using advanced MATLAB coding (Release 14). The program was successfully applied to pathway models from the literature for both cases. The results were compared to implementations using alternative tools for dynamic modelling and simulation of biochemical networks. The aim is to provide a concise set of MATLAB functions that encourage the experimentation with systems biology models. All the script files are available from www.sbi.uni-rostock.de/ publications_matlab-paper.html.

High-resolution mapping of bifurcations in nonlinear biochemical circuits

NASA Astrophysics Data System (ADS)

Genot, A. J.; Baccouche, A.; Sieskind, R.; Aubert-Kato, N.; Bredeche, N.; Bartolo, J. F.; Taly, V.; Fujii, T.; Rondelez, Y.

2016-08-01

Analog molecular circuits can exploit the nonlinear nature of biochemical reaction networks to compute low-precision outputs with fewer resources than digital circuits. This analog computation is similar to that employed by gene-regulation networks. Although digital systems have a tractable link between structure and function, the nonlinear and continuous nature of analog circuits yields an intricate functional landscape, which makes their design counter-intuitive, their characterization laborious and their analysis delicate. Here, using droplet-based microfluidics, we map with high resolution and dimensionality the bifurcation diagrams of two synthetic, out-of-equilibrium and nonlinear programs: a bistable DNA switch and a predator-prey DNA oscillator. The diagrams delineate where function is optimal, dynamics bifurcates and models fail. Inverse problem solving on these large-scale data sets indicates interference from enzymatic coupling. Additionally, data mining exposes the presence of rare, stochastically bursting oscillators near deterministic bifurcations.

Surrogate-Based Optimization of Biogeochemical Transport Models

NASA Astrophysics Data System (ADS)

Prieß, Malte; Slawig, Thomas

2010-09-01

First approaches towards a surrogate-based optimization method for a one-dimensional marine biogeochemical model of NPZD type are presented. The model, developed by Oschlies and Garcon [1], simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. A key issue is to minimize the misfit between the model output and given observational data. Our aim is to reduce the overall optimization cost avoiding expensive function and derivative evaluations by using a surrogate model replacing the high-fidelity model in focus. This in particular becomes important for more complex three-dimensional models. We analyse a coarsening in the discretization of the model equations as one way to create such a surrogate. Here the numerical stability crucially depends upon the discrete stepsize in time and space and the biochemical terms. We show that for given model parameters the level of grid coarsening can be choosen accordingly yielding a stable and satisfactory surrogate. As one example of a surrogate-based optimization method we present results of the Aggressive Space Mapping technique (developed by John W. Bandler [2, 3]) applied to the optimization of this one-dimensional biogeochemical transport model.

Biotinidase deficiency: Genotype-biochemical phenotype association in Brazilian patients

PubMed Central

Borsatto, Taciane; Sperb-Ludwig, Fernanda; Lima, Samyra E.; S. Carvalho, Maria R.; S. Fonseca, Pablo A.; S. Camelo, José; M. Ribeiro, Erlane; F. V. de Medeiros, Paula; M. Lourenço, Charles; F. M. de Souza, Carolina; Boy, Raquel; Félix, Têmis M.; M. Bittar, Camila; L. C. Pinto, Louise; C. Neto, Eurico; J. Blom, Henk; D. Schwartz, Ida V.

2017-01-01

Introduction The association between the BTD genotype and biochemical phenotype [profound biotinidase deficiency (BD), partial BD or heterozygous activity] is not always consistent. This study aimed to investigate the genotype-biochemical phenotype association in patients with low biotinidase activity. Methods All exons, the 5'UTR and the promoter of the BTD gene were sequenced in 72 Brazilian individuals who exhibited low biotinidase activity. For each patient, the expected biochemical phenotype based on the known genotype was compared with the observed biochemical phenotype. Additional non-genetic factors that could affect the biotinidase activity were also analysed. Results Most individuals were identified by neonatal screening (n = 66/72). When consecutive results for the same patient were compared, age, prematurity and neonatal jaundice appeared to affect the level of biotinidase activity. The biochemical phenotype at the time of the second blood collection changed in 11/22 patients compared to results from the first sample. Three novel variants were found: c.1337T>C (p.L446P), c.1466A>G (p.N489S) and c.962G>A (p.W321*). Some patients with the same genotype presented different biochemical phenotypes. The expected and observed biochemical phenotypes agreed in 68.5% of cases (concordant patients). The non-coding variants c.-183G>A, c.-315A>G and c.-514C>T were present in heterozygosis in 5/17 discordant patients. In addition, c.-183G>A and c.-514C>T were also present in 10/37 concordant patients. Conclusions The variants found in the promoter region do not appear to have a strong impact on biotinidase activity. Since there is a disparity between the BTD genotype and biochemical phenotype, and biotinidase activity may be affected by both genetic and non-genetic factors, we suggest that the diagnosis of BD should be based on more than one measurement of plasma biotinidase activity. DNA analysis can be of additional relevance to differentiate between partial BD and heterozygosity. PMID:28498829

Histologic and biochemical alterations predict pulmonary mechanical dysfunction in aging mice with chronic lung inflammation

PubMed Central

Laskin, Debra L.; Gow, Andrew J.

2017-01-01

Both aging and chronic inflammation produce complex structural and biochemical alterations to the lung known to impact work of breathing. Mice deficient in surfactant protein D (Sftpd) develop progressive age-related lung pathology characterized by tissue destruction/remodeling, accumulation of foamy macrophages and alteration in surfactant composition. This study proposes to relate changes in tissue structure seen in normal aging and in chronic inflammation to altered lung mechanics using a computational model. Alterations in lung function in aging and Sftpd -/- mice have been inferred from fitting simple mechanical models to respiratory impedance data (Zrs), however interpretation has been confounded by the simultaneous presence of multiple coexisting pathophysiologic processes. In contrast to the inverse modeling approach, this study uses simulation from experimental measurements to recapitulate how aging and inflammation alter Zrs. Histologic and mechanical measurements were made in C57BL6/J mice and congenic Sftpd-/- mice at 8, 27 and 80 weeks of age (n = 8/group). An anatomic computational model based on published airway morphometry was developed and Zrs was simulated between 0.5 and 20 Hz. End expiratory pressure dependent changes in airway caliber and recruitment were estimated from mechanical measurements. Tissue elements were simulated using the constant phase model of viscoelasticity. Baseline elastance distribution was estimated in 8-week-old wild type mice, and stochastically varied for each condition based on experimentally measured alteration in elastic fiber composition, alveolar geometry and surfactant composition. Weighing reduction in model error against increasing model complexity allowed for identification of essential features underlying mechanical pathology and their contribution to Zrs. Using a maximum likelihood approach, alteration in lung recruitment and diminished elastic fiber density were shown predictive of mechanical alteration at airway opening, to a greater extent than overt acinar wall destruction. Model-predicted deficits in PEEP-dependent lung recruitment correlate with altered lung lining fluid composition independent of age or genotype. PMID:28837561

Histologic and biochemical alterations predict pulmonary mechanical dysfunction in aging mice with chronic lung inflammation.

PubMed

Massa, Christopher B; Groves, Angela M; Jaggernauth, Smita U; Laskin, Debra L; Gow, Andrew J

2017-08-01

Both aging and chronic inflammation produce complex structural and biochemical alterations to the lung known to impact work of breathing. Mice deficient in surfactant protein D (Sftpd) develop progressive age-related lung pathology characterized by tissue destruction/remodeling, accumulation of foamy macrophages and alteration in surfactant composition. This study proposes to relate changes in tissue structure seen in normal aging and in chronic inflammation to altered lung mechanics using a computational model. Alterations in lung function in aging and Sftpd -/- mice have been inferred from fitting simple mechanical models to respiratory impedance data (Zrs), however interpretation has been confounded by the simultaneous presence of multiple coexisting pathophysiologic processes. In contrast to the inverse modeling approach, this study uses simulation from experimental measurements to recapitulate how aging and inflammation alter Zrs. Histologic and mechanical measurements were made in C57BL6/J mice and congenic Sftpd-/- mice at 8, 27 and 80 weeks of age (n = 8/group). An anatomic computational model based on published airway morphometry was developed and Zrs was simulated between 0.5 and 20 Hz. End expiratory pressure dependent changes in airway caliber and recruitment were estimated from mechanical measurements. Tissue elements were simulated using the constant phase model of viscoelasticity. Baseline elastance distribution was estimated in 8-week-old wild type mice, and stochastically varied for each condition based on experimentally measured alteration in elastic fiber composition, alveolar geometry and surfactant composition. Weighing reduction in model error against increasing model complexity allowed for identification of essential features underlying mechanical pathology and their contribution to Zrs. Using a maximum likelihood approach, alteration in lung recruitment and diminished elastic fiber density were shown predictive of mechanical alteration at airway opening, to a greater extent than overt acinar wall destruction. Model-predicted deficits in PEEP-dependent lung recruitment correlate with altered lung lining fluid composition independent of age or genotype.

Understanding Cooperative Chirality at the Nanoscale

NASA Astrophysics Data System (ADS)

Yu, Shangjie; Wang, Pengpeng; Govorov, Alexander; Ouyang, Min

Controlling chirality of organic and inorganic structures plays a key role in many physical, chemical and biochemical processes, and may offer new opportunity to create technology applications based on chiroptical effect. In this talk, we will present a theoretical model and simulation to demonstrate how to engineer nanoscale chirality in inorganic nanostructures via synergistic control of electromagnetic response of both lattice and geometry, leading to rich tunability of chirality at the nanoscale. Our model has also been applied to understand recent materials advancement of related control with excellent agreement, and can elucidate physical origins of circular dichroism features in the experiment.

Effects of size and temperature on metabolic rate.

PubMed

Gillooly, J F; Brown, J H; West, G B; Savage, V M; Charnov, E L

2001-09-21

We derive a general model, based on principles of biochemical kinetics and allometry, that characterizes the effects of temperature and body mass on metabolic rate. The model fits metabolic rates of microbes, ectotherms, endotherms (including those in hibernation), and plants in temperatures ranging from 0 degrees to 40 degrees C. Mass- and temperature-compensated resting metabolic rates of all organisms are similar: The lowest (for unicellular organisms and plants) is separated from the highest (for endothermic vertebrates) by a factor of about 20. Temperature and body size are primary determinants of biological time and ecological roles.

Length of positive surgical margin after radical prostatectomy as a predictor of biochemical recurrence.

PubMed

Shikanov, Sergey; Song, Jie; Royce, Cassandra; Al-Ahmadie, Hikmat; Zorn, Kevin; Steinberg, Gary; Zagaja, Gregory; Shalhav, Arieh; Eggener, Scott

2009-07-01

Length and location of positive surgical margins are independent predictors of biochemical recurrence after open radical prostatectomy. We assessed their impact on biochemical recurrence in a large robotic prostatectomy series. Data were collected prospectively from 1,398 men undergoing robotic radical prostatectomy for clinically localized prostate cancer from 2003 to 2008 at a single institution. The associations of preoperative prostate specific antigen, pathological Gleason score, pathological stage and positive surgical margin parameters (location, length and focality) with biochemical recurrence rate were evaluated. Margin status and length were measured by a single uropathologist. Biochemical recurrence was defined as serum prostate specific antigen greater than 0.1 ng/ml on 2 consecutive tests. Cox regression models were constructed to evaluate predictors of biochemical recurrence. Of 1,398 consecutive patients who underwent robotic prostatectomy positive margins were present in 243 (17%) (11% of pathological T2 and 41% of T3). Preoperative prostate specific antigen, pathological stage, Gleason score, margin status, and margin length as a continuous and categorical variable (less than 1, 1 to 3, more than 3 mm) were independent predictors of biochemical recurrence. Patients with negative margins and those with a positive margin less than 1 mm had similar rates of biochemical recurrence (log rank test p = 0.18). Surgical margin location was not independently associated with biochemical recurrence. Margin status and length are independent predictors of biochemical recurrence following robotic radical prostatectomy. Although longer followup and validation studies are necessary for confirmation, patients with a positive margin less than 1 mm appear to have similar recurrence rates as those with negative margins.

Biochemical component identification by plasmonic improved whispering gallery mode optical resonance based sensor

NASA Astrophysics Data System (ADS)

Saetchnikov, Vladimir A.; Tcherniavskaia, Elina A.; Saetchnikov, Anton V.; Schweiger, Gustav; Ostendorf, Andreas

2014-05-01

Experimental data on detection and identification of variety of biochemical agents, such as proteins, microelements, antibiotic of different generation etc. in both single and multi component solutions under varied in wide range concentration analyzed on the light scattering parameters of whispering gallery mode optical resonance based sensor are represented. Multiplexing on parameters and components has been realized using developed fluidic sensor cell with fixed in adhesive layer dielectric microspheres and data processing. Biochemical component identification has been performed by developed network analysis techniques. Developed approach is demonstrated to be applicable both for single agent and for multi component biochemical analysis. Novel technique based on optical resonance on microring structures, plasmon resonance and identification tools has been developed. To improve a sensitivity of microring structures microspheres fixed by adhesive had been treated previously by gold nanoparticle solution. Another technique used thin film gold layers deposited on the substrate below adhesive. Both biomolecule and nanoparticle injections caused considerable changes of optical resonance spectra. Plasmonic gold layers under optimized thickness also improve parameters of optical resonance spectra. Biochemical component identification has been also performed by developed network analysis techniques both for single and for multi component solution. So advantages of plasmon enhancing optical microcavity resonance with multiparameter identification tools is used for development of a new platform for ultra sensitive label-free biomedical sensor.

Application of water quality models to rivers in Johor

NASA Astrophysics Data System (ADS)

Chii, Puah Lih; Rahman, Haliza Abd.

2017-08-01

River pollution is one the most common hazard in many countries in the world, which includes Malaysia. Many rivers have been polluted because of the rapid growth in industrialization to support the country's growing population and economy. Domestic and industrial sewage, agricultural wastes have polluted the rivers and will affect the water quality. Based on the Malaysia Environment Quality Report 2007, the Department of Environment (DOE) has described that one of the major pollutants is Biochemical Oxygen Demand (BOD). Data from DOE in 2004, based on BOD, 18 river basins were classified polluted, 37 river basins were slightly polluted and 65 river basins were in clean condition. In this paper, two models are fitted the data of rivers in Johor state namely Streeter-Phelps model and nonlinear regression (NLR) model. The BOD concentration data for the two rivers in Johor state from year 1981 to year 1990 is analyzed. To estimate the parameters for the Streeter-Phelps model and NLR model, this study focuses on the weighted least squares and Gauss-Newton method respectively. Based on the value of Mean Square Error, NLR model is a better model compared to Streeter-Phelps model.

Gel-Based Purification and Biochemical Study of Laccase Isozymes from Ganoderma sp. and Its Role in Enhanced Cotton Callogenesis

PubMed Central

Kumar, Amit; Singh, Deepti; Sharma, Krishna K.; Arora, Sakshi; Singh, Amarjeet K.; Gill, Sarvajeet S.; Singhal, Barkha

2017-01-01

Basidiomycetous fungi, Ganoderma lucidum MDU-7 and Ganoderma sp. kk-02 secreted multiple laccase isozymes under diverse growth condition. Aromatic compounds and metal salts were also found to regulate the differential expression of laccase isozymes from both the Ganoderma sp. Laccase isozymes induced in the presence of copper from G. lucidum MDU-7 were purified by gel-based (native-PAGE) purification method. The purity of laccase isozymes was checked by zymogram and SDS-PAGE. The SDS-PAGE of purified proteins confirmed the multimeric nature of laccase isozymes. The molecular mass of isozymes was found to be in the range of 40–66 kDa. Further, the purified laccase isozymes and their peptides were confirmed with the help of MALDI-TOF peptide fingerprinting. The biochemical characterization of laccase isozymes viz. Glac L2, Glac L3, Glac L4, and Glac L5 have shown the optimum temperature in the range of 30°–45°C and pH 3.0. The Km values of all the laccase isozymes determined for guaiacol were (96–281 μM), ABTS (15–83 μM) and O-tolidine (78–724 μM). Further, laccase isozymes from G. lucidum whole genome were studied using bioinformatics tools. The molecular modeling and docking of laccase isozymes with different substrates showed a significant binding affinity, which further validates our experimental results. Interestingly, copper induced laccase of 40 U/ml in culture medium was found to significantly induce cotton callogenesis. Interestingly, all the laccase isozymes were found to have an antioxidative role and therefore capable in free radicals scavenging during callogenesis. This is the first detailed study on the biochemical characterization of all the laccase isozymes purified by a gel-based novel method. PMID:28473815

Dissolved oxygen in the Tualatin River, Oregon, during winter flow conditions, 1991 and 1992

USGS Publications Warehouse

Kelly, V.J.

1996-01-01

Throughout the winter period, November through April, wastewater treatment plants in the Tualatin River Basin discharge from 10,000 to 15,000 pounds per day of biochemical oxygen demand to the river. These loads often increase substantially during storms when streamflow is high. During the early winter season, when streamflow is frequently less than the average winter flow, the treatment plants discharge about 2,000 pounds per day of ammonia. This study focused on the capacity of the Tualatin River to assimilat oxygen-demanding loads under winter streamflow conditions during the 1992 water year, with an emphasis on peak-flow conditions in the river, and winter-base-flow conditions during November 1992. Concentrations of dissolved oxygen throughout the main stem of the river during the winter remained generally high relative to the State standard for Oregon of 6 milligrams per liter. The most important factors controlling oxygen consumption during winter-low-flow conditions were carbonaceous biochemical oxygen demand and input of oxygen-depleted waters from tributaries. During peak-flow conditions, reduced travel time and increased dilution associated with the increased streamflow minimized the effect of increased oxygen-demanding loads. During the base-flow period in November 1992, concentrations of dissolved oxygen were consistently below 6 milligrams per liter. A hydrodynamic water-quality model was used to identify the processes depleting dissolved oxygen, including sediment oxygen demand, nitrification, and carbonaceous biochemical oxygen demand. Sediment oxygen demand was the most significant factor; nitrification was also important. Hypothetical scenarios were posed to evaluate the effect of different wastewater treatment plant loads during winter-base-flow conditions. Streamflow and temperature were significant factors governing concentrations of dissolved oxygen in the main-stem river.

Metabolomic profiles in individuals with negative affectivity and social inhibition: a population-based study of Type D personality.

PubMed

Altmaier, Elisabeth; Emeny, Rebecca T; Krumsiek, Jan; Lacruz, Maria E; Lukaschek, Karoline; Häfner, Sibylle; Kastenmüller, Gabi; Römisch-Margl, Werner; Prehn, Cornelia; Mohney, Robert P; Evans, Anne M; Milburn, Michael V; Illig, Thomas; Adamski, Jerzy; Theis, Fabian; Suhre, Karsten; Ladwig, Karl-Heinz

2013-08-01

Individuals with negative affectivity who are inhibited in social situations are characterized as distressed, or Type D, and have an increased risk of cardiovascular disease (CVD). The underlying biomechanisms that link this psychological affect to a pathological state are not well understood. This study applied a metabolomic approach to explore biochemical pathways that may contribute to the Type D personality. Type D personality was determined by the Type D Scale-14. Small molecule biochemicals were measured using two complementary mass-spectrometry based metabolomics platforms. Metabolic profiles of Type D and non-Type D participants within a population-based study in Southern Germany were compared in cross-sectional regression analyses. The PHQ-9 and GAD-7 instruments were also used to assess symptoms of depression and anxiety, respectively, within this metabolomic study. 668 metabolites were identified in the serum of 1502 participants (age 32-77); 386 of these individuals were classified as Type D. While demographic and biomedical characteristics were equally distributed between the groups, a higher level of depression and anxiety was observed in Type D individuals. Significantly lower levels of the tryptophan metabolite kynurenine were associated with Type D (p-value corrected for multiple testing=0.042), while no significant associations could be found for depression and anxiety. A Gaussian graphical model analysis enabled the identification of four potentially interesting metabolite networks that are enriched in metabolites (androsterone sulfate, tyrosine, indoxyl sulfate or caffeine) that associate nominally with Type D personality. This study identified novel biochemical pathways associated with Type D personality and demonstrates that the application of metabolomic approaches in population studies can reveal mechanisms that may contribute to psychological health and disease. Copyright © 2012 Elsevier Ltd. All rights reserved.

[Interpretation of false positive results of biochemical prenatal tests].

PubMed

Sieroszewski, Piotr; Słowakiewicz, Katarzyna; Perenc, Małgorzata

2010-03-01

Modern, non-invasive prenatal diagnostics based on biochemical and ultrasonographic markers of fetal defects allows us to calculate the risk of fetal chromosomal aneuploidies with high sensitivity and specificity An introduction of biochemical, non-invasive prenatal tests turned out to result in frequent false positive results of these tests in cases when invasive diagnostics does not confirm fetal defects. However prospective analysis of these cases showed numerous complications in the third trimester of the pregnancies.

A master equation and moment approach for biochemical systems with creation-time-dependent bimolecular rate functions

PubMed Central

Chevalier, Michael W.; El-Samad, Hana

2014-01-01

Noise and stochasticity are fundamental to biology and derive from the very nature of biochemical reactions where thermal motion of molecules translates into randomness in the sequence and timing of reactions. This randomness leads to cell-to-cell variability even in clonal populations. Stochastic biochemical networks have been traditionally modeled as continuous-time discrete-state Markov processes whose probability density functions evolve according to a chemical master equation (CME). In diffusion reaction systems on membranes, the Markov formalism, which assumes constant reaction propensities is not directly appropriate. This is because the instantaneous propensity for a diffusion reaction to occur depends on the creation times of the molecules involved. In this work, we develop a chemical master equation for systems of this type. While this new CME is computationally intractable, we make rational dimensional reductions to form an approximate equation, whose moments are also derived and are shown to yield efficient, accurate results. This new framework forms a more general approach than the Markov CME and expands upon the realm of possible stochastic biochemical systems that can be efficiently modeled. PMID:25481130

A master equation and moment approach for biochemical systems with creation-time-dependent bimolecular rate functions

NASA Astrophysics Data System (ADS)

Chevalier, Michael W.; El-Samad, Hana

2014-12-01

Noise and stochasticity are fundamental to biology and derive from the very nature of biochemical reactions where thermal motion of molecules translates into randomness in the sequence and timing of reactions. This randomness leads to cell-to-cell variability even in clonal populations. Stochastic biochemical networks have been traditionally modeled as continuous-time discrete-state Markov processes whose probability density functions evolve according to a chemical master equation (CME). In diffusion reaction systems on membranes, the Markov formalism, which assumes constant reaction propensities is not directly appropriate. This is because the instantaneous propensity for a diffusion reaction to occur depends on the creation times of the molecules involved. In this work, we develop a chemical master equation for systems of this type. While this new CME is computationally intractable, we make rational dimensional reductions to form an approximate equation, whose moments are also derived and are shown to yield efficient, accurate results. This new framework forms a more general approach than the Markov CME and expands upon the realm of possible stochastic biochemical systems that can be efficiently modeled.

Integrated modeling of protein-coding genes in the Manduca sexta genome using RNA-Seq data from the biochemical model insect

PubMed Central

Cao, Xiaolong; Jiang, Haobo

2015-01-01

The genome sequence of Manduca sexta was recently determined using 454 technology. Cufflinks and MAKER2 were used to establish gene models in the genome assembly based on the RNA-Seq data and other species' sequences. Aided by the extensive RNA-Seq data from 50 tissue samples at various life stages, annotators over the world (including the present authors) have manually confirmed and improved a small percentage of the models after spending months of effort. While such collaborative efforts are highly commendable, many of the predicted genes still have problems which may hamper future research on this insect species. As a biochemical model representing lepidopteran pests, M. sexta has been used extensively to study insect physiological processes for over five decades. In this work, we assembled Manduca datasets Cufflinks 3.0, Trinity 4.0, and Oases 4.0 to assist the manual annotation efforts and development of Official Gene Set (OGS) 2.0. To further improve annotation quality, we developed methods to evaluate gene models in the MAKER2, Cufflinks, Oases and Trinity assemblies and selected the best ones to constitute MCOT 1.0 after thorough crosschecking. MCOT 1.0 has 18,089 genes encoding 31,666 proteins: 32.8% match OGS 2.0 models perfectly or near perfectly, 11,747 differ considerably, and 29.5% are absent in OGS 2.0. Future automation of this process is anticipated to greatly reduce human efforts in generating comprehensive, reliable models of structural genes in other genome projects where extensive RNA-Seq data are available. PMID:25612938

Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model.

PubMed

Jiang, Jingyi; Comar, Alexis; Burger, Philippe; Bancal, Pierre; Weiss, Marie; Baret, Frédéric

2018-01-01

Leaf biochemical composition corresponds to traits related to the plant state and its functioning. This study puts the emphasis on the main leaf absorbers: chlorophyll a and b ([Formula: see text]), carotenoids ([Formula: see text]), water ([Formula: see text]) and dry mater ([Formula: see text]) contents. Two main approaches were used to estimate [[Formula: see text] [Formula: see text], [Formula: see text], [Formula: see text

Chemical oscillator as a generalized Rayleigh oscillator.

PubMed

Ghosh, Shyamolina; Ray, Deb Shankar

2013-10-28

We derive the conditions under which a set of arbitrary two dimensional autonomous kinetic equations can be reduced to the form of a generalized Rayleigh oscillator which admits of limit cycle solution. This is based on a linear transformation of field variables which can be found by inspection of the kinetic equations. We illustrate the scheme with the help of several chemical and bio-chemical oscillator models to show how they can be cast as a generalized Rayleigh oscillator.

A hydraulic-photosynthetic model based on extended HLH and its application to Coast redwood (Sequoia sempervirens).

PubMed

Du, Ning; Fan, Jintu; Chen, Shuo; Liu, Yang

2008-07-21

Although recent investigations [Ryan, M.G., Yoder, B.J., 1997. Hydraulic limits to tree height and tree growth. Bioscience 47, 235-242; Koch, G.W., Sillett, S.C.,Jennings, G.M.,Davis, S.D., 2004. The limits to tree height. Nature 428, 851-854; Niklas, K.J., Spatz, H., 2004. Growth and hydraulic (not mechanical) constraints govern the scaling of tree height and mass. Proc. Natl Acad. Sci. 101, 15661-15663; Ryan, M.G., Phillips, N., Bond, B.J., 2006. Hydraulic limitation hypothesis revisited. Plant Cell Environ. 29, 367-381; Niklas, K.J., 2007. Maximum plant height and the biophysical factors that limit it. Tree Physiol. 27, 433-440; Burgess, S.S.O., Dawson, T.E., 2007. Predicting the limits to tree height using statistical regressions of leaf traits. New Phytol. 174, 626-636] suggested that the hydraulic limitation hypothesis (HLH) is the most plausible theory to explain the biophysical limits to maximum tree height and the decline in tree growth rate with age, the analysis is largely qualitative or based on statistical regression. Here we present an integrated biophysical model based on the principle that trees develop physiological compensations (e.g. the declined leaf water potential and the tapering of conduits with heights [West, G.B., Brown, J.H., Enquist, B.J., 1999. A general model for the structure and allometry of plant vascular systems. Nature 400, 664-667]) to resist the increasing water stress with height, the classical HLH and the biochemical limitations on photosynthesis [von Caemmerer, S., 2000. Biochemical Models of Leaf Photosynthesis. CSIRO Publishing, Australia]. The model has been applied to the tallest trees in the world (viz. Coast redwood (Sequoia sempervirens)). Xylem water potential, leaf carbon isotope composition, leaf mass to area ratio at different heights derived from the model show good agreements with the experimental measurements of Koch et al. [2004. The limits to tree height. Nature 428, 851-854]. The model also well explains the universal trend of declining growth rate with age.

Toward improving fine needle aspiration cytology by applying Raman microspectroscopy

NASA Astrophysics Data System (ADS)

Becker-Putsche, Melanie; Bocklitz, Thomas; Clement, Joachim; Rösch, Petra; Popp, Jürgen

2013-04-01

Medical diagnosis of biopsies performed by fine needle aspiration has to be very reliable. Therefore, pathologists/cytologists need additional biochemical information on single cancer cells for an accurate diagnosis. Accordingly, we applied three different classification models for discriminating various features of six breast cancer cell lines by analyzing Raman microspectroscopic data. The statistical evaluations are implemented by linear discriminant analysis (LDA) and support vector machines (SVM). For the first model, a total of 61,580 Raman spectra from 110 single cells are discriminated at the cell-line level with an accuracy of 99.52% using an SVM. The LDA classification based on Raman data achieved an accuracy of 94.04% by discriminating cell lines by their origin (solid tumor versus pleural effusion). In the third model, Raman cell spectra are classified by their cancer subtypes. LDA results show an accuracy of 97.45% and specificities of 97.78%, 99.11%, and 98.97% for the subtypes basal-like, HER2+/ER-, and luminal, respectively. These subtypes are confirmed by gene expression patterns, which are important prognostic features in diagnosis. This work shows the applicability of Raman spectroscopy and statistical data handling in analyzing cancer-relevant biochemical information for advanced medical diagnosis on the single-cell level.

Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism

PubMed Central

Fleming, R.M.T.; Thiele, I.; Provan, G.; Nasheuer, H.P.

2010-01-01

The quantitative analysis of biochemical reactions and metabolites is at frontier of biological sciences. The recent availability of high-throughput technology data sets in biology has paved the way for new modelling approaches at various levels of complexity including the metabolome of a cell or an organism. Understanding the metabolism of a single cell and multi-cell organism will provide the knowledge for the rational design of growth conditions to produce commercially valuable reagents in biotechnology. Here, we demonstrate how equations representing steady state mass conservation, energy conservation, the second law of thermodynamics, and reversible enzyme kinetics can be formulated as a single system of linear equalities and inequalities, in addition to linear equalities on exponential variables. Even though the feasible set is non-convex, the reformulation is exact and amenable to large-scale numerical analysis, a prerequisite for computationally feasible genome scale modelling. Integrating flux, concentration and kinetic variables in a unified constraint-based formulation is aimed at increasing the quantitative predictive capacity of flux balance analysis. Incorporation of experimental and theoretical bounds on thermodynamic and kinetic variables ensures that the predicted steady state fluxes are both thermodynamically and biochemically feasible. The resulting in silico predictions are tested against fluxomic data for central metabolism in E. coli and compare favourably with in silico prediction by flux balance analysis. PMID:20230840

Development of a model based on biochemical, real-time tissue elastography and ultrasound data for the staging of liver fibrosis and cirrhosis in patients with chronic hepatitis B

PubMed Central

Xu, Shi-Hao; Li, Qiao; Hu, Yuan-Ping; Ying, Li

2016-01-01

The liver fibrosis index (LFI), based on real-time tissue elastography (RTE), is a method currently used to assess liver fibrosis. However, this method may not consistently distinguish between the different stages of fibrosis, which limits its accuracy. The aim of the present study was to develop novel models based on biochemical, RTE and ultrasound data for predicting significant liver fibrosis and cirrhosis. A total of 85 consecutive patients with chronic hepatitis B (CHB) were prospectively enrolled and underwent a liver biopsy and RTE. The parameters for predicting significant fibrosis and cirrhosis were determined by conducting multivariate analyses. The splenoportal index (SPI; P=0.002) and LFI (P=0.023) were confirmed as independent predictors of significant fibrosis. Using multivariate analyses for identifying parameters that predict cirrhosis, significant differences in γ-glutamyl transferase (GGT; P=0.049), SPI (P=0.002) and LFI (P=0.001) were observed. Based on these observations, the novel model LFI-SPI score (LSPS) was developed to predict the occurrence of significant liver fibrosis, with an area under receiver operating characteristic curves (AUROC) of 0.87. The diagnostic accuracy of the LSPS model was superior to that of the LFI (AUROC=0.76; P=0.0109), aspartate aminotransferase-to-platelet ratio index (APRI; AUROC=0.64; P=0.0031), fibrosis-4 index (FIB-4; AUROC= 0.67; P= 0.0044) and FibroScan (AUROC=0.68; P=0.0021) models. In addition, the LFI-SPI-GGT score (LSPGS) was developed for the purposes of predicting liver cirrhosis, demonstrating an AUROC value of 0.93. The accuracy of LSPGS was similar to that of FibroScan (AUROC=0.85; P=0.134), but was superior to LFI (AUROC= 0.81; P= 0.0113), APRI (AUROC= 0.67; P<0.0001) and FIB-4 (AUROC=0.719; P=0.0005). In conclusion, the results of the present study suggest that the use of LSPS and LSPGS may complement current methods of diagnosing significant liver fibrosis and cirrhosis in patients with CHB. PMID:27573619

Development of a model based on biochemical, real‑time tissue elastography and ultrasound data for the staging of liver fibrosis and cirrhosis in patients with chronic hepatitis B.

PubMed

Xu, Shi-Hao; Li, Qiao; Hu, Yuan-Ping; Ying, Li

2016-10-01

The liver fibrosis index (LFI), based on real‑time tissue elastography (RTE), is a method currently used to assess liver fibrosis. However, this method may not consistently distinguish between the different stages of fibrosis, which limits its accuracy. The aim of the present study was to develop novel models based on biochemical, RTE and ultrasound data for predicting significant liver fibrosis and cirrhosis. A total of 85 consecutive patients with chronic hepatitis B (CHB) were prospectively enrolled and underwent a liver biopsy and RTE. The parameters for predicting significant fibrosis and cirrhosis were determined by conducting multivariate analyses. The splenoportal index (SPI; P=0.002) and LFI (P=0.023) were confirmed as independent predictors of significant fibrosis. Using multivariate analyses for identifying parameters that predict cirrhosis, significant differences in γ‑glutamyl transferase (GGT; P=0.049), SPI (P=0.002) and LFI (P=0.001) were observed. Based on these observations, the novel model LFI‑SPI score (LSPS) was developed to predict the occurrence of significant liver fibrosis, with an area under receiver operating characteristic curves (AUROC) of 0.87. The diagnostic accuracy of the LSPS model was superior to that of the LFI (AUROC=0.76; P=0.0109), aspartate aminotransferase‑to‑platelet ratio index (APRI; AUROC=0.64; P=0.0031), fibrosis‑4 index (FIB‑4; AUROC=0.67; P=0.0044) and FibroScan (AUROC=0.68; P=0.0021) models. In addition, the LFI‑SPI‑GGT score (LSPGS) was developed for the purposes of predicting liver cirrhosis, demonstrating an AUROC value of 0.93. The accuracy of LSPGS was similar to that of FibroScan (AUROC=0.85; P=0.134), but was superior to LFI (AUROC=0.81; P=0.0113), APRI (AUROC=0.67; P<0.0001) and FIB‑4 (AUROC=0.719; P=0.0005). In conclusion, the results of the present study suggest that the use of LSPS and LSPGS may complement current methods of diagnosing significant liver fibrosis and cirrhosis in patients with CHB.

High-resolution modeling of a marine ecosystem using the FRESCO hydroecological model

NASA Astrophysics Data System (ADS)

Zalesny, V. B.; Tamsalu, R.

2009-02-01

The FRESCO (Finnish Russian Estonian Cooperation) mathematical model describing a marine hydroecosystem is presented. The methodology of the numerical solution is based on the method of multicomponent splitting into physical and biological processes, spatial coordinates, etc. The model is used for the reproduction of physical and biological processes proceeding in the Baltic Sea. Numerical experiments are performed with different spatial resolutions for four marine basins that are enclosed into one another: the Baltic Sea, the Gulf of Finland, the Tallinn-Helsinki water area, and Tallinn Bay. Physical processes are described by the equations of nonhydrostatic dynamics, including the k-ω parametrization of turbulence. Biological processes are described by the three-dimensional equations of an aquatic ecosystem with the use of a size-dependent parametrization of biochemical reactions. The main goal of this study is to illustrate the efficiency of the developed numerical technique and to demonstrate the importance of a high spatial resolution for water basins that have complex bottom topography, such as the Baltic Sea. Detailed information about the atmospheric forcing, bottom topography, and coastline is very important for the description of coastal dynamics and specific features of a marine ecosystem. Experiments show that the spatial inhomogeneity of hydroecosystem fields is caused by the combined effect of upwelling, turbulent mixing, surface-wave breaking, and temperature variations, which affect biochemical reactions.

The establishment and characterization of the first canine hepatocellular carcinoma cell line, which resembles human oncogenic expression patterns

PubMed Central

Boomkens, Sacha Y; Spee, Bart; IJzer, Jooske; Kisjes, Ronald; Egberink, Herman F; van den Ingh, Ted SGAM; Rothuizen, Jan; Penning, Louis C

2004-01-01

Background Hepatocellular carcinoma (HCC) is one of the most worldwide frequent primary carcinomas resulting in the death of many cirrhotic patients. Unfortunately, the molecular mechanisms of this cancer are not well understood; therefore, we need a good model system to study HCC. The dog is recognized as a promising model for human medical research, namely compared with rodents. The objective of this study was to establish and characterize a spontaneous canine tumor cell line as a potential model for studies on HCC. Results Histomorphological, biochemical, molecular biological and quantitative assays were performed to characterize the canine HCC cell line that originated from a dog with a spontaneous liver tumor. Morphological investigations provided strong evidence for the hepatocytic and neoplastic nature of the cell line, while biochemical assays showed that they produced liver-specific enzymes. PCR analysis confirmed expression of ceruloplasmin, alpha-fetoprotein and serum albumin. Quantitative RT-PCR showed that the canine HCC cell line resembles human HCC based on the measurements of expression profiles of genes involved in cell proliferation and apoptosis. Conclusions We have developed a novel, spontaneous tumor liver cell line of canine origin that has many characteristics of human HCC. Therefore, the canine HCC cell line might be an excellent model for comparative studies on the molecular pathogenesis of HCC. PMID:15566568

Bicyclic-Capped Histone Deacetylase 6 Inhibitors with Improved Activity in a Model of Axonal Charcot-Marie-Tooth Disease.

PubMed

Shen, Sida; Benoy, Veronick; Bergman, Joel A; Kalin, Jay H; Frojuello, Mariana; Vistoli, Giulio; Haeck, Wanda; Van Den Bosch, Ludo; Kozikowski, Alan P

2016-02-17

Charcot-Marie-Tooth (CMT) disease is a disorder of the peripheral nervous system where progressive degeneration of motor and sensory nerves leads to motor problems and sensory loss and for which no pharmacological treatment is available. Recently, it has been shown in a model for the axonal form of CMT that histone deacetylase 6 (HDAC6) can serve as a target for the development of a pharmacological therapy. Therefore, we aimed at developing new selective and activity-specific HDAC6 inhibitors with improved biochemical properties. By utilizing a bicyclic cap as the structural scaffold from which to build upon, we developed several analogues that showed improved potency compared to tubastatin A while maintaining excellent selectivity compared to HDAC1. Further screening in N2a cells examining both the acetylation of α-tubulin and histones narrowed down the library of compounds to three potent and selective HDAC6 inhibitors. In mutant HSPB1-expressing DRG neurons, serving as an in vitro model for CMT2, these inhibitors were able to restore the mitochondrial axonal transport deficits. Combining structure-based development of HDAC6 inhibitors, screening in N2a cells and in a neuronal model for CMT2F, and preliminary ADMET and pharmacokinetic profiles, resulted in the selection of compound 23d that possesses improved biochemical, functional, and druglike properties compared to tubastatin A.

Therapeutic evaluation of grain based functional food formulation in a geriatric animal model.

PubMed

Teradal, Deepa; Joshi, Neena; Aladakatti, Ravindranath H

2017-08-01

This study investigates the effect of wholesome grain based functional food formulation, on clinical and biochemical parameters in 24-30 months old Wistar albino geriatric rats, corresponding to human age 60-75 years. Animals were randomly divided into five, groups. Experimental diets were compared to the basal rat diet (Group I). Four food, formulation were-wheat based (Group II), finger millet based (Group III), wheat based, diet + fenugreek seed powder (Group IV), finger millet based diet + fenugreek powder, (Group V). These five types of diets were fed to the experimental rats for 6 weeks. Hematological and biochemical parameters were evaluated. The results showed that, feed intake was influenced by the type of feed. Diets supplemented with, fenugreek (Group IV) caused a significant increase in serum hemoglobin. The total serum protein values were significantly highest in Group III. Total serum albumin was found to be lower in Group I and highest in Group II. The concentration of BUN was highest in Group I and the lowest in control diet. Serum cholesterol and glucose were significantly reduced in Group IV. Several hematological and serum mineral values were influenced by the type of diet. The type of diet did not influence the organs weight. A moderate hypoglycemic and hypercholesterolemic effect was observed in composite mix fed rats. This study clearly justifies the recommendation to use wholesome grain based functional foods for geriatric population.

A Study of the Competency of Third Year Medical Students to Interpret Biochemically Based Clinical Scenarios Using Knowledge and Skills Gained in Year 1 and 2

ERIC Educational Resources Information Center

Gowda, Veena Bhaskar S.; Nagaiah, Bhaskar Hebbani; Sengodan, Bharathi

2016-01-01

Medical students build clinical knowledge on the grounds of previously obtained basic knowledge. The study aimed to evaluate the competency of third year medical students to interpret biochemically based clinical scenarios using knowledge and skills gained during year 1 and 2 of undergraduate medical training. Study was conducted on year 3 MBBS…

Novel Hydrogen Production Systems Operative at Thermodynamic Extremes

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

Gunsalus, Robert

2012-11-30

We have employed a suite of molecular, bioinformatics, and biochemical tools to interrogate the thermodynamically limiting steps of H{sub 2} production from fatty acids in syntrophic communities. We also developed a new microbial model system that generates high H{sub 2} concentrations (over 17% of the gas phase) with high H{sub 2} yields of over 3 moles H{sub 2} per mole glucose. Lastly, a systems-based study of biohydrogen production in model anaerobic consortia was performed to begin identifying key regulated steps as a precursor to modeling co-metabolism. The results of these studies significantly expand our ability to predict and model systemsmore » for H{sub 2} production in novel anaerobes that are currently very poorly documented or understood.« less

Prediction of fetal growth restriction using estimated fetal weight vs a combined screening model in the third trimester.

PubMed

Miranda, J; Rodriguez-Lopez, M; Triunfo, S; Sairanen, M; Kouru, H; Parra-Saavedra, M; Crovetto, F; Figueras, F; Crispi, F; Gratacós, E

2017-11-01

To compare the performance of third-trimester screening, based on estimated fetal weight centile (EFWc) vs a combined model including maternal baseline characteristics, fetoplacental ultrasound and maternal biochemical markers, for the prediction of small-for-gestational-age (SGA) neonates and late-onset fetal growth restriction (FGR). This was a nested case-control study within a prospective cohort of 1590 singleton gestations undergoing third-trimester (32 + 0 to 36 + 6 weeks' gestation) evaluation. Maternal baseline characteristics, mean arterial pressure, fetoplacental ultrasound and circulating biochemical markers (placental growth factor (PlGF), lipocalin-2, unconjugated estriol and inhibin A) were assessed in all women who subsequently delivered a SGA neonate (n = 175), defined as birth weight < 10 th centile according to customized standards, and in a control group (n = 875). Among SGA cases, those with birth weight < 3 rd centile and/or abnormal uterine artery pulsatility index (UtA-PI) and/or abnormal cerebroplacental ratio (CPR) were classified as FGR. Logistic regression predictive models were developed for SGA and FGR, and their performance was compared with that obtained using EFWc alone. In SGA cases, EFWc, CPR Z-score and maternal serum concentrations of unconjugated estriol and PlGF were significantly lower, while mean UtA-PI Z-score and lipocalin-2 and inhibin A concentrations were significantly higher, compared with controls. Using EFWc alone, 52% (area under receiver-operating characteristics curve (AUC), 0.82 (95% CI, 0.77-0.85)) of SGA and 64% (AUC, 0.86 (95% CI, 0.81-0.91)) of FGR cases were predicted at a 10% false-positive rate. A combined screening model including a-priori risk (maternal characteristics), EFWc, UtA-PI, PlGF and estriol (with lipocalin-2 for SGA) achieved a detection rate of 61% (AUC, 0.86 (95% CI, 0.83-0.89)) for SGA cases and 77% (AUC, 0.92 (95% CI, 0.88-0.95)) for FGR. The combined model for the prediction of SGA and FGR performed significantly better than did using EFWc alone (P < 0.001 and P = 0.002, respectively). A multivariable integrative model of maternal characteristics, fetoplacental ultrasound and maternal biochemical markers modestly improved the detection of SGA and FGR cases at 32-36 weeks' gestation when compared with screening based on EFWc alone. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

Oxidative DNA damage background estimated by a system model of base excision repair

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

Sokhansanj, B A; Wilson, III, D M

Human DNA can be damaged by natural metabolism through free radical production. It has been suggested that the equilibrium between innate damage and cellular DNA repair results in an oxidative DNA damage background that potentially contributes to disease and aging. Efforts to quantitatively characterize the human oxidative DNA damage background level based on measuring 8-oxoguanine lesions as a biomarker have led to estimates varying over 3-4 orders of magnitude, depending on the method of measurement. We applied a previously developed and validated quantitative pathway model of human DNA base excision repair, integrating experimentally determined endogenous damage rates and model parametersmore » from multiple sources. Our estimates of at most 100 8-oxoguanine lesions per cell are consistent with the low end of data from biochemical and cell biology experiments, a result robust to model limitations and parameter variation. Our results show the power of quantitative system modeling to interpret composite experimental data and make biologically and physiologically relevant predictions for complex human DNA repair pathway mechanisms and capacity.« less

Microsatellite Interruptions Stabilize Primate Genomes and Exist as Population-Specific Single Nucleotide Polymorphisms within Individual Human Genomes

PubMed Central

Ananda, Guruprasad; Hile, Suzanne E.; Breski, Amanda; Wang, Yanli; Kelkar, Yogeshwar; Makova, Kateryna D.; Eckert, Kristin A.

2014-01-01

Interruptions of microsatellite sequences impact genome evolution and can alter disease manifestation. However, human polymorphism levels at interrupted microsatellites (iMSs) are not known at a genome-wide scale, and the pathways for gaining interruptions are poorly understood. Using the 1000 Genomes Phase-1 variant call set, we interrogated mono-, di-, tri-, and tetranucleotide repeats up to 10 units in length. We detected ∼26,000–40,000 iMSs within each of four human population groups (African, European, East Asian, and American). We identified population-specific iMSs within exonic regions, and discovered that known disease-associated iMSs contain alleles present at differing frequencies among the populations. By analyzing longer microsatellites in primate genomes, we demonstrate that single interruptions result in a genome-wide average two- to six-fold reduction in microsatellite mutability, as compared with perfect microsatellites. Centrally located interruptions lowered mutability dramatically, by two to three orders of magnitude. Using a biochemical approach, we tested directly whether the mutability of a specific iMS is lower because of decreased DNA polymerase strand slippage errors. Modeling the adenomatous polyposis coli tumor suppressor gene sequence, we observed that a single base substitution interruption reduced strand slippage error rates five- to 50-fold, relative to a perfect repeat, during synthesis by DNA polymerases α, β, or η. Computationally, we demonstrate that iMSs arise primarily by base substitution mutations within individual human genomes. Our biochemical survey of human DNA polymerase α, β, δ, κ, and η error rates within certain microsatellites suggests that interruptions are created most frequently by low fidelity polymerases. Our combined computational and biochemical results demonstrate that iMSs are abundant in human genomes and are sources of population-specific genetic variation that may affect genome stability. The genome-wide identification of iMSs in human populations presented here has important implications for current models describing the impact of microsatellite polymorphisms on gene expression. PMID:25033203

Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

PubMed

Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

2004-12-01

As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

Advances in citric acid fermentation by Aspergillus niger: biochemical aspects, membrane transport and modeling.

PubMed

Papagianni, Maria

2007-01-01

Citric acid is regarded as a metabolite of energy metabolism, of which the concentration will rise to appreciable amounts only under conditions of substantive metabolic imbalances. Citric acid fermentation conditions were established during the 1930s and 1940s, when the effects of various medium components were evaluated. The biochemical mechanism by which Aspergillus niger accumulates citric acid has continued to attract interest even though its commercial production by fermentation has been established for decades. Although extensive basic biochemical research has been carried out with A. niger, the understanding of the events relevant for citric acid accumulation is not completely understood. This review is focused on citric acid fermentation by A. niger. Emphasis is given to aspects of fermentation biochemistry, membrane transport in A. niger and modeling of the production process.

Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era

PubMed Central

Tebani, Abdellah; Abily-Donval, Lenaig; Afonso, Carlos; Marret, Stéphane; Bekri, Soumeya

2016-01-01

Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era. PMID:27447622

Writing and compiling code into biochemistry.

PubMed

Shea, Adam; Fett, Brian; Riedel, Marc D; Parhi, Keshab

2010-01-01

This paper presents a methodology for translating iterative arithmetic computation, specified as high-level programming constructs, into biochemical reactions. From an input/output specification, we generate biochemical reactions that produce output quantities of proteins as a function of input quantities performing operations such as addition, subtraction, and scalar multiplication. Iterative constructs such as "while" loops and "for" loops are implemented by transferring quantities between protein types, based on a clocking mechanism. Synthesis first is performed at a conceptual level, in terms of abstract biochemical reactions - a task analogous to high-level program compilation. Then the results are mapped onto specific biochemical reactions selected from libraries - a task analogous to machine language compilation. We demonstrate our approach through the compilation of a variety of standard iterative functions: multiplication, exponentiation, discrete logarithms, raising to a power, and linear transforms on time series. The designs are validated through transient stochastic simulation of the chemical kinetics. We are exploring DNA-based computation via strand displacement as a possible experimental chassis.

Nonequilibrium steady state of biochemical cycle kinetics under non-isothermal conditions

NASA Astrophysics Data System (ADS)

Jin, Xiao; Ge, Hao

2018-04-01

The nonequilibrium steady state of isothermal biochemical cycle kinetics has been extensively studied, but that under non-isothermal conditions has been much less extensively investigated. When the heat exchange between subsystems is slow, the isothermal assumption of the whole system breaks down, as is true for many types of living organisms. Here, starting with a four-state model of molecular transporter across the cell membrane, we generalize the nonequilibrium steady-state theory of isothermal biochemical cycle kinetics to the circumstances with non-uniform temperatures of subsystems in terms of general master equation models. We obtain a new thermodynamic relationship between the chemical reaction rates and thermodynamic potentials in non-isothermal circumstances, based on the overdamped dynamics along the continuous reaction coordinate. We show that the entropy production can vary up to 3% in real cells, even when the temperature difference across the cell membrane is only approximately 1 K. We then decompose the total thermodynamic driving force into its thermal and chemical components and predict that the net flux of molecules transported by the molecular transporter can potentially go against the temperature gradient in the absence of a chemical driving force. Furthermore, we demonstrate that the simple application of the isothermal transition-state rate formula for each chemical reaction in terms of only the reactant’ temperature is not thermodynamically consistent. Therefore, we mathematically derive several revised reaction rate formulas that are not only consistent with the new thermodynamic relationship but also approximate the exact reaction rate better than Kramers’ rate formula under isothermal conditions.

Serum microRNA expression patterns that predict early treatment failure in prostate cancer patients.

PubMed

Singh, Prashant K; Preus, Leah; Hu, Qiang; Yan, Li; Long, Mark D; Morrison, Carl D; Nesline, Mary; Johnson, Candace S; Koochekpour, Shahriar; Kohli, Manish; Liu, Song; Trump, Donald L; Sucheston-Campbell, Lara E; Campbell, Moray J

2014-02-15

We aimed to identify microRNA (miRNA) expression patterns in the serum of prostate cancer (CaP) patients that predict the risk of early treatment failure following radical prostatectomy (RP). Microarray and Q-RT-PCR analyses identified 43 miRNAs as differentiating disease stages within 14 prostate cell lines and reflectedpublically available patient data. 34 of these miRNA were detectable in the serum of CaP patients. Association with time to biochemical progression was examined in a cohort of CaP patients following RP. A greater than two-fold increase in hazard of biochemical progression associated with altered expression of miR-103, miR-125b and miR-222 (p<.0008) in the serum of CaP patients. Prediction models based on penalized regression analyses showed that the levels of the miRNAs and PSA together were better at detecting false positives than models without miRNAs, for similar level of sensitivity. Analyses of publically available data revealed significant and reciprocal relationships between changes in CpG methylation and miRNA expression patterns suggesting a role for CpG methylation to regulate miRNA. Exploratory validation supported roles for miR-222 and miR-125b to predict progression risk in CaP. The current study established that expression patterns of serum-detectable miRNAs taken at the time of RP are prognostic for men who are at risk of experiencing subsequent early biochemical progression. These non-invasive approaches could be used to augment treatment decisions.

Biochemical phenotypes to discriminate microbial subpopulations and improve outbreak detection.

PubMed

Galar, Alicia; Kulldorff, Martin; Rudnick, Wallis; O'Brien, Thomas F; Stelling, John

2013-01-01

Clinical microbiology laboratories worldwide constitute an invaluable resource for monitoring emerging threats and the spread of antimicrobial resistance. We studied the growing number of biochemical tests routinely performed on clinical isolates to explore their value as epidemiological markers. Microbiology laboratory results from January 2009 through December 2011 from a 793-bed hospital stored in WHONET were examined. Variables included patient location, collection date, organism, and 47 biochemical and 17 antimicrobial susceptibility test results reported by Vitek 2. To identify biochemical tests that were particularly valuable (stable with repeat testing, but good variability across the species) or problematic (inconsistent results with repeat testing), three types of variance analyses were performed on isolates of K. pneumonia: descriptive analysis of discordant biochemical results in same-day isolates, an average within-patient variance index, and generalized linear mixed model variance component analysis. 4,200 isolates of K. pneumoniae were identified from 2,485 patients, 32% of whom had multiple isolates. The first two variance analyses highlighted SUCT, TyrA, GlyA, and GGT as "nuisance" biochemicals for which discordant within-patient test results impacted a high proportion of patient results, while dTAG had relatively good within-patient stability with good heterogeneity across the species. Variance component analyses confirmed the relative stability of dTAG, and identified additional biochemicals such as PHOS with a large between patient to within patient variance ratio. A reduced subset of biochemicals improved the robustness of strain definition for carbapenem-resistant K. pneumoniae. Surveillance analyses suggest that the reduced biochemical profile could improve the timeliness and specificity of outbreak detection algorithms. The statistical approaches explored can improve the robust recognition of microbial subpopulations with routinely available biochemical test results, of value in the timely detection of outbreak clones and evolutionarily important genetic events.

A modified culture-based study of bacterial community composition in a tannery wastewater treatment plant.

PubMed

Desta, Adey F; Dalhammer, Gunnel; Kittuva, Gunatrana R

2010-01-01

Though culture-independent methods have been used in preference to traditional isolation techniques for characterization of microbial community of wastewater treatment plants, it is difficult to widely apply this approach in resource-poor countries. The present study aimed to develop a test to identify the culturable portion of bacterial community in a high-strength wastewater. Wastewater samples were collected from nitrification-denitrification and settling tanks of the treatment plant of Elmo Leather AB tannery located in Borås, Sweden. After cultivating on nutrient agar with the optimal dilution (10⁻²), phenotypic and biochemical identification of the bacteria were done with colony morphology, Gram reaction, growth on MacConkey, phenylethanol media, triple sugar Iron agar slants, catalase and oxidase tests. Biochemical grouping of the isolates was done based on their test results for MacConkey, phenylethanol media, triple sugar Iron agar and oxidase test reaction. From the biochemical groups, isolates were randomly selected for API test and 16SrRNA gene sequencing. The isolates from the denitrification, nitrification tank were identified to be Paracoccus denitrificans (67%), Azoarcus spp (3%) and Spingomonas wittichii (1%). From the settling tank, Paracoccus denitrificans (22%), Corynebacterium freneyi (20%) and Bacillus cereus (1%) were identified. The grouping based on biochemical test results as well as the identification based on sequencing has shown coherence except for discrepancies with the API test. The preliminary implications of the grouping based on culture-based characteristics and its potential application for resource-limited environmental microbial studies is discussed.

Simulating imaging spectrometer data of a mixed old-growth forest: A parameterization of a 3D radiative transfer model based on airborne and terrestrial laser scanning

NASA Astrophysics Data System (ADS)

Schneider, F. D.; Leiterer, R.; Morsdorf, F.; Gastellu-Etchegorry, J.; Lauret, N.; Pfeifer, N.; Schaepman, M. E.

2013-12-01

Remote sensing offers unique potential to study forest ecosystems by providing spatially and temporally distributed information that can be linked with key biophysical and biochemical variables. The estimation of biochemical constituents of leaves from remotely sensed data is of high interest revealing insight on photosynthetic processes, plant health, plant functional types, and speciation. However, the scaling of observations at the canopy level to the leaf level or vice versa is not trivial due to the structural complexity of forests. Thus, a common solution for scaling spectral information is the use of physically-based radiative transfer models. The discrete anisotropic radiative transfer model (DART), being one of the most complete coupled canopy-atmosphere 3D radiative transfer models, was parameterized based on airborne and in-situ measurements. At-sensor radiances were simulated and compared with measurements from an airborne imaging spectrometer. The study was performed on the Laegern site, a temperate mixed forest characterized by steep slopes, a heterogeneous spectral background, and deciduous and coniferous trees at different development stages (dominated by beech trees; 47°28'42.0' N, 8°21'51.8' E, 682 m asl, Switzerland). It is one of the few studies conducted on an old-growth forest. Particularly the 3D modeling of the complex canopy architecture is crucial to model the interaction of photons with the vegetation canopy and its background. Thus, we developed two forest reconstruction approaches: 1) based on a voxel grid, and 2) based on individual tree detection. Both methods are transferable to various forest ecosystems and applicable at scales between plot and landscape. Our results show that the newly developed voxel grid approach is favorable over a parameterization based on individual trees. In comparison to the actual imaging spectrometer data, the simulated images exhibit very similar spatial patterns, whereas absolute radiance values are partially differing depending on the respective wavelength. We conclude that our proposed method provides a representation of the 3D radiative regime within old-growth forests that is suitable for simulating most spectral and spatial features of imaging spectrometer data. It indicates the potential of simulating future Earth observation missions, such as ESA's Sentinel-2. However, the high spectral variability of leaf optical properties among species has to be addressed in future radiative transfer modeling. The results further reveal that research emphasis has to be put on the accurate parameterization of small-scale structures, such as the clumping of needles into shoots or the distribution of leaf angles.

Large animal models and new therapies for glycogen storage disease.

PubMed

Brooks, Elizabeth D; Koeberl, Dwight D

2015-05-01

Glycogen storage diseases (GSD), a unique category of inherited metabolic disorders, were first described early in the twentieth century. Since then, the biochemical and genetic bases of these disorders have been determined, and an increasing number of animal models for GSD have become available. At least seven large mammalian models have been developed for laboratory research on GSDs. These models have facilitated the development of new therapies, including gene therapy, which are undergoing clinical translation. For example, gene therapy prolonged survival and prevented hypoglycemia during fasting for greater than one year in dogs with GSD type Ia, and the need for periodic re-administration to maintain efficacy was demonstrated in that dog model. The further development of gene therapy could provide curative therapy for patients with GSD and other inherited metabolic disorders.

Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

PubMed Central

Ramadan Suleiman, Ahmed; Nehdi, Moncef L.

2017-01-01

This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials. PMID:28772495

Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm-Artificial Neural Network.

PubMed

Ramadan Suleiman, Ahmed; Nehdi, Moncef L

2017-02-07

This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.

BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA.

PubMed

Schomburg, Ida; Chang, Antje; Placzek, Sandra; Söhngen, Carola; Rother, Michael; Lang, Maren; Munaretto, Cornelia; Ulas, Susanne; Stelzer, Michael; Grote, Andreas; Scheer, Maurice; Schomburg, Dietmar

2013-01-01

The BRENDA (BRaunschweig ENzyme DAtabase) enzyme portal (http://www.brenda-enzymes.org) is the main information system of functional biochemical and molecular enzyme data and provides access to seven interconnected databases. BRENDA contains 2.7 million manually annotated data on enzyme occurrence, function, kinetics and molecular properties. Each entry is connected to a reference and the source organism. Enzyme ligands are stored with their structures and can be accessed via their names, synonyms or via a structure search. FRENDA (Full Reference ENzyme DAta) and AMENDA (Automatic Mining of ENzyme DAta) are based on text mining methods and represent a complete survey of PubMed abstracts with information on enzymes in different organisms, tissues or organelles. The supplemental database DRENDA provides more than 910 000 new EC number-disease relations in more than 510 000 references from automatic search and a classification of enzyme-disease-related information. KENDA (Kinetic ENzyme DAta), a new amendment extracts and displays kinetic values from PubMed abstracts. The integration of the EnzymeDetector offers an automatic comparison, evaluation and prediction of enzyme function annotations for prokaryotic genomes. The biochemical reaction database BKM-react contains non-redundant enzyme-catalysed and spontaneous reactions and was developed to facilitate and accelerate the construction of biochemical models.

Application of Biochemical Markers for Population Level Assessment of a White Sucker (Catostomus commersoni) Population Exposed to Bleached Kraft Pulp Mill Effluent

EPA Science Inventory

A need in ecological risk assessment is an approach that can be used to link chemically-induced alterations in molecular and biochemical endpoints to adverse outcomes in whole organisms and populations. A predictive population model was developed to translate changes in fecundit...

Anthropometric and Biochemical Characteristics of Polycystic Ovarian Syndrome in South Indian Women Using AES-2006 Criteria.

PubMed

Thathapudi, Sujatha; Kodati, Vijayalakshmi; Erukkambattu, Jayashankar; Katragadda, Anuradha; Addepally, Uma; Hasan, Qurratulain

2014-01-01

Polycystic ovarian syndrome (PCOS) is one of the most common endocrine conditions affecting women of reproductive age with a prevalence of approximately 5-10% worldwide. PCOS can be viewed as a heterogeneous androgen excess disorder with varying degrees of reproductive and metabolic abnormalities, whose diagnosis is based on anthropometric, biochemical and radiological abnormalities. To our knowledge, this is the first study investigating the anthropometric, biochemical and ultrasonographic characteristics of PCOS in Asian Indians of South India, using the Androgen Excess Society (AES-2006) diagnostic criteria. To assess anthropometric, biochemical and ultrasonographic features of PCOS subgroups and controls among South Indian women using the AES-2006 criteria. Two hundred and four women clinically diagnosed with PCOS, and 204 healthy women controls aged 17 to 35 years were evaluated. PCOS was diagnosed by clinical hyperandrogenism (HA), irregular menstruation (IM), and polycystic ovary (PCO). PCOS was further categorized into phenotypic subgroups including the IM+HA+PCO (n = 181, 89%), HA+PCO (n = 23, 11%), IM+HA (n = 0), and also into obese PCOS (n = 142, 70%) and lean PCOS (n = 62, 30%) using body mass index (BMI). Anthropometric measurements and biochemical characteristics were compared among the PCOS subgroups. The PCOS subgroups with regular menstrual cycles (HA+PCO), had more luteinizing hormone (LH), follicle stimulating hormone (FSH), fasting glucose, fasting insulin, and high insulin resistance (IR) expressed as the Homeostasis Model Assessment (HOMA) score, compared with the IM+HA+PCO subgroups and controls. Similarly, the obese PCOS had high BMI, waist to hip ratio (WHR), fasting glucose, LH, LH/FSH, fasting insulin, HOMA score (IR), and dyslipidemia, compared with lean PCOS and controls. Unilateral polycystic ovary was seen in 32 (15.7%) patients, and bilateral involvement in 172 (84.3%) patients. All the controls showed normal ovaries. Anthropometric, biochemical, and ultrasonographic findings showed significant differences among PCOS subgroups. The PCOS subgroups with regular menstrual cycles (HA+PCO), had high insulin resistance (IR) and gonadotropic hormonal abnormalities, compared with the IM+HA+PCO subgroups and controls.

Anthropometric and Biochemical Characteristics of Polycystic Ovarian Syndrome in South Indian Women Using AES-2006 Criteria

PubMed Central

Thathapudi, Sujatha; Kodati, Vijayalakshmi; Erukkambattu, Jayashankar; Katragadda, Anuradha; Addepally, Uma; Hasan, Qurratulain

2014-01-01

Background: Polycystic ovarian syndrome (PCOS) is one of the most common endocrine conditions affecting women of reproductive age with a prevalence of approximately 5-10% worldwide. PCOS can be viewed as a heterogeneous androgen excess disorder with varying degrees of reproductive and metabolic abnormalities, whose diagnosis is based on anthropometric, biochemical and radiological abnormalities. To our knowledge, this is the first study investigating the anthropometric, biochemical and ultrasonographic characteristics of PCOS in Asian Indians of South India, using the Androgen Excess Society (AES-2006) diagnostic criteria. Objectives: To assess anthropometric, biochemical and ultrasonographic features of PCOS subgroups and controls among South Indian women using the AES-2006 criteria. Materials and Methods: Two hundred and four women clinically diagnosed with PCOS, and 204 healthy women controls aged 17 to 35 years were evaluated. PCOS was diagnosed by clinical hyperandrogenism (HA), irregular menstruation (IM), and polycystic ovary (PCO). PCOS was further categorized into phenotypic subgroups including the IM+HA+PCO (n = 181, 89%), HA+PCO (n = 23, 11%), IM+HA (n = 0), and also into obese PCOS (n = 142, 70%) and lean PCOS (n = 62, 30%) using body mass index (BMI). Anthropometric measurements and biochemical characteristics were compared among the PCOS subgroups. Results: The PCOS subgroups with regular menstrual cycles (HA+PCO), had more luteinizing hormone (LH), follicle stimulating hormone (FSH), fasting glucose, fasting insulin, and high insulin resistance (IR) expressed as the Homeostasis Model Assessment (HOMA) score, compared with the IM+HA+PCO subgroups and controls. Similarly, the obese PCOS had high BMI, waist to hip ratio (WHR), fasting glucose, LH, LH/FSH, fasting insulin, HOMA score (IR), and dyslipidemia, compared with lean PCOS and controls. Unilateral polycystic ovary was seen in 32 (15.7%) patients, and bilateral involvement in 172 (84.3%) patients. All the controls showed normal ovaries. Conclusions: Anthropometric, biochemical, and ultrasonographic findings showed significant differences among PCOS subgroups. The PCOS subgroups with regular menstrual cycles (HA+PCO), had high insulin resistance (IR) and gonadotropic hormonal abnormalities, compared with the IM+HA+PCO subgroups and controls. PMID:24696694

Experimental ocean acidification alters the allocation of metabolic energy

PubMed Central

Pan, T.-C. Francis; Applebaum, Scott L.; Manahan, Donal T.

2015-01-01

Energy is required to maintain physiological homeostasis in response to environmental change. Although responses to environmental stressors frequently are assumed to involve high metabolic costs, the biochemical bases of actual energy demands are rarely quantified. We studied the impact of a near-future scenario of ocean acidification [800 µatm partial pressure of CO2 (pCO2)] during the development and growth of an important model organism in developmental and environmental biology, the sea urchin Strongylocentrotus purpuratus. Size, metabolic rate, biochemical content, and gene expression were not different in larvae growing under control and seawater acidification treatments. Measurements limited to those levels of biological analysis did not reveal the biochemical mechanisms of response to ocean acidification that occurred at the cellular level. In vivo rates of protein synthesis and ion transport increased ∼50% under acidification. Importantly, the in vivo physiological increases in ion transport were not predicted from total enzyme activity or gene expression. Under acidification, the increased rates of protein synthesis and ion transport that were sustained in growing larvae collectively accounted for the majority of available ATP (84%). In contrast, embryos and prefeeding and unfed larvae in control treatments allocated on average only 40% of ATP to these same two processes. Understanding the biochemical strategies for accommodating increases in metabolic energy demand and their biological limitations can serve as a quantitative basis for assessing sublethal effects of global change. Variation in the ability to allocate ATP differentially among essential functions may be a key basis of resilience to ocean acidification and other compounding environmental stressors. PMID:25825763

Experimental ocean acidification alters the allocation of metabolic energy.

PubMed

Pan, T-C Francis; Applebaum, Scott L; Manahan, Donal T

2015-04-14

Energy is required to maintain physiological homeostasis in response to environmental change. Although responses to environmental stressors frequently are assumed to involve high metabolic costs, the biochemical bases of actual energy demands are rarely quantified. We studied the impact of a near-future scenario of ocean acidification [800 µatm partial pressure of CO2 (pCO2)] during the development and growth of an important model organism in developmental and environmental biology, the sea urchin Strongylocentrotus purpuratus. Size, metabolic rate, biochemical content, and gene expression were not different in larvae growing under control and seawater acidification treatments. Measurements limited to those levels of biological analysis did not reveal the biochemical mechanisms of response to ocean acidification that occurred at the cellular level. In vivo rates of protein synthesis and ion transport increased ∼50% under acidification. Importantly, the in vivo physiological increases in ion transport were not predicted from total enzyme activity or gene expression. Under acidification, the increased rates of protein synthesis and ion transport that were sustained in growing larvae collectively accounted for the majority of available ATP (84%). In contrast, embryos and prefeeding and unfed larvae in control treatments allocated on average only 40% of ATP to these same two processes. Understanding the biochemical strategies for accommodating increases in metabolic energy demand and their biological limitations can serve as a quantitative basis for assessing sublethal effects of global change. Variation in the ability to allocate ATP differentially among essential functions may be a key basis of resilience to ocean acidification and other compounding environmental stressors.

Universal dynamical properties preclude standard clustering in a large class of biochemical data.

PubMed

Gomez, Florian; Stoop, Ralph L; Stoop, Ruedi

2014-09-01

Clustering of chemical and biochemical data based on observed features is a central cognitive step in the analysis of chemical substances, in particular in combinatorial chemistry, or of complex biochemical reaction networks. Often, for reasons unknown to the researcher, this step produces disappointing results. Once the sources of the problem are known, improved clustering methods might revitalize the statistical approach of compound and reaction search and analysis. Here, we present a generic mechanism that may be at the origin of many clustering difficulties. The variety of dynamical behaviors that can be exhibited by complex biochemical reactions on variation of the system parameters are fundamental system fingerprints. In parameter space, shrimp-like or swallow-tail structures separate parameter sets that lead to stable periodic dynamical behavior from those leading to irregular behavior. We work out the genericity of this phenomenon and demonstrate novel examples for their occurrence in realistic models of biophysics. Although we elucidate the phenomenon by considering the emergence of periodicity in dependence on system parameters in a low-dimensional parameter space, the conclusions from our simple setting are shown to continue to be valid for features in a higher-dimensional feature space, as long as the feature-generating mechanism is not too extreme and the dimension of this space is not too high compared with the amount of available data. For online versions of super-paramagnetic clustering see http://stoop.ini.uzh.ch/research/clustering. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Metstoich--Teaching Quantitative Metabolism and Energetics in Biochemical Engineering

ERIC Educational Resources Information Center

Wong, Kelvin W. W.; Barford, John P.

2010-01-01

Metstoich, a metabolic calculator developed for teaching, can provide a novel way to teach quantitative metabolism to biochemical engineering students. It can also introduce biochemistry/life science students to the quantitative aspects of life science subjects they have studied. Metstoich links traditional biochemistry-based metabolic approaches…

Temperature-programmed natural convection for micromixing and biochemical reaction in a single microfluidic chamber.

PubMed

Kim, Sung-Jin; Wang, Fang; Burns, Mark A; Kurabayashi, Katsuo

2009-06-01

Micromixing is a crucial step for biochemical reactions in microfluidic networks. A critical challenge is that the system containing micromixers needs numerous pumps, chambers, and channels not only for the micromixing but also for the biochemical reactions and detections. Thus, a simple and compatible design of the micromixer element for the system is essential. Here, we propose a simple, yet effective, scheme that enables micromixing and a biochemical reaction in a single microfluidic chamber without using any pumps. We accomplish this process by using natural convection in conjunction with alternating heating of two heaters for efficient micromixing, and by regulating capillarity for sample transport. As a model application, we demonstrate micromixing and subsequent polymerase chain reaction (PCR) for an influenza viral DNA fragment. This process is achieved in a platform of a microfluidic cartridge and a microfabricated heating-instrument with a fast thermal response. Our results will significantly simplify micromixing and a subsequent biochemical reaction that involves reagent heating in microfluidic networks.

Nanoparticles as biochemical sensors

PubMed Central

El-Ansary, Afaf; Faddah, Layla M

2010-01-01

There is little doubt that nanoparticles offer real and new opportunities in many fields, such as biomedicine and materials science. Such particles are small enough to enter almost all areas of the body, including cells and organelles, potentially leading to new approaches in nanomedicine. Sensors for small molecules of biochemical interest are of critical importance. This review is an attempt to trace the use of nanomaterials in biochemical sensor design. The possibility of using nanoparticles functionalized with antibodies as markers for proteins will be elucidated. Moreover, capabilities and applications for nanoparticles based on gold, silver, magnetic, and semiconductor materials (quantum dots), used in optical (absorbance, luminescence, surface enhanced Raman spectroscopy, surface plasmon resonance), electrochemical, and mass-sensitive sensors will be highlighted. The unique ability of nanosensors to improve the analysis of biochemical fluids is discussed either through considering the use of nanoparticles for in vitro molecular diagnosis, or in the biological/biochemical analysis for in vivo interaction with the human body. PMID:24198472

Validation of a time-resolved fluorescence spectroscopy apparatus in a rabbit atherosclerosis model

NASA Astrophysics Data System (ADS)

Fang, Qiyin; Jo, Javier A.; Papaioannou, Thanassis; Dorafshar, Amir; Reil, Todd; Qiao, Jian-Hua; Fishbein, Michael C.; Freischlag, Julie A.; Marcu, Laura

2004-07-01

Time-resolved laser-induced fluorescence spectroscopy (tr-LIFS) has been studied as a potential tool for in vivo diagnosis of atherosclerotic lesions. This study is to evaluate the potential of a compact fiber-optics based tr-LIFS instrument developed in our laboratory for in vivo analysis of atherosclerotic plaque composition. Time-resolved fluorescence spectroscopy studies were performed in vivo on fifteen New Zealand White rabbits (atherosclerotic: N=8, control: N=7). Time-resolved fluorescence spectra were acquired (range: 360-600 nm, increment: 5 nm, total acquisition time: 65 s) from normal aorta wall and lesions in the abdominal aorta. Data were analyzed in terms of fluorescence emission spectra and wavelength specific lifetimes. Following trichrome staining, tissue specimens were analyzed histopathologically in terms of intima/media thickness and biochemical composition (collagen, elastin, foam cells, and etc). Based on intimal thickness, the lesions were divided into thin and thick lesions. Each group was further separated into two categories: collagen rich lesions and foam cell rich lesions based on their biochemical composition. The obtained spectral and time domain fluorescence signatures were subsequently correlated to the histopathological findings. The results have shown that time-domain fluorescence spectral features can be used in vivo to separate atherosclerotic lesions from normal aorta wall as well discrimination within certain types of lesions.

Super-Joule heating in graphene and silver nanowire network

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

Maize, Kerry; Das, Suprem R.; Sadeque, Sajia

Transistors, sensors, and transparent conductors based on randomly assembled nanowire networks rely on multi-component percolation for unique and distinctive applications in flexible electronics, biochemical sensing, and solar cells. While conduction models for 1-D and 1-D/2-D networks have been developed, typically assuming linear electronic transport and self-heating, the model has not been validated by direct high-resolution characterization of coupled electronic pathways and thermal response. In this letter, we show the occurrence of nonlinear “super-Joule” self-heating at the transport bottlenecks in networks of silver nanowires and silver nanowire/single layer graphene hybrid using high resolution thermoreflectance (TR) imaging. TR images at the microscopicmore » self-heating hotspots within nanowire network and nanowire/graphene hybrid network devices with submicron spatial resolution are used to infer electrical current pathways. The results encourage a fundamental reevaluation of transport models for network-based percolating conductors.« less

Effect of Adipose-Derived Stromal Cells and BMP12 on Intrasynovial Tendon Repair: A Biomechanical, Biochemical, and Proteomics Study

PubMed Central

Gelberman, Richard H.; Shen, Hua; Kormpakis, Ioannis; Rothrauff, Benjamin; Yang, Guang; Tuan, Rocky S.; Xia, Younan; Sakiyama-Elbert, Shelly; Silva, Matthew J.; Thomopoulos, Stavros

2016-01-01

The outcomes of flexor tendon repair are highly variable. As recent efforts to improve healing have demonstrated promise for growth factor- and cell-based therapies, the objective of the current study was to enhance repair via application of autologous adipose derived stromal cells (ASCs) and the tenogenic growth factor bone morphogenetic protein (BMP) 12. Controlled delivery of cells and growth factor was achieved in a clinically relevant canine model using a nanofiber/fibrin-based scaffold. Control groups consisted of repair-only (no scaffold) and acellular scaffold. Repairs were evaluated after 28 days of healing using biomechanical, biochemical, and proteomics analyses. Range of motion was reduced in the groups that received scaffolds compared to normal. There was no effect of ASC+BMP12 treatment for range of motion or tensile properties outcomes versus repair-only. Biochemical assays demonstrated increased DNA, glycosaminoglycans, and crosslink concentration in all repair groups compared to normal, but no effect of ASC+BMP12. Total collagen was significantly decreased in the acellular scaffold group compared to normal and significantly increased in the ASC+BMP12 group compared to the acellular scaffold group. Proteomics analysis comparing healing tendons to uninjured tendons revealed significant increases in proteins associated with inflammation, stress response, and matrix degradation. Treatment with ASC+BMP12 amplified these unfavorable changes. In summary, the treatment approach used in this study induced a negative inflammatory reaction at the repair site leading to poor healing. Future approaches should consider cell and growth factor delivery methods that do not incite negative local reactions. PMID:26445383

A probabilistic approach to identify putative drug targets in biochemical networks.

PubMed

Murabito, Ettore; Smallbone, Kieran; Swinton, Jonathan; Westerhoff, Hans V; Steuer, Ralf

2011-06-06

Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual components, responds to specific perturbations in different physiological conditions. Proteins exerting little control over normal cells and larger control over altered cells may be considered as good candidates for drug targets. The application of network-based drug design would greatly benefit from using an explicit computational model describing the dynamics of the system under investigation. However, creating a fully characterized kinetic model is not an easy task, even for relatively small networks, as it is still significantly hampered by the lack of data about kinetic mechanisms and parameters values. Here, we propose a Monte Carlo approach to identify the differences between flux control profiles of a metabolic network in different physiological states, when information about the kinetics of the system is partially or totally missing. Based on experimentally accessible information on metabolic phenotypes, we develop a novel method to determine probabilistic differences in the flux control coefficients between the two observable phenotypes. Knowledge of how differences in flux control are distributed among the different enzymatic steps is exploited to identify points of fragility in one of the phenotypes. Using a prototypical cancerous phenotype as an example, we demonstrate how our approach can assist researchers in developing compounds with high efficacy and low toxicity. © 2010 The Royal Society

Analyzing Arabidopsis thaliana root proteome provides insights into the molecular bases of enantioselective imazethapyr toxicity

PubMed Central

Qian, Haifeng; Lu, Haiping; Ding, Haiyan; Lavoie, Michel; Li, Yali; Liu, Weiping; Fu, Zhengwei

2015-01-01

Imazethapyr (IM) is a widely used chiral herbicide that inhibits the synthesis of branched-chain amino acids (BCAAs). IM is thought to exert its toxic effects on amino acid synthesis mainly through inhibition of acetolactate synthase activity, but little is known about the potential effects of IM on other key biochemical pathways. Here, we exposed the model plant Arabidospsis thaliana to trace S- and R-IM enantiomer concentrations and examined IM toxicity effects on the root proteome using iTRAQ. Conventional analyses of root carbohydrates, organic acids, and enzyme activities were also performed. We discovered several previously unknown key biochemical pathways targeted by IM in Arabidospsis. 1,322 and 987 proteins were differentially expressed in response to R- and S-IM treatments, respectively. Bioinformatics and physiological analyses suggested that IM reduced the BCAA tissue content not only by strongly suppressing BCAA synthesis but also by increasing BCAA catabolism. IM also affected sugar and starch metabolism, changed the composition of root cell walls, increased citrate production and exudation, and affected the microbial community structure of the rhizosphere. The present study shed new light on the multiple toxicity mechanisms of a selective herbicide on a model plant. PMID:26153126

Analyzing Arabidopsis thaliana root proteome provides insights into the molecular bases of enantioselective imazethapyr toxicity

NASA Astrophysics Data System (ADS)

Qian, Haifeng; Lu, Haiping; Ding, Haiyan; Lavoie, Michel; Li, Yali; Liu, Weiping; Fu, Zhengwei

2015-07-01

Imazethapyr (IM) is a widely used chiral herbicide that inhibits the synthesis of branched-chain amino acids (BCAAs). IM is thought to exert its toxic effects on amino acid synthesis mainly through inhibition of acetolactate synthase activity, but little is known about the potential effects of IM on other key biochemical pathways. Here, we exposed the model plant Arabidospsis thaliana to trace S- and R-IM enantiomer concentrations and examined IM toxicity effects on the root proteome using iTRAQ. Conventional analyses of root carbohydrates, organic acids, and enzyme activities were also performed. We discovered several previously unknown key biochemical pathways targeted by IM in Arabidospsis. 1,322 and 987 proteins were differentially expressed in response to R- and S-IM treatments, respectively. Bioinformatics and physiological analyses suggested that IM reduced the BCAA tissue content not only by strongly suppressing BCAA synthesis but also by increasing BCAA catabolism. IM also affected sugar and starch metabolism, changed the composition of root cell walls, increased citrate production and exudation, and affected the microbial community structure of the rhizosphere. The present study shed new light on the multiple toxicity mechanisms of a selective herbicide on a model plant.

Biochemical Markers of Brain Injury: An Integrated Proteomics-Based Approach

DTIC Science & Technology

2006-02-01

Anthony J Williams, X-C May Lu, Renwu Chen, Zhilin Liao, Rebeca Connors, Kevin K Wang, Ron L Hayes, Frank C Tortella, Jitendra R Dave. High throughput... YANG , A., et al. (2002). Evalu- ation of two-dimensional differential gel electrophoresis for proteomic expression analysis of a model breast cancer cell...apoptosis. J. Biol. Chem. 279, 1030–1039. Kuida K., Zheng T. S., Na S., Kuan C., Yang D., Karasuyama H., Rakic P. and Flavell R. A. (1996) Decreased apoptosis

A heuristic model for working memory deficit in schizophrenia.

PubMed

Qi, Zhen; Yu, Gina P; Tretter, Felix; Pogarell, Oliver; Grace, Anthony A; Voit, Eberhard O

2016-11-01

The life of schizophrenia patients is severely affected by deficits in working memory. In various brain regions, the reciprocal interactions between excitatory glutamatergic neurons and inhibitory GABAergic neurons are crucial. Other neurotransmitters, in particular dopamine, serotonin, acetylcholine, and norepinephrine, modulate the local balance between glutamate and GABA and therefore regulate the function of brain regions. Persistent alterations in the balances between the neurotransmitters can result in working memory deficits. Here we present a heuristic computational model that accounts for interactions among neurotransmitters across various brain regions. The model is based on the concept of a neurochemical interaction matrix at the biochemical level and combines this matrix with a mobile model representing physiological dynamic balances among neurotransmitter systems associated with working memory. The comparison of clinical and simulation results demonstrates that the model output is qualitatively very consistent with the available data. In addition, the model captured how perturbations migrated through different neurotransmitters and brain regions. Results showed that chronic administration of ketamine can cause a variety of imbalances, and application of an antagonist of the D2 receptor in PFC can also induce imbalances but in a very different manner. The heuristic computational model permits a variety of assessments of genetic, biochemical, and pharmacological perturbations and serves as an intuitive tool for explaining clinical and biological observations. The heuristic model is more intuitive than biophysically detailed models. It can serve as an important tool for interdisciplinary communication and even for psychiatric education of patients and relatives. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016 Elsevier B.V. All rights reserved.

The evaluation of renal ischaemic damage: the value of CD10 monoclonal antibody staining and of biochemical assessments of tissue viability

PubMed Central

Tagboto, S; Griffiths, A Paul

2007-01-01

Background It is well recognised that there is often a disparity between the structural changes observed in the kidney following renal injury and the function of the organ. For this reason, we carried out studies to explore possible means of studying and quantifying the severity of renal ischaemic damage using a laboratory model. Methods To do this, freshly isolated rabbit kidney tissue was subjected to warm (37°C) or cold (1°C) ischaemia for 20 hours. Following this, the tissue was stained using Haematoxylin and Eosin (H+E), Periodic Schiff reagent (PAS) and the novel monoclonal antibody CD10 stain. Additionally, ischaemic damage to the kidneys was assessed by biochemical tests of tissue viability using formazan-based colorimetry. Results CD 10 antibody intensely stained the brush border of control kidney tissue with mild or no cytoplasmic staining. Cell injury was accompanied by a redistribution of CD10 into the lumen and cell cytoplasm. There was good correlation between a score of histological damage using the CD 10 monoclonal antibody stain and the biochemical assessment of viability. Similarly, a score of histological damage using traditional PAS staining correlated well with that using the CD10 antibody stain. In particular, the biochemical assay and the monoclonal antibody staining techniques were able to demonstrate the efficacy of Soltran (this solution is used cold to preserve freshly isolated human kidneys prior to transplantation) in preserving renal tissue at cold temperatures compared to other randomly selected solutions. Conclusion We conclude that the techniques described using the CD10 monoclonal antibody stain may be helpful in the diagnosis and assessment of ischaemic renal damage. In addition, biochemical tests of viability may have an important role in routine histopathological work by giving additional information about cellular viability which may have implications on the function of the organ. PMID:17531101

BioNetCAD: design, simulation and experimental validation of synthetic biochemical networks

PubMed Central

Rialle, Stéphanie; Felicori, Liza; Dias-Lopes, Camila; Pérès, Sabine; El Atia, Sanaâ; Thierry, Alain R.; Amar, Patrick; Molina, Franck

2010-01-01

Motivation: Synthetic biology studies how to design and construct biological systems with functions that do not exist in nature. Biochemical networks, although easier to control, have been used less frequently than genetic networks as a base to build a synthetic system. To date, no clear engineering principles exist to design such cell-free biochemical networks. Results: We describe a methodology for the construction of synthetic biochemical networks based on three main steps: design, simulation and experimental validation. We developed BioNetCAD to help users to go through these steps. BioNetCAD allows designing abstract networks that can be implemented thanks to CompuBioTicDB, a database of parts for synthetic biology. BioNetCAD enables also simulations with the HSim software and the classical Ordinary Differential Equations (ODE). We demonstrate with a case study that BioNetCAD can rationalize and reduce further experimental validation during the construction of a biochemical network. Availability and implementation: BioNetCAD is freely available at http://www.sysdiag.cnrs.fr/BioNetCAD. It is implemented in Java and supported on MS Windows. CompuBioTicDB is freely accessible at http://compubiotic.sysdiag.cnrs.fr/ Contact: stephanie.rialle@sysdiag.cnrs.fr; franck.molina@sysdiag.cnrs.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20628073

Direct real-time detection of the structural and biochemical events in the myosin power stroke.

PubMed

Muretta, Joseph M; Rohde, John A; Johnsrud, Daniel O; Cornea, Sinziana; Thomas, David D

2015-11-17

A principal goal of molecular biophysics is to show how protein structural transitions explain physiology. We have developed a strategic tool, transient time-resolved FRET [(TR)(2)FRET], for this purpose and use it here to measure directly, with millisecond resolution, the structural and biochemical kinetics of muscle myosin and to determine directly how myosin's power stroke is coupled to the thermodynamic drive for force generation, actin-activated phosphate release, and the weak-to-strong actin-binding transition. We find that actin initiates the power stroke before phosphate dissociation and not after, as many models propose. This result supports a model for muscle contraction in which power output and efficiency are tuned by the distribution of myosin structural states. This technology should have wide application to other systems in which questions about the temporal coupling of allosteric structural and biochemical transitions remain unanswered.

Smartphone-based analysis of biochemical tests for health monitoring support at home.

PubMed

Velikova, Marina; Smeets, Ruben L; van Scheltinga, Josien Terwisscha; Lucas, Peter J F; Spaanderman, Marc

2014-09-01

In the context of home-based healthcare monitoring systems, it is desirable that the results obtained from biochemical tests - tests of various body fluids such as blood and urine - are objective and automatically generated to reduce the number of man-made errors. The authors present the StripTest reader - an innovative smartphone-based interpreter of biochemical tests based on paper-based strip colour using image processing techniques. The working principles of the reader include image acquisition of the colour strip pads using the camera phone, analysing the images within the phone and comparing them with reference colours provided by the manufacturer to obtain the test result. The detection of kidney damage was used as a scenario to illustrate the application of, and test, the StripTest reader. An extensive evaluation using laboratory and human urine samples demonstrates the reader's accuracy and precision of detection, indicating the successful development of a cheap, mobile and smart reader for home-monitoring of kidney functioning, which can facilitate the early detection of health problems and a timely treatment intervention.

Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

NASA Technical Reports Server (NTRS)

Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)

2001-01-01

Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.

On The Development of Biophysical Models for Space Radiation Risk Assessment

NASA Technical Reports Server (NTRS)

Cucinotta, F. A.; Dicello, J. F.

1999-01-01

Experimental techniques in molecular biology are being applied to study biological risks from space radiation. The use of molecular assays presents a challenge to biophysical models which in the past have relied on descriptions of energy deposition and phenomenological treatments of repair. We describe a biochemical kinetics model of cell cycle control and DNA damage response proteins in order to model cellular responses to radiation exposures. Using models of cyclin-cdk, pRB, E2F's, p53, and GI inhibitors we show that simulations of cell cycle populations and GI arrest can be described by our biochemical approach. We consider radiation damaged DNA as a substrate for signal transduction processes and consider a dose and dose-rate reduction effectiveness factor (DDREF) for protein expression.

CaPTHUS scoring model in primary hyperparathyroidism: can it eliminate the need for ioPTH testing?

PubMed

Elfenbein, Dawn M; Weber, Sara; Schneider, David F; Sippel, Rebecca S; Chen, Herbert

2015-04-01

The CaPTHUS model was reported to have a positive predictive value of 100 % to correctly predict single-gland disease in patients with primary hyperparathyroidism, thus obviating the need for intraoperative parathyroid hormone (ioPTH) testing. We sought to apply the CaPTHUS scoring model in our patient population and assess its utility in predicting long-term biochemical cure. We retrospective reviewed all parathyroidectomies for primary hyperparathyroidism performed at our university hospital from 2003 to 2012. We routinely perform ioPTH testing. Biochemical cure was defined as a normal calcium level at 6 months. A total of 1,421 patients met the inclusion criteria: 78 % of patients had a single adenoma at the time of surgery, 98 % had a normal serum calcium at 1 week postoperatively, and 96 % had a normal serum calcium level 6 months postoperatively. Using the CaPTHUS scoring model, 307 patients (22.5 %) had a score of ≥ 3, with a positive predictive value of 91 % for single adenoma. A CaPTHUS score of ≥ 3 had a positive predictive value of 98 % for biochemical cure at 1 week as well as at 6 months. In our population, where ioPTH testing is used routinely to guide use of bilateral exploration, patients with a preoperative CaPTHUS score of ≥ 3 had good long-term biochemical cure rates. However, the model only predicted adenoma in 91 % of cases. If minimally invasive parathyroidectomy without ioPTH testing had been done for these patients, the cure rate would have dropped from 98 % to an unacceptable 89 %. Even in these patients with high CaPTHUS scores, multigland disease is present in almost 10 %, and ioPTH testing is necessary.

Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells

PubMed Central

Sahoo, Swagatika; Thiele, Ines

2013-01-01

Small intestinal epithelial cells (sIECs) have a significant share in whole body metabolism as they perform enzymatic digestion and absorption of nutrients. Furthermore, the diet plays a key role in a number of complex diseases including obesity and diabetes. The impact of diet and altered genetic backgrounds on human metabolism may be studied by using computational modeling. A metabolic reconstruction of human sIECs was manually assembled using the literature. The resulting sIEC model was subjected to two different diets to obtain condition-specific metabolic models. Fifty defined metabolic tasks evaluated the functionalities of these models, along with the respective secretion profiles, which distinguished between impacts of different dietary regimes. Under the average American diet, the sIEC model resulted in higher secretion flux for metabolites implicated in metabolic syndrome. In addition, enzymopathies were analyzed in the context of the sIEC metabolism. Computed results were compared with reported gastrointestinal (GI) pathologies and biochemical defects as well as with biomarker patterns used in their diagnosis. Based on our simulations, we propose that (i) sIEC metabolism is perturbed by numerous enzymopathies, which can be used to study cellular adaptive mechanisms specific for such disorders, and in the identification of novel co-morbidities, (ii) porphyrias are associated with both heme synthesis and degradation and (iii) disturbed intestinal gamma-aminobutyric acid synthesis may be linked to neurological manifestations of various enzymopathies. Taken together, the sIEC model represents a comprehensive, biochemically accurate platform for studying the function of sIEC and their role in whole body metabolism. PMID:23492669

Polyamine and ethylene biosynthesis in relation to somatic embryogenesis in carrot (Daucus carota L.) cell cultures

Treesearch

Subhash C. Minocha; Cheryl A. Robie; Akhtar J. Khan; Nancy S. Papa; Andrew I. Samuelsen; Rakesh Minocha

1990-01-01

Carrot cell cultures provide a model experimental system for the analysis of biochemical and molecular events associated with morphogenesis in plants (3, 4, 5, 14). Among the biochemical changes accompanying somatic embryogenesis in this tissue is an increased biosynthesis ofpolyamines (1, 2, 7, 10, 11, 13). A variety of inhibitors of polyamine biosynthetic enzymes...

A master equation and moment approach for biochemical systems with creation-time-dependent bimolecular rate functions

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

Chevalier, Michael W., E-mail: Michael.Chevalier@ucsf.edu; El-Samad, Hana, E-mail: Hana.El-Samad@ucsf.edu

Noise and stochasticity are fundamental to biology and derive from the very nature of biochemical reactions where thermal motion of molecules translates into randomness in the sequence and timing of reactions. This randomness leads to cell-to-cell variability even in clonal populations. Stochastic biochemical networks have been traditionally modeled as continuous-time discrete-state Markov processes whose probability density functions evolve according to a chemical master equation (CME). In diffusion reaction systems on membranes, the Markov formalism, which assumes constant reaction propensities is not directly appropriate. This is because the instantaneous propensity for a diffusion reaction to occur depends on the creation timesmore » of the molecules involved. In this work, we develop a chemical master equation for systems of this type. While this new CME is computationally intractable, we make rational dimensional reductions to form an approximate equation, whose moments are also derived and are shown to yield efficient, accurate results. This new framework forms a more general approach than the Markov CME and expands upon the realm of possible stochastic biochemical systems that can be efficiently modeled.« less

Protective effect of gallic acid in experimental model of ketamine-induced psychosis: possible behaviour, biochemical, neurochemical and cellular alterations.

PubMed

Yadav, Monu; Jindal, Deepak Kumar; Dhingra, Mamta Sachdeva; Kumar, Anil; Parle, Milind; Dhingra, Sameer

2018-04-01

Gallic acid has been reported to possess a number of psychopharmacological activities. These activities are attributed to the antioxidant potential due to the presence of phenolic moeity. The present study was carried out to investigate the protective effects of gallic acid in an experimental model of ketamine-induced psychosis in mice. Ketamine (50 mg/kg, i.p.) was used to induce stereotyped psychotic behavioural symptoms in mice. Behavioural studies (locomotor activity, stereotype behaviour, immobility duration and memory retention) were carried out to investigate the protective of gallic acid on ketamine-induced psychotic symptoms, followed by biochemical and neurochemical changes and cellular alterations in the brain. Chronic treatment with gallic acid for 15 consecutive days significantly attenuated stereotyped behavioural symptoms in mice. Biochemical estimations revealed that gallic acid reduced the lipid peroxidation and restored the total brain proteins. Furthermore, gallic acid remarkably reduced the dopamine levels, AChE activity and inflammatory surge (serum TNF-α), and increased the levels of GABA and increased glutathione in mice. The study revealed that gallic acid could ameliorate psychotic symptoms and biochemical changes in mice, indicating protective effects in psychosis.

Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine.

PubMed

Aurich, Maike K; Thiele, Ines

2016-01-01

Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model's topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine.

Lego bricks and the octet rule: Molecular models for biochemical pathways with plastic, interlocking toy bricks.

PubMed

Lin, Henry J; Lehoang, Jennifer; Kwan, Isabel; Baghaee, Anita; Prasad, Priya; Ha-Chen, Stephanie J; Moss, Tanesha; Woods, Jeremy D

2018-01-01

The 8 studs on a 2 × 4 Lego brick conveniently represent the outer shell of electrons for carbon, nitrogen, and oxygen atoms. We used Lego bricks to model these atoms, which are then joined together to form molecules by following the Lewis octet rule. A variety of small biological molecules can be modeled in this way, such as most amino acids, fatty acids, glucose, and various intermediate metabolites. Model building with these familiar toys can be a helpful, hands-on exercise for learning-or re-learning-biochemical pathways. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(1):54-57, 2018. © 2017 The International Union of Biochemistry and Molecular Biology.

Reconstruction and in silico analysis of an Actinoplanes sp. SE50/110 genome-scale metabolic model for acarbose production

PubMed Central

Wang, Yali; Xu, Nan; Ye, Chao; Liu, Liming; Shi, Zhongping; Wu, Jing

2015-01-01

Actinoplanes sp. SE50/110 produces the α-glucosidase inhibitor acarbose, which is used to treat type 2 diabetes mellitus. To obtain a comprehensive understanding of its cellular metabolism, a genome-scale metabolic model of strain SE50/110, iYLW1028, was reconstructed on the bases of the genome annotation, biochemical databases, and extensive literature mining. Model iYLW1028 comprises 1028 genes, 1128 metabolites, and 1219 reactions. One hundred and twenty-two and eighty one genes were essential for cell growth on acarbose synthesis and sucrose media, respectively, and the acarbose biosynthetic pathway in SE50/110 was expounded completely. Based on model predictions, the addition of arginine and histidine to the media increased acarbose production by 78 and 59%, respectively. Additionally, dissolved oxygen has a great effect on acarbose production based on model predictions. Furthermore, genes to be overexpressed for the overproduction of acarbose were identified, and the deletion of treY eliminated the formation of by-product component C. Model iYLW1028 is a useful platform for optimizing and systems metabolic engineering for acarbose production in Actinoplanes sp. SE50/110. PMID:26161077

Model-based optimal design of experiments - semidefinite and nonlinear programming formulations

PubMed Central

Duarte, Belmiro P.M.; Wong, Weng Kee; Oliveira, Nuno M.C.

2015-01-01

We use mathematical programming tools, such as Semidefinite Programming (SDP) and Nonlinear Programming (NLP)-based formulations to find optimal designs for models used in chemistry and chemical engineering. In particular, we employ local design-based setups in linear models and a Bayesian setup in nonlinear models to find optimal designs. In the latter case, Gaussian Quadrature Formulas (GQFs) are used to evaluate the optimality criterion averaged over the prior distribution for the model parameters. Mathematical programming techniques are then applied to solve the optimization problems. Because such methods require the design space be discretized, we also evaluate the impact of the discretization scheme on the generated design. We demonstrate the techniques for finding D–, A– and E–optimal designs using design problems in biochemical engineering and show the method can also be directly applied to tackle additional issues, such as heteroscedasticity in the model. Our results show that the NLP formulation produces highly efficient D–optimal designs but is computationally less efficient than that required for the SDP formulation. The efficiencies of the generated designs from the two methods are generally very close and so we recommend the SDP formulation in practice. PMID:26949279

Model-based optimal design of experiments - semidefinite and nonlinear programming formulations.

PubMed

Duarte, Belmiro P M; Wong, Weng Kee; Oliveira, Nuno M C

2016-02-15

We use mathematical programming tools, such as Semidefinite Programming (SDP) and Nonlinear Programming (NLP)-based formulations to find optimal designs for models used in chemistry and chemical engineering. In particular, we employ local design-based setups in linear models and a Bayesian setup in nonlinear models to find optimal designs. In the latter case, Gaussian Quadrature Formulas (GQFs) are used to evaluate the optimality criterion averaged over the prior distribution for the model parameters. Mathematical programming techniques are then applied to solve the optimization problems. Because such methods require the design space be discretized, we also evaluate the impact of the discretization scheme on the generated design. We demonstrate the techniques for finding D -, A - and E -optimal designs using design problems in biochemical engineering and show the method can also be directly applied to tackle additional issues, such as heteroscedasticity in the model. Our results show that the NLP formulation produces highly efficient D -optimal designs but is computationally less efficient than that required for the SDP formulation. The efficiencies of the generated designs from the two methods are generally very close and so we recommend the SDP formulation in practice.

Advances in the integration of transcriptional regulatory information into genome-scale metabolic models.

PubMed

Vivek-Ananth, R P; Samal, Areejit

2016-09-01

A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Notes on stochastic (bio)-logic gates: computing with allosteric cooperativity

PubMed Central

Agliari, Elena; Altavilla, Matteo; Barra, Adriano; Dello Schiavo, Lorenzo; Katz, Evgeny

2015-01-01

Recent experimental breakthroughs have finally allowed to implement in-vitro reaction kinetics (the so called enzyme based logic) which code for two-inputs logic gates and mimic the stochastic AND (and NAND) as well as the stochastic OR (and NOR). This accomplishment, together with the already-known single-input gates (performing as YES and NOT), provides a logic base and paves the way to the development of powerful biotechnological devices. However, as biochemical systems are always affected by the presence of noise (e.g. thermal), standard logic is not the correct theoretical reference framework, rather we show that statistical mechanics can work for this scope: here we formulate a complete statistical mechanical description of the Monod-Wyman-Changeaux allosteric model for both single and double ligand systems, with the purpose of exploring their practical capabilities to express noisy logical operators and/or perform stochastic logical operations. Mixing statistical mechanics with logics, and testing quantitatively the resulting findings on the available biochemical data, we successfully revise the concept of cooperativity (and anti-cooperativity) for allosteric systems, with particular emphasis on its computational capabilities, the related ranges and scaling of the involved parameters and its differences with classical cooperativity (and anti-cooperativity). PMID:25976626

Synthetic biology, inspired by synthetic chemistry.

PubMed

Malinova, V; Nallani, M; Meier, W P; Sinner, E K

2012-07-16

The topic synthetic biology appears still as an 'empty basket to be filled'. However, there is already plenty of claims and visions, as well as convincing research strategies about the theme of synthetic biology. First of all, synthetic biology seems to be about the engineering of biology - about bottom-up and top-down approaches, compromising complexity versus stability of artificial architectures, relevant in biology. Synthetic biology accounts for heterogeneous approaches towards minimal and even artificial life, the engineering of biochemical pathways on the organismic level, the modelling of molecular processes and finally, the combination of synthetic with nature-derived materials and architectural concepts, such as a cellular membrane. Still, synthetic biology is a discipline, which embraces interdisciplinary attempts in order to have a profound, scientific base to enable the re-design of nature and to compose architectures and processes with man-made matter. We like to give an overview about the developments in the field of synthetic biology, regarding polymer-based analogs of cellular membranes and what questions can be answered by applying synthetic polymer science towards the smallest unit in life, namely a cell. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Notes on stochastic (bio)-logic gates: computing with allosteric cooperativity.

PubMed

Agliari, Elena; Altavilla, Matteo; Barra, Adriano; Dello Schiavo, Lorenzo; Katz, Evgeny

2015-05-15

Recent experimental breakthroughs have finally allowed to implement in-vitro reaction kinetics (the so called enzyme based logic) which code for two-inputs logic gates and mimic the stochastic AND (and NAND) as well as the stochastic OR (and NOR). This accomplishment, together with the already-known single-input gates (performing as YES and NOT), provides a logic base and paves the way to the development of powerful biotechnological devices. However, as biochemical systems are always affected by the presence of noise (e.g. thermal), standard logic is not the correct theoretical reference framework, rather we show that statistical mechanics can work for this scope: here we formulate a complete statistical mechanical description of the Monod-Wyman-Changeaux allosteric model for both single and double ligand systems, with the purpose of exploring their practical capabilities to express noisy logical operators and/or perform stochastic logical operations. Mixing statistical mechanics with logics, and testing quantitatively the resulting findings on the available biochemical data, we successfully revise the concept of cooperativity (and anti-cooperativity) for allosteric systems, with particular emphasis on its computational capabilities, the related ranges and scaling of the involved parameters and its differences with classical cooperativity (and anti-cooperativity).

Notes on stochastic (bio)-logic gates: computing with allosteric cooperativity

NASA Astrophysics Data System (ADS)

Agliari, Elena; Altavilla, Matteo; Barra, Adriano; Dello Schiavo, Lorenzo; Katz, Evgeny

2015-05-01

Recent experimental breakthroughs have finally allowed to implement in-vitro reaction kinetics (the so called enzyme based logic) which code for two-inputs logic gates and mimic the stochastic AND (and NAND) as well as the stochastic OR (and NOR). This accomplishment, together with the already-known single-input gates (performing as YES and NOT), provides a logic base and paves the way to the development of powerful biotechnological devices. However, as biochemical systems are always affected by the presence of noise (e.g. thermal), standard logic is not the correct theoretical reference framework, rather we show that statistical mechanics can work for this scope: here we formulate a complete statistical mechanical description of the Monod-Wyman-Changeaux allosteric model for both single and double ligand systems, with the purpose of exploring their practical capabilities to express noisy logical operators and/or perform stochastic logical operations. Mixing statistical mechanics with logics, and testing quantitatively the resulting findings on the available biochemical data, we successfully revise the concept of cooperativity (and anti-cooperativity) for allosteric systems, with particular emphasis on its computational capabilities, the related ranges and scaling of the involved parameters and its differences with classical cooperativity (and anti-cooperativity).

Identification of a Novel Class of BRD4 Inhibitors by Computational Screening and Binding Simulations

PubMed Central

2017-01-01

Computational screening is a method to prioritize small-molecule compounds based on the structural and biochemical attributes built from ligand and target information. Previously, we have developed a scalable virtual screening workflow to identify novel multitarget kinase/bromodomain inhibitors. In the current study, we identified several novel N-[3-(2-oxo-pyrrolidinyl)phenyl]-benzenesulfonamide derivatives that scored highly in our ensemble docking protocol. We quantified the binding affinity of these compounds for BRD4(BD1) biochemically and generated cocrystal structures, which were deposited in the Protein Data Bank. As the docking poses obtained in the virtual screening pipeline did not align with the experimental cocrystal structures, we evaluated the predictions of their precise binding modes by performing molecular dynamics (MD) simulations. The MD simulations closely reproduced the experimentally observed protein–ligand cocrystal binding conformations and interactions for all compounds. These results suggest a computational workflow to generate experimental-quality protein–ligand binding models, overcoming limitations of docking results due to receptor flexibility and incomplete sampling, as a useful starting point for the structure-based lead optimization of novel BRD4(BD1) inhibitors. PMID:28884163

Integrated modeling of protein-coding genes in the Manduca sexta genome using RNA-Seq data from the biochemical model insect.

PubMed

Cao, Xiaolong; Jiang, Haobo

2015-07-01

The genome sequence of Manduca sexta was recently determined using 454 technology. Cufflinks and MAKER2 were used to establish gene models in the genome assembly based on the RNA-Seq data and other species' sequences. Aided by the extensive RNA-Seq data from 50 tissue samples at various life stages, annotators over the world (including the present authors) have manually confirmed and improved a small percentage of the models after spending months of effort. While such collaborative efforts are highly commendable, many of the predicted genes still have problems which may hamper future research on this insect species. As a biochemical model representing lepidopteran pests, M. sexta has been used extensively to study insect physiological processes for over five decades. In this work, we assembled Manduca datasets Cufflinks 3.0, Trinity 4.0, and Oases 4.0 to assist the manual annotation efforts and development of Official Gene Set (OGS) 2.0. To further improve annotation quality, we developed methods to evaluate gene models in the MAKER2, Cufflinks, Oases and Trinity assemblies and selected the best ones to constitute MCOT 1.0 after thorough crosschecking. MCOT 1.0 has 18,089 genes encoding 31,666 proteins: 32.8% match OGS 2.0 models perfectly or near perfectly, 11,747 differ considerably, and 29.5% are absent in OGS 2.0. Future automation of this process is anticipated to greatly reduce human efforts in generating comprehensive, reliable models of structural genes in other genome projects where extensive RNA-Seq data are available. Copyright © 2015 Elsevier Ltd. All rights reserved.

Management Options for Biochemically Recurrent Prostate Cancer.

PubMed

Fakhrejahani, Farhad; Madan, Ravi A; Dahut, William L

2017-05-01

Prostate cancer is the most common solid tumor malignancy in men worldwide. Treatment with surgery and radiation can be curative in organ-confined disease. Unfortunately, about one third of men develop biochemically recurrent disease based only on rising prostate-specific antigen (PSA) in the absence of visible disease on conventional imaging. For these patients with biochemical recurrent prostate cancer, there is no uniform guideline for subsequent management. Based on available data, it seems prudent that biochemical recurrent prostate cancer should initially be evaluated for salvage radiation or prostatectomy, with curative intent. In selected cases, high-intensity focused ultrasound and cryotherapy may be considered in patients that meet very narrow criteria as defined by non-randomized trials. If salvage options are not practical or unsuccessful, androgen deprivation therapy (ADT) is a standard option for disease control. While some patients prefer ADT to manage the disease immediately, others defer treatment because of the associated toxicity. In the absence of definitive randomized data, patients may be followed using PSA doubling time as a trigger to initiate ADT. Based on retrospective data, a PSA doubling time of less than 3-6 months has been associated with near-term development of metastasis and thus could be used signal to initiate ADT. Once treatment is begun, patients and their providers can choose between an intermittent and continuous ADT strategy. The intermittent approach may limit side effects but in patients with metastatic disease studies could not exclude a 20% greater risk of death. In men with biochemical recurrence, large studies have shown that intermittent therapy is non-inferior to continuous therapy, thus making this a reasonable option. Since biochemically recurrent prostate cancer is defined by technological limitations of radiographic detection, as new imaging (i.e., PSMA) strategies are developed, it may alter how the disease is monitored and perhaps managed. Furthermore, patients have no symptoms related to their disease and thus many prefer options that minimize toxicity. For this reason, herbal agents and immunotherapy are under investigation as potential alternatives to ADT and its accompanying side effects. New therapeutic options combined with improved imaging to evaluate the disease may markedly change how biochemically recurrent prostate cancer is managed in the future.

Structure-based design of a novel series of azetidine inhibitors of the hepatitis C virus NS3/4A serine protease.

PubMed

Parsy, Christophe; Alexandre, François-René; Brandt, Guillaume; Caillet, Catherine; Cappelle, Sylvie; Chaves, Dominique; Convard, Thierry; Derock, Michel; Gloux, Damien; Griffon, Yann; Lallos, Lisa; Leroy, Frédéric; Liuzzi, Michel; Loi, Anna-Giulia; Moulat, Laure; Musiu, Chiara; Rahali, Houcine; Roques, Virginie; Seifer, Maria; Standring, David; Surleraux, Dominique

2014-09-15

Structural homology between thrombin inhibitors and the early tetrapeptide HCV protease inhibitor led to the bioisosteric replacement of the P2 proline by a 2,4-disubstituted azetidine within the macrocyclic β-strand mimic. Molecular modeling guided the design of the series. This approach was validated by the excellent activity and selectivity in biochemical and cell based assays of this novel series and confirmed by the co-crystal structure of the inhibitor with the NS3/4A protein (PDB code: 4TYD). Copyright © 2014 Elsevier Ltd. All rights reserved.

Mass balances for a biological life support system simulation model

NASA Technical Reports Server (NTRS)

Volk, Tyler; Rummel, John D.

1987-01-01

Design decisions to aid the development of future space based biological life support systems (BLSS) can be made with simulation models. The biochemistry stoichiometry was developed for: (1) protein, carbohydrate, fat, fiber, and lignin production in the edible and inedible parts of plants; (2) food consumption and production of organic solids in urine, feces, and wash water by the humans; and (3) operation of the waste processor. Flux values for all components are derived for a steady state system with wheat as the sole food source. The large scale dynamics of a materially closed (BLSS) computer model is described in a companion paper. An extension of this methodology can explore multifood systems and more complex biochemical dynamics while maintaining whole system closure as a focus.

Systems pharmacology modeling of drug‐induced hyperbilirubinemia: Differentiating hepatotoxicity and inhibition of enzymes/transporters

PubMed Central

Battista, C; Woodhead, JL; Stahl, SH; Mettetal, JT; Watkins, PB; Siler, SQ; Howell, BA

2017-01-01

Elevations in serum bilirubin during drug treatment may indicate global liver dysfunction and a high risk of liver failure. However, drugs also can increase serum bilirubin in the absence of hepatic injury by inhibiting specific enzymes/transporters. We constructed a mechanistic model of bilirubin disposition based on known functional polymorphisms in bilirubin metabolism/transport. Using physiologically based pharmacokinetic (PBPK) model‐predicted drug exposure and enzyme/transporter inhibition constants determined in vitro, our model correctly predicted indinavir‐mediated hyperbilirubinemia in humans and rats. Nelfinavir was predicted not to cause hyperbilirubinemia, consistent with clinical observations. We next examined a new drug candidate that caused both elevations in serum bilirubin and biochemical evidence of liver injury in rats. Simulations suggest that bilirubin elevation primarily resulted from inhibition of transporters rather than global liver dysfunction. We conclude that mechanistic modeling of bilirubin can help elucidate underlying mechanisms of drug‐induced hyperbilirubinemia, and thereby distinguish benign from clinically important elevations in serum bilirubin. PMID:28074467

Multiscale Cancer Modeling

PubMed Central

Macklin, Paul; Cristini, Vittorio

2013-01-01

Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insight on the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community. PMID:21529163

MOCCASIN: converting MATLAB ODE models to SBML.

PubMed

Gómez, Harold F; Hucka, Michael; Keating, Sarah M; Nudelman, German; Iber, Dagmar; Sealfon, Stuart C

2016-06-15

MATLAB is popular in biological research for creating and simulating models that use ordinary differential equations (ODEs). However, sharing or using these models outside of MATLAB is often problematic. A community standard such as Systems Biology Markup Language (SBML) can serve as a neutral exchange format, but translating models from MATLAB to SBML can be challenging-especially for legacy models not written with translation in mind. We developed MOCCASIN (Model ODE Converter for Creating Automated SBML INteroperability) to help. MOCCASIN can convert ODE-based MATLAB models of biochemical reaction networks into the SBML format. MOCCASIN is available under the terms of the LGPL 2.1 license (http://www.gnu.org/licenses/lgpl-2.1.html). Source code, binaries and test cases can be freely obtained from https://github.com/sbmlteam/moccasin : mhucka@caltech.edu More information is available at https://github.com/sbmlteam/moccasin. © The Author 2016. Published by Oxford University Press.

A novel approach of modeling continuous dark hydrogen fermentation.

PubMed

Alexandropoulou, Maria; Antonopoulou, Georgia; Lyberatos, Gerasimos

2018-02-01

In this study a novel modeling approach for describing fermentative hydrogen production in a continuous stirred tank reactor (CSTR) was developed, using the Aquasim modeling platform. This model accounts for the key metabolic reactions taking place in a fermentative hydrogen producing reactor, using fixed stoichiometry but different reaction rates. Biomass yields are determined based on bioenergetics. The model is capable of describing very well the variation in the distribution of metabolic products for a wide range of hydraulic retention times (HRT). The modeling approach is demonstrated using the experimental data obtained from a CSTR, fed with food industry waste (FIW), operating at different HRTs. The kinetic parameters were estimated through fitting to the experimental results. Hydrogen and total biogas production rates were predicted very well by the model, validating the basic assumptions regarding the implicated stoichiometric biochemical reactions and their kinetic rates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Misdiagnosis of Thyroid Disorders in Down Syndrome: Time to Re-Examine the Myth?

ERIC Educational Resources Information Center

Prasher, V.; Haque, M. S.

2005-01-01

There is a reported association between thyroid disorders and Down syndrome, but is this association based on valid and reliable research evidence? We evaluated thyroid function test results of 110 healthy adults with Down syndrome to determine biochemical thyroid status. Approximately two thirds were biochemically euthyroid when assessed by…

Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow

DOE PAGES

Brunk, Elizabeth; George, Kevin W.; Alonso-Gutierrez, Jorge; ...

2016-05-19

Understanding the complex interactions that occur between heterologous and native biochemical pathways represents a major challenge in metabolic engineering and synthetic biology. We present a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host. This workflow incorporates complementary approaches from computational systems biology, metabolic engineering, and synthetic biology; provides molecular insight into how the host organism microenvironment changes due to pathway engineering; and demonstrates how biological mechanisms underlying strain variation can be exploited as an engineering strategy to increase product yield. As a proofmore » of concept, we present the analysis of eight engineered strains producing three biofuels: isopentenol, limonene, and bisabolene. Application of this workflow identified the roles of candidate genes, pathways, and biochemical reactions in observed experimental phenomena and facilitated the construction of a mutant strain with improved productivity. The contributed workflow is available as an open-source tool in the form of iPython notebooks.« less

Ultrasensitive dual phosphorylation dephosphorylation cycle kinetics exhibits canonical competition behavior

NASA Astrophysics Data System (ADS)

Huang, Qingdao; Qian, Hong

2009-09-01

We establish a mathematical model for a cellular biochemical signaling module in terms of a planar differential equation system. The signaling process is carried out by two phosphorylation-dephosphorylation reaction steps that share common kinase and phosphatase with saturated enzyme kinetics. The pair of equations is particularly simple in the present mathematical formulation, but they are singular. A complete mathematical analysis is developed based on an elementary perturbation theory. The dynamics exhibits the canonical competition behavior in addition to bistability. Although widely understood in ecological context, we are not aware of a full range of biochemical competition in a simple signaling network. The competition dynamics has broad implications to cellular processes such as cell differentiation and cancer immunoediting. The concepts of homogeneous and heterogeneous multisite phosphorylation are introduced and their corresponding dynamics are compared: there is no bistability in a heterogeneous dual phosphorylation system. A stochastic interpretation is also provided that further gives intuitive understanding of the bistable behavior inside the cells.

Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

NASA Astrophysics Data System (ADS)

Thomas, Alex; Rahmanian, Sorena; Bordbar, Aarash; Palsson, Bernhard Ø.; Jamshidi, Neema

2014-01-01

Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.

Biochemical characterization and phylogenetic analysis based on 16S rRNA sequences for V-factor dependent members of Pasteurellaceae derived from laboratory rats.

PubMed

Hayashimoto, Nobuhito; Ueno, Masami; Tkakura, Akira; Itoh, Toshio

2007-06-01

Phylogenetic analysis based on 16S rRNA sequences with sequence data of some bacterial species of Pasteurellaceae related to rodents deposited in GenBank was performed along with biochemical characterization for the 20 strains of V-factor dependent members of Pasteurellaceae derived from laboratory rats to obtain basic information and to investigate the taxonomic positions. The results of biochemical tests for all strains were identical except for three tests, the ornithine decarboxylase test, and fermentation tests of D(+) mannose and D(+) xylose. The biochemical properties of 8 of 20 strains that showed negative results for the fermentation test of D(+) xylose agreed with those of Haemophilus parainfluenzae complex. By phylogenetic analysis, the strains were divided into two clusters that agreed with the results of the fermentation test of xylose (group I: negative reaction for xylose, group II: positive reaction for xylose). The clusters were independent of other bacterial species of Pasteurellaceae tested. The sequences of the strains in group I showed 99.7-99.8% similarity and the strains in group II showed 99.3-99.7% similarity. None of the strains in group I had a close relation with Haemophilus parainfluenzae by phylogenetic analysis, although they showed the same biochemical properties. In conclusion, the strains had characteristic biochemical properties and formed two independent groups within the "rodent cluster" of Pasteurellaceae that differed in the results of the fermentation test of xylose. Therefore, they seemed to be hitherto undescribed taxa in Pasteurellaceae.

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

Hay, J.; Schwender, J.

Computational simulation of large-scale biochemical networks can be used to analyze and predict the metabolic behavior of an organism, such as a developing seed. Based on the biochemical literature, pathways databases and decision rules defining reaction directionality we reconstructed bna572, a stoichiometric metabolic network model representing Brassica napus seed storage metabolism. In the highly compartmentalized network about 25% of the 572 reactions are transport reactions interconnecting nine subcellular compartments and the environment. According to known physiological capabilities of developing B. napus embryos, four nutritional conditions were defined to simulate heterotrophy or photoheterotrophy, each in combination with the availability of inorganicmore » nitrogen (ammonia, nitrate) or amino acids as nitrogen sources. Based on mathematical linear optimization the optimal solution space was comprehensively explored by flux variability analysis, thereby identifying for each reaction the range of flux values allowable under optimality. The range and variability of flux values was then categorized into flux variability types. Across the four nutritional conditions, approximately 13% of the reactions have variable flux values and 10-11% are substitutable (can be inactive), both indicating metabolic redundancy given, for example, by isoenzymes, subcellular compartmentalization or the presence of alternative pathways. About one-third of the reactions are never used and are associated with pathways that are suboptimal for storage synthesis. Fifty-seven reactions change flux variability type among the different nutritional conditions, indicating their function in metabolic adjustments. This predictive modeling framework allows analysis and quantitative exploration of storage metabolism of a developing B. napus oilseed.« less

LCE: leaf carbon exchange data set for tropical, temperate, and boreal species of North and Central America.

PubMed

Smith, Nicholas G; Dukes, Jeffrey S

2017-11-01

Leaf canopy carbon exchange processes, such as photosynthesis and respiration, are substantial components of the global carbon cycle. Climate models base their simulations of photosynthesis and respiration on an empirical understanding of the underlying biochemical processes, and the responses of those processes to environmental drivers. As such, data spanning large spatial scales are needed to evaluate and parameterize these models. Here, we present data on four important biochemical parameters defining leaf carbon exchange processes from 626 individuals of 98 species at 12 North and Central American sites spanning ~53° of latitude. The four parameters are the maximum rate of Rubisco carboxylation (V cmax ), the maximum rate of electron transport for the regeneration of Ribulose-1,5,-bisphosphate (J max ), the maximum rate of phosphoenolpyruvate carboxylase carboxylation (V pmax ), and leaf dark respiration (R d ). The raw net photosynthesis by intercellular CO 2 (A/C i ) data used to calculate V cmax , J max , and V pmax rates are also presented. Data were gathered on the same leaf of each individual (one leaf per individual), allowing for the examination of each parameter relative to others. Additionally, the data set contains a number of covariates for the plants measured. Covariate data include (1) leaf-level traits (leaf mass, leaf area, leaf nitrogen and carbon content, predawn leaf water potential), (2) plant-level traits (plant height for herbaceous individuals and diameter at breast height for trees), (3) soil moisture at the time of measurement, (4) air temperature from nearby weather stations for the day of measurement and each of the 90 d prior to measurement, and (5) climate data (growing season mean temperature, precipitation, photosynthetically active radiation, vapor pressure deficit, and aridity index). We hope that the data will be useful for obtaining greater understanding of the abiotic and biotic determinants of these important biochemical parameters and for evaluating and improving large-scale models of leaf carbon exchange. © 2017 by the Ecological Society of America.

Activation of sorbitol pathway in metabolic syndrome and increased susceptibility to cataract in Wistar-Obese rats

PubMed Central

Giridharan, Nappan Veettil

2012-01-01

Purpose Obesity is a major public health problem worldwide, and of late, epidemiological studies indicate a preponderance of cataracts under obesity conditions. Although cataract is a multifactorial disorder and various biochemical mechanisms have been proposed, the influence of obesity on cataractogenesis has yet to be investigated. In such a scenario, a suitable animal model that develops cataract following the onset of obesity will be a welcome tool for biomedical research. Therefore, we investigated the molecular and biochemical basis for predisposition to cataract in the obese mutant rat models established in our institute because 15%–20% of these rats develop cataracts spontaneously as they reach 12–15 months of age. Methods We analyzed the major biochemical pathways in the normal lenses of different age groups of our obese mutant rat strains, Wistar/Obese (WNIN/Ob) and WNIN/GR-Ob, the former with euglycemia and the latter with an additional impaired glucose tolerance trait. In addition, sorbitol levels were estimated in the cataractous lenses of the obese rats. Results Except for the polyol pathway, all the principal pathways of the lens remained unaltered. Therefore, sorbitol levels were found to be high in the normal eye lenses of obese rats (WNIN/Ob and WNIN/GR-Ob) compared to their lean controls from three months of age onwards. Between WNIN/Ob and WNIN/GR-Ob, the levels of sorbitol were higher in the latter, suggesting a synergistic effect of impaired glucose tolerance along with obesity in the activation of the sorbitol pathway. Either way, an elevated sorbitol pathway seemed to be the predisposing factor responsible for cataract formation in these mutant rats. Conclusions Activation of the sorbitol pathway indeed enhances the risk of cataract development in conditions such as metabolic syndrome. These rat models thus may be valuable tools for investigating obesity-associated cataract and for developing intervention strategies, based on these findings. PMID:22393276

Activation of sorbitol pathway in metabolic syndrome and increased susceptibility to cataract in Wistar-Obese rats.

PubMed

Reddy, Paduru Yadagiri; Giridharan, Nappan Veettil; Reddy, Geereddy Bhanuprakash

2012-01-01

Obesity is a major public health problem worldwide, and of late, epidemiological studies indicate a preponderance of cataracts under obesity conditions. Although cataract is a multifactorial disorder and various biochemical mechanisms have been proposed, the influence of obesity on cataractogenesis has yet to be investigated. In such a scenario, a suitable animal model that develops cataract following the onset of obesity will be a welcome tool for biomedical research. Therefore, we investigated the molecular and biochemical basis for predisposition to cataract in the obese mutant rat models established in our institute because 15%-20% of these rats develop cataracts spontaneously as they reach 12-15 months of age. We analyzed the major biochemical pathways in the normal lenses of different age groups of our obese mutant rat strains, Wistar/Obese (WNIN/Ob) and WNIN/GR-Ob, the former with euglycemia and the latter with an additional impaired glucose tolerance trait. In addition, sorbitol levels were estimated in the cataractous lenses of the obese rats. Except for the polyol pathway, all the principal pathways of the lens remained unaltered. Therefore, sorbitol levels were found to be high in the normal eye lenses of obese rats (WNIN/Ob and WNIN/GR-Ob) compared to their lean controls from three months of age onwards. Between WNIN/Ob and WNIN/GR-Ob, the levels of sorbitol were higher in the latter, suggesting a synergistic effect of impaired glucose tolerance along with obesity in the activation of the sorbitol pathway. Either way, an elevated sorbitol pathway seemed to be the predisposing factor responsible for cataract formation in these mutant rats. Activation of the sorbitol pathway indeed enhances the risk of cataract development in conditions such as metabolic syndrome. These rat models thus may be valuable tools for investigating obesity-associated cataract and for developing intervention strategies, based on these findings.

Chemical kinetic mechanistic models to investigate cancer biology and impact cancer medicine.

PubMed

Stites, Edward C

2013-04-01

Traditional experimental biology has provided a mechanistic understanding of cancer in which the malignancy develops through the acquisition of mutations that disrupt cellular processes. Several drugs developed to target such mutations have now demonstrated clinical value. These advances are unequivocal testaments to the value of traditional cellular and molecular biology. However, several features of cancer may limit the pace of progress that can be made with established experimental approaches alone. The mutated genes (and resultant mutant proteins) function within large biochemical networks. Biochemical networks typically have a large number of component molecules and are characterized by a large number of quantitative properties. Responses to a stimulus or perturbation are typically nonlinear and can display qualitative changes that depend upon the specific values of variable system properties. Features such as these can complicate the interpretation of experimental data and the formulation of logical hypotheses that drive further research. Mathematical models based upon the molecular reactions that define these networks combined with computational studies have the potential to deal with these obstacles and to enable currently available information to be more completely utilized. Many of the pressing problems in cancer biology and cancer medicine may benefit from a mathematical treatment. As work in this area advances, one can envision a future where such models may meaningfully contribute to the clinical management of cancer patients.

Animal Models for Studying the In Vivo Functions of Cell Cycle CDKs.

PubMed

Risal, Sanjiv; Adhikari, Deepak; Liu, Kui

2016-01-01

Multiple Cdks (Cdk4, Cdk6, and Cdk2) and a mitotic Cdk (Cdk1) are involved in cell cycle progression in mammals. Cyclins, Cdk inhibitors, and phosphorylations (both activating and inhibitory) at different cellular levels tightly modulate the activities of these kinases. Based on the results of biochemical studies, it was long believed that different Cdks functioned at specific stages during cell cycle progression. However, deletion of all three interphase Cdks in mice affected cell cycle entry and progression only in certain specialized cells such as hematopoietic cells, beta cells of the pancreas, pituitary lactotrophs, and cardiomyocytes. These genetic experiments challenged the prevailing biochemical model and established that Cdks function in a cell-specific, but not a stage-specific, manner during cell cycle entry and the progression of mitosis. Recent in vivo studies have further established that Cdk1 is the only Cdk that is both essential and sufficient for driving the resumption of meiosis during mouse oocyte maturation. These genetic studies suggest a minimal-essential cell cycle model in which Cdk1 is the central regulator of cell cycle progression. Cdk1 can compensate for the loss of the interphase Cdks by forming active complexes with A-, B-, E-, and D-type Cyclins in a stepwise manner. Thus, Cdk1 plays an essential role in both mitosis and meiosis in mammals, whereas interphase Cdks are dispensable.

Prediction of renal crystalline size distributions in space using a PBE analytic model. 2. Effect of dietary countermeasures.

PubMed

Kassemi, Mohammad; Thompson, David

2016-09-01

An analytic Population Balance Equation model is used to assess the efficacy of citrate, pyrophosphate, and augmented fluid intake as dietary countermeasures aimed at reducing the risk of renal stone formation for astronauts. The model uses the measured biochemical profile of the astronauts as input and predicts the steady-state size distribution of the nucleating, growing, and agglomerating renal calculi subject to biochemical changes brought about by administration of these dietary countermeasures. Numerical predictions indicate that an increase in citrate levels beyond its average normal ground-based urinary values is beneficial but only to a limited extent. Unfortunately, results also indicate that any decline in the citrate levels during space travel below its normal urinary values on Earth can easily move the astronaut into the stone-forming risk category. Pyrophosphate is found to be an effective inhibitor since numerical predictions indicate that even at quite small urinary concentrations, it has the potential of shifting the maximum crystal aggregate size to a much smaller and plausibly safer range. Finally, our numerical results predict a decline in urinary volume below 1.5 liters/day can act as a dangerous promoter of renal stone development in microgravity while urinary volume levels of 2.5-3 liters/day can serve as effective space countermeasures. Copyright © 2016 the American Physiological Society.

Biochemical Phenotypes to Discriminate Microbial Subpopulations and Improve Outbreak Detection

PubMed Central

Galar, Alicia; Kulldorff, Martin; Rudnick, Wallis; O'Brien, Thomas F.; Stelling, John

2013-01-01

Background Clinical microbiology laboratories worldwide constitute an invaluable resource for monitoring emerging threats and the spread of antimicrobial resistance. We studied the growing number of biochemical tests routinely performed on clinical isolates to explore their value as epidemiological markers. Methodology/Principal Findings Microbiology laboratory results from January 2009 through December 2011 from a 793-bed hospital stored in WHONET were examined. Variables included patient location, collection date, organism, and 47 biochemical and 17 antimicrobial susceptibility test results reported by Vitek 2. To identify biochemical tests that were particularly valuable (stable with repeat testing, but good variability across the species) or problematic (inconsistent results with repeat testing), three types of variance analyses were performed on isolates of K. pneumonia: descriptive analysis of discordant biochemical results in same-day isolates, an average within-patient variance index, and generalized linear mixed model variance component analysis. Results: 4,200 isolates of K. pneumoniae were identified from 2,485 patients, 32% of whom had multiple isolates. The first two variance analyses highlighted SUCT, TyrA, GlyA, and GGT as “nuisance” biochemicals for which discordant within-patient test results impacted a high proportion of patient results, while dTAG had relatively good within-patient stability with good heterogeneity across the species. Variance component analyses confirmed the relative stability of dTAG, and identified additional biochemicals such as PHOS with a large between patient to within patient variance ratio. A reduced subset of biochemicals improved the robustness of strain definition for carbapenem-resistant K. pneumoniae. Surveillance analyses suggest that the reduced biochemical profile could improve the timeliness and specificity of outbreak detection algorithms. Conclusions The statistical approaches explored can improve the robust recognition of microbial subpopulations with routinely available biochemical test results, of value in the timely detection of outbreak clones and evolutionarily important genetic events. PMID:24391936

Hyponatremia and fractures: should hyponatremia be further studied as a potential biochemical risk factor to be included in FRAX algorithms?

PubMed

Ayus, J C; Bellido, T; Negri, A L

2017-05-01

The Fracture Risk Assessment Tool (FRAX®) was developed by the WHO Collaborating Centre for metabolic bone diseases to evaluate fracture risk of patients. It is based on patient models that integrate the risk associated with clinical variables and bone mineral density (BMD) at the femoral neck. The clinical risk factors included in FRAX were chosen to include only well-established and independent variables related to skeletal fracture risk. The FRAX tool has acquired worldwide acceptance despite having several limitations. FRAX models have not included biochemical derangements in estimation of fracture risk due to the lack of validation in large prospective studies. Recently, there has been an increasing number of studies showing a relationship between hyponatremia and the occurrence of fractures. Hyponatremia is the most frequent electrolyte abnormality measured in the clinic, and serum sodium concentration is a very reproducible, affordable, and readily obtainable measurement. Thus, we think that hyponatremia should be further studied as a biochemical risk factor for skeletal fractures prediction, particularly those at the hip which carries the greatest morbidity and mortality. To achieve this will require the collection of large patient cohorts from diverse geographical locations that include a measure of serum sodium in addition to the other FRAX variables in large numbers, in both sexes, over a wide age range and with wide geographical representation. It would also require the inclusion of data on duration and severity of hyponatremia. Information will be required both on the risk of fracture associated with the occurrence and length of exposure to hyponatremia and to the relationship with the other risk variables included in FRAX and also the independent effect on the occurrence of death which is increased by hyponatremia.

Toward efficiency in heterogeneous multispecies reactive transport modeling: A particle-tracking solution for first-order network reactions

NASA Astrophysics Data System (ADS)

Henri, Christopher; Fernàndez-Garcia, Daniel

2015-04-01

Modeling multi-species reactive transport in natural systems with strong heterogeneities and complex biochemical reactions is a major challenge for assessing groundwater polluted sites with organic and inorganic contaminants. A large variety of these contaminants react according to serial-parallel reaction networks commonly simplified by a combination of first-order kinetic reactions. In this context, a random-walk particle tracking method is presented. This method is capable of efficiently simulating the motion of particles affected by first-order network reactions in three-dimensional systems, which are represented by spatially variable physical and biochemical coefficients described at high resolution. The approach is based on the development of transition probabilities that describe the likelihood that particles belonging to a given species and location at a given time will be transformed into and moved to another species and location afterwards. These probabilities are derived from the solution matrix of the spatial moments governing equations. The method is fully coupled with reactions, free of numerical dispersion and overcomes the inherent numerical problems stemming from the incorporation of heterogeneities to reactive transport codes. In doing this, we demonstrate that the motion of particles follows a standard random walk with time-dependent effective retardation and dispersion parameters that depend on the initial and final chemical state of the particle. The behavior of effective parameters develops as a result of differential retardation effects among species. Moreover, explicit analytic solutions of the transition probability matrix and related particle motions are provided for serial reactions. An example of the effect of heterogeneity on the dechlorination of organic solvents in a three-dimensional random porous media shows that the power-law behavior typically observed in conservative tracers breakthrough curves can be largely compromised by the effect of biochemical reactions.

Toward efficiency in heterogeneous multispecies reactive transport modeling: A particle-tracking solution for first-order network reactions

NASA Astrophysics Data System (ADS)

Henri, Christopher V.; Fernàndez-Garcia, Daniel

2014-09-01

Modeling multispecies reactive transport in natural systems with strong heterogeneities and complex biochemical reactions is a major challenge for assessing groundwater polluted sites with organic and inorganic contaminants. A large variety of these contaminants react according to serial-parallel reaction networks commonly simplified by a combination of first-order kinetic reactions. In this context, a random-walk particle tracking method is presented. This method is capable of efficiently simulating the motion of particles affected by first-order network reactions in three-dimensional systems, which are represented by spatially variable physical and biochemical coefficients described at high resolution. The approach is based on the development of transition probabilities that describe the likelihood that particles belonging to a given species and location at a given time will be transformed into and moved to another species and location afterward. These probabilities are derived from the solution matrix of the spatial moments governing equations. The method is fully coupled with reactions, free of numerical dispersion and overcomes the inherent numerical problems stemming from the incorporation of heterogeneities to reactive transport codes. In doing this, we demonstrate that the motion of particles follows a standard random walk with time-dependent effective retardation and dispersion parameters that depend on the initial and final chemical state of the particle. The behavior of effective parameters develops as a result of differential retardation effects among species. Moreover, explicit analytic solutions of the transition probability matrix and related particle motions are provided for serial reactions. An example of the effect of heterogeneity on the dechlorination of organic solvents in a three-dimensional random porous media shows that the power-law behavior typically observed in conservative tracers breakthrough curves can be largely compromised by the effect of biochemical reactions.

Biochemical studies on Francisella tularensis RelA in (p)ppGpp biosynthesis

PubMed Central

Wilkinson, Rachael C.; Batten, Laura E.; Wells, Neil J.; Oyston, Petra C.F.; Roach, Peter L.

2015-01-01

The bacterial stringent response is induced by nutrient deprivation and is mediated by enzymes of the RSH (RelA/SpoT homologue; RelA, (p)ppGpp synthetase I; SpoT, (p)ppGpp synthetase II) superfamily that control concentrations of the ‘alarmones’ (p)ppGpp (guanosine penta- or tetra-phosphate). This regulatory pathway is present in the vast majority of pathogens and has been proposed as a potential anti-bacterial target. Current understanding of RelA-mediated responses is based on biochemical studies using Escherichia coli as a model. In comparison, the Francisella tularensis RelA sequence contains a truncated regulatory C-terminal region and an unusual synthetase motif (EXSD). Biochemical analysis of F. tularensis RelA showed the similarities and differences of this enzyme compared with the model RelA from Escherichia coli. Purification of the enzyme yielded a stable dimer capable of reaching concentrations of 10 mg/ml. In contrast with other enzymes from the RelA/SpoT homologue superfamily, activity assays with F. tularensis RelA demonstrate a high degree of specificity for GTP as a pyrophosphate acceptor, with no measurable turnover for GDP. Steady state kinetic analysis of F. tularensis RelA gave saturation activity curves that best fitted a sigmoidal function. This kinetic profile can result from allosteric regulation and further measurements with potential allosteric regulators demonstrated activation by ppGpp (5′,3′-dibisphosphate guanosine) with an EC50 of 60±1.9 μM. Activation of F. tularensis RelA by stalled ribosomal complexes formed with ribosomes purified from E. coli MRE600 was observed, but interestingly, significantly weaker activation with ribosomes isolated from Francisella philomiragia. PMID:26450927

Fibre optic system for biochemical and microbiological sensing

NASA Astrophysics Data System (ADS)

Penwill, L. A.; Slater, J. H.; Hayes, N. W.; Tremlett, C. J.

2007-07-01

This poster will discuss state-of-the-art fibre optic sensors based on evanescent wave technology emphasising chemophotonic sensors for biochemical reactions and microbe detection. Devices based on antibody specificity and unique DNA sequences will be described. The development of simple sensor devices with disposable single use sensor probes will be illustrated with a view to providing cost effective field based or point of care analysis of major themes such as hospital acquired infections or bioterrorism events. This presentation will discuss the nature and detection thresholds required, the optical detection techniques investigated, results of sensor trials and the potential for wider commercial application.

Heparin/heparan sulfate 6-O-sulfatase from Flavobacterium heparinum: integrated structural and biochemical investigation of enzyme active site and substrate specificity.

PubMed

Myette, James R; Soundararajan, Venkataramanan; Shriver, Zachary; Raman, Rahul; Sasisekharan, Ram

2009-12-11

Heparin and heparan sulfate glycosaminoglycans (HSGAGs) comprise a chemically heterogeneous class of sulfated polysaccharides. The development of structure-activity relationships for this class of polysaccharides requires the identification and characterization of degrading enzymes with defined substrate specificity and enzymatic activity. Toward this end, we report here the molecular cloning and extensive structure-function analysis of a 6-O-sulfatase from the Gram-negative bacterium Flavobacterium heparinum. In addition, we report the recombinant expression of this enzyme in Escherichia coli in a soluble, active form and identify it as a specific HSGAG sulfatase. We further define the mechanism of action of the enzyme through biochemical and structural studies. Through the use of defined substrates, we investigate the kinetic properties of the enzyme. This analysis was complemented by homology-based molecular modeling studies that sought to rationalize the substrate specificity of the enzyme and mode of action through an analysis of the active-site topology of the enzyme including identifying key enzyme-substrate interactions and assigning key amino acids within the active site of the enzyme. Taken together, our structural and biochemical studies indicate that 6-O-sulfatase is a predominantly exolytic enzyme that specifically acts on N-sulfated or N-acetylated 6-O-sulfated glucosamines present at the non-reducing end of HSGAG oligosaccharide substrates. This requirement for the N-acetyl or N-sulfo groups on the glucosamine substrate can be explained through eliciting favorable interactions with key residues within the active site of the enzyme. These findings provide a framework that enables the use of 6-O-sulfatase as a tool for HSGAG structure-activity studies as well as expand our biochemical and structural understanding of this important class of enzymes.

Proinflammatory Cytokines, Enolase and S-100 as Early Biochemical Indicators of Hypoxic-Ischemic Encephalopathy Following Perinatal Asphyxia in Newborns.

PubMed

Chaparro-Huerta, Verónica; Flores-Soto, Mario Eduardo; Merin Sigala, Mario Ernesto; Barrera de León, Juan Carlos; Lemus-Varela, María de Lourdes; Torres-Mendoza, Blanca Miriam de Guadalupe; Beas-Zárate, Carlos

2017-02-01

Estimation of the neurological prognosis of infants suffering from perinatal asphyxia and signs of hypoxic-ischemic encephalopathy is of great clinical importance; however, it remains difficult to satisfactorily assess these signs with current standard medical practices. Prognoses are typically based on data obtained from clinical examinations and neurological tests, such as electroencephalography (EEG) and neuroimaging, but their sensitivities and specificities are far from optimal, and they do not always reliably predict future neurological sequelae. In an attempt to improve prognostic estimates, neurological research envisaged various biochemical markers detectable in the umbilical cord blood of newborns (NB). Few studies examining these biochemical factors in the whole blood of newborns exist. Thus, the aim of this study was to determine the expression and concentrations of proinflammatory cytokines (TNF-α, IL-1β and IL-6) and specific CNS enzymes (S-100 and enolase) in infants with perinatal asphyxia. These data were compared between the affected infants and controls and were related to the degree of HIE to determine their utilities as biochemical markers for early diagnosis and prognosis. The levels of the proinflammatory cytokines and enzymes were measured by enzyme-linked immunosorbent assay (ELISA) and Reverse Transcription polymerase chain reaction (RT-PCR). The expression and serum levels of the proinflammatory cytokines, enolase and S-100 were significantly increased in the children with asphyxia compared with the controls. The role of cytokines after hypoxic-ischemic insult has been determined in studies of transgenic mice that support the use of these molecules as candidate biomarkers. Similarly, S-100 and enolase are considered promising candidates because these markers have been correlated with tissue damage in different experimental models. Copyright © 2016. Published by Elsevier B.V.

Automated analysis of information processing, kinetic independence and modular architecture in biochemical networks using MIDIA.

PubMed

Bowsher, Clive G

2011-02-15

Understanding the encoding and propagation of information by biochemical reaction networks and the relationship of such information processing properties to modular network structure is of fundamental importance in the study of cell signalling and regulation. However, a rigorous, automated approach for general biochemical networks has not been available, and high-throughput analysis has therefore been out of reach. Modularization Identification by Dynamic Independence Algorithms (MIDIA) is a user-friendly, extensible R package that performs automated analysis of how information is processed by biochemical networks. An important component is the algorithm's ability to identify exact network decompositions based on both the mass action kinetics and informational properties of the network. These modularizations are visualized using a tree structure from which important dynamic conditional independence properties can be directly read. Only partial stoichiometric information needs to be used as input to MIDIA, and neither simulations nor knowledge of rate parameters are required. When applied to a signalling network, for example, the method identifies the routes and species involved in the sequential propagation of information between its multiple inputs and outputs. These routes correspond to the relevant paths in the tree structure and may be further visualized using the Input-Output Path Matrix tool. MIDIA remains computationally feasible for the largest network reconstructions currently available and is straightforward to use with models written in Systems Biology Markup Language (SBML). The package is distributed under the GNU General Public License and is available, together with a link to browsable Supplementary Material, at http://code.google.com/p/midia. Further information is at www.maths.bris.ac.uk/~macgb/Software.html.

Cancer growth and metastasis as a metaphor of Go gaming: An Ising model approach.

PubMed

Barradas-Bautista, Didier; Alvarado-Mentado, Matias; Agostino, Mark; Cocho, Germinal

2018-01-01

This work aims for modeling and simulating the metastasis of cancer, via the analogy between the cancer process and the board game Go. In the game of Go, black stones that play first could correspond to a metaphor of the birth, growth, and metastasis of cancer. Moreover, playing white stones on the second turn could correspond the inhibition of cancer invasion. Mathematical modeling and algorithmic simulation of Go may therefore benefit the efforts to deploy therapies to surpass cancer illness by providing insight into the cellular growth and expansion over a tissue area. We use the Ising Hamiltonian, that models the energy exchange in interacting particles, for modeling the cancer dynamics. Parameters in the energy function refer the biochemical elements that induce cancer birth, growth, and metastasis; as well as the biochemical immune system process of defense.

Performance limits and trade-offs in entropy-driven biochemical computers.

PubMed

Chu, Dominique

2018-04-14

It is now widely accepted that biochemical reaction networks can perform computations. Examples are kinetic proof reading, gene regulation, or signalling networks. For many of these systems it was found that their computational performance is limited by a trade-off between the metabolic cost, the speed and the accuracy of the computation. In order to gain insight into the origins of these trade-offs, we consider entropy-driven computers as a model of biochemical computation. Using tools from stochastic thermodynamics, we show that entropy-driven computation is subject to a trade-off between accuracy and metabolic cost, but does not involve time-trade-offs. Time trade-offs appear when it is taken into account that the result of the computation needs to be measured in order to be known. We argue that this measurement process, although usually ignored, is a major contributor to the cost of biochemical computation. Copyright © 2018 Elsevier Ltd. All rights reserved.

Modeling biochemical transformation processes and information processing with Narrator.

PubMed

Mandel, Johannes J; Fuss, Hendrik; Palfreyman, Niall M; Dubitzky, Werner

2007-03-27

Software tools that model and simulate the dynamics of biological processes and systems are becoming increasingly important. Some of these tools offer sophisticated graphical user interfaces (GUIs), which greatly enhance their acceptance by users. Such GUIs are based on symbolic or graphical notations used to describe, interact and communicate the developed models. Typically, these graphical notations are geared towards conventional biochemical pathway diagrams. They permit the user to represent the transport and transformation of chemical species and to define inhibitory and stimulatory dependencies. A critical weakness of existing tools is their lack of supporting an integrative representation of transport, transformation as well as biological information processing. Narrator is a software tool facilitating the development and simulation of biological systems as Co-dependence models. The Co-dependence Methodology complements the representation of species transport and transformation together with an explicit mechanism to express biological information processing. Thus, Co-dependence models explicitly capture, for instance, signal processing structures and the influence of exogenous factors or events affecting certain parts of a biological system or process. This combined set of features provides the system biologist with a powerful tool to describe and explore the dynamics of life phenomena. Narrator's GUI is based on an expressive graphical notation which forms an integral part of the Co-dependence Methodology. Behind the user-friendly GUI, Narrator hides a flexible feature which makes it relatively easy to map models defined via the graphical notation to mathematical formalisms and languages such as ordinary differential equations, the Systems Biology Markup Language or Gillespie's direct method. This powerful feature facilitates reuse, interoperability and conceptual model development. Narrator is a flexible and intuitive systems biology tool. It is specifically intended for users aiming to construct and simulate dynamic models of biology without recourse to extensive mathematical detail. Its design facilitates mappings to different formal languages and frameworks. The combined set of features makes Narrator unique among tools of its kind. Narrator is implemented as Java software program and available as open-source from http://www.narrator-tool.org.

Modeling biochemical transformation processes and information processing with Narrator

PubMed Central

Mandel, Johannes J; Fuß, Hendrik; Palfreyman, Niall M; Dubitzky, Werner

2007-01-01

Background Software tools that model and simulate the dynamics of biological processes and systems are becoming increasingly important. Some of these tools offer sophisticated graphical user interfaces (GUIs), which greatly enhance their acceptance by users. Such GUIs are based on symbolic or graphical notations used to describe, interact and communicate the developed models. Typically, these graphical notations are geared towards conventional biochemical pathway diagrams. They permit the user to represent the transport and transformation of chemical species and to define inhibitory and stimulatory dependencies. A critical weakness of existing tools is their lack of supporting an integrative representation of transport, transformation as well as biological information processing. Results Narrator is a software tool facilitating the development and simulation of biological systems as Co-dependence models. The Co-dependence Methodology complements the representation of species transport and transformation together with an explicit mechanism to express biological information processing. Thus, Co-dependence models explicitly capture, for instance, signal processing structures and the influence of exogenous factors or events affecting certain parts of a biological system or process. This combined set of features provides the system biologist with a powerful tool to describe and explore the dynamics of life phenomena. Narrator's GUI is based on an expressive graphical notation which forms an integral part of the Co-dependence Methodology. Behind the user-friendly GUI, Narrator hides a flexible feature which makes it relatively easy to map models defined via the graphical notation to mathematical formalisms and languages such as ordinary differential equations, the Systems Biology Markup Language or Gillespie's direct method. This powerful feature facilitates reuse, interoperability and conceptual model development. Conclusion Narrator is a flexible and intuitive systems biology tool. It is specifically intended for users aiming to construct and simulate dynamic models of biology without recourse to extensive mathematical detail. Its design facilitates mappings to different formal languages and frameworks. The combined set of features makes Narrator unique among tools of its kind. Narrator is implemented as Java software program and available as open-source from . PMID:17389034

Fourier transform infrared spectroscopy to quantify collagen and elastin in an in vitro model of extracellular matrix degradation in aorta.

PubMed

Cheheltani, Rabee; McGoverin, Cushla M; Rao, Jayashree; Vorp, David A; Kiani, Mohammad F; Pleshko, Nancy

2014-06-21

Extracellular matrix (ECM) is a key component and regulator of many biological tissues including aorta. Several aortic pathologies are associated with significant changes in the composition of the matrix, especially in the content, quality and type of aortic structural proteins, collagen and elastin. The purpose of this study was to develop an infrared spectroscopic methodology that is comparable to biochemical assays to quantify collagen and elastin in aorta. Enzymatically degraded porcine aorta samples were used as a model of ECM degradation in abdominal aortic aneurysm (AAA). After enzymatic treatment, Fourier transform infrared (FTIR) spectra of the aortic tissue were acquired by an infrared fiber optic probe (IFOP) and FTIR imaging spectroscopy (FT-IRIS). Collagen and elastin content were quantified biochemically and partial least squares (PLS) models were developed to predict collagen and elastin content in aorta based on FTIR spectra. PLS models developed from FT-IRIS spectra were able to predict elastin and collagen content of the samples with strong correlations (RMSE of validation = 8.4% and 11.1% of the range respectively), and IFOP spectra were successfully used to predict elastin content (RMSE = 11.3% of the range). The PLS regression coefficients from the FT-IRIS models were used to map collagen and elastin in tissue sections of degraded porcine aortic tissue as well as a human AAA biopsy tissue, creating a similar map of each component compared to histology. These results support further application of FTIR spectroscopic techniques for evaluation of AAA tissues.

Fourier Transform Infrared Spectroscopy to Quantify Collagen and Elastin in an In Vitro Model of Extracellular Matrix Degradation in Aorta

PubMed Central

Cheheltani, Rabee; McGoverin, Cushla M.; Rao, Jayashree; Vorp, David A.; Kiani, Mohammad F.; Pleshko, N.

2014-01-01

Extracellular matrix (ECM) is a key component and regulator of many biological tissues including aorta. Several aortic pathologies are associated with significant changes in the composition of the matrix, especially in the content, quality and type of aortic structural proteins, collagen and elastin. The purpose of this study was to develop an infrared spectroscopic methodology that is comparable to biochemical assays to quantify collagen and elastin in aorta. Enzymatically degraded porcine aorta samples were used as a model of ECM degradation in abdominal aortic aneurysm (AAA). After enzymatic treatment, Fourier transform infrared (FTIR) spectra of the aortic tissue were acquired by an infrared fiber optic probe (IFOP) and FTIR imaging spectroscopy (FT-IRIS). Collagen and elastin content were quantified biochemically and partial least squares (PLS) models were developed to predict collagen and elastin content in aorta based on FTIR spectra. PLS models developed from FT-IRIS spectra were able to predict elastin and collagen content of the samples with strong correlations (RMSE of validation = 8.4% and 11.1% of the range respectively), and IFOP spectra were successfully used to predict elastin content (RMSE = 11.3% of the range). The PLS regression coefficients from the FT-IRIS models were used to map collagen and elastin in tissue sections of degraded porcine aortic tissue as well as a human AAA biopsy tissue, creating a similar map of each component compared to histology. These results support further application of FTIR spectroscopic techniques for evaluation of AAA tissues. PMID:24761431

Reproducing Phenomenology of Peroxidation Kinetics via Model Optimization

NASA Astrophysics Data System (ADS)

Ruslanov, Anatole D.; Bashylau, Anton V.

2010-06-01

We studied mathematical modeling of lipid peroxidation using a biochemical model system of iron (II)-ascorbate-dependent lipid peroxidation of rat hepatocyte mitochondrial fractions. We found that antioxidants extracted from plants demonstrate a high intensity of peroxidation inhibition. We simplified the system of differential equations that describes the kinetics of the mathematical model to a first order equation, which can be solved analytically. Moreover, we endeavor to algorithmically and heuristically recreate the processes and construct an environment that closely resembles the corresponding natural system. Our results demonstrate that it is possible to theoretically predict both the kinetics of oxidation and the intensity of inhibition without resorting to analytical and biochemical research, which is important for cost-effective discovery and development of medical agents with antioxidant action from the medicinal plants.

Numerical experiments based on a coupled physical-biochemical ocean model to study the Kuroshio-induced nutrient supply on the shelf-slope region off the southwestern coast of Japan

NASA Astrophysics Data System (ADS)

Kuroda, Hiroshi; Takasuka, Akinori; Hirota, Yuichi; Kodama, Taketoshi; Ichikawa, Tadafumi; Takahashi, Daisuke; Aoki, Kazuhiro; Setou, Takashi

2018-03-01

We developed a triply nested 1/50° ocean model coupled with a NPZD-type lower trophic level ecosystem model and used it to conduct numerical experiments to identify the major processes that supply nutrients on the shelf-slope region north of the Kuroshio. Tosa Bay, an open-type bay facing the Kuroshio, was selected for our experiment. Comparisons of numerical simulations using different grid sizes revealed that a grid size no larger than 1/50° was essential to reproduce a time-independent density structure related to the Kuroshio jet that uplifted nitrate from subsurface waters into the euphotic zone north of the Kuroshio front. The monthly mean budget of nitrate within the euphotic zone on the shelf showed that primary production was nearly balanced by physical advection and the biochemical supply of nitrate via mechanisms such as remineralization of detritus. Eddy advection of nitrate based on Reynolds decomposition, attributable primarily to submesoscale variations, had both positive and negative values within the bay, the indication being that eddy advection functioned regionally to supply or remove nitrate. Lagrangian particle-tracking experiments were performed to examine the major pathways of the nitrate used for primary production in Tosa Bay during the summer, when subsurface maxima of primary production typically appeared. The experiments revealed that when the Kuroshio took a stable nearshore path, nitrate was frequently uplifted around the Kuroshio front and horizontally transported along the front and into the bay via the counterclockwise circulation within the bay; it was sometimes further uplifted onto the shelf.

Biochemical and genetic analysis of Leigh syndrome patients in Korea.

PubMed

Chae, Jong-Hee; Lee, Jin Sook; Kim, Ki Joong; Hwang, Yong Seung; Hirano, Michio

2008-06-01

Sixteen Korean patients with Leigh syndrome were identified at the Seoul National University Children's Hospital in 2001-2006. Biochemical or molecular defects were identified in 14 patients (87.5%). Thirteen patients had respiratory chain enzyme defects; 9 had complex I deficiency, and 4 had combined defects of complex I+III+IV. Based on the biochemical defects, targeted genetic studies in 4 patients with complex I deficiency revealed two heteroplasmic mitochondrial DNA mutations in ND genes. One patient had the mitochondrial DNA T8993G point mutation. No mitochondrial DNA defects were identified in 11 (68.7%) of our LS patients, who probably have mutations in nuclear DNA. Although a limited study based in a single tertiary medical center, our findings suggest that isolated complex I deficiency may be the most common cause of Leigh syndrome in Korea.

"Chemical transformers" from nanoparticle ensembles operated with logic.

PubMed

Motornov, Mikhail; Zhou, Jian; Pita, Marcos; Gopishetty, Venkateshwarlu; Tokarev, Ihor; Katz, Evgeny; Minko, Sergiy

2008-09-01

The pH-responsive nanoparticles were coupled with information-processing enzyme-based systems to yield "smart" signal-responsive hybrid systems with built-in Boolean logic. The enzyme systems performed AND/OR logic operations, transducing biochemical input signals into reversible structural changes (signal-directed self-assembly) of the nanoparticle assemblies, thus resulting in the processing and amplification of the biochemical signals. The hybrid system mimics biological systems in effective processing of complex biochemical information, resulting in reversible changes of the self-assembled structures of the nanoparticles. The bioinspired approach to the nanostructured morphing materials could be used in future self-assembled molecular robotic systems.

Synthesis and DNA interaction of a mixed proflavine-phenanthroline Tröger base.

PubMed

Baldeyrou, Brigitte; Tardy, Christelle; Bailly, Christian; Colson, Pierre; Houssier, Claude; Charmantray, Franck; Demeunynck, Martine

2002-04-01

We report the synthesis of an asymmetric Tröger base containing the two well characterised DNA binding chromophores, proflavine and phenanthroline. The mode of interaction of the hybrid molecule was investigated by circular and linear dichroism experiments and a biochemical assay using DNA topoisomerase I. The data are compatible with a model in which the proflavine moiety intercalates between DNA base pairs and the phenanthroline ring occupies the DNA groove. DNase I cleavage experiments were carried out to investigate the sequence preference of the hybrid ligand and a well resolved footprint was detected at a site encompassing two adjacent 5'-GTC.5-GAC triplets. The sequence preference of the asymmetric molecule is compared to that of the symmetric analogues.

Chronobiology of epilepsy: diagnostic and therapeutic implications of chrono-epileptology.

PubMed

Loddenkemper, Tobias; Lockley, Steven W; Kaleyias, Joseph; Kothare, Sanjeev V

2011-04-01

The combination of chronobiology and epilepsy offers novel diagnostic and therapeutic management options. Knowledge of the interactions between circadian periodicity, entrainment, sleep patterns, and epilepsy may provide additional diagnostic options beyond sleep deprivation and extended release medication formulations. It may also provide novel insights into the physiologic, biochemical, and genetic regulation processes of epilepsy and the circadian clock, rendering new treatment options. Temporal fluctuations of seizure susceptibility based on sleep homeostasis and circadian phase in selected epilepsies may provide predictability based on mathematical models. Chrono-epileptology offers opportunities for individualized patient-oriented treatment paradigms based on chrono-pharmacology, differential medication dosing, chrono-drug delivery systems, and utilization of "zeitgebers" such as chronobiotics or light-therapy and desynchronization strategies among others.

Fibrous Hydrogels for Cell Encapsulation: A Modular and Supramolecular Approach.

PubMed

Włodarczyk-Biegun, Małgorzata K; Farbod, Kambiz; Werten, Marc W T; Slingerland, Cornelis J; de Wolf, Frits A; van den Beucken, Jeroen J J P; Leeuwenburgh, Sander C G; Cohen Stuart, Martien A; Kamperman, Marleen

2016-01-01

Artificial 3-dimensional (3D) cell culture systems, which mimic the extracellular matrix (ECM), hold great potential as models to study cellular processes under controlled conditions. The natural ECM is a 3D structure composed of a fibrous hydrogel that provides both mechanical and biochemical cues to instruct cell behavior. Here we present an ECM-mimicking genetically engineered protein-based hydrogel as a 3D cell culture system that combines several key features: (1) Mild and straightforward encapsulation meters (1) ease of ut I am not so sure.encapsulation of the cells, without the need of an external crosslinker. (2) Supramolecular assembly resulting in a fibrous architecture that recapitulates some of the unique mechanical characteristics of the ECM, i.e. strain-stiffening and self-healing behavior. (3) A modular approach allowing controlled incorporation of the biochemical cue density (integrin binding RGD domains). We tested the gels by encapsulating MG-63 osteoblastic cells and found that encapsulated cells not only respond to higher RGD density, but also to overall gel concentration. Cells in 1% and 2% (weight fraction) protein gels showed spreading and proliferation, provided a relative RGD density of at least 50%. In contrast, in 4% gels very little spreading and proliferation occurred, even for a relative RGD density of 100%. The independent control over both mechanical and biochemical cues obtained in this modular approach renders our hydrogels suitable to study cellular responses under highly defined conditions.

Mechanotransduction through Cytoskeleton

NASA Technical Reports Server (NTRS)

Ingber, Donald

2002-01-01

The goal of this project was to characterize the molecular mechanism by which cells recognize and respond to physical forces in their local environment. The project was based on the working hypothesis that cells sense mechanical stresses, such as those due to gravity, through their cell surface adhesion receptors (e.g., integrins) and that they respond as a result of structural arrangements with their internal cytoskeleton (CSK) which are orchestrated through use of tensegrity architecture. In this project, we carried out studies to define the architectural and molecular basis of cellular mechanotransduction. Our major goal was to define the molecular pathway that mediates mechanical force transfer between integrins and the CSK and to determine how mechanical deformation of integrin-CSK linkages is transduced into a biochemical response. Elucidation of the mechanism by which cells sense mechanical stresses through integrins and translate them into a biochemical response should help us to understand the molecular basis of the cellular response to gravity as well as many other forms of mechanosensation and tissue regulation. The specific aims of this proposal were: 1. To define the molecular basis of mechanical coupling between integrins, vinculin, and the actin CSK; 2. To develop a computer simulation of how mechanical stresses alter CSK structure and test this model in living cells; 3. To determine how mechanical deformation of integrin-CSK linkages is transduced into a biochemical response.

Discovery and Biochemical Characterization of the UDP-Xylose Biosynthesis Pathway in Sphaerobacter thermophilus.

PubMed

Gu, Bin; Laborda, Pedro; Wei, Shuang; Duan, Xu-Chu; Song, Hui-Bo; Liu, Li; Voglmeir, Josef

2016-01-01

The biosynthesis of UDP-xylose requires the stepwise oxidation/ decarboxylation of UDP-glucose, which is catalyzed by the enzymes UDPglucuronic acid dehydrogenase (UGD) and UDP-xylose synthase (UXS). UDPxylose biosynthesis is ubiquitous in animals and plants. However, only a few UGD and UXS isoforms of bacterial origin have thus far been biochemically characterized. Sphaerobacter thermophilus DSM 20745 is a bacterium isolated from heated sewage sludge, and therefore can be a valuable source of thermostable enzymes of biotechnological interest. However, no biochemical characterizations of any S. thermophilus enzymes have yet been reported. Herein, we describe the cloning and characterization of putative UGD (StUGD) and UXS (StUXS) isoforms from this organism. HPLC- and plate reader-based activity tests of the recombinantly expressed StUGD and StUXS showed that they are indeed active enzymes. Both StUGD and StUXS showed a temperature optimum of 70°C, and a reasonable thermal stability up to 60°C. No metal ions were required for enzymatic activities. StUGD had a higher pH optimum than StUXS. The simple purification procedures and the thermotolerance of StUGD and StUXS make them valuable biocatalysts for the synthesis of UDP-glucuronic acid and UDP-xylose at elevated temperatures. The biosynthetic potential of StUGD was further exemplified in a coupled enzymatic reaction with an UDP-glucuronosyltransferase, allowing the glucuronylation of the natural model substrate bilirubin.

On the precision of quasi steady state assumptions in stochastic dynamics

NASA Astrophysics Data System (ADS)

Agarwal, Animesh; Adams, Rhys; Castellani, Gastone C.; Shouval, Harel Z.

2012-07-01

Many biochemical networks have complex multidimensional dynamics and there is a long history of methods that have been used for dimensionality reduction for such reaction networks. Usually a deterministic mass action approach is used; however, in small volumes, there are significant fluctuations from the mean which the mass action approach cannot capture. In such cases stochastic simulation methods should be used. In this paper, we evaluate the applicability of one such dimensionality reduction method, the quasi-steady state approximation (QSSA) [L. Menten and M. Michaelis, "Die kinetik der invertinwirkung," Biochem. Z 49, 333369 (1913)] for dimensionality reduction in case of stochastic dynamics. First, the applicability of QSSA approach is evaluated for a canonical system of enzyme reactions. Application of QSSA to such a reaction system in a deterministic setting leads to Michaelis-Menten reduced kinetics which can be used to derive the equilibrium concentrations of the reaction species. In the case of stochastic simulations, however, the steady state is characterized by fluctuations around the mean equilibrium concentration. Our analysis shows that a QSSA based approach for dimensionality reduction captures well the mean of the distribution as obtained from a full dimensional simulation but fails to accurately capture the distribution around that mean. Moreover, the QSSA approximation is not unique. We have then extended the analysis to a simple bistable biochemical network model proposed to account for the stability of synaptic efficacies; the substrate of learning and memory [J. E. Lisman, "A mechanism of memory storage insensitive to molecular turnover: A bistable autophosphorylating kinase," Proc. Natl. Acad. Sci. U.S.A. 82, 3055-3057 (1985)], 10.1073/pnas.82.9.3055. Our analysis shows that a QSSA based dimensionality reduction method results in errors as big as two orders of magnitude in predicting the residence times in the two stable states.

Mathematical modelling of disintegration-limited co-digestion of OFMSW and sewage sludge.

PubMed

Esposito, G; Frunzo, L; Panico, A; d'Antonio, G

2008-01-01

This paper presents a mathematical model able to simulate under dynamic conditions the physical, chemical and biological processes prevailing in a OFMSW and sewage sludge anaerobic digestion system. The model proposed is based on differential mass balance equations for substrates, products and bacterial groups involved in the co-digestion process and includes the biochemical reactions of the substrate conversion and the kinetics of microbial growth and decay. The main peculiarity of the model is the surface based kinetic description of the OFMSW disintegration process, whereas the pH determination is based on a nine-order polynomial equation derived by acid-base equilibria. The model can be applied to simulate the co-digestion process for several purposes, such as the evaluation of the optimal process conditions in terms of OFMSW/sewage sludge ratio, temperature, OFMSW particle size, solid mixture retention time, reactor stirring rate, etc. Biogas production and composition can also be evaluated to estimate the potential energy production under different process conditions. In particular, model simulations reported in this paper show the model capability to predict the OFMSW amount which can be treated in the digester of an existing MWWTP and to assess the OFMSW particle size diminution pre-treatment required to increase the rate of the disintegration process, which otherwise can highly limit the co-digestion system. Copyright IWA Publishing 2008.

Phase computations and phase models for discrete molecular oscillators.

PubMed

Suvak, Onder; Demir, Alper

2012-06-11

Biochemical oscillators perform crucial functions in cells, e.g., they set up circadian clocks. The dynamical behavior of oscillators is best described and analyzed in terms of the scalar quantity, phase. A rigorous and useful definition for phase is based on the so-called isochrons of oscillators. Phase computation techniques for continuous oscillators that are based on isochrons have been used for characterizing the behavior of various types of oscillators under the influence of perturbations such as noise. In this article, we extend the applicability of these phase computation methods to biochemical oscillators as discrete molecular systems, upon the information obtained from a continuous-state approximation of such oscillators. In particular, we describe techniques for computing the instantaneous phase of discrete, molecular oscillators for stochastic simulation algorithm generated sample paths. We comment on the accuracies and derive certain measures for assessing the feasibilities of the proposed phase computation methods. Phase computation experiments on the sample paths of well-known biological oscillators validate our analyses. The impact of noise that arises from the discrete and random nature of the mechanisms that make up molecular oscillators can be characterized based on the phase computation techniques proposed in this article. The concept of isochrons is the natural choice upon which the phase notion of oscillators can be founded. The isochron-theoretic phase computation methods that we propose can be applied to discrete molecular oscillators of any dimension, provided that the oscillatory behavior observed in discrete-state does not vanish in a continuous-state approximation. Analysis of the full versatility of phase noise phenomena in molecular oscillators will be possible if a proper phase model theory is developed, without resorting to such approximations.

Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Su, Shiliang; Zhi, Junjun; Lou, Liping; Huang, Fang; Chen, Xia; Wu, Jiaping

Characterizing the spatio-temporal patterns and apportioning the pollution sources of water bodies are important for the management and protection of water resources. The main objective of this study is to describe the dynamics of water quality and provide references for improving river pollution control practices. Comprehensive application of neural-based modeling and different multivariate methods was used to evaluate the spatio-temporal patterns and source apportionment of pollution in Qiantang River, China. Measurement data were obtained and pretreated for 13 variables from 41 monitoring sites for the period of 2001-2004. A self-organizing map classified the 41 monitoring sites into three groups (Group A, B and C), representing different pollution characteristics. Four significant parameters (dissolved oxygen, biochemical oxygen demand, total phosphorus and total lead) were identified by discriminant analysis for distinguishing variations of different years, with about 80% correct assignment for temporal variation. Rotated principal component analysis (PCA) identified four potential pollution sources for Group A (domestic sewage and agricultural pollution, industrial wastewater pollution, mineral weathering, vehicle exhaust and sand mining), five for Group B (heavy metal pollution, agricultural runoff, vehicle exhaust and sand mining, mineral weathering, chemical plants discharge) and another five for Group C (vehicle exhaust and sand mining, chemical plants discharge, soil weathering, biochemical pollution, mineral weathering). The identified potential pollution sources explained 75.6% of the total variances for Group A, 75.0% for Group B and 80.0% for Group C, respectively. Receptor-based source apportionment was applied to further estimate source contributions for each pollution variable in the three groups, which facilitated and supported the PCA results. These results could assist managers to develop optimal strategies and determine priorities for river pollution control and effective water resources management.

Phase computations and phase models for discrete molecular oscillators

PubMed Central

2012-01-01

Background Biochemical oscillators perform crucial functions in cells, e.g., they set up circadian clocks. The dynamical behavior of oscillators is best described and analyzed in terms of the scalar quantity, phase. A rigorous and useful definition for phase is based on the so-called isochrons of oscillators. Phase computation techniques for continuous oscillators that are based on isochrons have been used for characterizing the behavior of various types of oscillators under the influence of perturbations such as noise. Results In this article, we extend the applicability of these phase computation methods to biochemical oscillators as discrete molecular systems, upon the information obtained from a continuous-state approximation of such oscillators. In particular, we describe techniques for computing the instantaneous phase of discrete, molecular oscillators for stochastic simulation algorithm generated sample paths. We comment on the accuracies and derive certain measures for assessing the feasibilities of the proposed phase computation methods. Phase computation experiments on the sample paths of well-known biological oscillators validate our analyses. Conclusions The impact of noise that arises from the discrete and random nature of the mechanisms that make up molecular oscillators can be characterized based on the phase computation techniques proposed in this article. The concept of isochrons is the natural choice upon which the phase notion of oscillators can be founded. The isochron-theoretic phase computation methods that we propose can be applied to discrete molecular oscillators of any dimension, provided that the oscillatory behavior observed in discrete-state does not vanish in a continuous-state approximation. Analysis of the full versatility of phase noise phenomena in molecular oscillators will be possible if a proper phase model theory is developed, without resorting to such approximations. PMID:22687330

Exploitation of Nontraditional Corp, Yacon, in Breast Cancer Prevention Using Preclinical Rat Model

DTIC Science & Technology

2011-07-01

liver glucose disposal evident along sorbitol, PPP, and hexosamine pathways. • Gut microbiome : A significant impact of diet on levels of...biochemicals reflecting metabolism of the gut microbiome was evident in plasma and liver and observed for several classes of metabolites. Biochemicals...acid metabolites reflecting activity of the gut microbiome contribute to host metabolic pathways and/or must be metabolized further by the liver

Systems biology of the structural proteome.

PubMed

Brunk, Elizabeth; Mih, Nathan; Monk, Jonathan; Zhang, Zhen; O'Brien, Edward J; Bliven, Spencer E; Chen, Ke; Chang, Roger L; Bourne, Philip E; Palsson, Bernhard O

2016-03-11

The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology. Here, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository ( https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/). Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism's genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.

The future of crystallography in drug discovery

PubMed Central

Zheng, Heping; Hou, Jing; Zimmerman, Matthew D; Wlodawer, Alexander; Minor, Wladek

2014-01-01

Introduction X-ray crystallography plays an important role in structure-based drug design (SBDD), and accurate analysis of crystal structures of target macromolecules and macromolecule–ligand complexes is critical at all stages. However, whereas there has been significant progress in improving methods of structural biology, particularly in X-ray crystallography, corresponding progress in the development of computational methods (such as in silico high-throughput screening) is still on the horizon. Crystal structures can be overinterpreted and thus bias hypotheses and follow-up experiments. As in any experimental science, the models of macromolecular structures derived from X-ray diffraction data have their limitations, which need to be critically evaluated and well understood for structure-based drug discovery. Areas covered This review describes how the validity, accuracy and precision of a protein or nucleic acid structure determined by X-ray crystallography can be evaluated from three different perspectives: i) the nature of the diffraction experiment; ii) the interpretation of an electron density map; and iii) the interpretation of the structural model in terms of function and mechanism. The strategies to optimally exploit a macromolecular structure are also discussed in the context of ‘Big Data’ analysis, biochemical experimental design and structure-based drug discovery. Expert opinion Although X-ray crystallography is one of the most detailed ‘microscopes’ available today for examining macromolecular structures, the authors would like to re-emphasize that such structures are only simplified models of the target macromolecules. The authors also wish to reinforce the idea that a structure should not be thought of as a set of precise coordinates but rather as a framework for generating hypotheses to be explored. Numerous biochemical and biophysical experiments, including new diffraction experiments, can and should be performed to verify or falsify these hypotheses. X-ray crystallography will find its future application in drug discovery by the development of specific tools that would allow realistic interpretation of the outcome coordinates and/or support testing of these hypotheses. PMID:24372145

Estimating Biochemical Parameters of Tea (camellia Sinensis (L.)) Using Hyperspectral Techniques

NASA Astrophysics Data System (ADS)

Bian, M.; Skidmore, A. K.; Schlerf, M.; Liu, Y.; Wang, T.

2012-07-01

Tea (Camellia Sinensis (L.)) is an important economic crop and the market price of tea depends largely on its quality. This research aims to explore the potential of hyperspectral remote sensing on predicting the concentration of biochemical components, namely total tea polyphenols, as indicators of tea quality at canopy scale. Experiments were carried out for tea plants growing in the field and greenhouse. Partial least squares regression (PLSR), which has proven to be the one of the most successful empirical approach, was performed to establish the relationship between reflectance and biochemical concentration across six tea varieties in the field. Moreover, a novel integrated approach involving successive projections algorithms as band selection method and neural networks was developed and applied to detect the concentration of total tea polyphenols for one tea variety, in order to explore and model complex nonlinearity relationships between independent (wavebands) and dependent (biochemicals) variables. The good prediction accuracies (r2 > 0.8 and relative RMSEP < 10 %) achieved for tea plants using both linear (partial lease squares regress) and nonlinear (artificial neural networks) modelling approaches in this study demonstrates the feasibility of using airborne and spaceborne sensors to cover wide areas of tea plantation for in situ monitoring of tea quality cheaply and rapidly.

Recent advances in mathematical modeling of developmental abnormalities using mechanistic information.

PubMed

Kavlock, R J

1997-01-01

During the last several years, significant changes in the risk assessment process for developmental toxicity of environmental contaminants have begun to emerge. The first of these changes is the development and beginning use of statistically based dose-response models [the benchmark dose (BMD) approach] that better utilize data derived from existing testing approaches. Accompanying this change is the greater emphasis placed on understanding and using mechanistic information to yield more accurate, reliable, and less uncertain risk assessments. The next stage in the evolution of risk assessment will be the use of biologically based dose-response (BBDR) models that begin to build into the statistically based models factors related to the underlying kinetic, biochemical, and/or physiologic processes perturbed by a toxicant. Such models are now emerging from several research laboratories. The introduction of quantitative models and the incorporation of biologic information into them has pointed to the need for even more sophisticated modifications for which we offer the term embryologically based dose-response (EBDR) models. Because these models would be based upon the understanding of normal morphogenesis, they represent a quantum leap in our thinking, but their complexity presents daunting challenges both to the developmental biologist and the developmental toxicologist. Implementation of these models will require extensive communication between developmental toxicologists, molecular embryologists, and biomathematicians. The remarkable progress in the understanding of mammalian embryonic development at the molecular level that has occurred over the last decade combined with advances in computing power and computational models should eventually enable these as yet hypothetical models to be brought into use.

Active cell-matrix coupling regulates cellular force landscapes of cohesive epithelial monolayers

NASA Astrophysics Data System (ADS)

Zhao, Tiankai; Zhang, Yao; Wei, Qiong; Shi, Xuechen; Zhao, Peng; Chen, Long-Qing; Zhang, Sulin

2018-03-01

Epithelial cells can assemble into cohesive monolayers with rich morphologies on substrates due to competition between elastic, edge, and interfacial effects. Here we present a molecularly based thermodynamic model, integrating monolayer and substrate elasticity, and force-mediated focal adhesion formation, to elucidate the active biochemical regulation over the cellular force landscapes in cohesive epithelial monolayers, corroborated by microscopy and immunofluorescence studies. The predicted extracellular traction and intercellular tension are both monolayer size and substrate stiffness dependent, suggestive of cross-talks between intercellular and extracellular activities. Our model sets a firm ground toward a versatile computational framework to uncover the molecular origins of morphogenesis and disease in multicellular epithelia.

Cooperative ethylene receptor signaling

PubMed Central

Liu, Qian; Wen, Chi-Kuang

2012-01-01

The gaseous plant hormone ethylene is perceived by a family of five ethylene receptor members in the dicotyledonous model plant Arabidopsis. Genetic and biochemical studies suggest that the ethylene response is suppressed by ethylene receptor complexes, but the biochemical nature of the receptor signal is unknown. Without appropriate biochemical measures to trace the ethylene receptor signal and quantify the signal strength, the biological significance of the modulation of ethylene responses by multiple ethylene receptors has yet to be fully addressed. Nevertheless, the ethylene receptor signal strength can be reflected by degrees in alteration of various ethylene response phenotypes and in expression levels of ethylene-inducible genes. This mini-review highlights studies that have advanced our understanding of cooperative ethylene receptor signaling. PMID:22827938

Potential of FTIR spectroscopy for analysis of tears for diagnosis purposes.

PubMed

Travo, Adrian; Paya, Clément; Déléris, Gérard; Colin, Joseph; Mortemousque, Bruno; Forfar, Isabelle

2014-04-01

It has been widely reported that the tear film, which is crucially important as a protective barrier of the eye, undergoes biochemical changes as a result of a wide range of ocular pathology. This tends to suggest the possibility of early detection of ocular diseases on the basis of biochemical analysis of tears. However, studies of tears by conventional methods of biomolecular and biochemical analysis are often limited by methodological difficulties. Moreover, such analysis could not be applied in the clinic, where structural and morphological analyses by, mainly, slit-lamp biomicroscopy remains the recommended method. In this study, we assessed, for the first time, the potential of FTIR spectroscopy combined with advanced chemometric processing of spectral data for analysis of raw tears for diagnosis purposes. We first optimized sampling and spectral acquisition (tears collection method, tear sample volume, and preservation of the samples) for accurate spectral measurement. On the basis of the results, we focused our study on the possibility of discriminating tears from normal individuals from those of patients with different ocular pathologies, and showed that the most discriminating spectral range is that corresponding to variations of CH2 and CH3 of lipid aliphatic chains. We also report more subtle discrimination of tears from patients with keratoconus and those from patients with non-specific inflammatory ocular diseases, on the basis of variations in spectral ranges attributed notably to lipid and carbohydrate vibrations. Finally, we also succeeded in distinguishing tears from patients with early-stage and late-stage keratoconus on the basis of spectral features attributed to protein structure. Therefore, this study strongly suggests that FTIR spectral analysis of tears could be developed as a valuable and cost-saving tool for biochemical-based detection of ocular diseases, potentially before the appearance of the first morphological signs of diseases. Combined with supervised modelling methods and with use of a spectral data base acquired for representative patients, such a spectral approach could be a useful addition to current methods of clinical analysis for improvement of patient care.

Temperature Sensitivities of Extracellular Enzyme Vmax and Km Across Thermal Environments

NASA Astrophysics Data System (ADS)

Allison, S. D.; Romero-Olivares, A.; Lu, Y.; Taylor, J.; Treseder, K. K.

2017-12-01

The magnitude and direction of carbon cycle feedbacks under climate warming remain uncertain due to insufficient knowledge about the temperature sensitivity of microbial processes in soil. Enzymatic rates could increase at higher temperatures, but this response is determined by multiple parameters that may change over time if soil microbes adapt to warming. We used the Michaelis-Menten relationship, the Arrhenius relationship, and biochemical transition state theory to construct hypotheses about the responses of extracellular enzyme Vmax and Km to temperature. Based on the Arrhenius relationship, we hypothesized that Vmax and Km would show positive temperature sensitivities. For enzymes from warmer environments, we expected to find lower Vmax, Km, and Km temperature sensitivity but higher Vmax temperature sensitivity. We tested these hypotheses with enzymes from isolates of the filamentous fungus Neurospora discreta collected around the globe and from decomposing leaf litter in a warming experiment in Alaskan boreal forest. Vmax and Km of most Neurospora extracellular enzymes were temperature sensitive with average Vmax Q10 ranging from 1.48 to 2.25 and Km Q10 ranging from 0.71 to 2.80. For both Vmax and Km, there was a tendency for the parameter to correlate negatively with its temperature sensitivity, a pattern predicted by biochemical theory. Also in agreement with theory, Vmax and Km were positively correlated for some enzymes. In contrast, there was little support for biochemical theory when comparing Vmax and Km across thermal environments. There was no relationship between temperature sensitivity of Vmax or Km and mean annual temperature of the isolation site for Neurospora strains. There was some evidence for greater Vmax under experimental warming in Alaskan litter, but the temperature sensitivities of Vmax and Km did not vary with warming as expected. We conclude that relationships among Vmax, Km, and temperature are largely consistent with biochemical theory, and our enzyme data should be useful for parameterizing trait-based models of microbial processes. However, theoretical predictions about adaptation to thermal environment were not supported by our data, suggesting that covarying edaphic and ecological factors may play a dominant role in soil enzyme responses to climate warming.

Biosensor-based small molecule fragment screening with biolayer interferometry

NASA Astrophysics Data System (ADS)

Wartchow, Charles A.; Podlaski, Frank; Li, Shirley; Rowan, Karen; Zhang, Xiaolei; Mark, David; Huang, Kuo-Sen

2011-07-01

Biosensor-based fragment screening is a valuable tool in the drug discovery process. This method is advantageous over many biochemical methods because primary hits can be distinguished from non-specific or non-ideal interactions by examining binding profiles and responses, resulting in reduced false-positive rates. Biolayer interferometry (BLI), a technique that measures changes in an interference pattern generated from visible light reflected from an optical layer and a biolayer containing proteins of interest, is a relatively new method for monitoring small molecule interactions. The BLI format is based on a disposable sensor that is immersed in 96-well or 384-well plates. BLI has been validated for small molecule detection and fragment screening with model systems and well-characterized targets where affinity constants and binding profiles are generally similar to those obtained with surface plasmon resonsance (SPR). Screens with challenging targets involved in protein-protein interactions including BCL-2, JNK1, and eIF4E were performed with a fragment library of 6,500 compounds, and hit rates were compared for these targets. For eIF4E, a protein containing a PPI site and a nucleotide binding site, results from a BLI fragment screen were compared to results obtained in biochemical HTS screens. Overlapping hits were observed for the PPI site, and hits unique to the BLI screen were identified. Hit assessments with SPR and BLI are described.

[Effect of Novonukutskaya mineral water extracts on the clinical course of wound disease and wound healing in experiment].

PubMed

Malyshkina, N A; Iushkov, G G; Benemanskiĭ, V V; Shpeĭzer, G M; Khutorianskiĭ, V A; Smirnov, A I; Rodionova, V A; Mineeva, L A

2010-01-01

The objective of the present work was to study the wound-healing potential of the new preparation, Extramin (a 32% ethanol extract of organic substances from Novonukutskaya mineral water) in a series of experiments on a model of chemical burns in rabbits. The wound healing process was monitored based on biochemical, hematiological, and morphological indicators. Analysis of the results allows for the conclusion that Extramin is a powerful stimulator of the wound-healing processes and can be recommended for further clinical studies.

The molecular biology of inflammatory bowel diseases.

PubMed

Corfield, Anthony P; Wallace, Heather M; Probert, Chris S J

2011-08-01

IBDs (inflammatory bowel diseases) are a group of diseases affecting the gastrointestinal tract. The diseases are multifactorial and cover genetic aspects: susceptibility genes, innate and adaptive responses to inflammation, and structure and efficacy of the mucosal protective barrier. Animal models of IBD have been developed to gain further knowledge of the disease mechanisms. These topics form an overlapping background to enable an improved understanding of the molecular features of these diseases. A series of articles is presented based on the topics covered at the Biochemical Society Focused Meeting The Molecular Biology of Inflammatory Bowel Diseases.

The role of networks and artificial intelligence in nanotechnology design and analysis.

PubMed

Hudson, D L; Cohen, M E

2004-05-01

Techniques with their origins in artificial intelligence have had a great impact on many areas of biomedicine. Expert-based systems have been used to develop computer-assisted decision aids. Neural networks have been used extensively in disease classification and more recently in many bioinformatics applications including genomics and drug design. Network theory in general has proved useful in modeling all aspects of biomedicine from healthcare organizational structure to biochemical pathways. These methods show promise in applications involving nanotechnology both in the design phase and in interpretation of system functioning.

How genetic errors in GPCRs affect their function: Possible therapeutic strategies

PubMed Central

Stoy, Henriette; Gurevich, Vsevolod V.

2015-01-01

Activating and inactivating mutations in numerous human G protein-coupled receptors (GPCRs) are associated with a wide range of disease phenotypes. Here we use several class A GPCRs with a particularly large set of identified disease-associated mutations, many of which were biochemically characterized, along with known GPCR structures and current models of GPCR activation, to understand the molecular mechanisms yielding pathological phenotypes. Based on this mechanistic understanding we also propose different therapeutic approaches, both conventional, using small molecule ligands, and novel, involving gene therapy. PMID:26229975

Proteomic Analysis to Identify Functional Molecules in Drug Resistance Caused by E-Cadherin Knockdown in 3D-Cultured Colorectal Cancer Models

DTIC Science & Technology

2013-09-01

REFERENCES (1) Harsha, H. C.; Pandey, A. Phosphoproteomics in cancer. Mol. Oncol. 2010, 4 (6), 482−95. (2) Iliuk , A.; Liu, X. S.; Xue, L.; Liu, X...based proteomics and peptidomics for biomarker discovery in neurodegenerative diseases. Int. J. Clin. Exp. Pathol. 2009, 2 (2), 132−48. (4) Iliuk , A...phosphoproteins by immobilized metal (Fe3+) affinity chromatography. Anal. Biochem. 1986, 154 (1), 250−4. (6) Iliuk , A. B.; Martin, V. A.; Alicie, B. M

ECOPASS - a multivariate model used as an index of growth performance of poplar clones

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

Ceulemans, R.; Impens, I.

The model (ECOlogical PASSport) reported was constructed by principal component analysis from a combination of biochemical, anatomical/morphological and ecophysiological gas exchange parameters measured on 5 fast growing poplar clones. Productivity data were 10 selected trees in 3 plantations in Belgium and given as m.a.i.(b.a.). The model is shown to be able to reflect not only genetic origin and the relative effects of the different parameters of the clones, but also their production potential. Multiple regression analysis of the 4 principal components showed a high cumulative correlation (96%) between the 3 components related to ecophysiological, biochemical and morphological parameters, and productivity;more » the ecophysiological component alone correlated 85% with productivity.« less

Progress in understanding the neuronal SNARE function and its regulation.

PubMed

Yoon, T-Y; Shin, Y-K

2009-02-01

Vesicle budding and fusion underlies many essential biochemical deliveries in eukaryotic cells, and its core fusion machinery is thought to be built on one protein family named soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE). Recent technical advances based on site-directed fluorescence labelling and nano-scale detection down to the single-molecule level rapidly unveiled the protein and the lipid intermediates along the fusion pathway as well as the molecular actions of fusion effectors. Here we summarize these new exciting findings in context with a new mechanistic model that reconciles two existing fusion models: the proteinaceous pore model and the hemifusion model. Further, we attempt to locate the points of action for the fusion effectors along the fusion pathway and to delineate the energetic interplay between the SNARE complexes and the fusion effectors.

Emergent cell and tissue dynamics from subcellular modeling of active biomechanical processes

NASA Astrophysics Data System (ADS)

Sandersius, S. A.; Weijer, C. J.; Newman, T. J.

2011-08-01

Cells and the tissues they form are not passive material bodies. Cells change their behavior in response to external biochemical and biomechanical cues. Behavioral changes, such as morphological deformation, proliferation and migration, are striking in many multicellular processes such as morphogenesis, wound healing and cancer progression. Cell-based modeling of these phenomena requires algorithms that can capture active cell behavior and their emergent tissue-level phenotypes. In this paper, we report on extensions of the subcellular element model to model active biomechanical subcellular processes. These processes lead to emergent cell and tissue level phenotypes at larger scales, including (i) adaptive shape deformations in cells responding to slow stretching, (ii) viscous flow of embryonic tissues, and (iii) streaming patterns of chemotactic cells in epithelial-like sheets. In each case, we connect our simulation results to recent experiments.

XML Encoding of Features Describing Rule-Based Modeling of Reaction Networks with Multi-Component Molecular Complexes

PubMed Central

Blinov, Michael L.; Moraru, Ion I.

2011-01-01

Multi-state molecules and multi-component complexes are commonly involved in cellular signaling. Accounting for molecules that have multiple potential states, such as a protein that may be phosphorylated on multiple residues, and molecules that combine to form heterogeneous complexes located among multiple compartments, generates an effect of combinatorial complexity. Models involving relatively few signaling molecules can include thousands of distinct chemical species. Several software tools (StochSim, BioNetGen) are already available to deal with combinatorial complexity. Such tools need information standards if models are to be shared, jointly evaluated and developed. Here we discuss XML conventions that can be adopted for modeling biochemical reaction networks described by user-specified reaction rules. These could form a basis for possible future extensions of the Systems Biology Markup Language (SBML). PMID:21464833

Developing a new intelligent system for the diagnosis of tuberculous pleural effusion.

PubMed

Li, Chengye; Hou, Lingxian; Sharma, Bishundat Yanesh; Li, Huaizhong; Chen, ChengShui; Li, Yuping; Zhao, Xuehua; Huang, Hui; Cai, Zhennao; Chen, Huiling

2018-01-01

In countries with high prevalence of tuberculosis (TB), clinicians often diagnose tuberculous pleural effusion (TPE) by using diagnostic tests, which have not only poor sensitivity, but poor availability as well. The aim of our study is to develop a new artificial intelligence based diagnostic model that is accurate, fast, non-invasive and cost effective to diagnose TPE. It is expected that a tool derived based on the model be installed on simple computer devices (such as smart phones and tablets) and be used by clinicians widely. For this study, data of 140 patients whose clinical signs, routine blood test results, blood biochemistry markers, pleural fluid cell type and count, and pleural fluid biochemical tests' results were prospectively collected into a database. An Artificial intelligence based diagnostic model, which employs moth flame optimization based support vector machine with feature selection (FS-MFO-SVM), is constructed to predict the diagnosis of TPE. The optimal model results in an average of 95% accuracy (ACC), 0.9564 the area under the receiver operating characteristic curve (AUC), 93.35% sensitivity, and 97.57% specificity for FS-MFO-SVM. The proposed artificial intelligence based diagnostic model is found to be highly reliable for diagnosing TPE based on simple clinical signs, blood samples and pleural effusion samples. Therefore, the proposed model can be widely used in clinical practice and further evaluated for use as a substitute of invasive pleural biopsies. Copyright © 2017 Elsevier B.V. All rights reserved.

The Underestimated Role of Mechanical Stimuli in Brain Diseases and the Relate d In Vitro Models.

PubMed

Guo, Tingwang; Ren, Peng; Hao, Shilei; Wang, Bochu

2017-01-01

Besides the well-documented biochemical and electrophysiological effects, the mechanical stimuli also have prominent roles in the initiation and development of brain diseases but yet have been underestimated. To explore the role of mechanical stimuli and the followed mechanical-biochemical effects in the brain diseases. In this review, we discussed the initiation and effect of mechanical stimuli and the surrounding topography in brain diseases, especially for the intracerebral hemorrhage (ICH), Alzheimer's disease (AD), diffuse axonal injury (DAI) and primary brain tumors. The induced cascades of biological pathways by mechanical stimuli prior to and during the brain diseases were summarized. Strategies aiming to reduce the mechanical stimuli related damages or poor outcomes were also discussed, despite some could only prevent rather than cure. Literatures have indicated mechanical stimuli were the connection between the exogenous mechanotransduction and the inherent biochemical cascades. Therefore, we also reviewed in vitro models in the literatures that simulated the diverse range of mechanical stimuli, which connected the neural network with the tissue engineering, biomaterials and potential therapeutic strategies together. At the microscopic and macroscopic levels, the hydrostatic pressure, tensile/compressive force, shear force, and even the roughness of topography from the physical surrounding exert the influence on the neural network not only by themselves but also through the interaction with other factors, e.g. biochemical or electrophysiological effects. In the clinical management, taking the undervalued mechanical stimuli and the followed mechanical- biochemical effects into consideration are important and inevitable in preventing and treating brain diseases. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A Biochemical Oscillator Using Excitatory Molecules for Nanonetworks.

PubMed

Shitiri, Ethungshan; Cho, Ho-Shin

2016-10-01

For nanonetworks to be able to achieve large-scale functionality, such as to respond collectively to a trigger, synchrony between nanomachines is essential. However, to facilitate synchronization, some sort of physical clocking mechanism is required, such as the oscillators driven by auto-inhibitory molecules or by auto-inducing molecules. In this study, taking inspiration from the widely studied biological oscillatory phenomena called Calcium (Ca 2+ ) oscillations, we undertake a different approach to design an oscillator. Our model employs three different types of excitatory molecules that work in tandem to generate oscillatory phenomenon in the concentration levels of the molecule of interest. The main objective of the study is to model a high frequency biochemical oscillator, along with the investigations to identify and determine the parameters that affect the period of the oscillations. The investigations entail and highlight the design of the reserve unit, a reservoir of the molecule of interest, as a key factor in realizing a high frequency stable biochemical oscillator.

Prostate cancer: role of pretreatment multiparametric 3-T MRI in predicting biochemical recurrence after radical prostatectomy.

PubMed

Park, Jung Jae; Kim, Chan Kyo; Park, Sung Yoon; Park, Byung Kwan; Lee, Hyun Moo; Cho, Seong Whi

2014-05-01

The purpose of this study is to retrospectively investigate whether pretreatment multiparametric MRI findings can predict biochemical recurrence in patients who underwent radical prostatectomy (RP) for localized prostate cancer. In this study, 282 patients with biopsy-proven prostate cancer who received RP underwent pretreatment MRI using a phased-array coil at 3 T, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI). MRI variables included apparent tumor presence on combined imaging sequences, extracapsular extension, and tumor size on DWI or DCE-MRI. Clinical variables included baseline prostate-specific antigen (PSA) level, clinical stage, and Gleason score at biopsy. The relationship between clinical and imaging variables and biochemical recurrence was evaluated using Cox regression analysis. After a median follow-up of 26 months, biochemical recurrence developed in 61 patients (22%). Univariate analysis revealed that all the imaging and clinical variables were significantly associated with biochemical recurrence (p < 0.01). On multivariate analysis, however, baseline PSA level (p = 0.002), Gleason score at biopsy (p = 0.024), and apparent tumor presence on combined T2WI, DWI, and DCE-MRI (p = 0.047) were the only significant independent predictors of biochemical recurrence. Of the independent predictors, apparent tumor presence on combined T2WI, DWI, and DCE-MRI showed the highest hazard ratio (2.38) compared with baseline PSA level (hazard ratio, 1.05) and Gleason score at biopsy (hazard ratio, 1.34). The apparent tumor presence on combined T2WI, DWI, and DCE-MRI of pretreatment MRI is an independent predictor of biochemical recurrence after RP. This finding may be used to construct a predictive model for biochemical recurrence after surgery.

Reassessment of the risk factors for biochemical recurrence in D'Amico intermediate-risk prostate cancer treated using radical prostatectomy.

PubMed

Narita, Shintaro; Mitsuzuka, Koji; Tsuchiya, Norihiko; Koie, Takuya; Kawamura, Sadafumi; Ohyama, Chikara; Tochigi, Tatsuo; Yamaguchi, Takuhiro; Arai, Yoichi; Habuchi, Tomonori

2015-11-01

To assess the risk factors for biochemical recurrence in D'Amico intermediate-risk prostate cancer patients treated using radical prostatectomy. We retrospectively reviewed the medical records of 1268 men with prostate cancer treated using radical prostatectomy without neoadjuvant therapy. The association between various risk factors and biochemical recurrence was then statistically evaluated. The Kaplan-Meier method, log-rank tests and Cox proportional hazards models were used for statistical analysis. In the intermediate-risk group, 96 patients (14.5%) experienced biochemical recurrence during a median follow up of 41 months. In the intermediate-risk group, preoperative prostate-specific antigen level, prostate volume and prostate-specific antigen density were significant preoperative risk factors for biochemical recurrence, whereas other factors including age, primary Gleason 4, clinical stage >T2 and percentage of positive biopsies were not. In multivariate analysis, higher preoperative prostate-specific antigen level and density, and a smaller prostate volume were independent risk factors for biochemical recurrence in the intermediate-risk group. Biochemical recurrence-free survival of patients in the intermediate-risk group with a higher prostate-specific antigen level and density (≥15 ng/mL, ≥0.6 ng/mL/cm(3), respectively), and lower prostate volume (≤10 mL) was comparable with that of high-risk group individuals (P = 0.632, 0.494 and 0.961, respectively). Preoperative prostate-specific antigen, prostate volume and prostate-specific antigen density are significant risk factors for biochemical recurrence in D'Amico intermediate-risk prostate cancer patients treated using radical prostatectomy. Using these variables, a subset of the intermediate-risk patients can be identified as having equivalent outcomes to high-risk patients. © 2015 The Japanese Urological Association.

Computer Simulation in Predicting Biochemical Processes and Energy Balance at WWTPs

NASA Astrophysics Data System (ADS)

Drewnowski, Jakub; Zaborowska, Ewa; Hernandez De Vega, Carmen

2018-02-01

Nowadays, the use of mathematical models and computer simulation allow analysis of many different technological solutions as well as testing various scenarios in a short time and at low financial budget in order to simulate the scenario under typical conditions for the real system and help to find the best solution in design or operation process. The aim of the study was to evaluate different concepts of biochemical processes and energy balance modelling using a simulation platform GPS-x and a comprehensive model Mantis2. The paper presents the example of calibration and validation processes in the biological reactor as well as scenarios showing an influence of operational parameters on the WWTP energy balance. The results of batch tests and full-scale campaign obtained in the former work were used to predict biochemical and operational parameters in a newly developed plant model. The model was extended with sludge treatment devices, including anaerobic digester. Primary sludge removal efficiency was found as a significant factor determining biogas production and further renewable energy production in cogeneration. Water and wastewater utilities, which run and control WWTP, are interested in optimizing the process in order to save environment, their budget and decrease the pollutant emissions to water and air. In this context, computer simulation can be the easiest and very useful tool to improve the efficiency without interfering in the actual process performance.

Development of transplantable human chordoma xenograft for preclinical assessment of novel therapeutic strategies.

PubMed

Bozzi, Fabio; Manenti, Giacomo; Conca, Elena; Stacchiotti, Silvia; Messina, Antonella; Dagrada, GianPaolo; Gronchi, Alessandro; Panizza, Pietro; Pierotti, Marco A; Tamborini, Elena; Pilotti, Silvana

2014-01-01

Chordomas are rare and indolent bone tumors that arise in the skull base and mobile spine. Distant metastases occur in >20% of cases, but morbidity and mortality are mainly related to local relapses that affect the majority of patients. Standard chemotherapy has modest activity, whereas new targeted therapies (alone or in combination) have some activity in controlling disease progression. However, the scarcity of preclinical models capable of testing in vivo responses to these therapies hampers the development of new medical strategies. In this study, 8 chordoma samples taken from 8 patients were implanted in nude mice. Four engrafted successfully and gave rise to tumor masses that were analyzed histologically, by means of fluorescence in situ hybridization and biochemical techniques. The data relating to each of the mouse tumors were compared with those obtained from the corresponding human tumor. All 4 engraftments retained the histological, genetic and biochemical features of the human tumors they came from. In one epidermal growth factor receptor(EGFR)-positive xenograft, responsiveness to lapatinib was evaluated by comparing the pre- and post treatment findings. The treatment induced a low-level, heterogeneous switching off of EGFR and its downstream signaling effectors. Overall, this model is very close to human chordoma and represents a new means of undertaking preclinical investigations and developing tailored therapies.

A cellular reporter to evaluate CRM1 nuclear export activity: functional analysis of the cancer-related mutant E571K.

PubMed

García-Santisteban, Iraia; Arregi, Igor; Alonso-Mariño, Marián; Urbaneja, María A; Garcia-Vallejo, Juan J; Bañuelos, Sonia; Rodríguez, Jose A

2016-12-01

The exportin CRM1 binds nuclear export signals (NESs), and mediates active transport of NES-bearing proteins from the nucleus to the cytoplasm. Structural and biochemical analyses have uncovered the molecular mechanisms underlying CRM1/NES interaction. CRM1 binds NESs through a hydrophobic cleft, whose open or closed conformation facilitates NES binding and release. Several cofactors allosterically modulate the conformation of the NES-binding cleft through intramolecular interactions involving an acidic loop and a C-terminal helix in CRM1. This current model of CRM1-mediated nuclear export has not yet been evaluated in a cellular setting. Here, we describe SRV100, a cellular reporter to interrogate CRM1 nuclear export activity. Using this novel tool, we provide evidence further validating the model of NES binding and release by CRM1. Furthermore, using both SRV100-based cellular assays and in vitro biochemical analyses, we investigate the functional consequences of a recurrent cancer-related mutation, which targets a residue near CRM1 NES-binding cleft. Our data indicate that this mutation does not necessarily abrogate the nuclear export activity of CRM1, but may increase its affinity for NES sequences bearing a more negatively charged C-terminal end.

Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming

PubMed Central

Colins, Andrea; Gerdtzen, Ziomara P.; Nuñez, Marco T.; Salgado, J. Cristian

2017-01-01

Iron is a trace metal, key for the development of living organisms. Its absorption process is complex and highly regulated at the transcriptional, translational and systemic levels. Recently, the internalization of the DMT1 transporter has been proposed as an additional regulatory mechanism at the intestinal level, associated to the mucosal block phenomenon. The short-term effect of iron exposure in apical uptake and initial absorption rates was studied in Caco-2 cells at different apical iron concentrations, using both an experimental approach and a mathematical modeling framework. This is the first report of short-term studies for this system. A non-linear behavior in the apical uptake dynamics was observed, which does not follow the classic saturation dynamics of traditional biochemical models. We propose a method for developing mathematical models for complex systems, based on a genetic programming algorithm. The algorithm is aimed at obtaining models with a high predictive capacity, and considers an additional parameter fitting stage and an additional Jackknife stage for estimating the generalization error. We developed a model for the iron uptake system with a higher predictive capacity than classic biochemical models. This was observed both with the apical uptake dataset used for generating the model and with an independent initial rates dataset used to test the predictive capacity of the model. The model obtained is a function of time and the initial apical iron concentration, with a linear component that captures the global tendency of the system, and a non-linear component that can be associated to the movement of DMT1 transporters. The model presented in this paper allows the detailed analysis, interpretation of experimental data, and identification of key relevant components for this complex biological process. This general method holds great potential for application to the elucidation of biological mechanisms and their key components in other complex systems. PMID:28072870

Analytical modeling of drug dynamics induced by eluting stents in the coronary multi-layered curved domain.

PubMed

d'Errico, Michele; Sammarco, Paolo; Vairo, Giuseppe

2015-09-01

Pharmacokinetics induced by drug eluting stents (DES) in coronary walls is modeled by means of a one-dimensional multi-layered model, accounting for vessel curvature and non-homogeneous properties of the arterial tissues. The model includes diffusion mechanisms, advection effects related to plasma filtration through the walls, and bio-chemical drug reactions. A non-classical Sturm-Liouville problem with discontinuous coefficients is derived, whose closed-form analytical solution is obtained via an eigenfunction expansion. Soundness and consistency of the proposed approach are shown by numerical computations based on possible clinical treatments involving both hydrophilic and hydrophobic drugs. The influence of the main model parameters on drug delivery mechanisms is analyzed, highlighting the effects induced by vessel curvature and yielding comparative indications and useful insights into the concurring mechanisms governing the pharmacokinetics. Copyright © 2015. Published by Elsevier Inc.

Biomimetic strategies for the glioblastoma microenvironment

NASA Astrophysics Data System (ADS)

Cha, Junghwa; Kim, Pilnam

2017-12-01

Glioblastoma multiforme (GBM) is a devastating type of tumor with high mortality, caused by extensive infiltration into adjacent tissue and rapid recurrence. Most therapies for GBM have focused on the cytotoxicity, and have not targeted GBM spread. However, there have been numerous attempts to improve therapy by addressing GBM invasion, through understanding and mimicking its behavior using three-dimensional (3D) experimental models. Compared with two-dimensional models and in vivo animal models, 3D GBM models can capture the invasive motility of glioma cells within a 3D environment comprising many cellular and non-cellular components. Based on tissue engineering techniques, GBM invasion has been investigated within a biologically relevant environment, from biophysical and biochemical perspectives, to clarify the pro-invasive factors of GBM. This review discusses the recent progress in techniques for modeling the microenvironments of GBM tissue and suggests future directions with respect to recreating the GBM microenvironment and preclinical applications.

Cancer growth and metastasis as a metaphor of Go gaming: An Ising model approach

PubMed Central

Barradas-Bautista, Didier; Agostino, Mark; Cocho, Germinal

2018-01-01

This work aims for modeling and simulating the metastasis of cancer, via the analogy between the cancer process and the board game Go. In the game of Go, black stones that play first could correspond to a metaphor of the birth, growth, and metastasis of cancer. Moreover, playing white stones on the second turn could correspond the inhibition of cancer invasion. Mathematical modeling and algorithmic simulation of Go may therefore benefit the efforts to deploy therapies to surpass cancer illness by providing insight into the cellular growth and expansion over a tissue area. We use the Ising Hamiltonian, that models the energy exchange in interacting particles, for modeling the cancer dynamics. Parameters in the energy function refer the biochemical elements that induce cancer birth, growth, and metastasis; as well as the biochemical immune system process of defense. PMID:29718932

Hybrid ODE/SSA methods and the cell cycle model

NASA Astrophysics Data System (ADS)

Wang, S.; Chen, M.; Cao, Y.

2017-07-01

Stochastic effect in cellular systems has been an important topic in systems biology. Stochastic modeling and simulation methods are important tools to study stochastic effect. Given the low efficiency of stochastic simulation algorithms, the hybrid method, which combines an ordinary differential equation (ODE) system with a stochastic chemically reacting system, shows its unique advantages in the modeling and simulation of biochemical systems. The efficiency of hybrid method is usually limited by reactions in the stochastic subsystem, which are modeled and simulated using Gillespie's framework and frequently interrupt the integration of the ODE subsystem. In this paper we develop an efficient implementation approach for the hybrid method coupled with traditional ODE solvers. We also compare the efficiency of hybrid methods with three widely used ODE solvers RADAU5, DASSL, and DLSODAR. Numerical experiments with three biochemical models are presented. A detailed discussion is presented for the performances of three ODE solvers.

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

The markup is the model: reasoning about systems biology models in the Semantic Web era.

PubMed

Kell, Douglas B; Mendes, Pedro

2008-06-07

Metabolic control analysis, co-invented by Reinhart Heinrich, is a formalism for the analysis of biochemical networks, and is a highly important intellectual forerunner of modern systems biology. Exchanging ideas and exchanging models are part of the international activities of science and scientists, and the Systems Biology Markup Language (SBML) allows one to perform the latter with great facility. Encoding such models in SBML allows their distributed analysis using loosely coupled workflows, and with the advent of the Internet the various software modules that one might use to analyze biochemical models can reside on entirely different computers and even on different continents. Optimization is at the core of many scientific and biotechnological activities, and Reinhart made many major contributions in this area, stimulating our own activities in the use of the methods of evolutionary computing for optimization.

Biochemical and nutritional components of selected honey samples.

PubMed

Chua, Lee Suan; Adnan, Nur Ardawati

2014-01-01

The purpose of this study was to investigate the relationship of biochemical (enzymes) and nutritional components in the selected honey samples from Malaysia. The relationship is important to estimate the quality of honey based on the concentration of these nutritious components. Such a study is limited for honey samples from tropical countries with heavy rainfall throughout the year. A number of six honey samples that commonly consumed by local people were collected for the study. Both the biochemical and nutritional components were analysed by using standard methods from Association of Official Analytical Chemists (AOAC). Individual monosaccharides, disaccharides and 17 amino acids in honey were determined by using liquid chromatographic method. The results showed that the peroxide activity was positively correlated with moisture content (r = 0.8264), but negatively correlated with carbohydrate content (r = 0.7755) in honey. The chromatographic sugar and free amino acid profiles showed that the honey samples could be clustered based on the type and maturity of honey. Proline explained for 64.9% of the total variance in principle component analysis (PCA). The correlation between honey components and honey quality has been established for the selected honey samples based on their biochemical and nutritional concentrations. PCA results revealed that the ratio of sucrose to maltose could be used to measure honey maturity, whereas proline was the marker compound used to distinguish honey either as floral or honeydew.

Smoldyn: particle-based simulation with rule-based modeling, improved molecular interaction and a library interface.

PubMed

Andrews, Steven S

2017-03-01

Smoldyn is a spatial and stochastic biochemical simulator. It treats each molecule of interest as an individual particle in continuous space, simulating molecular diffusion, molecule-membrane interactions and chemical reactions, all with good accuracy. This article presents several new features. Smoldyn now supports two types of rule-based modeling. These are a wildcard method, which is very convenient, and the BioNetGen package with extensions for spatial simulation, which is better for complicated models. Smoldyn also includes new algorithms for simulating the diffusion of surface-bound molecules and molecules with excluded volume. Both are exact in the limit of short time steps and reasonably good with longer steps. In addition, Smoldyn supports single-molecule tracking simulations. Finally, the Smoldyn source code can be accessed through a C/C ++ language library interface. Smoldyn software, documentation, code, and examples are at http://www.smoldyn.org . steven.s.andrews@gmail.com. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Increase in the CO2 exchange rate of leaves of Ilex rotunda with elevated atmospheric CO2 concentration in an urban canyon

NASA Astrophysics Data System (ADS)

Takagi, M.; Gyokusen, Koichiro; Saito, Akira

It was found that the atmospheric carbon dioxide (CO2) concentration in an urban canyon in Fukuoka city, Japan during August 1997 was about 30 µmol mol-1 higher than that in the suburbs. When fully exposed to sunlight, in situ the rate of photosynthesis in single leaves of Ilex rotunda planted in the urban canyon was higher when the atmospheric CO2 concentration was elevated. A biochemically based model was able to predict the in situ rate of photosynthesis well. The model also predicted an increase in the daily CO2 exchange rate for leaves in the urban canyon with an increase in atmospheric CO2 concentration. However, in situ such an increase in the daily CO2 exchange rate may be offset by diminished sunlight, a higher air temperature and a lower relative humidity. Thus, the daily CO2 exchange rate predicted using the model based soleley on the environmental conditions prevailing in the urban canyon was lower than that predicted based only on environmental factors found in the suburbs.

A Bayesian alternative for multi-objective ecohydrological model specification

NASA Astrophysics Data System (ADS)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior distributions in such approaches.

Single-molecule detection: applications to ultrasensitive biochemical analysis

NASA Astrophysics Data System (ADS)

Castro, Alonso; Shera, E. Brooks

1995-06-01

Recent developments in laser-based detection of fluorescent molecules have made possible the implementation of very sensitive techniques for biochemical analysis. We present and discuss our experiments on the applications of our recently developed technique of single-molecule detection to the analysis of molecules of biological interest. These newly developed methods are capable of detecting and identifying biomolecules at the single-molecule level of sensitivity. In one case, identification is based on measuring fluorescence brightness from single molecules. In another, molecules are classified by determining their electrophoretic velocities.

Activity-based protein profiling for biochemical pathway discovery in cancer

PubMed Central

Nomura, Daniel K.; Dix, Melissa M.; Cravatt, Benjamin F.

2011-01-01

Large-scale profiling methods have uncovered numerous gene and protein expression changes that correlate with tumorigenesis. However, determining the relevance of these expression changes and which biochemical pathways they affect has been hindered by our incomplete understanding of the proteome and its myriad functions and modes of regulation. Activity-based profiling platforms enable both the discovery of cancer-relevant enzymes and selective pharmacological probes to perturb and characterize these proteins in tumour cells. When integrated with other large-scale profiling methods, activity-based proteomics can provide insight into the metabolic and signalling pathways that support cancer pathogenesis and illuminate new strategies for disease diagnosis and treatment. PMID:20703252

WATER QUALITY MODELING IN THE RIO CHONE ESTUARY

EPA Science Inventory

Water quality in the Rio Chone Estuary, a seasonally inverse, tropical estuary, in Ecuador was characterized by modeling the distribution of biochemical oxygen demand (BOD) and dissolved inorganic nitrogen (DIN) within the water column. These two variables are modeled using modif...

Structure-Guided Design and Optimization of Dipeptidyl Inhibitors of Norovirus 3CL Protease. Structure–Activity Relationships and Biochemical, X-ray Crystallographic, Cell-Based, and In Vivo Studies

DOE PAGES

Galasiti Kankanamalage, Anushka C.; Kim, Yunjeong; Weerawarna, Pathum M.; ...

2015-04-09

Norovirus infection constitutes the primary cause of acute viral gastroenteritis. There are currently no vaccines or norovirus-specific antiviral therapeutics available for the management of norovirus infection. Norovirus 3C-like protease is essential for viral replication, consequently, inhibition of this enzyme is a fruitful avenue of investigation that may lead to the emergence of antinorovirus therapeutics. We describe herein the optimization of dipeptidyl inhibitors of norovirus 3C-like protease using iterative SAR, X-ray crystallographic, and enzyme and cell-based studies. We also demonstrate herein in vivo efficacy of an inhibitor using the murine model of norovirus infection.

Chemical networks with inflows and outflows: a positive linear differential inclusions approach.

PubMed

Angeli, David; De Leenheer, Patrick; Sontag, Eduardo D

2009-01-01

Certain mass-action kinetics models of biochemical reaction networks, although described by nonlinear differential equations, may be partially viewed as state-dependent linear time-varying systems, which in turn may be modeled by convex compact valued positive linear differential inclusions. A result is provided on asymptotic stability of such inclusions, and applied to a ubiquitous biochemical reaction network with inflows and outflows, known as the futile cycle. We also provide a characterization of exponential stability of general homogeneous switched systems which is not only of interest in itself, but also plays a role in the analysis of the futile cycle. 2009 American Institute of Chemical Engineers

A limit cycle oscillator model for cycling mood variations of bipolar disorder patients derived from cellular biochemical reaction equations

NASA Astrophysics Data System (ADS)

Frank, T. D.

2013-08-01

We derive a nonlinear limit cycle model for oscillatory mood variations as observed in patients with cycling bipolar disorder. To this end, we consider two signaling pathways leading to the activation of two enzymes that play a key role for cellular and neural processes. We model pathway cross-talk in terms of an inhibitory impact of the first pathway on the second and an excitatory impact of the second on the first. The model also involves a negative feedback loop (inhibitory self-regulation) for the first pathway and a positive feedback loop (excitatory self-regulation) for the second pathway. We demonstrate that due to the cross-talk the biochemical dynamics is described by an oscillator equation. Under disease-free conditions the oscillatory system exhibits a stable fixed point. The breakdown of the self-inhibition of the first pathway at higher concentration levels is studied by means of a scalar control parameter ξ, where ξ equal to zero refers to intact self-inhibition at all concentration levels. Under certain conditions, stable limit cycle solutions emerge at critical parameter values of ξ larger than zero. These oscillations mimic pathological cycling mood variations that emerge due to a disease-induced bifurcation. Consequently, our modeling analysis supports the notion of bipolar disorder as a dynamical disease. In addition, our study establishes a connection between mechanistic biochemical modeling of bipolar disorder and phenomenological nonlinear oscillator approaches to bipolar disorder suggested in the literature.

BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

PubMed Central

2010-01-01

Background Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification. Description BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various formats. Reaction network diagrams generated from the models are also available in several formats. BioModels Database also provides features such as online simulation and the extraction of components from large scale models into smaller submodels. Finally, the system provides a range of web services that external software systems can use to access up-to-date data from the database. Conclusions BioModels Database has become a recognised reference resource for systems biology. It is being used by the community in a variety of ways; for example, it is used to benchmark different simulation systems, and to study the clustering of models based upon their annotations. Model deposition to the database today is advised by several publishers of scientific journals. The models in BioModels Database are freely distributed and reusable; the underlying software infrastructure is also available from SourceForge https://sourceforge.net/projects/biomodels/ under the GNU General Public License. PMID:20587024

Ocean Acidification Portends Acute Habitat Compression for Atlantic Cod (Gadus morhua) in a Physiologically-informed Metabolic Rate Model

NASA Astrophysics Data System (ADS)

Del Raye, G.; Weng, K.

2011-12-01

Ocean acidification affects organisms on a biochemical scale, yet its societal impacts manifest from changes that propagate through entire populations. Successful forecasting of the effects of ocean acidification therefore depends on at least two steps: (1) deducing systemic physiology based on subcellular stresses and (2) scaling individual physiology up to ecosystem processes. Predictions that are based on known biological processes (process-based models) may fare better than purely statistical models in both these steps because the latter are less robust to novel environmental conditions. Here we present a process-based model that uses temperature, pO2, and pCO2 to predict maximal aerobic scope in Atlantic cod. Using this model, we show that (i) experimentally-derived physiological parameters are sufficient to capture the response of cod aerobic scope to temperature and oxygen, and (ii) subcellular pH effects can be used to predict the systemic physiological response of cod to an acidified ocean. We predict that acute pH stress (on a scale of hours) could limit the mobility of Atlantic cod during diel vertical migration across a pCO2 gradient, promoting habitat compression. Finally, we use a global sensitivity analysis to identify opportunities for the improvement of model uncertainty as well as some physiological adaptations that could mitigate climate stresses on cod in the future.

Pre-clinical methods for detecting the hypersensitivity potential of pharmaceuticals: regulatory considerations.

PubMed

Hastings, K L

2001-02-02

Immune-based systemic hypersensitivities account for a significant number of adverse drug reactions. There appear to be no adequate nonclinical models to predict systemic hypersensitivity to small molecular weight drugs. Although there are very good methods for detecting drugs that can induce contact sensitization, these have not been successfully adapted for prediction of systemic hypersensitivity. Several factors have made the development of adequate models difficult. The term systemic hypersensitivity encompases many discrete immunopathologies. Each type of immunopathology presumably is the result of a specific cluster of immunologic and biochemical phenomena. Certainly other factors, such as genetic predisposition, metabolic idiosyncrasies, and concomitant diseases, further complicate the problem. Therefore, it may be difficult to find common mechanisms upon which to construct adequate models to predict specific types of systemic hypersensitivity reactions. There is some reason to hope, however, that adequate methods could be developed for at least identifying drugs that have the potential to produce signs indicative of a general hazard for immune-based reactions.

Quantitative multiphase model for hydrothermal liquefaction of algal biomass

DOE PAGES

Li, Yalin; Leow, Shijie; Fedders, Anna C.; ...

2017-01-17

Here, optimized incorporation of hydrothermal liquefaction (HTL, reaction in water at elevated temperature and pressure) within an integrated biorefinery requires accurate models to predict the quantity and quality of all HTL products. Existing models primarily focus on biocrude product yields with limited consideration for biocrude quality and aqueous, gas, and biochar co-products, and have not been validated with an extensive collection of feedstocks. In this study, HTL experiments (300 °C, 30 min) were conducted using 24 different batches of microalgae feedstocks with distinctive feedstock properties, which resulted in a wide range of biocrude (21.3–54.3 dry weight basis, dw%), aqueous (4.6–31.2more » dw%), gas (7.1–35.6 dw%), and biochar (1.3–35.0 dw%) yields. Based on these results, a multiphase component additivity (MCA) model was introduced to predict yields and characteristics of the HTL biocrude product and aqueous, gas, and biochar co-products, with only feedstock biochemical (lipid, protein, carbohydrate, and ash) and elemental (C/H/N) composition as model inputs. Biochemical components were determined to distribute across biocrude product/HTL co-products as follows: lipids to biocrude; proteins to biocrude > aqueous > gas; carbohydrates to gas ≈ biochar > biocrude; and ash to aqueous > biochar. Modeled quality indicators included biocrude C/H/N contents, higher heating value (HHV), and energy recovery (ER); aqueous total organic carbon (TOC) and total nitrogen (TN) contents; and biochar carbon content. The model was validated with HTL data from the literature, the potential to expand the application of this modeling framework to include waste biosolids (e.g., wastewater sludge, manure) was explored, and future research needs for industrial application were identified. Ultimately, the MCA model represents a critical step towards the integration of cultivation models with downstream HTL and biorefinery operations to enable system-level optimization, valorization of co-product streams (e.g., through catalytic hydrothermal gasification and nutrient recovery), and the navigation of tradeoffs across the value chain.« less

Soya bean Gα proteins with distinct biochemical properties exhibit differential ability to complement Saccharomyces cerevisiae gpa1 mutant.

PubMed

Roy Choudhury, Swarup; Wang, Yuqi; Pandey, Sona

2014-07-01

Signalling pathways mediated by heterotrimeric G-proteins are common to all eukaryotes. Plants have a limited number of each of the G-protein subunits, with the most elaborate G-protein network discovered so far in soya bean (Glycine max, also known as soybean) which has four Gα, four Gβ and ten Gγ proteins. Biochemical characterization of Gα proteins from plants suggests significant variation in their properties compared with the well-characterized non-plant proteins. Furthermore, the four soya bean Gα (GmGα) proteins exhibit distinct biochemical activities among themselves, but the extent to which such biochemical differences contribute to their in vivo function is also not known. We used the yeast gpa1 mutant which displays constitutive signalling and growth arrest in the pheromone-response pathway as an in vivo model to evaluate the effect of distinct biochemical activities of GmGα proteins. We showed that specific GmGα proteins can be activated during pheromone-dependent receptor-mediated signalling in yeast and they display different strengths towards complementation of yeast gpa1 phenotypes. We also identified amino acids that are responsible for differential complementation abilities of specific Gα proteins. These data establish that specific plant Gα proteins are functional in the receptor-mediated pheromone-response pathway in yeast and that the subtle biochemical differences in their activity are physiologically relevant.

In vitro reconstitution of interactions in the CARD9 signalosome

PubMed Central

Park, Jin Hee; Choi, Jae Young; Mustafa, Mir Faisal; Park, Hyun Ho

2017-01-01

The caspase-associated recruitment domain (CARD)-containing protein 9 (CARD9) signalosome is composed of CARD9, B-cell CLL/lymphoma 10 (BCL10) and mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1). The CARD9 signalosome has been reported to exert critical functions in the immunoreceptor tyrosine-based activation motif-coupled receptor-mediated activation of myeloid cells, through nuclear factor-κB pathways during innate immunity processes. During CARD9 signalosome assembly, BCL10 has been revealed to function as an adaptor protein and to interact with CARD9 via CARD-CARD interactions; BCL10 also interacts with MALT1 via its C-terminal Ser/Thr-rich region and the first immunoglobulin domain of MALT1. The CARD9 signalosome is implicated in critical biological processes; however, its structural and biochemical characteristics have yet to be elucidated. In the present study, CARD9 and BCL10 CARDs were successfully purified and characterized, and their biochemical properties were investigated. In addition, CARD9-BCL10 complexes were reconstituted in vitro under low salt and pH conditions. Furthermore, based on structural modeling data, a scheme was proposed to describe the interactions between CARD9 and BCL10. This provides a further understanding of the mechanism of how the CARD9 signalosome may be assembled. PMID:28765954

Alterations in Red Blood Cell Functionality Induced by an Indole Scaffold Containing a Y-Iminodiketo Moiety: Potential Antiproliferative Conditions

PubMed Central

Scala, Angela; Ficarra, Silvana; Russo, Annamaria; Giunta, Elena; Galtieri, Antonio; Tellone, Ester

2016-01-01

We have recently proposed a new erythrocyte-based model of study to predict the antiproliferative effects of selected heterocyclic scaffolds. Starting from the metabolic similarity between erythrocytes and cancer cells, we have demonstrated how the metabolic derangement induced by an indolone-based compound (DPIT) could be related to its antiproliferative effects. In order to prove the validity of our biochemical approach, in the present study the effects on erythrocyte functionality of its chemical precursor (PID), whose synthesis we reported, were investigated. The influence of the tested compound on band 3 protein (B3), oxidative state, ATP efflux, caspase 3, metabolism, intracellular pH, and Ca2+ homeostasis has been evaluated. PID crosses the membrane localizing into the cytosol, increases anion exchange, induces direct caspase activation, shifts the erythrocytes towards an oxidative state, and releases less ATP than in normal conditions. Analysis of phosphatidylserine externalization shows that PID slightly induces apoptosis. Our findings indicate that, due to its unique features, erythrocyte responses to exogenous molecular stimuli can be fruitfully correlated at structurally more complex cells, such as cancer cells. Overall, our work indicates that erythrocyte is a powerful study tool to elucidate the biochemical/biological effects of selected heterocycles opening considerable perspectives in the field of drug discovery. PMID:27651854

Plants, plant pathogens, and microgravity--a deadly trio.

PubMed

Leach, J E; Ryba-White, M; Sun, Q; Wu, C J; Hilaire, E; Gartner, C; Nedukha, O; Kordyum, E; Keck, M; Leung, H; Guikema, J A

2001-06-01

Plants grown in spaceflight conditions are more susceptible to colonization by plant pathogens. The underlying causes for this enhanced susceptibility are not known. Possibly the formation of structural barriers and the activation of plant defense response components are impaired in spaceflight conditions. Either condition would result from altered gene expression of the plant. Because of the tools available, past studies focused on a few physiological responses or biochemical pathways. With recent advances in genomics research, new tools, including microarray technologies, are available to examine the global impact of growth in the spacecraft on the plant's gene expression profile. In ground-based studies, we have developed cDNA subtraction libraries of rice that are enriched for genes induced during pathogen infection and the defense response. Arrays of these genes are being used to dissect plant defense response pathways in a model system involving wild-type rice plants and lesion mimic mutants. The lesion mimic mutants are ideal experimental tools because they erratically develop defense response-like lesions in the absence of pathogens. The gene expression profiles from these ground-based studies will provide the molecular basis for understanding the biochemical and physiological impacts of spaceflight on plant growth, development and disease defense responses. This, in turn, will allow the development of strategies to manage plant disease for life in the space environment.

Plants, plant pathogens, and microgravity--a deadly trio

NASA Technical Reports Server (NTRS)

Leach, J. E.; Ryba-White, M.; Sun, Q.; Wu, C. J.; Hilaire, E.; Gartner, C.; Nedukha, O.; Kordyum, E.; Keck, M.; Leung, H.;

Biochemical measurement of bilirubin with an evanescent wave optical sensor

NASA Astrophysics Data System (ADS)

Poscio, Patrick; Depeursinge, Christian D.; Emery, Y.; Parriaux, Olivier M.; Voirin, Guy

1991-09-01

Optical sensing techniques can be considered as powerful information sources on the biochemistry of tissue, blood, and physiological fluids. Various sensing modalities can be considered: spectroscopic determination of the fluorescence or optical absorption of the biological medium itself, or more generally, of a reagent in contact with the biological medium. The principle and realization of the optical sensor developed are based on the use of polished fibers: the cladding of a monomode fiber is removed on a longitudinal section. The device can then be inserted into an hypodermic needle for in-vivo measurements. Using this minute probe, local measurements of the tissue biochemistry or metabolic processes can be obtained. The sensing mechanism is based on the propagation of the evanescent wave in the tissues or reagent: the proximity of the fiber core allows the penetration of the model field tail into the sensed medium, with a uniquely defined field distribution. Single or multi-wavelength analysis of the light collected into the fiber yields the biochemical information. Here an example of this sensing technology is discussed. In-vitro measurement of bilirubin in gastric juice demonstrates that the evanescent wave optical sensor provides a sensitivity which matches the physiological concentrations. A device is proposed for in-vivo monitoring of bilirubin concentration in the gastro-oesophageal tract.

Exercise-induced biochemical changes and their potential influence on cancer: a scientific review

PubMed Central

Thomas, Robert James; Kenfield, Stacey A; Jimenez, Alfonso

2017-01-01

Aim To review and discuss the available international literature regarding the indirect and direct biochemical mechanisms that occur after exercise, which could positively, or negatively, influence oncogenic pathways. Methods The PubMed, MEDLINE, Embase and Cochrane libraries were searched for papers up to July 2016 addressing biochemical changes after exercise with a particular reference to cancer. The three authors independently assessed their appropriateness for inclusion in this review based on their scientific quality and relevance. Results 168 papers were selected and categorised into indirect and direct biochemical pathways. The indirect effects included changes in vitamin D, weight reduction, sunlight exposure and improved mood. The direct effects included insulin-like growth factor, epigenetic effects on gene expression and DNA repair, vasoactive intestinal peptide, oxidative stress and antioxidant pathways, heat shock proteins, testosterone, irisin, immunity, chronic inflammation and prostaglandins, energy metabolism and insulin resistance. Summary Exercise is one of several lifestyle factors known to lower the risk of developing cancer and is associated with lower relapse rates and better survival. This review highlights the numerous biochemical processes, which explain these potential anticancer benefits. PMID:27993842

A biochemical protocol for the isolation and identification of current species of Vibrio in seafood.

PubMed

Ottaviani, D; Masini, L; Bacchiocchi, S

2003-01-01

We report a biochemical method for the isolation and identification of the current species of vibrios using just one operative protocol. The method involves an enrichment phase with incubation at 30 degrees C for 8-24 h in alkaline peptone water and an isolation phase on thiosulphate-citrate-salt sucrose agar plates incubating at 30 degrees C for 24 h. Four biochemical tests and Alsina's scheme were performed for genus and species identification, respectively. All biochemical tests were optimized as regards conditions of temperature, time of incubation and media composition. The whole standardized protocol was always able to give a correct identification when applied to 25 reference strains of Vibrio and 134 field isolates. The data demonstrated that the assay method allows an efficient recovery, isolation and identification of current species of Vibrio in seafood obtaining results within 2-7 days. This method based on biochemical tests could be applicable even in basic microbiology laboratories, and can be used simultaneously to isolate and discriminate all clinically relevant species of Vibrio.

Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks

PubMed Central

Liao, Shuohao; Vejchodský, Tomáš; Erban, Radek

2015-01-01

Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org. PMID:26063822

Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks.

PubMed

Liao, Shuohao; Vejchodský, Tomáš; Erban, Radek

2015-07-06

Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org.

Inverse problems and computational cell metabolic models: a statistical approach

NASA Astrophysics Data System (ADS)

Calvetti, D.; Somersalo, E.

2008-07-01

In this article, we give an overview of the Bayesian modelling of metabolic systems at the cellular and subcellular level. The models are based on detailed description of key biochemical reactions occurring in tissue, which may in turn be compartmentalized into cytosol and mitochondria, and of transports between the compartments. The classical deterministic approach which models metabolic systems as dynamical systems with Michaelis-Menten kinetics, is replaced by a stochastic extension where the model parameters are interpreted as random variables with an appropriate probability density. The inverse problem of cell metabolism in this setting consists of estimating the density of the model parameters. After discussing some possible approaches to solving the problem, we address the issue of how to assess the reliability of the predictions of a stochastic model by proposing an output analysis in terms of model uncertainties. Visualization modalities for organizing the large amount of information provided by the Bayesian dynamic sensitivity analysis are also illustrated.

Fronts and waves of actin polymerization in a bistability-based mechanism of circular dorsal ruffles

NASA Astrophysics Data System (ADS)

Bernitt, Erik; Döbereiner, Hans-Günther; Gov, Nir S.; Yochelis, Arik

2017-06-01

During macropinocytosis, cells remodel their morphologies for the uptake of extracellular matter. This endocytotic mechanism relies on the collapse and closure of precursory structures, which are propagating actin-based, ring-shaped vertical undulations at the dorsal (top) cell membrane, a.k.a. circular dorsal ruffles (CDRs). As such, CDRs are essential to a range of vital and pathogenic processes alike. Here we show, based on both experimental data and theoretical analysis, that CDRs are propagating fronts of actin polymerization in a bistable system. The theory relies on a novel mass-conserving reaction-diffusion model, which associates the expansion and contraction of waves to distinct counter-propagating front solutions. Moreover, the model predicts that under a change in parameters (for example, biochemical conditions) CDRs may be pinned and fluctuate near the cell boundary or exhibit complex spiral wave dynamics due to a wave instability. We observe both phenomena also in our experiments indicating the conditions for which macropinocytosis is suppressed.

Fronts and waves of actin polymerization in a bistability-based mechanism of circular dorsal ruffles

PubMed Central

Bernitt, Erik; Döbereiner, Hans-Günther; Gov, Nir S.; Yochelis, Arik

2017-01-01

During macropinocytosis, cells remodel their morphologies for the uptake of extracellular matter. This endocytotic mechanism relies on the collapse and closure of precursory structures, which are propagating actin-based, ring-shaped vertical undulations at the dorsal (top) cell membrane, a.k.a. circular dorsal ruffles (CDRs). As such, CDRs are essential to a range of vital and pathogenic processes alike. Here we show, based on both experimental data and theoretical analysis, that CDRs are propagating fronts of actin polymerization in a bistable system. The theory relies on a novel mass-conserving reaction–diffusion model, which associates the expansion and contraction of waves to distinct counter-propagating front solutions. Moreover, the model predicts that under a change in parameters (for example, biochemical conditions) CDRs may be pinned and fluctuate near the cell boundary or exhibit complex spiral wave dynamics due to a wave instability. We observe both phenomena also in our experiments indicating the conditions for which macropinocytosis is suppressed. PMID:28627511

Non-equilibrium phase transition in mesoscopic biochemical systems: from stochastic to nonlinear dynamics and beyond

PubMed Central

Ge, Hao; Qian, Hong

2011-01-01

A theory for an non-equilibrium phase transition in a driven biochemical network is presented. The theory is based on the chemical master equation (CME) formulation of mesoscopic biochemical reactions and the mathematical method of large deviations. The large deviations theory provides an analytical tool connecting the macroscopic multi-stability of an open chemical system with the multi-scale dynamics of its mesoscopic counterpart. It shows a corresponding non-equilibrium phase transition among multiple stochastic attractors. As an example, in the canonical phosphorylation–dephosphorylation system with feedback that exhibits bistability, we show that the non-equilibrium steady-state (NESS) phase transition has all the characteristics of classic equilibrium phase transition: Maxwell construction, a discontinuous first-derivative of the ‘free energy function’, Lee–Yang's zero for a generating function and a critical point that matches the cusp in nonlinear bifurcation theory. To the biochemical system, the mathematical analysis suggests three distinct timescales and needed levels of description. They are (i) molecular signalling, (ii) biochemical network nonlinear dynamics, and (iii) cellular evolution. For finite mesoscopic systems such as a cell, motions associated with (i) and (iii) are stochastic while that with (ii) is deterministic. Both (ii) and (iii) are emergent properties of a dynamic biochemical network. PMID:20466813

Hyperspectral remote sensing of vegetation: knowledge gain and knowledge gap after 50 years of research (Conference Presentation)

NASA Astrophysics Data System (ADS)

Thenkabail, Prasad S.

2017-04-01

This presentation summarizes the advances made over 40+ years in understanding, modeling, and mapping terrestrial vegetation as reported in the new book on "Hyperspectral Remote Sensing of Vegetation" (Publisher:Taylor and Francis inc.). The advent of spaceborne hyperspectral sensors or imaging spectroscopy (e.g., NASA's Hyperion, ESA's PROBA, and upcoming Italy's ASI's Prisma, Germany's DLR's EnMAP, Japanese HIUSI, NASA's HyspIRI) as well as the advances made in processing when handling large volumes of hyperspectral data have generated tremendous interest in advancing the hyperspectral applications' knowledge base to large areas. Advances made in using hyperspectral data, relative to broadband data, include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) ability to discriminate plant species and vegetation types with high degree of accuracy, (c) reducing uncertainties in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (e) ability to assess stress resulting from causes such as management practices, pests and disease, water deficit or water excess, and (f) establishing more sensitive wavebands and indices to study vegetation characteristics. The presentation will discuss topics such as: (1) hyperspectral sensors and their characteristics, (2) methods of overcoming the Hughes phenomenon, (3) characterizing biophysical and biochemical properties, (4) advances made in using hyperspectral data in modeling evapotranspiration or actual water use by plants, (5) study of phenology, light use efficiency, and gross primary productivity, (5) improved accuracies in species identification and land cover classifications, and (6) applications in precision farming.

Vitreous humour - routine or alternative material for analysis in forensic medicine.

PubMed

Markowska, Joanna; Szopa, Monika; Zawadzki, Marcin; Piekoszewski, Wojciech

2017-01-01

Biological materials used in toxicological analyses in forensic medicine traditionally include blood, urine and vitreous humour. Forensic use of the vitreous body is mostly due to the need to assess the endogenous concentration of ethyl alcohol in the process of human body decomposition. The vitreous body is an underestimated biological material, even though its biochemical properties and anatomical location make it suitable for specific forensic toxicology tests as a reliable material for the preparation of forensic expert opinions. Based on the available literature the paper gathers information on the biochemical structure of the vitreous body, ways to secure the material after collection and its use in postmortem diagnostics. Specific applications of the vitreous humour for biochemical and toxicological tests are discussed, with a focus on its advantages and limitations in forensic medical assessment which are attributable to its biochemical properties, anatomical location and limited scientific studies on the distribution of xenobiotics in the vitreous body.

Effective screening strategy using ensembled pharmacophore models combined with cascade docking: application to p53-MDM2 interaction inhibitors.

PubMed

Xue, Xin; Wei, Jin-Lian; Xu, Li-Li; Xi, Mei-Yang; Xu, Xiao-Li; Liu, Fang; Guo, Xiao-Ke; Wang, Lei; Zhang, Xiao-Jin; Zhang, Ming-Ye; Lu, Meng-Chen; Sun, Hao-Peng; You, Qi-Dong

2013-10-28

Protein-protein interactions (PPIs) play a crucial role in cellular function and form the backbone of almost all biochemical processes. In recent years, protein-protein interaction inhibitors (PPIIs) have represented a treasure trove of potential new drug targets. Unfortunately, there are few successful drugs of PPIIs on the market. Structure-based pharmacophore (SBP) combined with docking has been demonstrated as a useful Virtual Screening (VS) strategy in drug development projects. However, the combination of target complexity and poor binding affinity prediction has thwarted the application of this strategy in the discovery of PPIIs. Here we report an effective VS strategy on p53-MDM2 PPI. First, we built a SBP model based on p53-MDM2 complex cocrystal structures. The model was then simplified by using a Receptor-Ligand complex-based pharmacophore model considering the critical binding features between MDM2 and its small molecular inhibitors. Cascade docking was subsequently applied to improve the hit rate. Based on this strategy, we performed VS on NCI and SPECS databases and successfully discovered 6 novel compounds from 15 hits with the best, compound 1 (NSC 5359), K(i) = 180 ± 50 nM. These compounds can serve as lead compounds for further optimization.

OvAge: a new methodology to quantify ovarian reserve combining clinical, biochemical and 3D-ultrasonographic parameters.

PubMed

Venturella, Roberta; Lico, Daniela; Sarica, Alessia; Falbo, Maria Pia; Gulletta, Elio; Morelli, Michele; Zupi, Errico; Cevenini, Gabriele; Cannataro, Mario; Zullo, Fulvio

2015-04-08

In the last decade, both endocrine and ultrasound data have been tested to verify their usefulness for assessing ovarian reserve, but the ideal marker does not yet exist. The purpose of this study was to find, if any, a statistical advanced model able to identify a simple, easy to understand and intuitive modality for defining ovarian age by combining clinical, biochemical and 3D-ultrasonographic data. This is a population-based observational study. From January 2012 to March 2014, we enrolled 652 healthy fertile women, 29 patients with clinical suspect of premature ovarian insufficiency (POI) and 29 patients with Polycystic Ovary syndrome (PCOS) at the Unit of Obstetrics & Gynecology of Magna Graecia University of Catanzaro (Italy). In all women we measured Anti Müllerian Hormone (AMH), Follicle Stimulating Hormone (FSH), Estradiol (E2), 3D Antral Follicle Count (AFC), ovarian volume, Vascular Index (VI) and Flow Index (FI) between days 1 and 4 of menstrual cycle. We applied the Generalized Linear Models (GzLM) for producing an equation combining these data to provide a ready to use information about women ovarian reserve, here called OvAge. To introduce this new variable, expression of ovarian reserve, we assumed that in healthy fertile women ovarian age is identical to chronological age. GzLM applied on the healthy fertile controls dataset produced the following equation OvAge = 48.05 - 3.14*AHM + 0.07*FSH - 0.77*AFC - 0.11*FI + 0.25*VI + 0.1*AMH*AFC + 0.02*FSH*AFC. This model showed a high statistical significance for each marker included in the equation. We applied the final equation on POI and PCOS datasets to test its ability of discovering significant deviation from normality and we obtained a mean of predicted ovarian age significantly different from the mean of chronological age in both groups. OvAge is one of the first reliable attempt to create a new method able to identify a simple, easy to understand and intuitive modality for defining ovarian reserve by combining clinical, biochemical and 3D-ultrasonographic data. Although design data prove a statistical high accuracy of the model, we are going to plan a clinical validation of model reliability in predicting reproductive prognosis and distance to menopause.

Fragment-Based, Structure-Enabled Discovery of Novel Pyridones and Pyridone Macrocycles as Potent Bromodomain and Extra-Terminal Domain (BET) Family Bromodomain Inhibitors

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

Wang, Le; Pratt, John K.; Soltwedel, Todd

Members of the BET family of bromodomain containing proteins have been identified as potential targets for blocking proliferation in a variety of cancer cell lines. A two-dimensional NMR fragment screen for binders to the bromodomains of BRD4 identified a phenylpyridazinone fragment with a weak binding affinity (1, Ki = 160 μM). SAR investigation of fragment 1, aided by X-ray structure-based design, enabled the synthesis of potent pyridone and macrocyclic pyridone inhibitors exhibiting single digit nanomolar potency in both biochemical and cell based assays. Advanced analogs in these series exhibited high oral exposures in rodent PK studies and demonstrated significant tumormore » growth inhibition efficacy in mouse flank xenograft models.« less

Molecular modelling and quantum biochemistry computations of a naturally occurring bioremediation enzyme: Alkane hydroxylase from Pseudomonas putida P1.

PubMed

de Sousa, B G; Oliveira, J I N; Albuquerque, E L; Fulco, U L; Amaro, V E; Blaha, C A G

2017-10-01

Many species of bacteria involved in degradation of n-alkanes have an important constitutional metabolic enzyme, the alkane hydroxylase called AlkB, specialized in the conversion of hydrocarbons molecules that can be used as carbon and/or energy source. This enzyme plays an important role in the microbial degradation of oil, chlorinated hydrocarbons, fuel additives, and many other compounds. A number of these enzymes has been biochemically characterized in detail because the potential of alkane hydroxylases to catalyse high added-value reactions is widely recognized. Nevertheless, the industrial and process bioremediation application of them is restricted, owing to their complex biochemistry, challenging process requirements, and the limited number of their three-dimensional structures. Furthermore, AlkB has great potential as biocatalysts for selective transformation of a wide range of chemically inert unreactive alkanes into reactive chemical precursors that can be used as tools for bioremediation and bioprocesses. Aiming to understand the possible ways the AlkB enzyme Pseudomonas putida P1 interacts with octane, octanol and 1-octyne, we consider its suitable biochemical structure taking into account a 3-D homology modelling. Besides, by using a quantum chemistry computational model based on the density functional theory (DFT), we determine possible protein-substrate interaction regions measured by means of its binding energy simulated throughout the Molecular Fractionation with Conjugated Caps (MFCC) approach. Copyright © 2017 Elsevier Inc. All rights reserved.

Identification and evaluation of air-pollution-tolerant plants around lignite-based thermal power station for greenbelt development.

PubMed

Govindaraju, M; Ganeshkumar, R S; Muthukumaran, V R; Visvanathan, P

2012-05-01

Thermal power plants emit various gaseous and particulate pollutants into the atmosphere. It is well known that trees help to reduce air pollution. Development of a greenbelt with suitable plant species around the source of emission will mitigate the air pollution. Selection of suitable plant species for a greenbelt is very important. Present study evaluates different plant species around Neyveli thermal power plant by calculating the Air Pollution Tolerance Index (APTI) which is based on their significant biochemical parameters. Also Anticipated Performance Index (API) was calculated for these plant species by combining APTI values with other socio-economic and biological parameters. Based on these indices, the most appropriate plant species were identified for the development of a greenbelt around the thermal power plant to mitigate air pollution. Among the 30 different plant species evaluated, Mangifere indica L. was identified as keystone species which is coming under the excellent category. Ambient air quality parameters were correlated with the biochemical characteristics of plant leaves and significant changes were observed in the plants biochemical characteristics due to the air pollution stress.

Biochemical characterization, homology modeling and docking studies of ornithine delta-aminotransferase--an important enzyme in proline biosynthesis of plants.

PubMed

Sekhar, P Nataraj; Amrutha, R Naga; Sangam, Shubhada; Verma, D P S; Kishor, P B Kavi

2007-11-01

Ornithine delta-aminotransferase (OAT) is an important enzyme in proline biosynthetic pathway and is implicated in salt tolerance in higher plants. OAT transaminates ornithine to pyrroline 5-carboxylate, which is further catalyzed to proline by pyrroline 5-carboxylate reductase. The Vigna aconitifolia OAT cDNA, encoding a polypeptide of 48.1 kDa, was expressed in Escherichia coli and the enzyme was partially characterized following its purification using (NH(4))(2)SO(4) precipitation and gel filtration techniques. Optimal activity of the enzyme was observed at a temperature of 25 degrees C and pH 8.0. The enzyme appeared to be a monomer and exhibited high activity at 4mM ornithine. Proline did not show any apparent effect but isoleucine, valine and serine inhibited the activity when added into the assay mixture along with ornithine. Omission of pyridoxal 5'-phosphate from the reaction mixture reduced the activity of this enzyme by 60%. To further evaluate these biochemical observations, homology modeling of the OAT was performed based on the crystal structure of the ornithine delta-aminotransferase from humans (PDB code 1OAT) by using the software MODELLER6v2. With the aid of the molecular mechanics and dynamics methods, the final model was obtained and assessed subsequently by PROCHECK and VERIFY-3D graph. With this model, a flexible docking study with the substrate and inhibitors was performed and the results indicated that Gly106 and Lys256 in OAT are the important determinant residues in binding as they have strong hydrogen bonding contacts with the substrate and inhibitors. These observations are in conformity with the results obtained from experimental investigations.

A quantitative systems approach to identify paracrine mechanisms that locally suppress immune response to Interleukin-12 in the B16 melanoma model

PubMed Central

Kulkarni, Yogesh M.; Chambers, Emily; McGray, A. J. Robert; Ware, Jason S.; Bramson, Jonathan L.

2012-01-01

Interleukin-12 (IL12) enhances anti-tumor immunity when delivered to the tumor microenvironment. However, local immunoregulatory elements dampen the efficacy of IL12. The identity of these local mechanisms used by tumors to suppress immunosurveillance represents a key knowledge gap for improving tumor immunotherapy. From a systems perspective, local suppression of anti-tumor immunity is a closed-loop system - where system response is determined by an unknown combination of external inputs and local cellular cross-talk. Here, we recreated this closed-loop system in vitro and combined quantitative high content assays, in silico model-based inference, and a proteomic workflow to identify the biochemical cues responsible for immunosuppression. Following an induction period, the B16 melanoma cell model, a transplantable model for spontaneous malignant melanoma, inhibited the response of a T helper cell model to IL12. This paracrine effect was not explained by induction of apoptosis or creation of a cytokine sink, despite both mechanisms present within the co-culture assay. Tumor-derived Wnt-inducible signaling protein-1 (WISP-1) was identified to exert paracrine action on immune cells by inhibiting their response to IL12. Moreover, WISP-1 was expressed in vivo following intradermal challenge with B16F10 cells and was inferred to be expressed at the tumor periphery. Collectively, the data suggest that (1) biochemical cues associated with epithelial-to-mesenchymal transition can shape anti-tumor immunity through paracrine action and (2) remnants of the immunoselective pressure associated with evolution in cancer include both sculpting of tumor antigens and expression of proteins that proactively shape anti-tumor immunity. PMID:22777646

A Comparison of Deterministic and Stochastic Modeling Approaches for Biochemical Reaction Systems: On Fixed Points, Means, and Modes.

PubMed

Hahl, Sayuri K; Kremling, Andreas

2016-01-01

In the mathematical modeling of biochemical reactions, a convenient standard approach is to use ordinary differential equations (ODEs) that follow the law of mass action. However, this deterministic ansatz is based on simplifications; in particular, it neglects noise, which is inherent to biological processes. In contrast, the stochasticity of reactions is captured in detail by the discrete chemical master equation (CME). Therefore, the CME is frequently applied to mesoscopic systems, where copy numbers of involved components are small and random fluctuations are thus significant. Here, we compare those two common modeling approaches, aiming at identifying parallels and discrepancies between deterministic variables and possible stochastic counterparts like the mean or modes of the state space probability distribution. To that end, a mathematically flexible reaction scheme of autoregulatory gene expression is translated into the corresponding ODE and CME formulations. We show that in the thermodynamic limit, deterministic stable fixed points usually correspond well to the modes in the stationary probability distribution. However, this connection might be disrupted in small systems. The discrepancies are characterized and systematically traced back to the magnitude of the stoichiometric coefficients and to the presence of nonlinear reactions. These factors are found to synergistically promote large and highly asymmetric fluctuations. As a consequence, bistable but unimodal, and monostable but bimodal systems can emerge. This clearly challenges the role of ODE modeling in the description of cellular signaling and regulation, where some of the involved components usually occur in low copy numbers. Nevertheless, systems whose bimodality originates from deterministic bistability are found to sustain a more robust separation of the two states compared to bimodal, but monostable systems. In regulatory circuits that require precise coordination, ODE modeling is thus still expected to provide relevant indications on the underlying dynamics.

Modeling and simulating networks of interdependent protein interactions.

PubMed

Stöcker, Bianca K; Köster, Johannes; Zamir, Eli; Rahmann, Sven

2018-05-21

Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of these systems emerge not only from the protein interactions themselves but also from the dependencies between these interactions, as generated by allosteric effects or mutual exclusion due to steric hindrance. Therefore, formal models for integrating and utilizing information about interaction dependencies are of high interest. Here, we describe an approach for endowing protein networks with interaction dependencies using propositional logic, thereby obtaining constrained protein interaction networks ("constrained networks"). The construction of these networks is based on public interaction databases as well as text-mined information about interaction dependencies. We present an efficient data structure and algorithm to simulate protein complex formation in constrained networks. The efficiency of the model allows fast simulation and facilitates the analysis of many proteins in large networks. In addition, this approach enables the simulation of perturbation effects, such as knockout of single or multiple proteins and changes of protein concentrations. We illustrate how our model can be used to analyze a constrained human adhesome protein network, which is responsible for the formation of diverse and dynamic cell-matrix adhesion sites. By comparing protein complex formation under known interaction dependencies versus without dependencies, we investigate how these dependencies shape the resulting repertoire of protein complexes. Furthermore, our model enables investigating how the interplay of network topology with interaction dependencies influences the propagation of perturbation effects across a large biochemical system. Our simulation software CPINSim (for Constrained Protein Interaction Network Simulator) is available under the MIT license at http://github.com/BiancaStoecker/cpinsim and as a Bioconda package (https://bioconda.github.io).

Suitability of temperature sum models to simulate the flowering period of birches on regional scale as basis for realistic predictions of the allergenic potential of atmospheric pollen loads

NASA Astrophysics Data System (ADS)

Biernath, Christian; Hauck, Julia; Klein, Christian; Thieme, Christoph; Heinlein, Florian; Priesack, Eckart

2014-05-01

Persons susceptible to allergenic pollen grains need to apply suppressive pharmacy before the occurrence of the first allergy symptoms. Patient targeted medication could be improved if forecasts of the allergenic potential of pollen (biochemical composition of the pollen grain) and the onset, duration, and end of the pollen season are precise on regional scale. In plant tissue the biochemical composition may change within hours due to the resource availability for plant growth and plant internal nutrient re-mobilization. As these processes highly depend on both, the environmental conditions and the development stage of a plant, precise simulations of the onset and duration of the flowering period are crucial to determine the allergenic potential of tissues and pollen. Here, dynamic plant models that consider the dependence of the chemical composition of tissue on the development stage of the plant embedded in process-based ecosystem models seem promising tools; however, today dynamic plant growth is widely ignored in simulations of atmospheric pollen loads. In this study we raise the question whether frequently applied temperature sum models (TSM) could precisely simulate the plant development stages in case of birches on regional scale. These TSM integrate average temperatures above a base temperature below which no further plant development is assumed. In this study, we therefore tested the ability of TSM to simulate the flowering period of birches on more than 100 sites in Bavaria, Germany over a period of three years (2010-2012). Our simulations indicate that the often applied base temperatures between 2.3°C and 3.5°C for the integration of daily or hourly average temperatures, respectively, in Europe are too high to adequately simulate the onset of birch flowering in Bavaria where a base temperature of 1°C seems more convenient. A more regional calibration of the models to sub-regions in Bavaria with comparable climatic conditions could further improve the simulation results if compared to simulations using a model that was adjusted to only one representative location in Bavaria. Our simulation results suggest that birch phenology needs to be modelled on a more regional scale to derive precise predictions of the flowering period. Some weak simulation results are suspected to be due to the high genetic diversity of birches and their high adaptive potential to a wide range of environmental conditions which indeed is a characteristic for many pioneer species. The high adaptive potential could be an explanation why authors who calibrate their models to other climatic regions observe better simulation results using higher base temperatures. However, our simulations indicate that the simulation results may be biased if the base temperatures are assumed constant for one species and transferred to larger or smaller scales, to other regions with different climatic conditions, or when applied to extrapolate birch pollen seasons to future climate conditions.

Hybrid pathwise sensitivity methods for discrete stochastic models of chemical reaction systems.

PubMed

Wolf, Elizabeth Skubak; Anderson, David F

2015-01-21

Stochastic models are often used to help understand the behavior of intracellular biochemical processes. The most common such models are continuous time Markov chains (CTMCs). Parametric sensitivities, which are derivatives of expectations of model output quantities with respect to model parameters, are useful in this setting for a variety of applications. In this paper, we introduce a class of hybrid pathwise differentiation methods for the numerical estimation of parametric sensitivities. The new hybrid methods combine elements from the three main classes of procedures for sensitivity estimation and have a number of desirable qualities. First, the new methods are unbiased for a broad class of problems. Second, the methods are applicable to nearly any physically relevant biochemical CTMC model. Third, and as we demonstrate on several numerical examples, the new methods are quite efficient, particularly if one wishes to estimate the full gradient of parametric sensitivities. The methods are rather intuitive and utilize the multilevel Monte Carlo philosophy of splitting an expectation into separate parts and handling each in an efficient manner.

A Systems Model of Parkinson's Disease Using Biochemical Systems Theory.

PubMed

Sasidharakurup, Hemalatha; Melethadathil, Nidheesh; Nair, Bipin; Diwakar, Shyam

2017-08-01

Parkinson's disease (PD), a neurodegenerative disorder, affects millions of people and has gained attention because of its clinical roles affecting behaviors related to motor and nonmotor symptoms. Although studies on PD from various aspects are becoming popular, few rely on predictive systems modeling approaches. Using Biochemical Systems Theory (BST), this article attempts to model and characterize dopaminergic cell death and understand pathophysiology of progression of PD. PD pathways were modeled using stochastic differential equations incorporating law of mass action, and initial concentrations for the modeled proteins were obtained from literature. Simulations suggest that dopamine levels were reduced significantly due to an increase in dopaminergic quinones and 3,4-dihydroxyphenylacetaldehyde (DOPAL) relating to imbalances compared to control during PD progression. Associating to clinically observed PD-related cell death, simulations show abnormal parkin and reactive oxygen species levels with an increase in neurofibrillary tangles. While relating molecular mechanistic roles, the BST modeling helps predicting dopaminergic cell death processes involved in the progression of PD and provides a predictive understanding of neuronal dysfunction for translational neuroscience.

Automatising the analysis of stochastic biochemical time-series

PubMed Central

2015-01-01

Background Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse complex models under a variety of configurations, eventually selecting the best setting of, e.g., parameters for a target system. Motivation This operational pipeline relies on the ability to interpret the predictions of a model, often represented as simulation time-series. Thus, an efficient data analysis pipeline is crucial to automatise time-series analyses, bearing in mind that errors in this phase might mislead the modeller's conclusions. Results For this reason we have developed an intuitive framework-independent Python tool to automate analyses common to a variety of modelling approaches. These include assessment of useful non-trivial statistics for simulation ensembles, e.g., estimation of master equations. Intuitive and domain-independent batch scripts will allow the researcher to automatically prepare reports, thus speeding up the usual model-definition, testing and refinement pipeline. PMID:26051821