Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

PubMed Central

Farasat, Iman; Kushwaha, Manish; Collens, Jason; Easterbrook, Michael; Guido, Matthew; Salis, Howard M

2014-01-01

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs. PMID:24952589

Predicting Cortical Dark/Bright Asymmetries from Natural Image Statistics and Early Visual Transforms

PubMed Central

Cooper, Emily A.; Norcia, Anthony M.

2015-01-01

The nervous system has evolved in an environment with structure and predictability. One of the ubiquitous principles of sensory systems is the creation of circuits that capitalize on this predictability. Previous work has identified predictable non-uniformities in the distributions of basic visual features in natural images that are relevant to the encoding tasks of the visual system. Here, we report that the well-established statistical distributions of visual features -- such as visual contrast, spatial scale, and depth -- differ between bright and dark image components. Following this analysis, we go on to trace how these differences in natural images translate into different patterns of cortical input that arise from the separate bright (ON) and dark (OFF) pathways originating in the retina. We use models of these early visual pathways to transform natural images into statistical patterns of cortical input. The models include the receptive fields and non-linear response properties of the magnocellular (M) and parvocellular (P) pathways, with their ON and OFF pathway divisions. The results indicate that there are regularities in visual cortical input beyond those that have previously been appreciated from the direct analysis of natural images. In particular, several dark/bright asymmetries provide a potential account for recently discovered asymmetries in how the brain processes visual features, such as violations of classic energy-type models. On the basis of our analysis, we expect that the dark/bright dichotomy in natural images plays a key role in the generation of both cortical and perceptual asymmetries. PMID:26020624

Modeling of nitrous oxide production by autotrophic ammonia-oxidizing bacteria with multiple production pathways.

PubMed

Ni, Bing-Jie; Peng, Lai; Law, Yingyu; Guo, Jianhua; Yuan, Zhiguo

2014-04-01

Autotrophic ammonia oxidizing bacteria (AOB) have been recognized as a major contributor to N2O production in wastewater treatment systems. However, so far N2O models have been proposed based on a single N2O production pathway by AOB, and there is still a lack of effective approach for the integration of these models. In this work, an integrated mathematical model that considers multiple production pathways is developed to describe N2O production by AOB. The pathways considered include the nitrifier denitrification pathway (N2O as the final product of AOB denitrification with NO2(-) as the terminal electron acceptor) and the hydroxylamine (NH2OH) pathway (N2O as a byproduct of incomplete oxidation of NH2OH to NO2(-)). In this model, the oxidation and reduction processes are modeled separately, with intracellular electron carriers introduced to link the two types of processes. The model is calibrated and validated using experimental data obtained with two independent nitrifying cultures. The model satisfactorily describes the N2O data from both systems. The model also predicts shifts of the dominating pathway at various dissolved oxygen (DO) and nitrite levels, consistent with previous hypotheses. This unified model is expected to enhance our ability to predict N2O production by AOB in wastewater treatment systems under varying operational conditions.

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework.

PubMed

Teeguarden, Justin G; Tan, Yu-Mei; Edwards, Stephen W; Leonard, Jeremy A; Anderson, Kim A; Corley, Richard A; Kile, Molly L; Simonich, Staci M; Stone, David; Tanguay, Robert L; Waters, Katrina M; Harper, Stacey L; Williams, David E

2016-05-03

Driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the "systems approaches" used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences. Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.

CMPF: class-switching minimized pathfinding in metabolic networks.

PubMed

Lim, Kevin; Wong, Limsoon

2012-01-01

The metabolic network is an aggregation of enzyme catalyzed reactions that converts one compound to another. Paths in a metabolic network are a sequence of enzymes that describe how a chemical compound of interest can be produced in a biological system. As the number of such paths is quite large, many methods have been developed to score paths so that the k-shortest paths represent the set of paths that are biologically meaningful or efficient. However, these approaches do not consider whether the sequence of enzymes can be manufactured in the same pathway/species/localization. As a result, a predicted sequence might consist of groups of enzymes that operate in distinct pathway/species/localization and may not truly reflect the events occurring within cell. We propose a path weighting method CMPF (Class-switching Minimized Pathfinder) to search for routes in a metabolic network which minimizes pathway switching. In biological terms, a pathway is a series of chemical reactions which define a specific function (e.g. glycolysis). We conjecture that routes that cross many pathways are inefficient since different pathways define different metabolic functions. In addition, native routes are also well characterized within pathways, suggesting that reasonable paths should not involve too many pathway switches. Our method can be generalized when reactions participate in a class set (e.g., pathways, species or cellular localization) so that the paths predicted have minimal class crossings. We show that our method generates k-paths that involve the least number of class switching. In addition, we also show that native paths are recoverable and alternative paths deviates less from native paths compared to other methods. This suggests that paths ranked by our method could be a way to predict paths that are likely to occur in biological systems.

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework

PubMed Central

Teeguarden, Justin. G.; Tan, Yu-Mei; Edwards, Stephen W.; Leonard, Jeremy A.; Anderson, Kim A.; Corley, Richard A.; Harding, Anna K; Kile, Molly L.; Simonich, Staci M; Stone, David; Tanguay, Robert L.; Waters, Katrina M.; Harper, Stacey L.; Williams, David E.

2016-01-01

Synopsis Driven by major scientific advances in analytical methods, biomonitoring, computational tools, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the Aggregate Exposure Pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the Adverse Outcome Pathway (AOP) concept in the toxicological sciences. Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more efficient integration of exposure assessment and hazard identification. Together, the two pathways form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making. PMID:26759916

The vacuole system is a significant intracellular pathway for longitudinal solute transport in basidiomycete fungi.

PubMed

Darrah, P R; Tlalka, M; Ashford, A; Watkinson, S C; Fricker, M D

2006-07-01

Mycelial fungi have a growth form which is unique among multicellular organisms. The data presented here suggest that they have developed a unique solution to internal solute translocation involving a complex, extended vacuole. In all filamentous fungi examined, this extended vacuole forms an interconnected network, dynamically linked by tubules, which has been hypothesized to act as an internal distribution system. We have tested this hypothesis directly by quantifying solute movement within the organelle by photobleaching a fluorescent vacuolar marker. Predictive simulation models were then used to determine the transport characteristics over extended length scales. This modeling showed that the vacuolar organelle forms a functionally important, bidirectional diffusive transport pathway over distances of millimeters to centimeters. Flux through the pathway is regulated by the dynamic tubular connections involving homotypic fusion and fission. There is also a strongly predicted interaction among vacuolar organization, predicted diffusion transport distances, and the architecture of the branching colony margin.

Predictive Models and Computational Embryology

EPA Science Inventory

EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

Multi-pathway Kinase Signatures of Multipotent Stromal Cells are Predictive for Osteogenic Differentiation

PubMed Central

Platt, Manu O.; Wilder, Catera L.; Wells, Alan; Griffith, Linda G.; Lauffenburger, Douglas A.

2010-01-01

Bone marrow-derived multi-potent stromal cells (MSCs) offer great promise for regenerating tissue. While certain transcription factors have been identified in association with tendency toward particular MSC differentiation phenotypes, the regulatory network of key receptor-mediated signaling pathways activated by extracellular ligands that induce various differentiation responses remain poorly understood. Attempts to predict differentiation fate tendencies from individual pathways in isolation are problematic due to the complex pathway interactions inherent in signaling networks. Accordingly, we have undertaken a multi-variate systems approach integrating experimental measurement of multiple kinase pathway activities and osteogenic differentiation in MSCs, together with computational analysis to elucidate quantitative combinations of kinase signals predictive of cell behavior across diverse contexts. In particular, for culture on polymeric biomaterials surfaces presenting tethered epidermal growth factor (tEGF), type-I collagen, neither, or both, we have found that a partial least-squares regression model yields successful prediction of phenotypic behavior on the basis of two principal components comprising the weighted sums of 8 intracellular phosphoproteins: p-EGFR, p-Akt, p-ERK1/2, p-Hsp27, p-c-jun, p-GSK3α/β, p-p38, and p-STAT3. This combination provides strongest predictive capability for 21-day differentiated phenotype status when calculated from day-7 signal measurements (99%); day-4 (88%) and day-14 (89%) signal measurements are also significantly predictive, indicating a broad time-frame during MSC osteogenesis wherein multiple pathways and states of the kinase signaling network are quantitatively integrated to regulate gene expression, cell processes, and ultimately, cell fate. PMID:19750537

Predictive Models and Computational Toxicology (II IBAMTOX)

EPA Science Inventory

EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

ReactPRED: a tool to predict and analyze biochemical reactions.

PubMed

Sivakumar, Tadi Venkata; Giri, Varun; Park, Jin Hwan; Kim, Tae Yong; Bhaduri, Anirban

2016-11-15

Biochemical pathways engineering is often used to synthesize or degrade target chemicals. In silico screening of the biochemical transformation space allows predicting feasible reactions, constituting these pathways. Current enabling tools are customized to predict reactions based on pre-defined biochemical transformations or reaction rule sets. Reaction rule sets are usually curated manually and tailored to specific applications. They are not exhaustive. In addition, current systems are incapable of regulating and refining data with an aim to tune specificity and sensitivity. A robust and flexible tool that allows automated reaction rule set creation along with regulated pathway prediction and analyses is a need. ReactPRED aims to address the same. ReactPRED is an open source flexible and customizable tool enabling users to predict biochemical reactions and pathways. The tool allows automated reaction rule creation from a user defined reaction set. Additionally, reaction rule degree and rule tolerance features allow refinement of predicted data. It is available as a flexible graphical user interface and a console application. ReactPRED is available at: https://sourceforge.net/projects/reactpred/ CONTACT: anirban.b@samsung.com or ty76.kim@samsung.comSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach "reverse pathway engineering".

PubMed

Liu, Mengjin; Bienfait, Bruno; Sacher, Oliver; Gasteiger, Johann; Siezen, Roland J; Nauta, Arjen; Geurts, Jan M W

2014-01-01

The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the "missing links" between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.

Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach

PubMed Central

Alaimo, Salvatore; Marceca, Gioacchino Paolo; Ferro, Alfredo; Pulvirenti, Alfredo

2017-01-01

In the era of network medicine, pathway analysis methods play a central role in the prediction of phenotype from high throughput experiments. In this paper, we present a network-based systems biology approach capable of extracting disease-perturbed subpathways within pathway networks in connection with expression data taken from The Cancer Genome Atlas (TCGA). Our system extends pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The framework enables the extraction, visualization, and analysis of statistically significant disease-specific subpathways through an easy to use web interface. Our analysis shows that the methodology is able to fill the gap in current techniques, allowing a more comprehensive analysis of the phenomena underlying disease states. PMID:29657291

Completing the link between exposure science and toxicology for improved environmental health decision making: The aggregate exposure pathway framework

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

Teeguarden, Justin G.; Tan, Yu -Mei; Edwards, Stephen W.

Here, driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences.more » Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.« less

Completing the link between exposure science and toxicology for improved environmental health decision making: The aggregate exposure pathway framework

DOE PAGES

Teeguarden, Justin G.; Tan, Yu -Mei; Edwards, Stephen W.; ...

2016-01-13

Here, driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences.more » Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.« less

Quantifying the Pathway and Predicting Spontaneous Emulsification during Material Exchange in a Two Phase Liquid System.

PubMed

Spooner, Stephen; Rahnama, Alireza; Warnett, Jason M; Williams, Mark A; Li, Zushu; Sridhar, Seetharaman

2017-10-30

Kinetic restriction of a thermodynamically favourable equilibrium is a common theme in materials processing. The interfacial instability in systems where rate of material exchange is far greater than the mass transfer through respective bulk phases is of specific interest when tracking the transient interfacial area, a parameter integral to short processing times for productivity streamlining in all manufacturing where interfacial reaction occurs. This is even more pertinent in high-temperature systems for energy and cost savings. Here the quantified physical pathway of interfacial area change due to material exchange in liquid metal-molten oxide systems is presented. In addition the predicted growth regime and emulsification behaviour in relation to interfacial tension as modelled using phase-field methodology is shown. The observed in-situ emulsification behaviour links quantitatively the geometry of perturbations as a validation method for the development of simulating the phenomena. Thus a method is presented to both predict and engineer the formation of micro emulsions to a desired specification.

Systems Biology and Birth Defects Prevention: Blockade of the Glucocorticoid Receptor Prevents Arsenic-Induced Birth Defects

PubMed Central

Ahir, Bhavesh K.; Sanders, Alison P.; Rager, Julia E.

2013-01-01

Background: The biological mechanisms by which environmental metals are associated with birth defects are largely unknown. Systems biology–based approaches may help to identify key pathways that mediate metal-induced birth defects as well as potential targets for prevention. Objectives: First, we applied a novel computational approach to identify a prioritized biological pathway that associates metals with birth defects. Second, in a laboratory setting, we sought to determine whether inhibition of the identified pathway prevents developmental defects. Methods: Seven environmental metals were selected for inclusion in the computational analysis: arsenic, cadmium, chromium, lead, mercury, nickel, and selenium. We used an in silico strategy to predict genes and pathways associated with both metal exposure and developmental defects. The most significant pathway was identified and tested using an in ovo whole chick embryo culture assay. We further evaluated the role of the pathway as a mediator of metal-induced toxicity using the in vitro midbrain micromass culture assay. Results: The glucocorticoid receptor pathway was computationally predicted to be a key mediator of multiple metal-induced birth defects. In the chick embryo model, structural malformations induced by inorganic arsenic (iAs) were prevented when signaling of the glucocorticoid receptor pathway was inhibited. Further, glucocorticoid receptor inhibition demonstrated partial to complete protection from both iAs- and cadmium-induced neurodevelopmental toxicity in vitro. Conclusions: Our findings highlight a novel approach to computationally identify a targeted biological pathway for examining birth defects prevention. PMID:23458687

Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping.

PubMed

Schumacher, Christian; Seyfarth, André

2017-01-01

In human and animal motor control several sensory organs contribute to a network of sensory pathways modulating the motion depending on the task and the phase of execution to generate daily motor tasks such as locomotion. To better understand the individual and joint contribution of reflex pathways in locomotor tasks, we developed a neuromuscular model that describes hopping movements. In this model, we consider the influence of proprioceptive length (LFB), velocity (VFB) and force feedback (FFB) pathways of a leg extensor muscle on hopping stability, performance and efficiency (metabolic effort). Therefore, we explore the space describing the blending of the monosynaptic reflex pathway gains. We call this reflex parameter space a sensor-motor map . The sensor-motor maps are used to visualize the functional contribution of sensory pathways in multisensory integration. We further evaluate the robustness of these sensor-motor maps to changes in tendon elasticity, body mass, segment length and ground compliance. The model predicted that different reflex pathway compositions selectively optimize specific hopping characteristics (e.g., performance and efficiency). Both FFB and LFB were pathways that enable hopping. FFB resulted in the largest hopping heights, LFB enhanced hopping efficiency and VFB had the ability to disable hopping. For the tested case, the topology of the sensor-motor maps as well as the location of functionally optimal compositions were invariant to changes in system designs (tendon elasticity, body mass, segment length) or environmental parameters (ground compliance). Our results indicate that different feedback pathway compositions may serve different functional roles. The topology of the sensor-motor map was predicted to be robust against changes in the mechanical system design indicating that the reflex system can use different morphological designs, which does not apply for most robotic systems (for which the control often follows a specific design). Consequently, variations in body mechanics are permitted with consistent compositions of sensory feedback pathways. Given the variability in human body morphology, such variations are highly relevant for human motor control.

Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism

PubMed Central

Sychev, Zoi E.; Hu, Alex; Lagunoff, Michael

2017-01-01

Kaposi’s Sarcoma associated Herpesvirus (KSHV), an oncogenic, human gamma-herpesvirus, is the etiological agent of Kaposi’s Sarcoma the most common tumor of AIDS patients world-wide. KSHV is predominantly latent in the main KS tumor cell, the spindle cell, a cell of endothelial origin. KSHV modulates numerous host cell-signaling pathways to activate endothelial cells including major metabolic pathways involved in lipid metabolism. To identify the underlying cellular mechanisms of KSHV alteration of host signaling and endothelial cell activation, we identified changes in the host proteome, phosphoproteome and transcriptome landscape following KSHV infection of endothelial cells. A Steiner forest algorithm was used to integrate the global data sets and, together with transcriptome based predicted transcription factor activity, cellular networks altered by latent KSHV were predicted. Several interesting pathways were identified, including peroxisome biogenesis. To validate the predictions, we showed that KSHV latent infection increases the number of peroxisomes per cell. Additionally, proteins involved in peroxisomal lipid metabolism of very long chain fatty acids, including ABCD3 and ACOX1, are required for the survival of latently infected cells. In summary, novel cellular pathways altered during herpesvirus latency that could not be predicted by a single systems biology platform, were identified by integrated proteomics and transcriptomics data analysis and when correlated with our metabolomics data revealed that peroxisome lipid metabolism is essential for KSHV latent infection of endothelial cells. PMID:28257516

Pursuit and prediction in the tracking of moving food by a teleost fish (Acanthaluteres spilomelanurus).

PubMed

Lanchester, B S; Mark, R F

1975-12-01

1. The path, eye and body movements of a teleost fish (the leatherjacket Acanthaluteres spilomelanurus) approaching and taking food were measured by cinematography. 2. Fixation of the food by movement of the eyes is an invariable feature of the approach. The eyes then remain aligned with the target while the body moves forward and round to bring the mouth to the food. 3. When pursuing pieces of food moving vertically at constant velocity through the water these fish normally trace out the pathway that can be calculated by assuming the fish aims constantly at the food. Predictive pathways that imply anticipation of the point of intersection with the food are not regularly seen. 4. Deviations from pursuit occur sporadically, usually in the direction of a predictive path, particularly when the fish approach falling food from below. 5. The geometry of the situation suggests that predictive paths may sometimes be generated if the alignment of eye and body during the pursuit of moving food can be delayed. In approaches from below this may be because forward movement of the fish would tend to stabilize the image of the falling food in the retina. 6. We suggest that a simple linked control system using both eye and body movements to fixate retinal images will on occasions generate predictive pathways without any need for the central nervous system to calculate them in advance.

Optimizing Technical Education Pathways: Does Dual-Credit Course Completion Predict Students' College and Labor Market Success?

ERIC Educational Resources Information Center

Phelps, L. Allen; Chan, Hsun-Yu

2016-01-01

Post-recession Federal policy initiatives, such as secondary/postsecondary career pathways and gainful employment higher education accountability standards, prioritize the alignment of education practices with market-driven outcomes. Using longitudinal student record data merged from college and state K-12 data systems with the Unemployment…

Mapping the Human Toxome by Systems Toxicology

PubMed Central

Bouhifd, Mounir; Hogberg, Helena T.; Kleensang, Andre; Maertens, Alexandra; Zhao, Liang; Hartung, Thomas

2014-01-01

Toxicity testing typically involves studying adverse health outcomes in animals subjected to high doses of toxicants with subsequent extrapolation to expected human responses at lower doses. The low-throughput of current toxicity testing approaches (which are largely the same for industrial chemicals, pesticides and drugs) has led to a backlog of more than 80,000 chemicals to which human beings are potentially exposed whose potential toxicity remains largely unknown. Employing new testing strategies that employ the use of predictive, high-throughput cell-based assays (of human origin) to evaluate perturbations in key pathways, referred as pathways of toxicity, and to conduct targeted testing against those pathways, we can begin to greatly accelerate our ability to test the vast “storehouses” of chemical compounds using a rational, risk-based approach to chemical prioritization, and provide test results that are more predictive of human toxicity than current methods. The NIH Transformative Research Grant project Mapping the Human Toxome by Systems Toxicology aims at developing the tools for pathway mapping, annotation and validation as well as the respective knowledge base to share this information. PMID:24443875

A conceptual framework to support exposure science research ...

EPA Pesticide Factsheets

While knowledge of exposure is fundamental to assessing and mitigating risks, exposure information has been costly and difficult to generate. Driven by major scientific advances in analytical methods, biomonitoring, computational tools, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition that allows it to be more agile, predictive, and data- and knowledge-driven. A necessary element of this evolved paradigm is an organizational and predictive framework for exposure science that furthers the application of systems-based approaches. To enable such systems-based approaches, we proposed the Aggregate Exposure Pathway (AEP) concept to organize data and information emerging from an invigorated and expanding field of exposure science. The AEP framework is a layered structure that describes the elements of an exposure pathway, as well as the relationship between those elements. The basic building blocks of an AEP adopt the naming conventions used for Adverse Outcome Pathways (AOPs): Key Events (KEs) to describe the measurable, obligate steps through the AEP; and Key Event Relationships (KERs) describe the linkages between KEs. Importantly, the AEP offers an intuitive approach to organize exposure information from sources to internal site of action, setting the stage for predicting stressor concentrations at an internal target site. These predicted concentrations can help inform the r

Evolution of branched regulatory genetic pathways: directional selection on pleiotropic loci accelerates developmental system drift.

PubMed

Johnson, Norman A; Porter, Adam H

2007-01-01

Developmental systems are regulated by a web of interacting loci. One common and useful approach in studying the evolution of development is to focus on classes of interacting elements within these systems. Here, we use individual-based simulations to study the evolution of traits controlled by branched developmental pathways involving three loci, where one locus regulates two different traits. We examined the system under a variety of selective regimes. In the case where one branch was under stabilizing selection and the other under directional selection, we observed "developmental system drift": the trait under stabilizing selection showed little phenotypic change even though the loci underlying that trait showed considerable evolutionary divergence. This occurs because the pleiotropic locus responds to directional selection and compensatory mutants are then favored in the pathway under stabilizing selection. Though developmental system drift may be caused by other mechanisms, it seems likely that it is accelerated by the same underlying genetic mechanism as that producing the Dobzhansky-Muller incompatibilities that lead to speciation in both linear and branched pathways. We also discuss predictions of our model for developmental system drift and how different selective regimes affect probabilities of speciation in the branched pathway system.

Pivotal role of the muscle-contraction pathway in cryptorchidism and evidence for genomic connections with cardiomyopathy pathways in RASopathies.

PubMed

Cannistraci, Carlo V; Ogorevc, Jernej; Zorc, Minja; Ravasi, Timothy; Dovc, Peter; Kunej, Tanja

2013-02-14

Cryptorchidism is the most frequent congenital disorder in male children; however the genetic causes of cryptorchidism remain poorly investigated. Comparative integratomics combined with systems biology approach was employed to elucidate genetic factors and molecular pathways underlying testis descent. Literature mining was performed to collect genomic loci associated with cryptorchidism in seven mammalian species. Information regarding the collected candidate genes was stored in MySQL relational database. Genomic view of the loci was presented using Flash GViewer web tool (http://gmod.org/wiki/Flashgviewer/). DAVID Bioinformatics Resources 6.7 was used for pathway enrichment analysis. Cytoscape plug-in PiNGO 1.11 was employed for protein-network-based prediction of novel candidate genes. Relevant protein-protein interactions were confirmed and visualized using the STRING database (version 9.0). The developed cryptorchidism gene atlas includes 217 candidate loci (genes, regions involved in chromosomal mutations, and copy number variations) identified at the genomic, transcriptomic, and proteomic level. Human orthologs of the collected candidate loci were presented using a genomic map viewer. The cryptorchidism gene atlas is freely available online: http://www.integratomics-time.com/cryptorchidism/. Pathway analysis suggested the presence of twelve enriched pathways associated with the list of 179 literature-derived candidate genes. Additionally, a list of 43 network-predicted novel candidate genes was significantly associated with four enriched pathways. Joint pathway analysis of the collected and predicted candidate genes revealed the pivotal importance of the muscle-contraction pathway in cryptorchidism and evidence for genomic associations with cardiomyopathy pathways in RASopathies. The developed gene atlas represents an important resource for the scientific community researching genetics of cryptorchidism. The collected data will further facilitate development of novel genetic markers and could be of interest for functional studies in animals and human. The proposed network-based systems biology approach elucidates molecular mechanisms underlying co-presence of cryptorchidism and cardiomyopathy in RASopathies. Such approach could also aid in molecular explanation of co-presence of diverse and apparently unrelated clinical manifestations in other syndromes.

Comparison of different two-pathway models for describing the combined effect of DO and nitrite on the nitrous oxide production by ammonia-oxidizing bacteria.

PubMed

Lang, Longqi; Pocquet, Mathieu; Ni, Bing-Jie; Yuan, Zhiguo; Spérandio, Mathieu

2017-02-01

The aim of this work is to compare the capability of two recently proposed two-pathway models for predicting nitrous oxide (N 2 O) production by ammonia-oxidizing bacteria (AOB) for varying ranges of dissolved oxygen (DO) and nitrite. The first model includes the electron carriers whereas the second model is based on direct coupling of electron donors and acceptors. Simulations are confronted to extensive sets of experiments (43 batches) from different studies with three different microbial systems. Despite their different mathematical structures, both models could well and similarly describe the combined effect of DO and nitrite on N 2 O production rate and emission factor. The model-predicted contributions for nitrifier denitrification pathway and hydroxylamine pathway also matched well with the available isotopic measurements. Based on sensitivity analysis, calibration procedures are described and discussed for facilitating the future use of those models.

Developing and applying the adverse outcome pathway concept for understanding and predicting neurotoxicity

PubMed Central

Bal-Price, Anna; Lein, Pamela J.; Keil, Kimberly P.; Sethi, Sunjay; Shafer, Timothy; Barenys, Marta; Fritsche, Ellen; Sachana, Magdalini; Meek, M.E. (Bette)

2016-01-01

The Adverse Outcome Pathway (AOP) concept has recently been proposed to support a paradigm shift in regulatory toxicology testing and risk assessment. This concept is similar to the Mode of Action (MOA), in that it describes a sequence of measurable key events triggered by a molecular initiating event in which a stressor interacts with a biological target. The resulting cascade of key events includes molecular, cellular, structural and functional changes in biological systems, resulting in a measurable adverse outcome. Thereby, an AOP ideally provides information relevant to chemical structure-activity relationships as a basis for predicting effects of structurally similar compounds. AOPs could potentially also form the basis for qualitative and quantitative predictive modeling of the human adverse outcome resulting from molecular initiating or other key events for which higher-throughput testing methods are available or can be developed. A variety of cellular and molecular processes are known to be critical for normal function of the central (CNS) and peripheral nervous systems (PNS). Because of the biological and functional complexity of the CNS and PNS, it has been challenging to establish causative links and quantitative relationships between key events that comprise the pathways leading from chemical exposure to an adverse outcome in the nervous system. Following introduction of the principles of MOA and AOPs, examples of potential or putative adverse outcome pathways specific for developmental or adult neurotoxicity are summarized and aspects of their assessment considered. Their possible application in developing mechanistically informed Integrated Approaches to Testing and Assessment (IATA) is also discussed. PMID:27212452

SYSTEMS APPROACH TO CHARACTERIZING AND PREDICTING THYROID TOXICITY

EPA Science Inventory

A systems approach is being undertaken in which in vivo and in vitro assays are integrated to understand the mechanisms of thyroid hormone mediated pathways controlling frog metamorphosis, and more generally the regulation and control of the HPT axis.

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

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

Young, M; Craft, D

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchicalmore » clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve cancer classification using biological pathways. Patients are classified with greater specificity and physiological relevance as compared to current gene-specific approaches. Focus now moves to utilizing PICS for pan-cancer patient-specific treatment response prediction.« less

Characterizing the roles of changing population size and selection on the evolution of flux control in metabolic pathways.

PubMed

Orlenko, Alena; Chi, Peter B; Liberles, David A

2017-05-25

Understanding the genotype-phenotype map is fundamental to our understanding of genomes. Genes do not function independently, but rather as part of networks or pathways. In the case of metabolic pathways, flux through the pathway is an important next layer of biological organization up from the individual gene or protein. Flux control in metabolic pathways, reflecting the importance of mutation to individual enzyme genes, may be evolutionarily variable due to the role of mutation-selection-drift balance. The evolutionary stability of rate limiting steps and the patterns of inter-molecular co-evolution were evaluated in a simulated pathway with a system out of equilibrium due to fluctuating selection, population size, or positive directional selection, to contrast with those under stabilizing selection. Depending upon the underlying population genetic regime, fluctuating population size was found to increase the evolutionary stability of rate limiting steps in some scenarios. This result was linked to patterns of local adaptation of the population. Further, during positive directional selection, as with more complex mutational scenarios, an increase in the observation of inter-molecular co-evolution was observed. Differences in patterns of evolution when systems are in and out of equilibrium, including during positive directional selection may lead to predictable differences in observed patterns for divergent evolutionary scenarios. In particular, this result might be harnessed to detect differences between compensatory processes and directional processes at the pathway level based upon evolutionary observations in individual proteins. Detecting functional shifts in pathways reflects an important milestone in predicting when changes in genotypes result in changes in phenotypes.

Prediction of GCRV virus-host protein interactome based on structural motif-domain interactions.

PubMed

Zhang, Aidi; He, Libo; Wang, Yaping

2017-03-02

Grass carp hemorrhagic disease, caused by grass carp reovirus (GCRV), is the most fatal causative agent in grass carp aquaculture. Protein-protein interactions between virus and host are one avenue through which GCRV can trigger infection and induce disease. Experimental approaches for the detection of host-virus interactome have many inherent limitations, and studies on protein-protein interactions between GCRV and its host remain rare. In this study, based on known motif-domain interaction information, we systematically predicted the GCRV virus-host protein interactome by using motif-domain interaction pair searching strategy. These proteins derived from different domain families and were predicted to interact with different motif patterns in GCRV. JAM-A protein was successfully predicted to interact with motifs of GCRV Sigma1-like protein, and shared the similar binding mode compared with orthoreovirus. Differentially expressed genes during GCRV infection process were extracted and mapped to our predicted interactome, the overlapped genes displayed different tissue expression distributions on the whole, the overall expression level in intestinal is higher than that of other three tissues, which may suggest that the functions of these genes are more active in intestinal. Function annotation and pathway enrichment analysis revealed that the host targets were largely involved in signaling pathway and immune pathway, such as interferon-gamma signaling pathway, VEGF signaling pathway, EGF receptor signaling pathway, B cell activation, and T cell activation. Although the predicted PPIs may contain some false positives due to limited data resource and poor research background in non-model species, the computational method still provide reasonable amount of interactions, which can be further validated by high throughput experiments. The findings of this work will contribute to the development of system biology for GCRV infectious diseases, and help guide the identification of novel receptors of GCRV in its host.

A gene expression signature of RAS pathway dependence predicts response to PI3K and RAS pathway inhibitors and expands the population of RAS pathway activated tumors.

PubMed

Loboda, Andrey; Nebozhyn, Michael; Klinghoffer, Rich; Frazier, Jason; Chastain, Michael; Arthur, William; Roberts, Brian; Zhang, Theresa; Chenard, Melissa; Haines, Brian; Andersen, Jannik; Nagashima, Kumiko; Paweletz, Cloud; Lynch, Bethany; Feldman, Igor; Dai, Hongyue; Huang, Pearl; Watters, James

2010-06-30

Hyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence. We used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets. The RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90%) sensitivity but relatively low (50%) specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer. These data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical utility in lung and breast tumors.

Energy landscape in frustrated systems: Cation hopping in pyrochlores

NASA Astrophysics Data System (ADS)

Brooks Hinojosa, Beverly; Asthagiri, Aravind; Nino, Juan C.

2013-07-01

We investigate the dynamics of the local environment and electronic structure in inherently dipolar frustrated pyrochlore compounds to help identify the fundamental nature of dipolar disorder in pyrochlore systems and determine the necessary and sufficient conditions for dielectric relaxation. We map out the energy landscape associated with cation hopping events in three compounds and correlate the hopping pathway with experimental dielectric response. Comprehensive analysis of the calculations allows us to postulate rules to predict the occurrence of relaxation and cation hopping pathways.

The kinetics and location of intra-host HIV evolution to evade cellular immunity are predictable

NASA Astrophysics Data System (ADS)

Barton, John; Goonetilleke, Nilu; Butler, Thomas; Walker, Bruce; McMichael, Andrew; Chakraborty, Arup

Human immunodeficiency virus (HIV) evolves within infected persons to escape targeting and clearance by the host immune system, thereby preventing effective immune control of infection. Knowledge of the timing and pathways of escape that result in loss of control of the virus could aid in the design of effective strategies to overcome the challenge of viral diversification and immune escape. We combined methods from statistical physics and evolutionary dynamics to predict the course of in vivo viral sequence evolution in response to T cell-mediated immune pressure in a cohort of 17 persons with acute HIV infection. Our predictions agree well with both the location of documented escape mutations and the clinically observed time to escape. We also find that that the mutational pathways to escape depend on the viral sequence background due to epistatic interactions. The ability to predict escape pathways, and the duration over which control is maintained by specific immune responses prior to escape, could be exploited for the rational design of immunotherapeutic strategies that may enable long-term control of HIV infection.

Completing the Link between Exposure Science and ...

EPA Pesticide Factsheets

Driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences. Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making. The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports G

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework

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

Teeguarden, Justin G.; Tan, Yu-Mei; Edwards, Stephen W.

Driven by major scientific advances in analytical methods, biomonitoring, and computational exposure assessment, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the computationally enabled “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) conceptmore » in the toxicological sciences. The AEP framework offers an intuitive approach to successful organization of exposure science data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathway and adverse outcome pathways, completing the source to outcome continuum and setting the stage for more efficient integration of exposure science and toxicity testing information. Together these frameworks form and inform a decision making framework with the flexibility for risk-based, hazard-based or exposure-based decisions.« less

Spatially organizing biochemistry: choosing a strategy to translate synthetic biology to the factory.

PubMed

Jakobson, Christopher M; Tullman-Ercek, Danielle; Mangan, Niall M

2018-05-29

Natural biochemical systems are ubiquitously organized both in space and time. Engineering the spatial organization of biochemistry has emerged as a key theme of synthetic biology, with numerous technologies promising improved biosynthetic pathway performance. One strategy, however, may produce disparate results for different biosynthetic pathways. We use a spatially resolved kinetic model to explore this fundamental design choice in systems and synthetic biology. We predict that two example biosynthetic pathways have distinct optimal organization strategies that vary based on pathway-dependent and cell-extrinsic factors. Moreover, we demonstrate that the optimal design varies as a function of kinetic and biophysical properties, as well as culture conditions. Our results suggest that organizing biosynthesis has the potential to substantially improve performance, but that choosing the appropriate strategy is key. The flexible design-space analysis we propose can be adapted to diverse biosynthetic pathways, and lays a foundation to rationally choose organization strategies for biosynthesis.

A gene expression signature of RAS pathway dependence predicts response to PI3K and RAS pathway inhibitors and expands the population of RAS pathway activated tumors

PubMed Central

2010-01-01

Background Hyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence. Methods We used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets. Results The RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90%) sensitivity but relatively low (50%) specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer. Conclusions These data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical utility in lung and breast tumors. PMID:20591134

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

PubMed

Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

2017-12-21

In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

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

PubMed

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

2004-05-05

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

Application of synthetic biology for production of chemicals in yeast Saccharomyces cerevisiae.

PubMed

Li, Mingji; Borodina, Irina

2015-02-01

Synthetic biology and metabolic engineering enable generation of novel cell factories that efficiently convert renewable feedstocks into biofuels, bulk, and fine chemicals, thus creating the basis for biosustainable economy independent on fossil resources. While over a hundred proof-of-concept chemicals have been made in yeast, only a very small fraction of those has reached commercial-scale production so far. The limiting factor is the high research cost associated with the development of a robust cell factory that can produce the desired chemical at high titer, rate, and yield. Synthetic biology has the potential to bring down this cost by improving our ability to predictably engineer biological systems. This review highlights synthetic biology applications for design, assembly, and optimization of non-native biochemical pathways in baker's yeast Saccharomyces cerevisiae We describe computational tools for the prediction of biochemical pathways, molecular biology methods for assembly of DNA parts into pathways, and for introducing the pathways into the host, and finally approaches for optimizing performance of the introduced pathways. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.

Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study.

PubMed

MacLean, Adam L; Harrington, Heather A; Stumpf, Michael P H; Byrne, Helen M

2016-01-01

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non-exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.

Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics.

PubMed

Hess, Becky M; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C; Wiley, H Steven; Ahring, Birgitte K; Linggi, Bryan

2013-01-01

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions.

Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics

PubMed Central

Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C.; Wiley, H. Steven; Ahring, Birgitte K.; Linggi, Bryan

2013-01-01

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions. PMID:23840410

Coregulation of terpenoid pathway genes and prediction of isoprene production in Bacillus subtilis using transcriptomics

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

Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng

2013-06-19

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding the system level regulation and control of the pathway. To address this limitation, we examined Bacillus subtilis grown under multiple conditions and then determined the relationship between altered isoprene production and the pattern of gene expression. Wemore » found that terpenoid genes appeared to fall into two distinct subsets with opposing correlations with respect to the amount of isoprene produced. The group whose expression levels positively correlated with isoprene production included dxs, the gene responsible for the commitment step in the pathway, as well as ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. This analysis showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model which accurately predicts production of this secondary metabolite across many simulated environmental conditions.« less

Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

PubMed

Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

2012-07-15

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of E<10(-5)) are included in 27 clusters. Five clusters are associated with metabolism, containing P450 genes restricted to the Brassica family and predicted to be involved in secondary metabolism. Operon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system. Copyright © 2012 Elsevier B.V. All rights reserved.

Determination of Highly Sensitive Biological Cell Model Systems to Screen BPA-Related Health Hazards Using Pathway Studio.

PubMed

Ryu, Do-Yeal; Rahman, Md Saidur; Pang, Myung-Geol

2017-09-06

Bisphenol-A (BPA) is a ubiquitous endocrine-disrupting chemical. Recently, many issues have arisen surrounding the disease pathogenesis of BPA. Therefore, several studies have been conducted to investigate the proteomic biomarkers of BPA that are associated with disease processes. However, studies on identifying highly sensitive biological cell model systems in determining BPA health risk are lacking. Here, we determined suitable cell model systems and potential biomarkers for predicting BPA-mediated disease using the bioinformatics tool Pathway Studio. We compiled known BPA-mediated diseases in humans, which were categorized into five major types. Subsequently, we investigated the differentially expressed proteins following BPA exposure in several cell types, and analyzed the efficacy of altered proteins to investigate their associations with BPA-mediated diseases. Our results demonstrated that colon cancer cells (SW480), mammary gland, and Sertoli cells were highly sensitive biological model systems, because of the efficacy of predicting the majority of BPA-mediated diseases. We selected glucose-6-phosphate dehydrogenase (G6PD), cytochrome b-c1 complex subunit 1 (UQCRC1), and voltage-dependent anion-selective channel protein 2 (VDAC2) as highly sensitive biomarkers to predict BPA-mediated diseases. Furthermore, we summarized proteomic studies in spermatozoa following BPA exposure, which have recently been considered as another suitable cell type for predicting BPA-mediated diseases.

Determination of Highly Sensitive Biological Cell Model Systems to Screen BPA-Related Health Hazards Using Pathway Studio

PubMed Central

Ryu, Do-Yeal

2017-01-01

Bisphenol-A (BPA) is a ubiquitous endocrine-disrupting chemical. Recently, many issues have arisen surrounding the disease pathogenesis of BPA. Therefore, several studies have been conducted to investigate the proteomic biomarkers of BPA that are associated with disease processes. However, studies on identifying highly sensitive biological cell model systems in determining BPA health risk are lacking. Here, we determined suitable cell model systems and potential biomarkers for predicting BPA-mediated disease using the bioinformatics tool Pathway Studio. We compiled known BPA-mediated diseases in humans, which were categorized into five major types. Subsequently, we investigated the differentially expressed proteins following BPA exposure in several cell types, and analyzed the efficacy of altered proteins to investigate their associations with BPA-mediated diseases. Our results demonstrated that colon cancer cells (SW480), mammary gland, and Sertoli cells were highly sensitive biological model systems, because of the efficacy of predicting the majority of BPA-mediated diseases. We selected glucose-6-phosphate dehydrogenase (G6PD), cytochrome b-c1 complex subunit 1 (UQCRC1), and voltage-dependent anion-selective channel protein 2 (VDAC2) as highly sensitive biomarkers to predict BPA-mediated diseases. Furthermore, we summarized proteomic studies in spermatozoa following BPA exposure, which have recently been considered as another suitable cell type for predicting BPA-mediated diseases. PMID:28878155

In vitro reconstitution of mevalonate pathway and targeted engineering of farnesene overproduction in Escherichia coli.

PubMed

Zhu, Fayin; Zhong, Xiaofang; Hu, Mengzhu; Lu, Lei; Deng, Zixin; Liu, Tiangang

2014-07-01

Approaches using metabolic engineering and synthetic biology to overproduce terpenoids, such as the precursors of taxol and artemisinin, in microbial systems have achieved initial success. However, due to the lack of steady-state kinetic information and incomplete understanding of the terpenoid biosynthetic pathway, it has been difficult to build a highly efficient, universal system. Here, we reconstituted the mevalonate pathway to produce farnesene (a precursor of new jet fuel) in vitro using purified protein components. The information from this in vitro reconstituted system guided us to rationally optimize farnesene production in E. coli by quantitatively overexpressing each component. Targeted proteomic assays and intermediate assays were used to determine the metabolic status of each mutant. Through targeted engineering, farnesene production could be increased predictably step by step, up to 1.1 g/L (∼ 2,000 fold) 96 h after induction at the shake-flask scale. The strategy developed to release the potential of the mevalonate pathway for terpenoid overproduction should also work in other multistep synthetic pathways. © 2014 Wiley Periodicals, Inc.

Modeling the plant uptake of organic chemicals, including the soil-air-plant pathway.

PubMed

Collins, Chris D; Finnegan, Eilis

2010-02-01

The soil-air-plant pathway is potentially important in the vegetative accumulation of organic pollutants from contaminated soils. While a number of qualitative frameworks exist for the prediction of plant accumulation of organic chemicals by this pathway, there are few quantitative models that incorporate this pathway. The aim of the present study was to produce a model that included this pathway and could quantify its contribution to the total plant contamination for a range of organic pollutants. A new model was developed from three submodels for the processes controlling plant contamination via this pathway: aerial deposition, soil volatilization, and systemic translocation. Using the combined model, the soil-air-plant pathway was predicted to account for a significant proportion of the total shoot contamination for those compounds with log K(OA) > 9 and log K(AW) < -3. For those pollutants with log K(OA) < 9 and log K(AW) > -3 there was a higher deposition of pollutant via the soil-air-plant pathway than for those chemicals with log K(OA) > 9 and log K(AW) < -3, but this was an insignificant proportion of the total shoot contamination because of the higher mobility of these compounds via the soil-root-shoot pathway. The incorporation of the soil-air-plant pathway into the plant uptake model did not significantly improve the prediction of the contamination of vegetation from polluted soils when compared across a range of studies. This was a result of the high variability between the experimental studies where the bioconcentration factors varied by 2 orders of magnitude at an equivalent log K(OA). One potential reason for this is the background air concentration of the pollutants under study. It was found background air concentrations would dominate those from soil volatilization in many situations unless there was a soil hot spot of contamination, i.e., >100 mg kg(-1).

Action selection performance of a reconfigurable basal ganglia inspired model with Hebbian–Bayesian Go-NoGo connectivity

PubMed Central

Berthet, Pierre; Hellgren-Kotaleski, Jeanette; Lansner, Anders

2012-01-01

Several studies have shown a strong involvement of the basal ganglia (BG) in action selection and dopamine dependent learning. The dopaminergic signal to striatum, the input stage of the BG, has been commonly described as coding a reward prediction error (RPE), i.e., the difference between the predicted and actual reward. The RPE has been hypothesized to be critical in the modulation of the synaptic plasticity in cortico-striatal synapses in the direct and indirect pathway. We developed an abstract computational model of the BG, with a dual pathway structure functionally corresponding to the direct and indirect pathways, and compared its behavior to biological data as well as other reinforcement learning models. The computations in our model are inspired by Bayesian inference, and the synaptic plasticity changes depend on a three factor Hebbian–Bayesian learning rule based on co-activation of pre- and post-synaptic units and on the value of the RPE. The model builds on a modified Actor-Critic architecture and implements the direct (Go) and the indirect (NoGo) pathway, as well as the reward prediction (RP) system, acting in a complementary fashion. We investigated the performance of the model system when different configurations of the Go, NoGo, and RP system were utilized, e.g., using only the Go, NoGo, or RP system, or combinations of those. Learning performance was investigated in several types of learning paradigms, such as learning-relearning, successive learning, stochastic learning, reversal learning and a two-choice task. The RPE and the activity of the model during learning were similar to monkey electrophysiological and behavioral data. Our results, however, show that there is not a unique best way to configure this BG model to handle well all the learning paradigms tested. We thus suggest that an agent might dynamically configure its action selection mode, possibly depending on task characteristics and also on how much time is available. PMID:23060764

Characterization of p38 MAPK isoforms for drug resistance study using systems biology approach.

PubMed

Peng, Huiming; Peng, Tao; Wen, Jianguo; Engler, David A; Matsunami, Risë K; Su, Jing; Zhang, Le; Chang, Chung-Che Jeff; Zhou, Xiaobo

2014-07-01

p38 mitogen-activated protein kinase activation plays an important role in resistance to chemotherapeutic cytotoxic drugs in treating multiple myeloma (MM). However, how the p38 mitogen-activated protein kinase signaling pathway is involved in drug resistance, in particular the roles that the various p38 isoforms play, remains largely unknown. To explore the underlying mechanisms, we developed a novel systems biology approach by integrating liquid chromatography-mass spectrometry and reverse phase protein array data from human MM cell lines with computational pathway models in which the unknown parameters were inferred using a proposed novel algorithm called modularized factor graph. New mechanisms predicted by our models suggest that combined activation of various p38 isoforms may result in drug resistance in MM via regulating the related pathways including extracellular signal-regulated kinase (ERK) pathway and NFкB pathway. ERK pathway regulating cell growth is synergistically regulated by p38δ isoform, whereas nuclear factor kappa B (NFкB) pathway regulating cell apoptosis is synergistically regulated by p38α isoform. This finding that p38δ isoform promotes the phosphorylation of ERK1/2 in MM cells treated with bortezomib was validated by western blotting. Based on the predicted mechanisms, we further screened drug combinations in silico and found that a promising drug combination targeting ERK1/2 and NFκB might reduce the effects of drug resistance in MM cells. This study provides a framework of a systems biology approach to studying drug resistance and drug combination selection. RPPA experimental Data and Matlab source codes of modularized factor graph for parameter estimation are freely available online at http://ctsb.is.wfubmc.edu/publications/modularized-factor-graph.php. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Accuracy of algorithms to predict accessory pathway location in children with Wolff-Parkinson-White syndrome.

PubMed

Wren, Christopher; Vogel, Melanie; Lord, Stephen; Abrams, Dominic; Bourke, John; Rees, Philip; Rosenthal, Eric

2012-02-01

The aim of this study was to examine the accuracy in predicting pathway location in children with Wolff-Parkinson-White syndrome for each of seven published algorithms. ECGs from 100 consecutive children with Wolff-Parkinson-White syndrome undergoing electrophysiological study were analysed by six investigators using seven published algorithms, six of which had been developed in adult patients. Accuracy and concordance of predictions were adjusted for the number of pathway locations. Accessory pathways were left-sided in 49, septal in 20 and right-sided in 31 children. Overall accuracy of prediction was 30-49% for the exact location and 61-68% including adjacent locations. Concordance between investigators varied between 41% and 86%. No algorithm was better at predicting septal pathways (accuracy 5-35%, improving to 40-78% including adjacent locations), but one was significantly worse. Predictive accuracy was 24-53% for the exact location of right-sided pathways (50-71% including adjacent locations) and 32-55% for the exact location of left-sided pathways (58-73% including adjacent locations). All algorithms were less accurate in our hands than in other authors' own assessment. None performed well in identifying midseptal or right anteroseptal accessory pathway locations.

Integrative pathway analysis of a genome-wide association study of V̇o2max response to exercise training

PubMed Central

Vivar, Juan C.; Sarzynski, Mark A.; Sung, Yun Ju; Timmons, James A.; Bouchard, Claude; Rankinen, Tuomo

2013-01-01

We previously reported the findings from a genome-wide association study of the response of maximal oxygen uptake (V̇o2max) to an exercise program. Here we follow up on these results to generate hypotheses on genes, pathways, and systems involved in the ability to respond to exercise training. A systems biology approach can help us better establish a comprehensive physiological description of what underlies V̇o2maxtrainability. The primary material for this exploration was the individual single-nucleotide polymorphism (SNP), SNP-gene mapping, and statistical significance levels. We aimed to generate novel hypotheses through analyses that go beyond statistical association of single-locus markers. This was accomplished through three complementary approaches: 1) building de novo evidence of gene candidacy through informatics-driven literature mining; 2) aggregating evidence from statistical associations to link variant enrichment in biological pathways to V̇o2max trainability; and 3) predicting possible consequences of variants residing in the pathways of interest. We started with candidate gene prioritization followed by pathway analysis focused on overrepresentation analysis and gene set enrichment analysis. Subsequently, leads were followed using in silico analysis of predicted SNP functions. Pathways related to cellular energetics (pantothenate and CoA biosynthesis; PPAR signaling) and immune functions (complement and coagulation cascades) had the highest levels of SNP burden. In particular, long-chain fatty acid transport and fatty acid oxidation genes and sequence variants were found to influence differences in V̇o2max trainability. Together, these methods allow for the hypothesis-driven ranking and prioritization of genes and pathways for future experimental testing and validation. PMID:23990238

Small peptide signaling pathways modulating macronutrient utilization in plants.

PubMed

de Bang, Thomas C; Lay, Katerina S; Scheible, Wolf-Rüdiger; Takahashi, Hideki

2017-10-01

Root system architecture (RSA) and physiological functions define macronutrient uptake efficiency. Small signaling peptides (SSPs), that act in manners similar to hormones, and their cognate receptors transmit signals both locally and systemically. Several SSPs controlling morphological and physiological traits of roots have been identified to be associated with macronutrient uptake. Recent development in plant genome research has provided an avenue toward systems-based identification and prediction of additional SSPs. This review highlights recent studies on SSP pathways important for optimization of macronutrient uptake and provides new insights into the diversity of SSPs regulated in response to changes in macronutrient availabilities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism of Neurospora crassa

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

Cannon, William; Zucker, Jeremy; Baxter, Douglas

We report the application of a recently proposed approach for modeling biological systems using a maximum entropy production rate principle in lieu of having in vivo rate constants. The method is applied in four steps: (1) a new ODE-based optimization approach based on Marcelin’s 1910 mass action equation is used to obtain the maximum entropy distribution, (2) the predicted metabolite concentrations are compared to those generally expected from experiment using a loss function from which post-translational regulation of enzymes is inferred, (3) the system is re-optimized with the inferred regulation from which rate constants are determined from the metabolite concentrationsmore » and reaction fluxes, and finally (4) a full ODE-based, mass action simulation with rate parameters and allosteric regulation is obtained. From the last step, the power characteristics and resistance of each reaction can be determined. The method is applied to the central metabolism of Neurospora crassa and the flow of material through the three competing pathways of upper glycolysis, the non-oxidative pentose phosphate pathway, and the oxidative pentose phosphate pathway are evaluated as a function of the NADP/NADPH ratio. It is predicted that regulation of phosphofructokinase (PFK) and flow through the pentose phosphate pathway are essential for preventing an extreme level of fructose 1, 6-bisphophate accumulation. Such an extreme level of fructose 1,6-bisphophate would otherwise result in a glassy cytoplasm with limited diffusion, dramatically decreasing the entropy and energy production rate and, consequently, biological competitiveness.« less

Modeling of the hypothalamic-pituitary-adrenal axis-mediated interaction between the serotonin regulation pathway and the stress response using a Boolean approximation: a novel study of depression

PubMed Central

2013-01-01

Major depressive disorder (MDD) is a multifactorial disorder known to be influenced by both genetic and environmental factors. MDD presents a heritability of 37%, and a genetic contribution has also been observed in studies of family members of individuals with MDD that imply that the probability of suffering the disorder is approximately three times higher if a first-degree family member is affected. Childhood maltreatment and stressful life events (SLEs) have been established as critical environmental factors that profoundly influence the onset of MDD. The serotonin pathway has been a strong candidate for genetic studies, but it only explains a small proportion of the heritability of the disorder, which implies the involvement of other pathways. The serotonin (5-HT) pathway interacts with the stress response pathway in a manner mediated by the hypothalamic-pituitary-adrenal (HPA) axis. To analyze the interaction between the pathways, we propose the use of a synchronous Boolean network (SBN) approximation. The principal aim of this work was to model the interaction between these pathways, taking into consideration the presence of selective serotonin reuptake inhibitors (SSRIs), in order to observe how the pathways interact and to examine if the system is stable. Additionally, we wanted to study which genes or metabolites have the greatest impact on model stability when knocked out in silico. We observed that the biological model generated predicts steady states (attractors) for each of the different runs performed, thereby proving that the system is stable. These attractors changed in shape, especially when anti-depressive drugs were also included in the simulation. This work also predicted that the genes with the greatest impact on model stability were those involved in the neurotrophin pathway, such as CREB, BDNF (which has been associated with major depressive disorder in a variety of studies) and TRkB, followed by genes and metabolites related to 5-HT synthesis. PMID:24093582

A dynamical systems approach to studying midlatitude weather extremes

NASA Astrophysics Data System (ADS)

Messori, Gabriele; Caballero, Rodrigo; Faranda, Davide

2017-04-01

Extreme weather occurrences carry enormous social and economic costs and routinely garner widespread scientific and media coverage. The ability to predict these events is therefore a topic of crucial importance. Here we propose a novel predictability pathway for extreme events, by building upon recent advances in dynamical systems theory. We show that simple dynamical systems metrics can be used to identify sets of large-scale atmospheric flow patterns with similar spatial structure and temporal evolution on time scales of several days to a week. In regions where these patterns favor extreme weather, they afford a particularly good predictability of the extremes. We specifically test this technique on the atmospheric circulation in the North Atlantic region, where it provides predictability of large-scale wintertime surface temperature extremes in Europe up to 1 week in advance.

Metabolic Engineering for the Production of Natural Products

PubMed Central

Pickens, Lauren B.; Tang, Yi; Chooi, Yit-Heng

2014-01-01

Natural products and natural product derived compounds play an important role in modern healthcare as frontline treatments for many diseases and as inspiration for chemically synthesized therapeutics. With advances in sequencing and recombinant DNA technology, many of the biosynthetic pathways responsible for the production of these chemically complex and pharmaceutically valuable compounds have been elucidated. With an ever expanding toolkit of biosynthetic components, metabolic engineering is an increasingly powerful method to improve natural product titers and generate novel compounds. Heterologous production platforms have enabled access to pathways from difficult to culture strains; systems biology and metabolic modeling tools have resulted in increasing predictive and analytic capabilities; advances in expression systems and regulation have enabled the fine-tuning of pathways for increased efficiency, and characterization of individual pathway components has facilitated the construction of hybrid pathways for the production of new compounds. These advances in the many aspects of metabolic engineering have not only yielded fascinating scientific discoveries but also make it an increasingly viable approach for the optimization of natural product biosynthesis. PMID:22432617

Deciphering the ubiquitin-mediated pathway in apicomplexan parasites: a potential strategy to interfere with parasite virulence.

PubMed

Ponts, Nadia; Yang, Jianfeng; Chung, Duk-Won Doug; Prudhomme, Jacques; Girke, Thomas; Horrocks, Paul; Le Roch, Karine G

2008-06-11

Reversible modification of proteins through the attachment of ubiquitin or ubiquitin-like modifiers is an essential post-translational regulatory mechanism in eukaryotes. The conjugation of ubiquitin or ubiquitin-like proteins has been demonstrated to play roles in growth, adaptation and homeostasis in all eukaryotes, with perturbation of ubiquitin-mediated systems associated with the pathogenesis of many human diseases, including cancer and neurodegenerative disorders. Here we describe the use of an HMM search of functional Pfam domains found in the key components of the ubiquitin-mediated pathway necessary to activate and reversibly modify target proteins in eight apicomplexan parasitic protozoa for which complete or late-stage genome projects exist. In parallel, the same search was conducted on five model organisms, single-celled and metazoans, to generate data to validate both the search parameters employed and aid paralog classification in Apicomplexa. For each of the 13 species investigated, a set of proteins predicted to be involved in the ubiquitylation pathway has been identified and demonstrates increasing component members of the ubiquitylation pathway correlating with organism and genome complexity. Sequence homology and domain architecture analyses facilitated prediction of apicomplexan-specific protein function, particularly those involved in regulating cell division during these parasite's complex life cycles. This study provides a comprehensive analysis of proteins predicted to be involved in the apicomplexan ubiquitin-mediated pathway. Given the importance of such pathway in a wide variety of cellular processes, our data is a key step in elucidating the biological networks that, in part, direct the pathogenicity of these parasites resulting in a massive impact on global health. Moreover, apicomplexan-specific adaptations of the ubiquitylation pathway may represent new therapeutic targets for much needed drugs against apicomplexan parasites.

Locate the Lesion: A Project-Based Learning Case that Stimulates Comprehension and Application of Neuroanatomy

PubMed Central

Lemons, Michele L.

2017-01-01

A fictitious patient, Mr. Challenge, is admitted to the emergency room and displays symptoms consistent with damage to the central nervous system. In this problem-based learning case, students are challenged to determine the location of a lesion that is consistent with Mr. Challenge’s symptoms. Students discover details about Mr. Challenge’s symptoms while exploring three anatomical pathways: corticospinal tract, spinothalamic tract and medial lemniscal pathway. Students make predictions as to which of these pathways may be damaged in Mr. Challenge and defend their predictions based on their research of the function and anatomical location of these tracts. This ultimately leads the student to identifying a single lesion site that can account for Mr. Challenge’s symptoms. This case is executed in an undergraduate neuroscience course and would be useful in anatomy and physiology course, as well as other courses that serve students interested in health science related careers. PMID:28690440

Locate the Lesion: A Project-Based Learning Case that Stimulates Comprehension and Application of Neuroanatomy.

PubMed

Lemons, Michele L

2017-01-01

A fictitious patient, Mr. Challenge, is admitted to the emergency room and displays symptoms consistent with damage to the central nervous system. In this problem-based learning case, students are challenged to determine the location of a lesion that is consistent with Mr. Challenge's symptoms. Students discover details about Mr. Challenge's symptoms while exploring three anatomical pathways: corticospinal tract, spinothalamic tract and medial lemniscal pathway. Students make predictions as to which of these pathways may be damaged in Mr. Challenge and defend their predictions based on their research of the function and anatomical location of these tracts. This ultimately leads the student to identifying a single lesion site that can account for Mr. Challenge's symptoms. This case is executed in an undergraduate neuroscience course and would be useful in anatomy and physiology course, as well as other courses that serve students interested in health science related careers.

Discordant predictions of residual activity could impact dolutegravir prescription upon raltegravir failure.

PubMed

Theys, Kristof; Abecasis, Ana; Libin, Pieter; Gomes, Perpe Tua; Cabanas, Joaquim; Camacho, Ricardo J; Van Laethem, Kristel

2015-09-01

Dolutegravir is approved for the treatment of HIV-1 patients exposed to other integrase inhibitors, but the decision to use dolutegravir in this setting should be informed by drug resistance testing. This study determined the extent of disagreement in predicted residual dolutegravir activity after raltegravir use, and identified individual mutational patterns for which uncertainty exists among HIV-1 expert systems. Mutation patterns were classified in raltegravir signature pathways including positions 143, 148 and 155, and interpreted into clinically informative resistance levels using genotypic drug resistance interpretation systems ANRS v24, HIVdb v7.0 and Rega v9.1.0, and instructions of dolutegravir use as approved by the Food and Drug Administration and the European Medicines Agency. In 216HIV-1 patients failing raltegravir-therapy, 87% patients displayed mutations associated with resistance towards integrase inhibitors. A total of 141 unique mutational patterns were observed, with N155H (25.4%), Q148H (16.2%) and Y143R (8.3%) the most prevalent signature mutations. The Q148 pathway occurred almost exclusively in HIV-1 subtype B viruses. Concordances in predicted dolutegravir susceptibility scores among 5 systems were obtained in 57.8% of patients, and concordant intermediate resistant and concordant resistant scores were only observed in 6.5% and 0.9% of patients, respectively. However, systems individually scored higher levels of dolutegravir intermediate resistance and resistance, ranging from 4.2% to 10.2% and from 14.8% to 22.7% of patients, respectively. A consensus on interpreting the extent of residual activity was lacking in 34.7% of patients and was highly resistance pathway-specific. Dolutegravir may potentially be effective in the majority of HIV-1 patients failing raltegravir, but concern over the uncertainty in predicted residual activity could withhold clinicians from prescribing dolutegravir during its clinical assessment. Copyright © 2015 Elsevier B.V. All rights reserved.

Computational prediction of secretion systems and secretomes of Brucella: identification of novel type IV effectors and their interaction with the host.

PubMed

Sankarasubramanian, Jagadesan; Vishnu, Udayakumar S; Dinakaran, Vasudevan; Sridhar, Jayavel; Gunasekaran, Paramasamy; Rajendhran, Jeyaprakash

2016-01-01

Brucella spp. are facultative intracellular pathogens that cause brucellosis in various mammals including humans. Brucella survive inside the host cells by forming vacuoles and subverting host defence systems. This study was aimed to predict the secretion systems and the secretomes of Brucella spp. from 39 complete genome sequences available in the databases. Furthermore, an attempt was made to identify the type IV secretion effectors and their interactions with host proteins. We predicted the secretion systems of Brucella by the KEGG pathway and SecReT4. Brucella secretomes and type IV effectors (T4SEs) were predicted through genome-wide screening using JVirGel and S4TE, respectively. Protein-protein interactions of Brucella T4SEs with their hosts were analyzed by HPIDB 2.0. Genes coding for Sec and Tat pathways of secretion and type I (T1SS), type IV (T4SS) and type V (T5SS) secretion systems were identified and they are conserved in all the species of Brucella. In addition to the well-known VirB operon coding for the type IV secretion system (T4SS), we have identified the presence of additional genes showing homology with T4SS of other organisms. On the whole, 10.26 to 14.94% of total proteomes were found to be either secreted (secretome) or membrane associated (membrane proteome). Approximately, 1.7 to 3.0% of total proteomes were identified as type IV secretion effectors (T4SEs). Prediction of protein-protein interactions showed 29 and 36 host-pathogen specific interactions between Bos taurus (cattle)-B. abortus and Ovis aries (sheep)-B. melitensis, respectively. Functional characterization of the predicted T4SEs and their interactions with their respective hosts may reveal the secrets of host specificity of Brucella.

Developing and applying the adverse outcome pathway ...

EPA Pesticide Factsheets

To support a paradigm shift in regulatory toxicology testing and risk assessment, the Adverse Outcome Pathway (AOP) concept has recently been proposed. This concept is similar to that for Mode of Action (MOA), describing a sequence of measurable key events triggered by a molecular initiating event in which a stressor interacts with a biological target. The resulting cascade of key events includes molecular, cellular, structural and functional changes in biological systems, resulting in a measurable adverse outcome. Thereby, an AOP ideally provides information relevant to chemical structure-activity relationships as a basis to predict effects for structurally similar compounds. AOPs could potentially also form the basis for qualitative and quantitative predictive modeling of the human adverse outcome resulting from molecular initiating or other key events for which higher-throughput testing methods are available or can be developed.A variety of cellular and molecular processes are known to be critical to normal function of the central (CNS) and peripheral nervous systems (PNS). Because of the biological and functional complexity of the CNS and PNS, it has been challenging to establish causative links and quantitative relationships between key events that comprise the pathways leading from chemical exposure to an adverse outcome in the nervous system. Following introduction of principles of the description and assessment of MOA and AOPs, examples of adverse out

Computationally-Predicted AOPs and Systems Toxicology

EPA Science Inventory

The Adverse Outcome Pathway has emerged as an internationally harmonized mechanism for organizing biological information in a chemical agnostic manner. This construct is valuable for interpreting the results from high-throughput toxicity (HTT) assessment by providing a mechanisti...

A Way Forward Commentary

EPA Science Inventory

Models for predicting adverse outcomes can help reduce and focus animal testing with new and existing chemicals. This short "thought starter" describes how quantitative-structure activity relationship and systems biology models can be used to help define toxicity pathways and li...

Discovery of new enzymes and metabolic pathways by using structure and genome context.

PubMed

Zhao, Suwen; Kumar, Ritesh; Sakai, Ayano; Vetting, Matthew W; Wood, B McKay; Brown, Shoshana; Bonanno, Jeffery B; Hillerich, Brandan S; Seidel, Ronald D; Babbitt, Patricia C; Almo, Steven C; Sweedler, Jonathan V; Gerlt, John A; Cronan, John E; Jacobson, Matthew P

2013-10-31

Assigning valid functions to proteins identified in genome projects is challenging: overprediction and database annotation errors are the principal concerns. We and others are developing computation-guided strategies for functional discovery with 'metabolite docking' to experimentally derived or homology-based three-dimensional structures. Bacterial metabolic pathways often are encoded by 'genome neighbourhoods' (gene clusters and/or operons), which can provide important clues for functional assignment. We recently demonstrated the synergy of docking and pathway context by 'predicting' the intermediates in the glycolytic pathway in Escherichia coli. Metabolite docking to multiple binding proteins and enzymes in the same pathway increases the reliability of in silico predictions of substrate specificities because the pathway intermediates are structurally similar. Here we report that structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster allowed the correct prediction of the in vitro activity of a structurally characterized enzyme of unknown function (PDB 2PMQ), 2-epimerization of trans-4-hydroxy-L-proline betaine (tHyp-B) and cis-4-hydroxy-D-proline betaine (cHyp-B), and also the correct identification of the catabolic pathway in which Hyp-B 2-epimerase participates. The substrate-liganded pose predicted by virtual library screening (docking) was confirmed experimentally. The enzymatic activities in the predicted pathway were confirmed by in vitro assays and genetic analyses; the intermediates were identified by metabolomics; and repression of the genes encoding the pathway by high salt concentrations was established by transcriptomics, confirming the osmolyte role of tHyp-B. This study establishes the utility of structure-guided functional predictions to enable the discovery of new metabolic pathways.

Outcome prediction of third ventriculostomy: a proposed hydrocephalus grading system.

PubMed

Kehler, U; Regelsberger, J; Gliemroth, J; Westphal, M

2006-08-01

An important factor in making a recommendation for different treatment modalities in hydrocephalus patients (VP shunt versus endoscopic third ventriculostomy) is the definition of the underlying pathology which determines the prognosis/outcome of the surgical procedure. Third ventriculostomies (3rd VS) are successful mainly in obstructive hydrocephalus but also in some subtypes of communicating hydrocephalus. A simple, easily applicable grading system that is designed to predict the outcome of 3rd VS is proposed. The hydrocephalus is graded on the basis of the extent of downward bulging of the floor of the third ventricle, which reflects the pressure gradient between the 3rd ventricle and the basal cisterns, presence of directly visualised CSF pathway obstruction in MRI, and the progression of the clinical symptoms resulting in five different grades. In this proposed grading system, grade 1 hydrocephalus subtype shows no downward bulged floor of the 3rd ventricle, no obstruction of the CSF pathway, and no progressive symptoms of hydrocephalus. There is no indication for 3rd VS. Grades 2 to 4 show different combinations of the described parameters. Grade 5 subtype shows a markedly downward bulged floor of the 3rd ventricle and direct detection of the CSF pathway obstruction (i.e., aqueductal stenosis) with progressive clinical deterioration. Retrospective application of this grading scheme to a series of 72 3rd VS has demonstrated a high correlation with the outcome: The success rate in grade 3 reached 40%, in grade 4: 58%, and in grade 5: 95%. This standardised grading system predicts the outcome of 3rd VS and helps in decision making for 3rd VS versus VP shunting.

NAViGaTing the Micronome – Using Multiple MicroRNA Prediction Databases to Identify Signalling Pathway-Associated MicroRNAs

PubMed Central

Shirdel, Elize A.; Xie, Wing; Mak, Tak W.; Jurisica, Igor

2011-01-01

Background MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome – referred to as the micronome – to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal — mirDIP (http://ophid.utoronto.ca/mirDIP). Results mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs. Conclusions Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level. PMID:21364759

Rab protein evolution and the history of the eukaryotic endomembrane system

PubMed Central

Brighouse, Andrew; Dacks, Joel B.

2010-01-01

Spectacular increases in the quantity of sequence data genome have facilitated major advances in eukaryotic comparative genomics. By exploiting homology with classical model organisms, this makes possible predictions of pathways and cellular functions currently impossible to address in intractable organisms. Echoing realization that core metabolic processes were established very early following evolution of life on earth, it is now emerging that many eukaryotic cellular features, including the endomembrane system, are ancient and organized around near-universal principles. Rab proteins are key mediators of vesicle transport and specificity, and via the presence of multiple paralogues, alterations in interaction specificity and modification of pathways, contribute greatly to the evolution of complexity of membrane transport. Understanding system-level contributions of Rab proteins to evolutionary history provides insight into the multiple processes sculpting cellular transport pathways and the exciting challenges that we face in delving further into the origins of membrane trafficking specificity. PMID:20582450

A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT.

PubMed

Bown, James L; Shovman, Mark; Robertson, Paul; Boiko, Andrei; Goltsov, Alexey; Mullen, Peter; Harrison, David J

2017-05-02

Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualization toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery.

Computational Reconstruction of NFκB Pathway Interaction Mechanisms during Prostate Cancer

PubMed Central

Börnigen, Daniela; Tyekucheva, Svitlana; Wang, Xiaodong; Rider, Jennifer R.; Lee, Gwo-Shu; Mucci, Lorelei A.; Sweeney, Christopher; Huttenhower, Curtis

2016-01-01

Molecular research in cancer is one of the largest areas of bioinformatic investigation, but it remains a challenge to understand biomolecular mechanisms in cancer-related pathways from high-throughput genomic data. This includes the Nuclear-factor-kappa-B (NFκB) pathway, which is central to the inflammatory response and cell proliferation in prostate cancer development and progression. Despite close scrutiny and a deep understanding of many of its members’ biomolecular activities, the current list of pathway members and a systems-level understanding of their interactions remains incomplete. Here, we provide the first steps toward computational reconstruction of interaction mechanisms of the NFκB pathway in prostate cancer. We identified novel roles for ATF3, CXCL2, DUSP5, JUNB, NEDD9, SELE, TRIB1, and ZFP36 in this pathway, in addition to new mechanistic interactions between these genes and 10 known NFκB pathway members. A newly predicted interaction between NEDD9 and ZFP36 in particular was validated by co-immunoprecipitation, as was NEDD9's potential biological role in prostate cancer cell growth regulation. We combined 651 gene expression datasets with 1.4M gene product interactions to predict the inclusion of 40 additional genes in the pathway. Molecular mechanisms of interaction among pathway members were inferred using recent advances in Bayesian data integration to simultaneously provide information specific to biological contexts and individual biomolecular activities, resulting in a total of 112 interactions in the fully reconstructed NFκB pathway: 13 (11%) previously known, 29 (26%) supported by existing literature, and 70 (63%) novel. This method is generalizable to other tissue types, cancers, and organisms, and this new information about the NFκB pathway will allow us to further understand prostate cancer and to develop more effective prevention and treatment strategies. PMID:27078000

Dynamical systems proxies of atmospheric predictability and mid-latitude extremes

NASA Astrophysics Data System (ADS)

Messori, Gabriele; Faranda, Davide; Caballero, Rodrigo; Yiou, Pascal

2017-04-01

Extreme weather ocurrences carry enormous social and economic costs and routinely garner widespread scientific and media coverage. Many extremes (for e.g. storms, heatwaves, cold spells, heavy precipitation) are tied to specific patterns of midlatitude atmospheric circulation. The ability to identify these patterns and use them to enhance the predictability of the extremes is therefore a topic of crucial societal and economic value. We propose a novel predictability pathway for extreme events, by building upon recent advances in dynamical systems theory. We use two simple dynamical systems metrics - local dimension and persistence - to identify sets of similar large-scale atmospheric flow patterns which present a coherent temporal evolution. When these patterns correspond to weather extremes, they therefore afford a particularly good forward predictability. We specifically test this technique on European winter temperatures, whose variability largely depends on the atmospheric circulation in the North Atlantic region. We find that our dynamical systems approach provides predictability of large-scale temperature extremes up to one week in advance.

Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks

DOE PAGES

Zhao, Suwen; Sakai, Ayano; Zhang, Xinshuai; ...

2014-06-30

Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins inmore » 12 families in the PRS that represent ~85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.« less

Dissecting dysfunctional crosstalk pathways regulated by miRNAs during glioma progression

PubMed Central

Li, Feng; Li, Xiang; Feng, Li; Shi, Xinrui; Wang, Lihua; Li, Xia

2016-01-01

Glioma is a malignant nervous system tumor with a high fatality rate and poor prognosis. MicroRNAs (miRNAs) are important post-transcriptional modulators of glioma initiation and progression. Tumor progression often results from dysfunctional co-operation between pathways regulated by miRNAs. We therefore constructed a glioma progression-related miRNA-pathway crosstalk network that not only revealed some key miRNA-pathway patterns, but also helped characterize the functional roles of miRNAs during glioma progression. Our data indicate that crosstalk between cell cycle and p53 pathways is associated with grade II to grade III progression, while cell communications-related pathways involving regulation of actin cytoskeleton and adherens junctions are associated with grade IV glioblastoma progression. Furthermore, miRNAs and their crosstalk pathways may be useful for stratifying glioma and glioblastoma patients into groups with short or long survival times. Our data indicate that a combination of miRNA and pathway crosstalk information can be used for survival prediction. PMID:27013589

Code System to Calculate Tornado-Induced Flow Material Transport.

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

ANDRAE, R. W.

1999-11-18

Version: 00 TORAC models tornado-induced flows, pressures, and material transport within structures. Its use is directed toward nuclear fuel cycle facilities and their primary release pathway, the ventilation system. However, it is applicable to other structures and can model other airflow pathways within a facility. In a nuclear facility, this network system could include process cells, canyons, laboratory offices, corridors, and offgas systems. TORAC predicts flow through a network system that also includes ventilation system components such as filters, dampers, ducts, and blowers. These ventilation system components are connected to the rooms and corridors of the facility to form amore » complete network for moving air through the structure and, perhaps, maintaining pressure levels in certain areas. The material transport capability in TORAC is very basic and includes convection, depletion, entrainment, and filtration of material.« less

A network pharmacology approach to discover active compounds and action mechanisms of San-Cao Granule for treatment of liver fibrosis

PubMed Central

Wei, Shizhang; Niu, Ming; Wang, Jian; Wang, Jiabo; Su, Haibin; Luo, Shengqiang; Zhang, Xiaomei; Guo, Yanlei; Liu, Liping; Liu, Fengqun; Zhao, Qingguo; Chen, Hongge; Xiao, Xiaohe; Zhao, Pan; Zhao, Yanling

2016-01-01

Ethnopharmacological relevance San-Cao Granule (SCG) has been used in patients with liver fibrosis for many years and has shown good effect. However, its mechanism of therapeutic action is not clear because of its complex chemical system. The purpose of our study is to establish a comprehensive and systemic method that can predict the mechanism of action of SCG in antihepatic fibrosis. Materials and methods In this study, a “compound–target–disease” network was constructed by combining the SCG-specific and liver fibrosis–specific target proteins with protein–protein interactions, and network pharmacology was used to screen out the underlying targets and mechanisms of SCG for treatment of liver fibrosis. Then, some key molecules of the enriched pathway were chosen to verify the effects of SCG on liver fibrosis induced by thioacetamide (TAA). Results This systematic approach had successfully revealed that 16 targets related to 11 SCG compounds were closely associated with liver fibrosis therapy. The pathway-enrichment analysis of them showed that the TGF-β1/Smad signaling pathway is relatively important. Animal experiments also proved that SCG could significantly ameliorate liver fibrosis by inhibiting the TGF-β1/Smad pathway. Conclusion SCG could alleviate liver fibrosis through the molecular mechanisms predicted by network pharmacology. Furthermore, network pharmacology could provide deep insight into the pharmacological mechanisms of Chinese herbal formulas. PMID:26929602

A network pharmacology approach to discover active compounds and action mechanisms of San-Cao Granule for treatment of liver fibrosis.

PubMed

Wei, Shizhang; Niu, Ming; Wang, Jian; Wang, Jiabo; Su, Haibin; Luo, Shengqiang; Zhang, Xiaomei; Guo, Yanlei; Liu, Liping; Liu, Fengqun; Zhao, Qingguo; Chen, Hongge; Xiao, Xiaohe; Zhao, Pan; Zhao, Yanling

2016-01-01

San-Cao Granule (SCG) has been used in patients with liver fibrosis for many years and has shown good effect. However, its mechanism of therapeutic action is not clear because of its complex chemical system. The purpose of our study is to establish a comprehensive and systemic method that can predict the mechanism of action of SCG in antihepatic fibrosis. In this study, a "compound-target-disease" network was constructed by combining the SCG-specific and liver fibrosis-specific target proteins with protein-protein interactions, and network pharmacology was used to screen out the underlying targets and mechanisms of SCG for treatment of liver fibrosis. Then, some key molecules of the enriched pathway were chosen to verify the effects of SCG on liver fibrosis induced by thioacetamide (TAA). This systematic approach had successfully revealed that 16 targets related to 11 SCG compounds were closely associated with liver fibrosis therapy. The pathway-enrichment analysis of them showed that the TGF-β1/Smad signaling pathway is relatively important. Animal experiments also proved that SCG could significantly ameliorate liver fibrosis by inhibiting the TGF-β1/Smad pathway. SCG could alleviate liver fibrosis through the molecular mechanisms predicted by network pharmacology. Furthermore, network pharmacology could provide deep insight into the pharmacological mechanisms of Chinese herbal formulas.

Multiscale systems pharmacological analysis of everolimus action in hepatocellular carcinoma.

PubMed

Ande, Anusha; Chaar, Maher; Ait-Oudhia, Sihem

2018-05-03

Dysregulation of mTOR pathway is common in hepatocellular carcinoma (HCC). A translational quantitative systems pharmacology (QSP), pharmacokinetic (PK), and pharmacodynamic (PD) model dissecting the circuitry of this pathway was developed to predict HCC patients' response to everolimus, an mTOR inhibitor. The time course of key signaling proteins in the mTOR pathway, HCC cells viability, tumor volume (TV) and everolimus plasma and tumor concentrations in xenograft mice, clinical PK of everolimus and progression free survival (PFS) in placebo and everolimus-treated patients were extracted from literature. A comprehensive and multiscale QSP/PK/PD model was developed, qualified, and translated to clinical settings. Model fittings and simulations were performed using Monolix software. The S6-kinase protein was identified as critical in the mTOR signaling pathway for describing everolimus lack of efficacy in HCC patients. The net growth rate constant (kg) of HCC cells was estimated at 0.02 h -1 (2.88%RSE). The partition coefficient of everolimus into the tumor (kp) was determined at 0.06 (12.98%RSE). The kg in patients was calculated from the doubling time of TV in naturally progressing HCC patients, and was determined at 0.004 day -1 . Model-predicted and observed PFS were in good agreement for placebo and everolimus-treated patients. In conclusion, a multiscale QSP/PK/PD model elucidating everolimus lack of efficacy in HCC patients was successfully developed and predicted PFS reasonably well compared to observed clinical findings. This model may provide insights into clinical response to everolimus-based therapy and serve as a valuable tool for the clinical translation of efficacy for novel mTOR inhibitors.

Comparison of the accuracy of three algorithms in predicting accessory pathways among adult Wolff-Parkinson-White syndrome patients.

PubMed

Maden, Orhan; Balci, Kevser Gülcihan; Selcuk, Mehmet Timur; Balci, Mustafa Mücahit; Açar, Burak; Unal, Sefa; Kara, Meryem; Selcuk, Hatice

2015-12-01

The aim of this study was to investigate the accuracy of three algorithms in predicting accessory pathway locations in adult patients with Wolff-Parkinson-White syndrome in Turkish population. A total of 207 adult patients with Wolff-Parkinson-White syndrome were retrospectively analyzed. The most preexcited 12-lead electrocardiogram in sinus rhythm was used for analysis. Two investigators blinded to the patient data used three algorithms for prediction of accessory pathway location. Among all locations, 48.5% were left-sided, 44% were right-sided, and 7.5% were located in the midseptum or anteroseptum. When only exact locations were accepted as match, predictive accuracy for Chiang was 71.5%, 72.4% for d'Avila, and 71.5% for Arruda. The percentage of predictive accuracy of all algorithms did not differ between the algorithms (p = 1.000; p = 0.875; p = 0.885, respectively). The best algorithm for prediction of right-sided, left-sided, and anteroseptal and midseptal accessory pathways was Arruda (p < 0.001). Arruda was significantly better than d'Avila in predicting adjacent sites (p = 0.035) and the percent of the contralateral site prediction was higher with d'Avila than Arruda (p = 0.013). All algorithms were similar in predicting accessory pathway location and the predicted accuracy was lower than previously reported by their authors. However, according to the accessory pathway site, the algorithm designed by Arruda et al. showed better predictions than the other algorithms and using this algorithm may provide advantages before a planned ablation.

Estimating epidemic arrival times using linear spreading theory

NASA Astrophysics Data System (ADS)

Chen, Lawrence M.; Holzer, Matt; Shapiro, Anne

2018-01-01

We study the dynamics of a spatially structured model of worldwide epidemics and formulate predictions for arrival times of the disease at any city in the network. The model is composed of a system of ordinary differential equations describing a meta-population susceptible-infected-recovered compartmental model defined on a network where each node represents a city and the edges represent the flight paths connecting cities. Making use of the linear determinacy of the system, we consider spreading speeds and arrival times in the system linearized about the unstable disease free state and compare these to arrival times in the nonlinear system. Two predictions are presented. The first is based upon expansion of the heat kernel for the linearized system. The second assumes that the dominant transmission pathway between any two cities can be approximated by a one dimensional lattice or a homogeneous tree and gives a uniform prediction for arrival times independent of the specific network features. We test these predictions on a real network describing worldwide airline traffic.

MaizeCyc: Metabolic networks in maize

USDA-ARS?s Scientific Manuscript database

MaizeCyc is a catalog of known and predicted metabolic and transport pathways that enables plant researchers to graphically represent the metabolome of maize (Zea mays), thereby supporting integrated systems-biology analysis. Supported analyses include molecular and genetic/phenotypic profiling (e.g...

Application of Adverse Outcome Pathways to U.S. EPA’s Endocrine Disruptor Screening Program

PubMed Central

Noyes, Pamela D.; Casey, Warren M.; Dix, David J.

2017-01-01

Background: The U.S. EPA’s Endocrine Disruptor Screening Program (EDSP) screens and tests environmental chemicals for potential effects in estrogen, androgen, and thyroid hormone pathways, and it is one of the only regulatory programs designed around chemical mode of action. Objectives: This review describes the EDSP’s use of adverse outcome pathway (AOP) and toxicity pathway frameworks to organize and integrate diverse biological data for evaluating the endocrine activity of chemicals. Using these frameworks helps to establish biologically plausible links between endocrine mechanisms and apical responses when those end points are not measured in the same assay. Results: Pathway frameworks can facilitate a weight of evidence determination of a chemical’s potential endocrine activity, identify data gaps, aid study design, direct assay development, and guide testing strategies. Pathway frameworks also can be used to evaluate the performance of computational approaches as alternatives for low-throughput and animal-based assays and predict downstream key events. In cases where computational methods can be validated based on performance, they may be considered as alternatives to specific assays or end points. Conclusions: A variety of biological systems affect apical end points used in regulatory risk assessments, and without mechanistic data, an endocrine mode of action cannot be determined. Because the EDSP was designed to consider mode of action, toxicity pathway and AOP concepts are a natural fit. Pathway frameworks have diverse applications to endocrine screening and testing. An estrogen pathway example is presented, and similar approaches are being used to evaluate alternative methods and develop predictive models for androgen and thyroid pathways. https://doi.org/10.1289/EHP1304 PMID:28934726

Finding off-targets, biological pathways, and target diseases for chymase inhibitors via structure-based systems biology approach.

PubMed

Arooj, Mahreen; Sakkiah, Sugunadevi; Cao, Guang Ping; Kim, Songmi; Arulalapperumal, Venkatesh; Lee, Keun Woo

2015-07-01

Off-target binding connotes the binding of a small molecule of therapeutic significance to a protein target in addition to the primary target for which it was proposed. Progressively such off-targeting is emerging to be regular practice to reveal side effects. Chymase is an enzyme of hydrolase class that catalyzes hydrolysis of peptide bonds. A link between heart failure and chymase is ascribed, and a chymase inhibitor is in clinical phase II for treatment of heart failure. However, the underlying mechanisms of the off-target effects of human chymase inhibitors are still unclear. Here, we develop a robust computational strategy that is applicable to any enzyme system and that allows the prediction of drug effects on biological processes. Putative off-targets for chymase inhibitors were identified through various structural and functional similarity analyses along with molecular docking studies. Finally, literature survey was performed to incorporate these off-targets into biological pathways and to establish links between pathways and particular adverse effects. Off-targets of chymase inhibitors are linked to various biological pathways such as classical and lectin pathways of complement system, intrinsic and extrinsic pathways of coagulation cascade, and fibrinolytic system. Tissue kallikreins, granzyme M, neutrophil elastase, and mesotrypsin are also identified as off-targets. These off-targets and their associated pathways are elucidated for the effects of inflammation, cancer, hemorrhage, thrombosis, and central nervous system diseases (Alzheimer's disease). Prospectively, our approach is helpful not only to better understand the mechanisms of chymase inhibitors but also for drug repurposing exercises to find novel uses for these inhibitors. © 2014 Wiley Periodicals, Inc.

A Feasibility Study for Perioperative Ventricular Tachycardia Prognosis and Detection and Noise Detection Using a Neural Network and Predictive Linear Operators

NASA Technical Reports Server (NTRS)

Moebes, T. A.

1994-01-01

To locate the accessory pathway(s) in preexicitation syndromes, epicardial and endocardial ventricular mapping is performed during anterograde ventricular activation via accessory pathway(s) from data originally received in signal form. As the number of channels increases, it is pertinent that more automated detection of coherent/incoherent signals is achieved as well as the prediction and prognosis of ventricular tachywardia (VT). Today's computers and computer program algorithms are not good in simple perceptual tasks such as recognizing a pattern or identifying a sound. This discrepancy, among other things, has been a major motivating factor in developing brain-based, massively parallel computing architectures. Neural net paradigms have proven to be effective at pattern recognition tasks. In signal processing, the picking of coherent/incoherent signals represents a pattern recognition task for computer systems. The picking of signals representing the onset ot VT also represents such a computer task. We attacked this problem by defining four signal attributes for each potential first maximal arrival peak and one signal attribute over the entire signal as input to a back propagation neural network. One attribute was the predicted amplitude value after the maximum amplitude over a data window. Then, by using a set of known (user selected) coherent/incoherent signals, and signals representing the onset of VT, we trained the back propagation network to recognize coherent/incoherent signals, and signals indicating the onset of VT. Since our output scheme involves a true or false decision, and since the output unit computes values between 0 and 1, we used a Fuzzy Arithmetic approach to classify data as coherent/incoherent signals. Furthermore, a Mean-Square Error Analysis was used to determine system stability. The neural net based picking coherent/incoherent signal system achieved high accuracy on picking coherent/incoherent signals on different patients. The system also achieved a high accuracy of picking signals which represent the onset of VT, that is, VT immediately followed these signals. A special binary representation of the input and output data allowed the neural network to train very rapidly as compared to another standard decimal or normalized representations of the data.

Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example.

PubMed

Gunalan, Kabilar; Chaturvedi, Ashutosh; Howell, Bryan; Duchin, Yuval; Lempka, Scott F; Patriat, Remi; Sapiro, Guillermo; Harel, Noam; McIntyre, Cameron C

2017-01-01

Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports. Provide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation. Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson's disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution. Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings. Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation.

Differential miRNA expression in B cells is associated with inter-individual differences in humoral immune response to measles vaccination.

PubMed

Haralambieva, Iana H; Kennedy, Richard B; Simon, Whitney L; Goergen, Krista M; Grill, Diane E; Ovsyannikova, Inna G; Poland, Gregory A

2018-01-01

MicroRNAs are important mediators of post-transcriptional regulation of gene expression through RNA degradation and translational repression, and are emerging biomarkers of immune system activation/response after vaccination. We performed Next Generation Sequencing (mRNA-Seq) of intracellular miRNAs in measles virus-stimulated B and CD4+ T cells from high and low antibody responders to measles vaccine. Negative binomial generalized estimating equation (GEE) models were used for miRNA assessment and the DIANA tool was used for gene/target prediction and pathway enrichment analysis. We identified a set of B cell-specific miRNAs (e.g., miR-151a-5p, miR-223, miR-29, miR-15a-5p, miR-199a-3p, miR-103a, and miR-15a/16 cluster) and biological processes/pathways, including regulation of adherens junction proteins, Fc-receptor signaling pathway, phosphatidylinositol-mediated signaling pathway, growth factor signaling pathway/pathways, transcriptional regulation, apoptosis and virus-related processes, significantly associated with neutralizing antibody titers after measles vaccination. No CD4+ T cell-specific miRNA expression differences between high and low antibody responders were found. Our study demonstrates that miRNA expression directly or indirectly influences humoral immunity to measles vaccination and suggests that B cell-specific miRNAs may serve as useful predictive biomarkers of vaccine humoral immune response.

Design principles for enhancing phase sensitivity and suppressing phase fluctuations simultaneously in biochemical oscillatory systems.

PubMed

Fei, Chenyi; Cao, Yuansheng; Ouyang, Qi; Tu, Yuhai

2018-04-12

Biological systems need to function accurately in the presence of strong noise and at the same time respond sensitively to subtle external cues. Here we study design principles in biochemical oscillatory circuits to achieve these two seemingly incompatible goals. We show that energy dissipation can enhance phase sensitivity linearly by driving the phase-amplitude coupling and increase timing accuracy by suppressing phase diffusion. Two general design principles in the key underlying reaction loop formed by two antiparallel pathways are found to optimize oscillation performance with a given energy budget: balancing the forward-to-backward flux ratio between the two pathways to reduce phase diffusion and maximizing the net flux of the phase-advancing pathway relative to that of the phase-retreating pathway to enhance phase sensitivity. Experimental evidences consistent with these design principles are found in the circadian clock of cyanobacteria. Future experiments to test the predicted dependence of phase sensitivity on energy dissipation are proposed.

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology

PubMed Central

Latendresse, Mario; Paley, Suzanne M.; Krummenacker, Markus; Ong, Quang D.; Billington, Richard; Kothari, Anamika; Weaver, Daniel; Lee, Thomas; Subhraveti, Pallavi; Spaulding, Aaron; Fulcher, Carol; Keseler, Ingrid M.; Caspi, Ron

2016-01-01

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms. PMID:26454094

Using metagenomic and metatranscriptomic observations to test a thermodynamic-based model of community metabolic expression over time and space

NASA Astrophysics Data System (ADS)

Vallino, J. J.; Huber, J. A.

2016-02-01

Marine biogeochemistry is orchestrated by a complex and dynamic community of microorganisms that attempt to maximize their own fecundity through a combination of competition and cooperation. At a systems level, the community can be described as a distributed metabolic network, where different species contribute their own unique set of metabolic capabilities. Our current project attempts to understand the governing principles that describe amplification or attenuation of metabolic pathways within the network through a combination of modeling and metagenomic, metatranscriptomic and biogeochemical observations. We will describe and present results from our thermodynamic-based model that determines optimal pathway expression from available resources based on the principle of maximum entropy production (MEP); that is, based on the hypothesis that non-equilibrium systems organize to maximize energy dissipation. The MEP model currently predicts metabolic pathway expression over time, and one spatial dimension. Model predictions will be compared to biogeochemical observations and gene presence and expression from samples collected over time and space from a costal meromictic basin (Siders Pond) located in Falmouth MA, US. Siders Pond permanent stratification, caused by occasional seawater intrusion, results in steep chemoclines and redox gradients, which supports both aerobic and anaerobic phototrophs as well as sulfur, Fe and Mn redox cycles. The diversity of metabolic capability and expression we have observed over depth makes it an ideal system to test our thermodynamic-based model.

REDUCTIVE DEHALOGENATION OF HALOMETHANES IN NATURAL AND MODEL SYSTEMS: QSAR ANALYSIS

EPA Science Inventory

Reductive dehalogenation is a dominant reaction pathway for halogenated organics in anoxic environments. Towards the goal of developing predictive tools for this reaction process, the reduction kinetics for a series of halomethanes were measured in batch studies with both natural...

Evolution of Antibody-Drug Conjugate Tumor Disposition Model to Predict Preclinical Tumor Pharmacokinetics of Trastuzumab-Emtansine (T-DM1).

PubMed

Singh, Aman P; Maass, Katie F; Betts, Alison M; Wittrup, K Dane; Kulkarni, Chethana; King, Lindsay E; Khot, Antari; Shah, Dhaval K

2016-07-01

A mathematical model capable of accurately characterizing intracellular disposition of ADCs is essential for a priori predicting unconjugated drug concentrations inside the tumor. Towards this goal, the objectives of this manuscript were to: (1) evolve previously published cellular disposition model of ADC with more intracellular details to characterize the disposition of T-DM1 in different HER2 expressing cell lines, (2) integrate the improved cellular model with the ADC tumor disposition model to a priori predict DM1 concentrations in a preclinical tumor model, and (3) identify prominent pathways and sensitive parameters associated with intracellular activation of ADCs. The cellular disposition model was augmented by incorporating intracellular ADC degradation and passive diffusion of unconjugated drug across tumor cells. Different biomeasures and chemomeasures for T-DM1, quantified in the companion manuscript, were incorporated into the modified model of ADC to characterize in vitro pharmacokinetics of T-DM1 in three HER2+ cell lines. When the cellular model was integrated with the tumor disposition model, the model was able to a priori predict tumor DM1 concentrations in xenograft mice. Pathway analysis suggested different contribution of antigen-mediated and passive diffusion pathways for intracellular unconjugated drug exposure between in vitro and in vivo systems. Global and local sensitivity analyses revealed that non-specific deconjugation and passive diffusion of the drug across tumor cell membrane are key parameters for drug exposure inside a cell. Finally, a systems pharmacokinetic model for intracellular processing of ADCs has been proposed to highlight our current understanding about the determinants of ADC activation inside a cell.

Genomancy: predicting tumour response to cancer therapy based on the oracle of genetics.

PubMed

Williams, P D; Lee, J K; Theodorescu, D

2009-01-01

Cells are complex systems that regulate a multitude of biologic pathways involving a diverse array of molecules. Cancer can develop when these pathways become deregulated as a result of mutations in the genes coding for these proteins or of epigenetic changes that affect gene expression, or both1,2. The diversity and interconnectedness of these pathways and their molecular components implies that a variety of mutations may lead to tumorigenic cellular deregulation3-6. This variety, combined with the requirement to overcome multiple anticancer defence mechanisms7, contributes to the heterogeneous nature of cancer. Consequently, tumours with similar histology may vary in their underlying molecular circuitry8-10, with resultant differences in biologic behaviour, manifested in proliferation rate, invasiveness, metastatic potential, and unfortunately, response to cytotoxic therapy. Thus, cancer can be thought of as a family of related tumour subtypes, highlighting the need for individualized prediction both of disease progression and of treatment response, based on the molecular characteristics of the tumour.

Systems Proteomics for Translational Network Medicine

PubMed Central

Arrell, D. Kent; Terzic, Andre

2012-01-01

Universal principles underlying network science, and their ever-increasing applications in biomedicine, underscore the unprecedented capacity of systems biology based strategies to synthesize and resolve massive high throughput generated datasets. Enabling previously unattainable comprehension of biological complexity, systems approaches have accelerated progress in elucidating disease prediction, progression, and outcome. Applied to the spectrum of states spanning health and disease, network proteomics establishes a collation, integration, and prioritization algorithm to guide mapping and decoding of proteome landscapes from large-scale raw data. Providing unparalleled deconvolution of protein lists into global interactomes, integrative systems proteomics enables objective, multi-modal interpretation at molecular, pathway, and network scales, merging individual molecular components, their plurality of interactions, and functional contributions for systems comprehension. As such, network systems approaches are increasingly exploited for objective interpretation of cardiovascular proteomics studies. Here, we highlight network systems proteomic analysis pipelines for integration and biological interpretation through protein cartography, ontological categorization, pathway and functional enrichment and complex network analysis. PMID:22896016

Chemical combination effects predict connectivity in biological systems

PubMed Central

Lehár, Joseph; Zimmermann, Grant R; Krueger, Andrew S; Molnar, Raymond A; Ledell, Jebediah T; Heilbut, Adrian M; Short, Glenn F; Giusti, Leanne C; Nolan, Garry P; Magid, Omar A; Lee, Margaret S; Borisy, Alexis A; Stockwell, Brent R; Keith, Curtis T

2007-01-01

Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured. PMID:17332758

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

PubMed Central

Forsberg, Erica M; Huan, Tao; Rinehart, Duane; Benton, H Paul; Warth, Benedikt; Hilmers, Brian; Siuzdak, Gary

2018-01-01

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LCLC)–mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5–10 min, depending on user experience; data processing typically takes 1–3 h, and data analysis takes ~30 min. PMID:29494574

Using Answer Set Programming to Integrate RNA Expression with Signalling Pathway Information to Infer How Mutations Affect Ageing

PubMed Central

Papatheodorou, Irene; Ziehm, Matthias; Wieser, Daniela; Alic, Nazif; Partridge, Linda; Thornton, Janet M.

2012-01-01

A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects. PMID:23251396

Using answer set programming to integrate RNA expression with signalling pathway information to infer how mutations affect ageing.

PubMed

Papatheodorou, Irene; Ziehm, Matthias; Wieser, Daniela; Alic, Nazif; Partridge, Linda; Thornton, Janet M

2012-01-01

A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects.

A framework for the use of single-chemical transcriptomics data in predicting the hazards associated with complex mixtures of polycyclic aromatic hydrocarbons.

PubMed

Labib, Sarah; Williams, Andrew; Kuo, Byron; Yauk, Carole L; White, Paul A; Halappanavar, Sabina

2017-07-01

The assumption of additivity applied in the risk assessment of environmental mixtures containing carcinogenic polycyclic aromatic hydrocarbons (PAHs) was investigated using transcriptomics. MutaTMMouse were gavaged for 28 days with three doses of eight individual PAHs, two defined mixtures of PAHs, or coal tar, an environmentally ubiquitous complex mixture of PAHs. Microarrays were used to identify differentially expressed genes (DEGs) in lung tissue collected 3 days post-exposure. Cancer-related pathways perturbed by the individual or mixtures of PAHs were identified, and dose-response modeling of the DEGs was conducted to calculate gene/pathway benchmark doses (BMDs). Individual PAH-induced pathway perturbations (the median gene expression changes for all genes in a pathway relative to controls) and pathway BMDs were applied to models of additivity [i.e., concentration addition (CA), generalized concentration addition (GCA), and independent action (IA)] to generate predicted pathway-specific dose-response curves for each PAH mixture. The predicted and observed pathway dose-response curves were compared to assess the sensitivity of different additivity models. Transcriptomics-based additivity calculation showed that IA accurately predicted the pathway perturbations induced by all mixtures of PAHs. CA did not support the additivity assumption for the defined mixtures; however, GCA improved the CA predictions. Moreover, pathway BMDs derived for coal tar were comparable to BMDs derived from previously published coal tar-induced mouse lung tumor incidence data. These results suggest that in the absence of tumor incidence data, individual chemical-induced transcriptomics changes associated with cancer can be used to investigate the assumption of additivity and to predict the carcinogenic potential of a mixture.

Modeling limb-bud dysmorphogenesis in a predictive virtual embryo model

EPA Science Inventory

ToxCast is profiling the bioactivity of thousands of chemicals based on high-throughput screening (HTS) and computational methods that integrate knowledge of biological systems and in vivo toxicities (www.epa.gov/ncct/toxcast/). Many ToxCast assays assess signaling pathways and c...

A probabilistic process model for pelagic marine ecosystems informed by Bayesian inverse analysis

EPA Science Inventory

Marine ecosystems are complex systems with multiple pathways that produce feedback cycles, which may lead to unanticipated effects. Models abstract this complexity and allow us to predict, understand, and hypothesize. In ecological models, however, the paucity of empirical data...

Possible pathways used to predict different stages of lung adenocarcinoma.

PubMed

Chen, Xiaodong; Duan, Qiongyu; Xuan, Ying; Sun, Yunan; Wu, Rong

2017-04-01

We aimed to find some specific pathways that can be used to predict the stage of lung adenocarcinoma.RNA-Seq expression profile data and clinical data of lung adenocarcinoma (stage I [37], stage II 161], stage III [75], and stage IV [45]) were obtained from the TCGA dataset. The differentially expressed genes were merged, correlation coefficient matrix between genes was constructed with correlation analysis, and unsupervised clustering was carried out with hierarchical clustering method. The specific coexpression network in every stage was constructed with cytoscape software. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed with KOBAS database and Fisher exact test. Euclidean distance algorithm was used to calculate total deviation score. The diagnostic model was constructed with SVM algorithm.Eighteen specific genes were obtained by getting intersection of 4 group differentially expressed genes. Ten significantly enriched pathways were obtained. In the distribution map of 10 pathways score in different groups, degrees that sample groups deviated from the normal level were as follows: stage I < stage II < stage III < stage IV. The pathway score of 4 stages exhibited linear change in some pathways, and the score of 1 or 2 stages were significantly different from the rest stages in some pathways. There was significant difference between dead and alive for these pathways except thyroid hormone signaling pathway.Those 10 pathways are associated with the development of lung adenocarcinoma and may be able to predict different stages of it. Furthermore, these pathways except thyroid hormone signaling pathway may be able to predict the prognosis.

Systems analysis of the single photon response in invertebrate photoreceptors.

PubMed

Pumir, Alain; Graves, Jennifer; Ranganathan, Rama; Shraiman, Boris I

2008-07-29

Photoreceptors of Drosophila compound eye employ a G protein-mediated signaling pathway that transduces single photons into transient electrical responses called "quantum bumps" (QB). Although most of the molecular components of this pathway are already known, the system-level understanding of the mechanism of QB generation has remained elusive. Here, we present a quantitative model explaining how QBs emerge from stochastic nonlinear dynamics of the signaling cascade. The model shows that the cascade acts as an "integrate and fire" device and explains how photoreceptors achieve reliable responses to light although keeping low background in the dark. The model predicts the nontrivial behavior of mutants that enhance or suppress signaling and explains the dependence on external calcium, which controls feedback regulation. The results provide insight into physiological questions such as single-photon response efficiency and the adaptation of response to high incident-light level. The system-level analysis enabled by modeling phototransduction provides a foundation for understanding G protein signaling pathways less amenable to quantitative approaches.

How predictable is the timing of a summer ice-free Arctic?

NASA Astrophysics Data System (ADS)

Jahn, Alexandra; Kay, Jennifer E.; Holland, Marika M.; Hall, David M.

2016-09-01

Climate model simulations give a large range of over 100 years for predictions of when the Arctic could first become ice free in the summer, and many studies have attempted to narrow this uncertainty range. However, given the chaotic nature of the climate system, what amount of spread in the prediction of an ice-free summer Arctic is inevitable? Based on results from large ensemble simulations with the Community Earth System Model, we show that internal variability alone leads to a prediction uncertainty of about two decades, while scenario uncertainty between the strong (Representative Concentration Pathway (RCP) 8.5) and medium (RCP4.5) forcing scenarios adds at least another 5 years. Common metrics of the past and present mean sea ice state (such as ice extent, volume, and thickness) as well as global mean temperatures do not allow a reduction of the prediction uncertainty from internal variability.

Predictive motor control of sensory dynamics in Auditory Active Sensing

PubMed Central

Morillon, Benjamin; Hackett, Troy A.; Kajikawa, Yoshinao; Schroeder, Charles E.

2016-01-01

Neuronal oscillations present potential physiological substrates for brain operations that require temporal prediction. We review this idea in the context of auditory perception. Using speech as an exemplar, we illustrate how hierarchically organized oscillations can be used to parse and encode complex input streams. We then consider the motor system as a major source of rhythms (temporal priors) in auditory processing, that act in concert with attention to sharpen sensory representations and link them across areas. We discuss the anatomo-functional pathways that could mediate this audio-motor interaction, and notably the potential role of the somatosensory cortex. Finally, we reposition temporal predictions in the context of internal models, discussing how they interact with feature-based or spatial predictions. We argue that complementary predictions interact synergistically according to the organizational principles of each sensory system, forming multidimensional filters crucial to perception. PMID:25594376

Ras-mediated deregulation of the circadian clock in cancer.

PubMed

Relógio, Angela; Thomas, Philippe; Medina-Pérez, Paula; Reischl, Silke; Bervoets, Sander; Gloc, Ewa; Riemer, Pamela; Mang-Fatehi, Shila; Maier, Bert; Schäfer, Reinhold; Leser, Ulf; Herzel, Hanspeter; Kramer, Achim; Sers, Christine

2014-01-01

Circadian rhythms are essential to the temporal regulation of molecular processes in living systems and as such to life itself. Deregulation of these rhythms leads to failures in biological processes and eventually to the manifestation of pathological phenotypes including cancer. To address the questions as to what are the elicitors of a disrupted clock in cancer, we applied a systems biology approach to correlate experimental, bioinformatics and modelling data from several cell line models for colorectal and skin cancer. We found strong and weak circadian oscillators within the same type of cancer and identified a set of genes, which allows the discrimination between the two oscillator-types. Among those genes are IFNGR2, PITX2, RFWD2, PPARγ, LOXL2, Rab6 and SPARC, all involved in cancer-related pathways. Using a bioinformatics approach, we extended the core-clock network and present its interconnection to the discriminative set of genes. Interestingly, such gene signatures link the clock to oncogenic pathways like the RAS/MAPK pathway. To investigate the potential impact of the RAS/MAPK pathway - a major driver of colorectal carcinogenesis - on the circadian clock, we used a computational model which predicted that perturbation of BMAL1-mediated transcription can generate the circadian phenotypes similar to those observed in metastatic cell lines. Using an inducible RAS expression system, we show that overexpression of RAS disrupts the circadian clock and leads to an increase of the circadian period while RAS inhibition causes a shortening of period length, as predicted by our mathematical simulations. Together, our data demonstrate that perturbations induced by a single oncogene are sufficient to deregulate the mammalian circadian clock.

Lvr, a Signaling System That Controls Global Gene Regulation and Virulence in Pathogenic Leptospira.

PubMed

Adhikarla, Haritha; Wunder, Elsio A; Mechaly, Ariel E; Mehta, Sameet; Wang, Zheng; Santos, Luciane; Bisht, Vimla; Diggle, Peter; Murray, Gerald; Adler, Ben; Lopez, Francesc; Townsend, Jeffrey P; Groisman, Eduardo; Picardeau, Mathieu; Buschiazzo, Alejandro; Ko, Albert I

2018-01-01

Leptospirosis is an emerging zoonotic disease with more than 1 million cases annually. Currently there is lack of evidence for signaling pathways involved during the infection process of Leptospira . In our comprehensive genomic analysis of 20 Leptospira spp. we identified seven pathogen-specific Two-Component System (TCS) proteins. Disruption of two these TCS genes in pathogenic Leptospira strain resulted in loss-of-virulence in a hamster model of leptospirosis. Corresponding genes lvrA and lvrB (leptospira virulence regulator ) are juxtaposed in an operon and are predicted to encode a hybrid histidine kinase and a hybrid response regulator, respectively. Transcriptome analysis of lvr mutant strains with disruption of one ( lvrB ) or both genes ( lvrA/B ) revealed global transcriptional regulation of 850 differentially expressed genes. Phosphotransfer assays demonstrated that LvrA phosphorylates LvrB and predicted further signaling downstream to one or more DNA-binding response regulators, suggesting that it is a branched pathway. Phylogenetic analyses indicated that lvrA and lvrB evolved independently within different ecological lineages in Leptospira via gene duplication. This study uncovers a novel-signaling pathway that regulates virulence in pathogenic Leptospira (Lvr), providing a framework to understand the molecular bases of regulation in this life-threatening bacterium.

Lvr, a Signaling System That Controls Global Gene Regulation and Virulence in Pathogenic Leptospira

PubMed Central

Adhikarla, Haritha; Wunder, Elsio A.; Mechaly, Ariel E.; Mehta, Sameet; Wang, Zheng; Santos, Luciane; Bisht, Vimla; Diggle, Peter; Murray, Gerald; Adler, Ben; Lopez, Francesc; Townsend, Jeffrey P.; Groisman, Eduardo; Picardeau, Mathieu; Buschiazzo, Alejandro; Ko, Albert I.

2018-01-01

Leptospirosis is an emerging zoonotic disease with more than 1 million cases annually. Currently there is lack of evidence for signaling pathways involved during the infection process of Leptospira. In our comprehensive genomic analysis of 20 Leptospira spp. we identified seven pathogen-specific Two-Component System (TCS) proteins. Disruption of two these TCS genes in pathogenic Leptospira strain resulted in loss-of-virulence in a hamster model of leptospirosis. Corresponding genes lvrA and lvrB (leptospira virulence regulator) are juxtaposed in an operon and are predicted to encode a hybrid histidine kinase and a hybrid response regulator, respectively. Transcriptome analysis of lvr mutant strains with disruption of one (lvrB) or both genes (lvrA/B) revealed global transcriptional regulation of 850 differentially expressed genes. Phosphotransfer assays demonstrated that LvrA phosphorylates LvrB and predicted further signaling downstream to one or more DNA-binding response regulators, suggesting that it is a branched pathway. Phylogenetic analyses indicated that lvrA and lvrB evolved independently within different ecological lineages in Leptospira via gene duplication. This study uncovers a novel-signaling pathway that regulates virulence in pathogenic Leptospira (Lvr), providing a framework to understand the molecular bases of regulation in this life-threatening bacterium. PMID:29600195

Kinetics of protein–ligand unbinding: Predicting pathways, rates, and rate-limiting steps

PubMed Central

Tiwary, Pratyush; Limongelli, Vittorio; Salvalaglio, Matteo; Parrinello, Michele

2015-01-01

The ability to predict the mechanisms and the associated rate constants of protein–ligand unbinding is of great practical importance in drug design. In this work we demonstrate how a recently introduced metadynamics-based approach allows exploration of the unbinding pathways, estimation of the rates, and determination of the rate-limiting steps in the paradigmatic case of the trypsin–benzamidine system. Protein, ligand, and solvent are described with full atomic resolution. Using metadynamics, multiple unbinding trajectories that start with the ligand in the crystallographic binding pose and end with the ligand in the fully solvated state are generated. The unbinding rate koff is computed from the mean residence time of the ligand. Using our previously computed binding affinity we also obtain the binding rate kon. Both rates are in agreement with reported experimental values. We uncover the complex pathways of unbinding trajectories and describe the critical rate-limiting steps with unprecedented detail. Our findings illuminate the role played by the coupling between subtle protein backbone fluctuations and the solvation by water molecules that enter the binding pocket and assist in the breaking of the shielded hydrogen bonds. We expect our approach to be useful in calculating rates for general protein–ligand systems and a valid support for drug design. PMID:25605901

Nanosystem self-assembly pathways discovered via all-atom multiscale analysis.

PubMed

Pankavich, Stephen D; Ortoleva, Peter J

2012-07-26

We consider the self-assembly of composite structures from a group of nanocomponents, each consisting of particles within an N-atom system. Self-assembly pathways and rates for nanocomposites are derived via a multiscale analysis of the classical Liouville equation. From a reduced statistical framework, rigorous stochastic equations for population levels of beginning, intermediate, and final aggregates are also derived. It is shown that the definition of an assembly type is a self-consistency criterion that must strike a balance between precision and the need for population levels to be slowly varying relative to the time scale of atomic motion. The deductive multiscale approach is complemented by a qualitative notion of multicomponent association and the ensemble of exact atomic-level configurations consistent with them. In processes such as viral self-assembly from proteins and RNA or DNA, there are many possible intermediates, so that it is usually difficult to predict the most efficient assembly pathway. However, in the current study, rates of assembly of each possible intermediate can be predicted. This avoids the need, as in a phenomenological approach, for recalibration with each new application. The method accounts for the feedback across scales in space and time that is fundamental to nanosystem self-assembly. The theory has applications to bionanostructures, geomaterials, engineered composites, and nanocapsule therapeutic delivery systems.

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

Ji, Yanzhou; Heo, Tae Wook; Zhang, Fan

Here we present our theoretical assessment of the kinetic pathways during phase transformations of multi-component Ti alloys. Employing the graphical thermodynamic approach and an integrated free energy function based on the realistic thermodynamic database and assuming that a displacive structural transformation occurs much faster than long-range diffusional processes, we analyze the phase stabilities of Ti-6Al -4V (Ti-6wt.%Al -4wt.%V). Our systematic analyses predict a variety of possible kinetic pathways for β to (α + β) transformations leading to different types of microstructures under various heat treatment conditions. In addition, the possibility of unconventional kinetic pathways is discussed. Lastly, we also brieflymore » discuss the application of our approach to general multicomponent/multiphase alloy systems.« less

Systems analysis of apoptosis protein expression allows the case-specific prediction of cell death responsiveness of melanoma cells

PubMed Central

Passante, E; Würstle, M L; Hellwig, C T; Leverkus, M; Rehm, M

2013-01-01

Many cancer entities and their associated cell line models are highly heterogeneous in their responsiveness to apoptosis inducers and, despite a detailed understanding of the underlying signaling networks, cell death susceptibility currently cannot be predicted reliably from protein expression profiles. Here, we demonstrate that an integration of quantitative apoptosis protein expression data with pathway knowledge can predict the cell death responsiveness of melanoma cell lines. By a total of 612 measurements, we determined the absolute expression (nM) of 17 core apoptosis regulators in a panel of 11 melanoma cell lines, and enriched these data with systems-level information on apoptosis pathway topology. By applying multivariate statistical analysis and multi-dimensional pattern recognition algorithms, the responsiveness of individual cell lines to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) or dacarbazine (DTIC) could be predicted with very high accuracy (91 and 82% correct predictions), and the most effective treatment option for individual cell lines could be pre-determined in silico. In contrast, cell death responsiveness was poorly predicted when not taking knowledge on protein–protein interactions into account (55 and 36% correct predictions). We also generated mathematical predictions on whether anti-apoptotic Bcl-2 family members or x-linked inhibitor of apoptosis protein (XIAP) can be targeted to enhance TRAIL responsiveness in individual cell lines. Subsequent experiments, making use of pharmacological Bcl-2/Bcl-xL inhibition or siRNA-based XIAP depletion, confirmed the accuracy of these predictions. We therefore demonstrate that cell death responsiveness to TRAIL or DTIC can be predicted reliably in a large number of melanoma cell lines when investigating expression patterns of apoptosis regulators in the context of their network-level interplay. The capacity to predict responsiveness at the cellular level may contribute to personalizing anti-cancer treatments in the future. PMID:23933815

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

PubMed Central

Sun, Xiaodian; Jin, Li; Xiong, Momiao

2008-01-01

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks. PMID:19018286

Network pharmacology-based strategy for predicting active ingredients and potential targets of Yangxinshi tablet for treating heart failure.

PubMed

Chen, Langdong; Cao, Yan; Zhang, Hai; Lv, Diya; Zhao, Yahong; Liu, Yanjun; Ye, Guan; Chai, Yifeng

2018-01-31

Yangxinshi tablet (YXST) is an effective treatment for heart failure and myocardial infarction; it consists of 13 herbal medicines formulated according to traditional Chinese Medicine (TCM) practices. It has been used for the treatment of cardiovascular disease for many years in China. In this study, a network pharmacology-based strategy was used to elucidate the mechanism of action of YXST for the treatment of heart failure. Cardiovascular disease-related protein target and compound databases were constructed for YXST. A molecular docking platform was used to predict the protein targets of YXST. The affinity between proteins and ingredients was determined using surface plasmon resonance (SPR) assays. The action modes between targets and representative ingredients were calculated using Glide docking, and the related pathways were predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A protein target database containing 924 proteins was constructed; 179 compounds in YXST were identified, and 48 compounds with high relevance to the proteins were defined as representative ingredients. Thirty-four protein targets of the 48 representative ingredients were analyzed and classified into two categories: immune and cardiovascular systems. The SPR assay and molecular docking partly validated the interplay between protein targets and representative ingredients. Moreover, 28 pathways related to heart failure were identified, which provided directions for further research on YXST. This study demonstrated that the cardiovascular protective effect of YXST mainly involved the immune and cardiovascular systems. Through the research strategy based on network pharmacology, we analysis the complex system of YXST and found 48 representative compounds, 34 proteins and 28 related pathways of YXST, which could help us understand the underlying mechanism of YSXT's anti-heart failure effect. The network-based investigation could help researchers simplify the complex system of YXSY. It may also offer a feasible approach to decipher the chemical and pharmacological bases of other TCM formulas. Copyright © 2018 Elsevier B.V. All rights reserved.

Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example

PubMed Central

Gunalan, Kabilar; Chaturvedi, Ashutosh; Howell, Bryan; Duchin, Yuval; Lempka, Scott F.; Patriat, Remi; Sapiro, Guillermo; Harel, Noam; McIntyre, Cameron C.

2017-01-01

Background Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports. Objective Provide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation. Methods Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson’s disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution. Results Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings. Conclusion Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation. PMID:28441410

Differential miRNA expression in B cells is associated with inter-individual differences in humoral immune response to measles vaccination

PubMed Central

Haralambieva, Iana H.; Kennedy, Richard B.; Simon, Whitney L.; Goergen, Krista M.; Grill, Diane E.; Ovsyannikova, Inna G.

2018-01-01

Background MicroRNAs are important mediators of post-transcriptional regulation of gene expression through RNA degradation and translational repression, and are emerging biomarkers of immune system activation/response after vaccination. Methods We performed Next Generation Sequencing (mRNA-Seq) of intracellular miRNAs in measles virus-stimulated B and CD4+ T cells from high and low antibody responders to measles vaccine. Negative binomial generalized estimating equation (GEE) models were used for miRNA assessment and the DIANA tool was used for gene/target prediction and pathway enrichment analysis. Results We identified a set of B cell-specific miRNAs (e.g., miR-151a-5p, miR-223, miR-29, miR-15a-5p, miR-199a-3p, miR-103a, and miR-15a/16 cluster) and biological processes/pathways, including regulation of adherens junction proteins, Fc-receptor signaling pathway, phosphatidylinositol-mediated signaling pathway, growth factor signaling pathway/pathways, transcriptional regulation, apoptosis and virus-related processes, significantly associated with neutralizing antibody titers after measles vaccination. No CD4+ T cell-specific miRNA expression differences between high and low antibody responders were found. Conclusion Our study demonstrates that miRNA expression directly or indirectly influences humoral immunity to measles vaccination and suggests that B cell-specific miRNAs may serve as useful predictive biomarkers of vaccine humoral immune response. PMID:29381765

Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery.

PubMed

Bosl, William J

2007-02-15

Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.

Challenges and Opportunities with Predicting in Vivo Phase II Metabolism via Glucuronidation from in Vitro Data

PubMed Central

Ge, Shufan; Tu, Yifan; Hu, Ming

2017-01-01

Glucuronidation is the most important phase II metabolic pathway which is responsible for the clearance of many endogenous and exogenous compounds. To better understand the elimination process for compounds undergoing glucuronidation and identify compounds with desirable in vivo pharmacokinetic properties, many efforts have been made to predict in vivo glucuronidation using in vitro data. In this article, we reviewed typical approaches used in previous predictions. The problems and challenges in prediction of glucuronidation were discussed. Besides that different incubation conditions can affect the prediction accuracy, other factors including efflux / uptake transporters, enterohepatic recycling, and deglucuronidation reactions also contribute to the disposition of glucuronides and make the prediction more difficult. PBPK modeling, which can describe more complicated process in vivo, is a promising prediction strategy which may greatly improve the prediction of glucuronidation and potential DDIs involving glucuronidation. Based on previous studies, we proposed a transport-glucuronidation classification system, which was built based on the kinetics of both glucuronidation and transport of the glucuronide. This system could be a very useful tool to achieve better in vivo predictions. PMID:28966903

Dyamical Systems Theory and Lagrangian Data Assimilation in 4D Geophysical Fluid Dynamics

DTIC Science & Technology

The long-term goal of our project (known as OCEAN 3D +1) was to better understand and predict ocean circulation features that are fundamentally three...dimensional in space and that vary in time. In particular, we sought to quantify the dynamical processes that govern the formation , evolution, and...predictability of 3D +1 transport pathways in the ocean. Our approach was to develop algorithms to thoroughly analyze a hierarchy of model and

Human Biomarker Discovery and Predictive Models for Disease Progression for Idiopathic Pneumonia Syndrome Following Allogeneic Stem Cell Transplantation*

PubMed Central

Schlatzer, Daniela M.; Dazard, Jean-Eudes; Ewing, Rob M.; Ilchenko, Serguei; Tomcheko, Sara E.; Eid, Saada; Ho, Vincent; Yanik, Greg; Chance, Mark R.; Cooke, Kenneth R.

2012-01-01

Allogeneic hematopoietic stem cell transplantation (SCT) is the only curative therapy for many malignant and nonmalignant conditions. Idiopathic pneumonia syndrome (IPS) is a frequently fatal complication that limits successful outcomes. Preclinical models suggest that IPS represents an immune mediated attack on the lung involving elements of both the adaptive and the innate immune system. However, the etiology of IPS in humans is less well understood. To explore the disease pathway and uncover potential biomarkers of disease, we performed two separate label-free, proteomics experiments defining the plasma protein profiles of allogeneic SCT patients with IPS. Samples obtained from SCT recipients without complications served as controls. The initial discovery study, intended to explore the disease pathway in humans, identified a set of 81 IPS-associated proteins. These data revealed similarities between the known IPS pathways in mice and the condition in humans, in particular in the acute phase response. In addition, pattern recognition pathways were judged to be significant as a function of development of IPS, and from this pathway we chose the lipopolysaccaharide-binding protein (LBP) protein as a candidate molecular diagnostic for IPS, and verified its increase as a function of disease using an ELISA assay. In a separately designed study, we identified protein-based classifiers that could predict, at day 0 of SCT, patients who: 1) progress to IPS and 2) respond to cytokine neutralization therapy. Using cross-validation strategies, we built highly predictive classifier models of both disease progression and therapeutic response. In sum, data generated in this report confirm previous clinical and experimental findings, provide new insights into the pathophysiology of IPS, identify potential molecular classifiers of the condition, and uncover a set of markers potentially of interest for patient stratification as a basis for individualized therapy. PMID:22337588

A New Approach to Predict Microbial Community Assembly and Function Using a Stochastic, Genome-Enabled Modeling Framework

NASA Astrophysics Data System (ADS)

King, E.; Brodie, E.; Anantharaman, K.; Karaoz, U.; Bouskill, N.; Banfield, J. F.; Steefel, C. I.; Molins, S.

2016-12-01

Characterizing and predicting the microbial and chemical compositions of subsurface aquatic systems necessitates an understanding of the metabolism and physiology of organisms that are often uncultured or studied under conditions not relevant for one's environment of interest. Cultivation-independent approaches are therefore important and have greatly enhanced our ability to characterize functional microbial diversity. The capability to reconstruct genomes representing thousands of populations from microbial communities using metagenomic techniques provides a foundation for development of predictive models for community structure and function. Here, we discuss a genome-informed stochastic trait-based model incorporated into a reactive transport framework to represent the activities of coupled guilds of hypothetical microorganisms. Metabolic pathways for each microbe within a functional guild are parameterized from metagenomic data with a unique combination of traits governing organism fitness under dynamic environmental conditions. We simulate the thermodynamics of coupled electron donor and acceptor reactions to predict the energy available for cellular maintenance, respiration, biomass development, and enzyme production. While `omics analyses can now characterize the metabolic potential of microbial communities, it is functionally redundant as well as computationally prohibitive to explicitly include the thousands of recovered organisms into biogeochemical models. However, one can derive potential metabolic pathways from genomes along with trait-linkages to build probability distributions of traits. These distributions are used to assemble groups of microbes that couple one or more of these pathways. From the initial ensemble of microbes, only a subset will persist based on the interaction of their physiological and metabolic traits with environmental conditions, competing organisms, etc. Here, we analyze the predicted niches of these hypothetical microbes and assess the ability of a stochastically assembled community of organisms to predict subsurface biogeochemical dynamics.

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology.

PubMed

Karp, Peter D; Latendresse, Mario; Paley, Suzanne M; Krummenacker, Markus; Ong, Quang D; Billington, Richard; Kothari, Anamika; Weaver, Daniel; Lee, Thomas; Subhraveti, Pallavi; Spaulding, Aaron; Fulcher, Carol; Keseler, Ingrid M; Caspi, Ron

2016-09-01

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

NiaoDuQing granules relieve chronic kidney disease symptoms by decreasing renal fibrosis and anemia

PubMed Central

Wang, Xu; Yu, Suyun; Jia, Qi; Chen, Lichuan; Zhong, Jinqiu; Pan, Yanhong; Shen, Peiliang; Shen, Yin; Wang, Siliang; Wei, Zhonghong; Cao, Yuzhu; Lu, Yin

2017-01-01

NiaoDuQing (NDQ) granules, a traditional Chinese medicine, has been clinically used in China for over fourteen years to treat chronic kidney disease (CKD). To elucidate the mechanisms underlying the therapeutic benefits of NDQ, we designed an approach incorporating chemoinformatics, bioinformatics, network biology methods, and cellular and molecular biology experiments. A total of 182 active compounds were identified in NDQ granules, and 397 putative targets associated with different diseases were derived through ADME modelling and target prediction tools. Protein-protein interaction networks of CKD-related and putative NDQ targets were constructed, and 219 candidate targets were identified based on topological features. Pathway enrichment analysis showed that the candidate targets were mostly related to the TGF-β, the p38MAPK, and the erythropoietin (EPO) receptor signaling pathways, which are known contributors to renal fibrosis and/or renal anemia. A rat model of CKD was established to validate the drug-target mechanisms predicted by the systems pharmacology analysis. Experimental results confirmed that NDQ granules exerted therapeutic effects on CKD and its comorbidities, including renal anemia, mainly by modulating the TGF-β and EPO signaling pathways. Thus, the pharmacological actions of NDQ on CKD symptoms correlated well with in silico predictions. PMID:28915563

Use of the Chemical Transformation Simulator as a Parameterization Tool for Modeling the Environmental Fate of Organic Chemicals and their Transformation Products

EPA Science Inventory

A Chemical Transformation Simulator is a web-based system for predicting transformation pathways and physicochemical properties of organic chemicals. Role in Environmental Modeling • Screening tool for identifying likely transformation products in the environment • Parameteri...

Harnessing system models of cell death signalling for cytotoxic chemotherapy: towards personalised medicine approaches?

PubMed

Huber, Heinrich J; McKiernan, Ross G; Prehn, Jochen H M

2014-03-01

Most cytotoxic chemotherapeutics are believed to kill cancer cells by inducing apoptosis. Understanding the factors that contribute to impairment of apoptosis in cancer cells is therefore critical for the development of novel therapies that circumvent the widespread chemoresistance. Apoptosis, however, is a complex and tightly controlled process that can be induced by different classes of chemotherapeutics targeting different signalling nodes and pathways. Moreover, apoptosis initiation and apoptosis execution strongly depend on patient-specific, genomic and proteomic signatures. Here, we will review recent translational studies that suggest a critical link between the sensitivity of cancer cells to initiate apoptosis and clinical outcome. Next we will discuss recent advances in the field of system modelling of apoptosis pathways for the prediction of treatment responses. We propose that initiation of mitochondrial apoptosis, defined as the process of mitochondrial outer membrane permeabilisation (MOMP), is a dose-dependent decision process that allows for a prediction of individual therapy responses and therapeutic windows. We provide evidence in contrast that apoptosis execution post-MOMP may be a binary decision that dictates whether apoptosis is executed or not. We will discuss the implications of this concept for the future use of novel adjuvant therapeutics that specifically target apoptosis signalling pathways or which may be used to reduce the impact of cell-to-cell heterogeneity on therapy responses. Finally, we will discuss the technical and regulatory requirements surrounding the use and implications of system-based patient stratification tools for the future of personalised oncology.

Theoretical Assessment on the Phase Transformation Kinetic Pathways of Multi-component Ti Alloys: Application to Ti-6Al- 4V

DOE PAGES

Ji, Yanzhou; Heo, Tae Wook; Zhang, Fan; ...

2015-12-21

Here we present our theoretical assessment of the kinetic pathways during phase transformations of multi-component Ti alloys. Employing the graphical thermodynamic approach and an integrated free energy function based on the realistic thermodynamic database and assuming that a displacive structural transformation occurs much faster than long-range diffusional processes, we analyze the phase stabilities of Ti-6Al -4V (Ti-6wt.%Al -4wt.%V). Our systematic analyses predict a variety of possible kinetic pathways for β to (α + β) transformations leading to different types of microstructures under various heat treatment conditions. In addition, the possibility of unconventional kinetic pathways is discussed. Lastly, we also brieflymore » discuss the application of our approach to general multicomponent/multiphase alloy systems.« less

Tissue-Specific Analysis of Pharmacological Pathways.

PubMed

Hao, Yun; Quinnies, Kayla; Realubit, Ronald; Karan, Charles; Tatonetti, Nicholas P

2018-06-19

Understanding the downstream consequences of pharmacologically targeted proteins is essential to drug design. Current approaches investigate molecular effects under tissue-naïve assumptions. Many target proteins, however, have tissue-specific expression. A systematic study connecting drugs to target pathways in in vivo human tissues is needed. We introduced a data-driven method that integrates drug-target relationships with gene expression, protein-protein interaction, and pathway annotation data. We applied our method to four independent genomewide expression datasets and built 467,396 connections between 1,034 drugs and 954 pathways in 259 human tissues or cell lines. We validated our results using data from L1000 and Pharmacogenomics Knowledgebase (PharmGKB), and observed high precision and recall. We predicted and tested anticoagulant effects of 22 compounds experimentally that were previously unknown, and used clinical data to validate these effects retrospectively. Our systematic study provides a better understanding of the cellular response to drugs and can be applied to many research topics in systems pharmacology. © 2018 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

miRnalyze: an interactive database linking tool to unlock intuitive microRNA regulation of cell signaling pathways

PubMed Central

Subhra Das, Sankha; James, Mithun; Paul, Sandip

2017-01-01

Abstract The various pathophysiological processes occurring in living systems are known to be orchestrated by delicate interplays and cross-talks between different genes and their regulators. Among the various regulators of genes, there is a class of small non-coding RNA molecules known as microRNAs. Although, the relative simplicity of miRNAs and their ability to modulate cellular processes make them attractive therapeutic candidates, their presence in large numbers make it challenging for experimental researchers to interpret the intricacies of the molecular processes they regulate. Most of the existing bioinformatic tools fail to address these challenges. Here, we present a new web resource ‘miRnalyze’ that has been specifically designed to directly identify the putative regulation of cell signaling pathways by miRNAs. The tool integrates miRNA-target predictions with signaling cascade members by utilizing TargetScanHuman 7.1 miRNA-target prediction tool and the KEGG pathway database, and thus provides researchers with in-depth insights into modulation of signal transduction pathways by miRNAs. miRnalyze is capable of identifying common miRNAs targeting more than one gene in the same signaling pathway—a feature that further increases the probability of modulating the pathway and downstream reactions when using miRNA modulators. Additionally, miRnalyze can sort miRNAs according to the seed-match types and TargetScan Context ++ score, thus providing a hierarchical list of most valuable miRNAs. Furthermore, in order to provide users with comprehensive information regarding miRNAs, genes and pathways, miRnalyze also links to expression data of miRNAs (miRmine) and genes (TiGER) and proteome abundance (PaxDb) data. To validate the capability of the tool, we have documented the correlation of miRnalyze’s prediction with experimental confirmation studies. Database URL: http://www.mirnalyze.in PMID:28365733

Biological Networks for Predicting Chemical Hepatocarcinogenicity Using Gene Expression Data from Treated Mice and Relevance across Human and Rat Species

PubMed Central

Thomas, Reuben; Thomas, Russell S.; Auerbach, Scott S.; Portier, Christopher J.

2013-01-01

Background Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. Objectives To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Methods Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Results Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Conclusions Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species. PMID:23737943

Biological networks for predicting chemical hepatocarcinogenicity using gene expression data from treated mice and relevance across human and rat species.

PubMed

Thomas, Reuben; Thomas, Russell S; Auerbach, Scott S; Portier, Christopher J

2013-01-01

Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species.

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway

PubMed Central

Benson, Helen E; Sharman, Joanna L; Mpamhanga, Chido P; Parton, Andrew; Southan, Christopher; Harmar, Anthony J; Ghazal, Peter

2017-01-01

Background and Purpose An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway. Experimental Approach Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment. Key Results We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects. Conclusion and Implications We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses. PMID:28910500

The Crosstalk of Pathways Involved in Immune Response Maybe the Shared Molecular Basis of Rheumatoid Arthritis and Type 2 Diabetes

PubMed Central

Ge, Na; Jiang, Miao; Li, Li; Bian, Yanqin; Xu, Gang; Bian, Zhaoxiang; Zhang, Ge; Lu, Aiping

2015-01-01

Rheumatoid arthritis (RA) and Type 2 diabetes (T2D) are both systemic diseases linked with altered immune response, moderate mortality when present together. The treatment for both RA and T2D are not satisfied, partly because of the linkage between them has not yet been appreciated. A comprehensive study for the potential associations between the two disorders is needed. In this study, we used RNA sequencing to explore the differently expressed genes (DEGs) in peripheral blood mononuclear cells (PBMC) of 10 RA and 10 T2D patients comparing with 10 healthy volunteers (control). We used bioinformatics analysis and the Ingenuity Pathways Analysis (IPA) to predict the commonalities on signaling pathways and molecular networks between those two diseases. 212 DEGs in RA and 114 DEGs in T2D patients were identified compared with healthy controls, respectively. 32 DEGs were shared between the two comparisons. The top 10 shared pathways interacted in cross-talking networks, regulated by 5 shared predicted upstream regulators, leading to the activated immune response were explored, which was considered as partly of the association mechanism of this two disorders. These discoveries would be considered as new understanding on the associations between RA and T2D, and provide novel treatment or prevention strategy. PMID:26252209

Predicting miRNA targets for head and neck squamous cell carcinoma using an ensemble method.

PubMed

Gao, Hong; Jin, Hui; Li, Guijun

2018-01-01

This study aimed to uncover potential microRNA (miRNA) targets in head and neck squamous cell carcinoma (HNSCC) using an ensemble method which combined 3 different methods: Pearson's correlation coefficient (PCC), Lasso and a causal inference method (i.e., intervention calculus when the directed acyclic graph (DAG) is absent [IDA]), based on Borda count election. The Borda count election method was used to integrate the top 100 predicted targets of each miRNA generated by individual methods. Afterwards, to validate the performance ability of our method, we checked the TarBase v6.0, miRecords v2013, miRWalk v2.0 and miRTarBase v4.5 databases to validate predictions for miRNAs. Pathway enrichment analysis of target genes in the top 1,000 miRNA-messenger RNA (mRNA) interactions was conducted to focus on significant KEGG pathways. Finally, we extracted target genes based on occurrence frequency ≥3. Based on an absolute value of PCC >0.7, we found 33 miRNAs and 288 mRNAs for further analysis. We extracted 10 target genes with predicted frequencies not less than 3. The target gene MYO5C possessed the highest frequency, which was predicted by 7 different miRNAs. Significantly, a total of 8 pathways were identified; the pathways of cytokine-cytokine receptor interaction and chemokine signaling pathway were the most significant. We successfully predicted target genes and pathways for HNSCC relying on miRNA expression data, mRNA expression profile, an ensemble method and pathway information. Our results may offer new information for the diagnosis and estimation of the prognosis of HNSCC.

ACTP: A webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds

PubMed Central

Ouyang, Liang; Cai, Haoyang; Liu, Bo

2016-01-01

Autophagy (macroautophagy) is well known as an evolutionarily conserved lysosomal degradation process for long-lived proteins and damaged organelles. Recently, accumulating evidence has revealed a series of small-molecule compounds that may activate or inhibit autophagy for therapeutic potential on human diseases. However, targeting autophagy for drug discovery still remains in its infancy. In this study, we developed a webserver called Autophagic Compound-Target Prediction (ACTP) (http://actp.liu-lab.com/) that could predict autophagic targets and relevant pathways for a given compound. The flexible docking of submitted small-molecule compound (s) to potential autophagic targets could be performed by backend reverse docking. The webpage would return structure-based scores and relevant pathways for each predicted target. Thus, these results provide a basis for the rapid prediction of potential targets/pathways of possible autophagy-activating or autophagy-inhibiting compounds without labor-intensive experiments. Moreover, ACTP will be helpful to shed light on identifying more novel autophagy-activating or autophagy-inhibiting compounds for future therapeutic implications. PMID:26824420

Heterogeneity in multiple transmission pathways: modelling the spread of cholera and other waterborne disease in networks with a common water source.

PubMed

Robertson, Suzanne L; Eisenberg, Marisa C; Tien, Joseph H

2013-01-01

Many factors influencing disease transmission vary throughout and across populations. For diseases spread through multiple transmission pathways, sources of variation may affect each transmission pathway differently. In this paper we consider a disease that can be spread via direct and indirect transmission, such as the waterborne disease cholera. Specifically, we consider a system of multiple patches with direct transmission occurring entirely within patch and indirect transmission via a single shared water source. We investigate the effect of heterogeneity in dual transmission pathways on the spread of the disease. We first present a 2-patch model for which we examine the effect of variation in each pathway separately and propose a measure of heterogeneity that incorporates both transmission mechanisms and is predictive of R(0). We also explore how heterogeneity affects the final outbreak size and the efficacy of intervention measures. We conclude by extending several results to a more general n-patch setting.

Channeling by Proximity: The Catalytic Advantages of Active Site Colocalization Using Brownian Dynamics.

PubMed

Bauler, Patricia; Huber, Gary; Leyh, Thomas; McCammon, J Andrew

2010-05-06

Nature often colocalizes successive steps in a metabolic pathway. Such organization is predicted to increase the effective concentration of pathway intermediates near their recipient active sites and to enhance catalytic efficiency. Here, the pathway of a two-step reaction is modeled using a simple spherical approximation for the enzymes and substrate particles. Brownian dynamics are used to simulate the trajectory of a substrate particle as it diffuses between the active site zones of two different enzyme spheres. The results approximate distances for the most effective reaction pathways, indicating that the most effective reaction pathway is one in which the active sites are closely aligned. However, when the active sites are too close, the ability of the substrate to react with the first enzyme was hindered, suggesting that even the most efficient orientations can be improved for a system that is allowed to rotate or change orientation to optimize the likelihood of reaction at both sites.

A Computer Simulation Approach to Assessing Therapeutic Intervention Points for the Prevention of Cytokine-Induced Cartilage Breakdown

PubMed Central

Proctor, CJ; Macdonald, C; Milner, JM; Rowan, AD; Cawston, TE

2014-01-01

Objective To use a novel computational approach to examine the molecular pathways involved in cartilage breakdown and to use computer simulation to test possible interventions for reducing collagen release. Methods We constructed a computational model of the relevant molecular pathways using the Systems Biology Markup Language, a computer-readable format of a biochemical network. The model was constructed using our experimental data showing that interleukin-1 (IL-1) and oncostatin M (OSM) act synergistically to up-regulate collagenase protein levels and activity and initiate cartilage collagen breakdown. Simulations were performed using the COPASI software package. Results The model predicted that simulated inhibition of JNK or p38 MAPK, and overexpression of tissue inhibitor of metalloproteinases 3 (TIMP-3) led to a reduction in collagen release. Overexpression of TIMP-1 was much less effective than that of TIMP-3 and led to a delay, rather than a reduction, in collagen release. Simulated interventions of receptor antagonists and inhibition of JAK-1, the first kinase in the OSM pathway, were ineffective. So, importantly, the model predicts that it is more effective to intervene at targets that are downstream, such as the JNK pathway, rather than those that are close to the cytokine signal. In vitro experiments confirmed the effectiveness of JNK inhibition. Conclusion Our study shows the value of computer modeling as a tool for examining possible interventions by which to reduce cartilage collagen breakdown. The model predicts that interventions that either prevent transcription or inhibit the activity of collagenases are promising strategies and should be investigated further in an experimental setting. PMID:24757149

A modeling approach to evaluate the balance between bioactivation and detoxification of MeIQx in human hepatocytes

PubMed Central

Delannée, Victorien; Théret, Nathalie; Siegel, Anne

2017-01-01

Background Heterocyclic aromatic amines (HAA) are environmental and food contaminants that are potentially carcinogenic for humans. 2-Amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) is one of the most abundant HAA formed in cooked meat. MeIQx is metabolized by cytochrome P450 1A2 in the human liver into detoxificated and bioactivated products. Once bioactivated, MeIQx metabolites can lead to DNA adduct formation responsible for further genome instability. Methods Using a computational approach, we developed a numerical model for MeIQx metabolism in the liver that predicts the MeIQx biotransformation into detoxification or bioactivation pathways according to the concentration of MeIQx. Results Our results demonstrate that (1) the detoxification pathway predominates, (2) the ratio between detoxification and bioactivation pathways is not linear and shows a maximum at 10 µM of MeIQx in hepatocyte cell models, and (3) CYP1A2 is a key enzyme in the system that regulates the balance between bioactivation and detoxification. Our analysis suggests that such a ratio could be considered as an indicator of MeIQx genotoxicity at a low concentration of MeIQx. Conclusions Our model permits the investigation of the balance between bioactivation (i.e., DNA adduct formation pathway through the prediction of potential genotoxic compounds) and detoxification of MeIQx in order to predict the behaviour of this environmental contaminant in the human liver. It highlights the importance of complex regulations of enzyme competitions that should be taken into account in any further multi-organ models. PMID:28879062

Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation.

PubMed

Polak, Marta E; Ung, Chuin Ying; Masapust, Joanna; Freeman, Tom C; Ardern-Jones, Michael R

2017-04-06

Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico modelling of a reconstructed gene regulatory network (GRN) with in vitro validation of the predictions, we sought to determine the mechanisms of regulation of immune responses in human primary LCs. The key role of Interferon regulatory factors (IRFs) as controllers of the human Langerhans cell response to epidermal cytokines was revealed by whole transcriptome analysis. Applying Boolean logic we assembled a Petri net-based model of the IRF-GRN which provides molecular pathway predictions for the induction of different transcriptional programmes in LCs. In silico simulations performed after model parameterisation with transcription factor expression values predicted that human LC activation of antigen-specific CD8 T cells would be differentially regulated by epidermal cytokine induction of specific IRF-controlled pathways. This was confirmed by in vitro measurement of IFN-γ production by activated T cells. As a proof of concept, this approach shows that stochastic modelling of a specific immune networks renders transcriptome data valuable for the prediction of functional outcomes of immune responses.

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

PubMed

Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

2011-08-01

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes

PubMed Central

Mohamad Zobir, Siti Zuraidah; Mohd Fauzi, Fazlin; Liggi, Sonia; Drakakis, Georgios; Fu, Xianjun; Fan, Tai-Ping; Bender, Andreas

2016-01-01

Traditional Chinese medicine (TCM) still needs more scientific rationale to be proven for it to be accepted further in the West. We are now in the position to propose computational hypotheses for the mode-of-actions (MOAs) of 45 TCM therapeutic action (sub)classes from in silico target prediction algorithms, whose target was later annotated with Kyoto Encyclopedia of Genes and Genomes pathway, and to discover the relationship between them by generating a hierarchical clustering. The results of 10,749 TCM compounds showed 183 enriched targets and 99 enriched pathways from Estimation Score ≤ 0 and ≥ 5% of compounds/targets in a (sub)class. The MOA of a (sub)class was established from supporting literature. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase nonreceptor type 2 (PTPN2) and digestive system such as mineral absorption. We found two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif, which agreed with the important treatment principle of TCM, “the foundation of acquired constitution” that includes spleen and stomach. In short, the TCM (sub)classes, in many cases share similar targets/pathways despite having different indications. PMID:26989424

Advancement in Watershed Modelling Using Dynamic Lateral and Longitudinal Sediment (Dis)connectivity Prediction

NASA Astrophysics Data System (ADS)

Mahoney, D. T.; al Aamery, N. M. H.; Fox, J.

2017-12-01

The authors find that sediment (dis)connectivity has seldom taken precedence within watershed models, and the present study advances this modeling framework and applies the modeling within a bedrock-controlled system. Sediment (dis)connectivity, defined as the detachment and transport of sediment from source to sink between geomorphic zones, is a major control on sediment transport. Given the availability of high resolution geospatial data, coupling sediment connectivity concepts within sediment prediction models offers an approach to simulate sediment sources and pathways within a watershed's sediment cascade. Bedrock controlled catchments are potentially unique due to the presence of rock outcrops causing longitudinal impedance to sediment transport pathways in turn impacting the longitudinal distribution of the energy gradient responsible for conveying sediment. Therefore, the authors were motivated by the need to formulate a sediment transport model that couples sediment (dis)connectivity knowledge to predict sediment flux for bedrock controlled catchments. A watershed-scale sediment transport model was formulated that incorporates sediment (dis)connectivity knowledge collected via field reconnaissance and predicts sediment flux through coupling with the Partheniades equation and sediment continuity model. Sediment (dis)connectivity was formulated by coupling probabilistic upland lateral connectivity prediction with instream longitudinal connectivity assessments via discretization of fluid and sediment pathways. Flux predictions from the upland lateral connectivity model served as an input to the instream longitudinal connectivity model. Disconnectivity in the instream model was simulated via the discretization of stream reaches due to barriers such as bedrock outcroppings and man-made check dams. The model was tested for a bedrock controlled catchment in Kentucky, USA for which extensive historic water and sediment flux data was available. Predicted sediment flux was validated via sediment flux measurements collected by the authors. Watershed configuration and the distribution of lateral and longitudinal impedances to sediment transport were found to have significant influence on sediment connectivity and thus sediment flux.

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.

Systems Pharmacology Dissection of the Anti-Inflammatory Mechanism for the Medicinal Herb Folium Eriobotryae

PubMed Central

Zhang, Jingxiao; Li, Yan; Chen, Su-Shing; Zhang, Lilei; Wang, Jinghui; Yang, Yinfeng; Zhang, Shuwei; Pan, Yanqiu; Wang, Yonghua; Yang, Ling

2015-01-01

Inflammation is a hallmark of many diseases like diabetes, cancers, atherosclerosis and arthritis. Thus, lots of concerns have been raised toward developing novel anti-inflammatory agents. Many alternative herbal medicines possess excellent anti-inflammatory properties, yet their precise mechanisms of action are yet to be elucidated. Here, a novel systems pharmacology approach based on a large number of chemical, biological and pharmacological data was developed and exemplified by a probe herb Folium Eriobotryae, a widely used clinical anti-inflammatory botanic drug. The results show that 11 ingredients of this herb with favorable pharmacokinetic properties are predicted as active compounds for anti-inflammatory treatment. In addition, via systematic network analyses, their targets are identified to be 43 inflammation-associated proteins including especially COX2, ALOX5, PPARG, TNF and RELA that are mainly involved in the mitogen-activated protein kinase (MAPK) signaling pathway, the rheumatoid arthritis pathway and NF-κB signaling pathway. All these demonstrate that the integrated systems pharmacology method provides not only an effective tool to illustrate the anti-inflammatory mechanisms of herbs, but also a new systems-based approach for drug discovery from, but not limited to, herbs, especially when combined with further experimental validations. PMID:25636035

Deciphering the pharmacological mechanism of the Chinese formula Huanglian-Jie-Du decoction in the treatment of ischemic stroke using a systems biology-based strategy

PubMed Central

Zhang, Yan-qiong; Wang, Song-song; Zhu, Wei-liang; Ma, Yan; Zhang, Fang-bo; Liang, Ri-xin; Xu, Hai-yu; Yang, Hong-jun

2015-01-01

Aim: Huanglian-Jie-Du decoction (HLJDD) is an important multiherb remedy in TCM, which is recently demonstrated to be effective to treat ischemic stroke. Here, we aimed to investigate the pharmacological mechanisms of HLJDD in the treatment of ischemic stroke using systems biology approaches. Methods: Putative targets of HLJDD were predicted using MetaDrug. An interaction network of putative HLJDD targets and known therapeutic targets for the treatment of ischemic stroke was then constructed, and candidate HLJDD targets were identified by calculating topological features, including 'Degree', 'Node-betweenness', 'Closeness', and 'K-coreness'. The binding efficiencies of the candidate HLJDD targets with the corresponding compositive compounds were further validated by a molecular docking simulation. Results: A total of 809 putative targets were obtained for 168 compositive compounds in HLJDD. Additionally, 39 putative targets were common to all four herbs of HLJDD. Next, 49 major nodes were identified as candidate HLJDD targets due to their network topological importance. The enrichment analysis based on the Gene Ontology (GO) annotation system and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway demonstrated that candidate HLJDD targets were more frequently involved in G-protein-coupled receptor signaling pathways, neuroactive ligand-receptor interactions and gap junctions, which all played important roles in the progression of ischemic stroke. Finally, the molecular docking simulation showed that 170 pairs of chemical components and candidate HLJDD targets had strong binding efficiencies. Conclusion: This study has developed for the first time a comprehensive systems approach integrating drug target prediction, network analysis and molecular docking simulation to reveal the relationships between the herbs contained in HLJDD and their putative targets and ischemic stroke-related pathways. PMID:25937634

Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways.

PubMed

Chen, Lei; Zhang, Yu-Hang; Wang, ShaoPeng; Zhang, YunHua; Huang, Tao; Cai, Yu-Dong

2017-01-01

Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems.

Inter-species pathway perturbation prediction via data-driven detection of functional homology.

PubMed

Hafemeister, Christoph; Romero, Roberto; Bilal, Erhan; Meyer, Pablo; Norel, Raquel; Rhrissorrakrai, Kahn; Bonneau, Richard; Tarca, Adi L

2015-02-15

Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli. We present two approaches, ranked second in this challenge, that do not rely on sequence-based orthology between rat and human genes to translate pathway perturbation state but instead identify transcriptional response orthologs across a set of training conditions. The translation from rat to human accomplished by these so-called direct methods is not dependent on the particular analysis method used to identify perturbed gene sets. In contrast, machine learning-based methods require performing a pathway analysis initially and then mapping the pathway activity between organisms. Unlike most machine learning approaches, direct methods can be used to predict the activation of a human pathway for a new (test) stimuli, even when that pathway was never activated by a training stimuli. Gene expression data are available from ArrayExpress (accession E-MTAB-2091), while software implementations are available from http://bioinformaticsprb.med.wayne.edu?p=50 and http://goo.gl/hJny3h. christoph.hafemeister@nyu.edu or atarca@med.wayne.edu. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Synthetic and systems biology for microbial production of commodity chemicals.

PubMed

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J; Keasling, Jay D; Martín, Héctor García

2016-01-01

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges start at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.

Synthetic and systems biology for microbial production of commodity chemicals

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

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J.

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges startmore » at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.« less

Synthetic and systems biology for microbial production of commodity chemicals

DOE PAGES

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J.; ...

2016-04-07

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges startmore » at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.« less

Production of bulk chemicals via novel metabolic pathways in microorganisms.

PubMed

Shin, Jae Ho; Kim, Hyun Uk; Kim, Dong In; Lee, Sang Yup

2013-11-01

Metabolic engineering has been playing important roles in developing high performance microorganisms capable of producing various chemicals and materials from renewable biomass in a sustainable manner. Synthetic and systems biology are also contributing significantly to the creation of novel pathways and the whole cell-wide optimization of metabolic performance, respectively. In order to expand the spectrum of chemicals that can be produced biotechnologically, it is necessary to broaden the metabolic capacities of microorganisms. Expanding the metabolic pathways for biosynthesizing the target chemicals requires not only the enumeration of a series of known enzymes, but also the identification of biochemical gaps whose corresponding enzymes might not actually exist in nature; this issue is the focus of this paper. First, pathway prediction tools, effectively combining reactions that lead to the production of a target chemical, are analyzed in terms of logics representing chemical information, and designing and ranking the proposed metabolic pathways. Then, several approaches for potentially filling in the gaps of the novel metabolic pathway are suggested along with relevant examples, including the use of promiscuous enzymes that flexibly utilize different substrates, design of novel enzymes for non-natural reactions, and exploration of hypothetical proteins. Finally, strain optimization by systems metabolic engineering in the context of novel metabolic pathways constructed is briefly described. It is hoped that this review paper will provide logical ways of efficiently utilizing 'big' biological data to design and develop novel metabolic pathways for the production of various bulk chemicals that are currently produced from fossil resources. Copyright © 2012 Elsevier Inc. All rights reserved.

Circulating Tumor Cells: What Is in It for the Patient? A Vision towards the Future

PubMed Central

van de Stolpe, Anja; den Toonder, Jaap M. J.

2014-01-01

Knowledge on cellular signal transduction pathways as drivers of cancer growth and metastasis has fuelled development of “targeted therapy” which “targets” aberrant oncogenic signal transduction pathways. These drugs require nearly invariably companion diagnostic tests to identify the tumor-driving pathway and the cause of the abnormal pathway activity in a tumor sample, both for therapy response prediction as well as for monitoring of therapy response and emerging secondary drug resistance. Obtaining sufficient tumor material for this analysis in the metastatic setting is a challenge, and circulating tumor cells (CTCs) may provide an attractive alternative to biopsy on the premise that they can be captured from blood and the companion diagnostic test results are correctly interpreted. We discuss novel companion diagnostic directions, including the challenges, to identify the tumor driving pathway in CTCs, which in combination with a digital pathology platform and algorithms to quantitatively interpret complex CTC diagnostic results may enable optimized therapy response prediction and monitoring. In contrast to CTC-based companion diagnostics, CTC enumeration is envisioned to be largely replaced by cell free tumor DNA measurements in blood for therapy response and recurrence monitoring. The recent emergence of novel in vitro human model systems in the form of cancer-on-a-chip may enable elucidation of some of the so far elusive characteristics of CTCs, and is expected to contribute to more efficient CTC capture and CTC-based diagnostics. PMID:24879438

Selection of an endogenous 2,3-butanediol pathway in Escherichia coli by fermentative redox balance.

PubMed

Liang, Keming; Shen, Claire R

2017-01-01

Fermentative redox balance has long been utilized as a metabolic evolution platform to improve efficiency of NADH-dependent pathways. However, such system relies on the complete recycling of NADH and may become limited when the target pathway results in excess NADH stoichiometrically. In this study, endogenous capability of Escherichia coli for 2,3-butanediol (2,3-BD) synthesis was explored using the anaerobic selection platform based on redox balance. To address the issue of NADH excess associated with the 2,3-BD pathway, we devised a substrate-decoupled system where a pathway intermediate is externally supplied in addition to the carbon source to decouple NADH recycling ratio from the intrinsic pathway stoichiometry. In this case, feeding of the 2,3-BD precursor acetoin effectively restored anaerobic growth of the mixed-acid fermentation mutant that remained otherwise inhibited even in the presence of a functional 2,3-BD pathway. Using established 2,3-BD dehydrogenases as model enzyme, we verified that the redox-based selection system is responsive to NADPH-dependent reactions but with lower sensitivity. Based on this substrate-decoupled selection scheme, we successfully identified the glycerol/1,2-propanediol dehydrogenase (Ec-GldA) as the major enzyme responsible for the acetoin reducing activity (k cat /K m ≈0.4mM -1 s -1 ) observed in E. coli. Significant shift of 2,3-BD configuration upon withdrawal of the heterologous acetolactate decarboxylase revealed that the endogenous synthesis of acetoin occurs via diacetyl. Among the predicted diacetyl reductase in E. coli, Ec-UcpA displayed the most significant activity towards diacetyl reduction into acetoin (V max ≈6U/mg). The final strain demonstrated a meso-2,3-BD production titer of 3g/L without introduction of foreign genes. The substrate-decoupled selection system allows redox balance regardless of the pathway stoichiometry thus enables segmented optimization of different reductive pathways through enzyme bioprospecting and metabolic evolution. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Computational identification of signalling pathways in Plasmodium falciparum.

PubMed

Oyelade, Jelili; Ewejobi, Itunu; Brors, Benedikt; Eils, Roland; Adebiyi, Ezekiel

2011-06-01

Malaria is one of the world's most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Reports have shown that the resistance of the parasite to existing drugs is increasing. Therefore, there is a huge and urgent need to discover and validate new drug or vaccine targets to enable the development of new treatments for malaria. The ability to discover these drug or vaccine targets can only be enhanced from our deep understanding of the detailed biology of the parasite, for example how cells function and how proteins organize into modules such as metabolic, regulatory and signal transduction pathways. It has been noted that the knowledge of signalling transduction pathways in Plasmodium is fundamental to aid the design of new strategies against malaria. This work uses a linear-time algorithm for finding paths in a network under modified biologically motivated constraints. We predicted several important signalling transduction pathways in Plasmodium falciparum. We have predicted a viable signalling pathway characterized in terms of the genes responsible that may be the PfPKB pathway recently elucidated in Plasmodium falciparum. We obtained from the FIKK family, a signal transduction pathway that ends up on a chloroquine resistance marker protein, which indicates that interference with FIKK proteins might reverse Plasmodium falciparum from resistant to sensitive phenotype. We also proposed a hypothesis that showed the FIKK proteins in this pathway as enabling the resistance parasite to have a mechanism for releasing chloroquine (via an efflux process). Furthermore, we also predicted a signalling pathway that may have been responsible for signalling the start of the invasion process of Red Blood Cell (RBC) by the merozoites. It has been noted that the understanding of this pathway will give insight into the parasite virulence and will facilitate rational vaccine design against merozoites invasion. And we have a host of other predicted pathways, some of which have been used in this work to predict the functionality of some proteins. Copyright © 2010 Elsevier B.V. All rights reserved.

Predicting the points of interaction of small molecules in the NF-κB pathway

PubMed Central

2011-01-01

Background The similarity property principle has been used extensively in drug discovery to identify small compounds that interact with specific drug targets. Here we show it can be applied to identify the interactions of small molecules within the NF-κB signalling pathway. Results Clusters that contain compounds with a predominant interaction within the pathway were created, which were then used to predict the interaction of compounds not included in the clustering analysis. Conclusions The technique successfully predicted the points of interactions of compounds that are known to interact with the NF-κB pathway. The method was also shown to be successful when compounds for which the interaction points were unknown were included in the clustering analysis. PMID:21342508

Replication and extension of the dual pathway model of disordered eating: The role of fear of negative evaluation, suggestibility, rumination, and self-compassion.

PubMed

Maraldo, Toni M; Zhou, Wanni; Dowling, Jessica; Vander Wal, Jillon S

2016-12-01

The dual pathway model, a theoretical model of eating disorder development, suggests that thin ideal internalization leads to body dissatisfaction which leads to disordered eating via the dual pathways of negative affect and dietary restraint. While the dual pathway model has been a valuable guide for eating disorder prevention, greater knowledge of characteristics that predict thin ideal internalization is needed. The present study replicated and extended the dual pathway model by considering the addition of fear of negative evaluation, suggestibility, rumination, and self-compassion in a sample of community women and female university students. Results showed that fear of negative evaluation and suggestibility predicted thin ideal internalization whereas rumination and self-compassion (inversely) predicted body dissatisfaction. Negative affect was predicted by fear of negative evaluation, rumination, and self-compassion (inversely). The extended model fit the data well in both samples. Analogue and longitudinal study of these constructs is warranted in future research. Copyright © 2016 Elsevier Ltd. All rights reserved.

Autophagy Regulatory Network - a systems-level bioinformatics resource for studying the mechanism and regulation of autophagy.

PubMed

Türei, Dénes; Földvári-Nagy, László; Fazekas, Dávid; Módos, Dezső; Kubisch, János; Kadlecsik, Tamás; Demeter, Amanda; Lenti, Katalin; Csermely, Péter; Vellai, Tibor; Korcsmáros, Tamás

2015-01-01

Autophagy is a complex cellular process having multiple roles, depending on tissue, physiological, or pathological conditions. Major post-translational regulators of autophagy are well known, however, they have not yet been collected comprehensively. The precise and context-dependent regulation of autophagy necessitates additional regulators, including transcriptional and post-transcriptional components that are listed in various datasets. Prompted by the lack of systems-level autophagy-related information, we manually collected the literature and integrated external resources to gain a high coverage autophagy database. We developed an online resource, Autophagy Regulatory Network (ARN; http://autophagy-regulation.org), to provide an integrated and systems-level database for autophagy research. ARN contains manually curated, imported, and predicted interactions of autophagy components (1,485 proteins with 4,013 interactions) in humans. We listed 413 transcription factors and 386 miRNAs that could regulate autophagy components or their protein regulators. We also connected the above-mentioned autophagy components and regulators with signaling pathways from the SignaLink 2 resource. The user-friendly website of ARN allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. ARN has the potential to facilitate the experimental validation of novel autophagy components and regulators. In addition, ARN helps the investigation of transcription factors, miRNAs and signaling pathways implicated in the control of the autophagic pathway. The list of such known and predicted regulators could be important in pharmacological attempts against cancer and neurodegenerative diseases.

Integrating genomics and proteomics data to predict drug effects using binary linear programming.

PubMed

Ji, Zhiwei; Su, Jing; Liu, Chenglin; Wang, Hongyan; Huang, Deshuang; Zhou, Xiaobo

2014-01-01

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy.

Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach.

PubMed

Wang, Yin-Yin; Li, Jie; Wu, Zeng-Rui; Zhang, Bo; Yang, Hong-Bin; Wang, Qin; Cai, Ying-Chun; Liu, Gui-Xia; Li, Wei-Hua; Tang, Yun

2017-05-01

An increasing number of cases of herb-induced liver injury (HILI) have been reported, presenting new clinical challenges. In this study, taking Polygonum multiflorum Thunb (PmT) as an example, we proposed a computational systems toxicology approach to explore the molecular mechanisms of HILI. First, the chemical components of PmT were extracted from 3 main TCM databases as well as the literature related to natural products. Then, the known targets were collected through data integration, and the potential compound-target interactions (CTIs) were predicted using our substructure-drug-target network-based inference (SDTNBI) method. After screening for hepatotoxicity-related genes by assessing the symptoms of HILI, a compound-target interaction network was constructed. A scoring function, namely, Ascore, was developed to estimate the toxicity of chemicals in the liver. We conducted network analysis to determine the possible mechanisms of the biphasic effects using the analysis tools, including BiNGO, pathway enrichment, organ distribution analysis and predictions of interactions with CYP450 enzymes. Among the chemical components of PmT, 54 components with good intestinal absorption were used for analysis, and 2939 CTIs were obtained. After analyzing the mRNA expression data in the BioGPS database, 1599 CTIs and 125 targets related to liver diseases were identified. In the top 15 compounds, seven with Ascore values >3000 (emodin, quercetin, apigenin, resveratrol, gallic acid, kaempferol and luteolin) were obviously associated with hepatotoxicity. The results from the pathway enrichment analysis suggest that multiple interactions between apoptosis and metabolism may underlie PmT-induced liver injury. Many of the pathways have been verified in specific compounds, such as glutathione metabolism, cytochrome P450 metabolism, and the p53 pathway, among others. Hepatitis symptoms, the perturbation of nine bile acids and yellow or tawny urine also had corresponding pathways, justifying our method. In conclusion, this computational systems toxicology method reveals possible toxic components and could be very helpful for understanding the mechanisms of HILI. In this way, the method might also facilitate the identification of novel hepatotoxic herbs.

Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours

PubMed Central

Yamada, Takuji; Waller, Alison S; Raes, Jeroen; Zelezniak, Aleksej; Perchat, Nadia; Perret, Alain; Salanoubat, Marcel; Patil, Kiran R; Weissenbach, Jean; Bork, Peer

2012-01-01

Despite the current wealth of sequencing data, one-third of all biochemically characterized metabolic enzymes lack a corresponding gene or protein sequence, and as such can be considered orphan enzymes. They represent a major gap between our molecular and biochemical knowledge, and consequently are not amenable to modern systemic analyses. As 555 of these orphan enzymes have metabolic pathway neighbours, we developed a global framework that utilizes the pathway and (meta)genomic neighbour information to assign candidate sequences to orphan enzymes. For 131 orphan enzymes (37% of those for which (meta)genomic neighbours are available), we associate sequences to them using scoring parameters with an estimated accuracy of 70%, implying functional annotation of 16 345 gene sequences in numerous (meta)genomes. As a case in point, two of these candidate sequences were experimentally validated to encode the predicted activity. In addition, we augmented the currently available genome-scale metabolic models with these new sequence–function associations and were able to expand the models by on average 8%, with a considerable change in the flux connectivity patterns and improved essentiality prediction. PMID:22569339

Neural and hybrid modeling: an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment.

PubMed

Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

2014-01-01

The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

PubMed Central

Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

2014-01-01

The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved. PMID:24516363

20171015 - Predicting Exposure Pathways with Machine Learning (ISES)

EPA Science Inventory

Prioritizing the risk posed to human health from the thousands of chemicals in the environment requires tools that can estimate exposure rates from limited information. High throughput models exist to make predictions of exposure via specific, important pathways such as residenti...

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

PubMed Central

2014-01-01

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

Different Pathways to Juvenile Delinquency: Characteristics of Early and Late Starters in a Sample of Previously Incarcerated Youth

ERIC Educational Resources Information Center

Alltucker, Kevin W.; Bullis, Michael; Close, Daniel; Yovanoff, Paul

2006-01-01

We examined the differences between early and late start juvenile delinquents in a sample of 531 previously incarcerated youth in Oregon's juvenile justice system. Data were analyzed with logistic regression to predict early start delinquency based on four explanatory variables: foster care experience, family criminality, special education…

Structural Equivalence of Involvement in Problem Behavior by Adolescents across Racial Groups Using Multiple Group Confirmatory Factor Analysis.

ERIC Educational Resources Information Center

Williams, James H.; And Others

1996-01-01

Problem behavior theory predicts that adolescent problem behaviors are manifestations of a single behavioral syndrome. This study tested the validity of the theory across racial groups. Results indicate that multiple pathways are necessary to account for the problem behaviors and they support previous research indicating system response bias in…

A superstatistical model of metastasis and cancer survival

NASA Astrophysics Data System (ADS)

Leon Chen, L.; Beck, Christian

2008-05-01

We introduce a superstatistical model for the progression statistics of malignant cancer cells. The metastatic cascade is modeled as a complex nonequilibrium system with several macroscopic pathways and inverse-chi-square distributed parameters of the underlying Poisson processes. The predictions of the model are in excellent agreement with observed survival-time probability distributions of breast cancer patients.

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis.

PubMed

Wang, QuanQiu; Xu, Rong

2017-07-01

Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved. MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E-19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E-7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict's potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known metabolite inhibitors for RA treatments (methotrexate:0.25%; leflunomide: 0.56%; sulfasalazine: 0.92%). MetabolitePredict is a generalizable disease metabolite prediction system. The only required input to the system is a disease name or a set of disease-associated genes. The web-based MetabolitePredict is available at:http://xulab. edu/MetabolitePredict. Copyright © 2017 Elsevier Inc. All rights reserved.

An integrated global chemomics and system biology approach to analyze the mechanisms of the traditional Chinese medicinal preparation Eriobotrya japonica - Fritillaria usuriensis dropping pills for pulmonary diseases.

PubMed

Tao, Jin; Hou, Yuanyuan; Ma, Xiaoyao; Liu, Dan; Tong, Yongling; Zhou, Hong; Gao, Jie; Bai, Gang

2016-01-08

Traditional Chinese medicine (TCM) herbal formulae provide valuable therapeutic strategies. However, the active ingredients and mechanisms of action remain unclear for most of these formulae. Therefore, the identification of complex mechanisms is a major challenge in TCM research. This study used a network pharmacology approach to clarify the anti-inflammatory and cough suppressing mechanisms of the Chinese medicinal preparation Eriobotrya japonica - Fritillaria usuriensis dropping pills (ChuanbeiPipa dropping pills, CBPP). The chemical constituents of CBPP were identified by high-quality ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS), and anti-inflammatory ingredients were selected and analyzed using the PharmMapper and Kyoto Encyclopedia of Genes and Genomes (KEGG) bioinformatics websites to predict the target proteins and related pathways, respectively. Then, an RNA-sequencing (RNA-Seq) analysis was carried out to investigate the different expression of genes in the lung tissue of rats with chronic bronchitis. Six main constituents affected 19 predicted pathways, including ursolic acid and oleanolic acid from Eriobotrya japonica (Thunb.) Lindl. (Eri), peiminine from Fritillaria usuriensis Maxim. (Fri), platycodigenin and polygalacic acid from Platycodon grandiflorum (Jacq.) A. DC. (Pla) and guanosine from Pinellia ternata (Thunb.) Makino. (Pin). Expression of 34 genes was significantly decreased after CBPP treatment, affecting four therapeutic functions: immunoregulation, anti-inflammation, collagen formation and muscle contraction. The active components acted on the mitogen activated protein kinase (MAPK) pathway, transforming growth factor (TGF)-beta pathway, focal adhesion, tight junctions and the action cytoskeleton to exert anti-inflammatory effects, resolve phlegm, and relieve cough. This novel approach of global chemomics-integrated systems biology represents an effective and accurate strategy for the study of TCM with multiple components and multiple target mechanisms.

RNA-Seq Expression Analysis of Enteric Neuron Cells with Rotenone Treatment and Prediction of Regulated Pathways.

PubMed

Guan, Qiang; Wang, Xijin; Jiang, Yanyan; Zhao, Lijuan; Nie, Zhiyu; Jin, Lingjing

2017-02-01

The enteric nervous system (ENS) is involved in the initiation and development of the pathological process of Parkinson's disease (PD). The effect of rotenone on the ENS may trigger the progression of PD through the central nervous system (CNS). In this study, we used RNA-sequencing (RNA-seq) analysis to examine differential expression genes (DEGs) and pathways induced by in vitro treatment of rotenone in the enteric nervous cells isolated from rats. We identified 45 up-regulated and 30 down-regulated genes. The functional categorization revealed that the DEGs were involved in the regulation of cell differentiation and development, response to various stimuli, and regulation of neurogenesis. In addition, the pathway and network analysis showed that the Mitogen Activated Protein Kinase (MAPK), Toll-like receptor, Wnt, and Ras signaling pathways were intensively involved in the effect of rotenone on the ENS. Additionally, the quantitative real-time polymerase chain reaction result for the selected seven DEGs matched those of the RNA-seq analysis. Our results present a significant step in the identification of DEGs and provide new insight into the progression of PD in the rotenone-induced model.

Radiogenomics: a systems biology approach to understanding genetic risk factors for radiotherapy toxicity ?

PubMed Central

Herskind, Carsten; Talbot, Christopher J.; Kerns, Sarah L.; Veldwijk, Marlon R.; Rosenstein, Barry S.; West, Catharine M. L.

2016-01-01

Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review ‘omics’ approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different ‘omics’ approaches may be more efficient in identifying critical pathways than pathway analysis based on single ‘omics’ data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterized by different mechanisms. Thus ‘omics’ and functional approaches may synergize if they are integrated into radiogenomics ‘systems biology’ to facilitate the goal of individualised radiotherapy. PMID:26944314

Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere1

PubMed Central

Hill, Kristine; Porco, Silvana; Lobet, Guillaume; Zappala, Susan; Mooney, Sacha; Draye, Xavier; Bennett, Malcolm J.

2013-01-01

Genetic and genomic approaches in model organisms have advanced our understanding of root biology over the last decade. Recently, however, systems biology and modeling have emerged as important approaches, as our understanding of root regulatory pathways has become more complex and interpreting pathway outputs has become less intuitive. To relate root genotype to phenotype, we must move beyond the examination of interactions at the genetic network scale and employ multiscale modeling approaches to predict emergent properties at the tissue, organ, organism, and rhizosphere scales. Understanding the underlying biological mechanisms and the complex interplay between systems at these different scales requires an integrative approach. Here, we describe examples of such approaches and discuss the merits of developing models to span multiple scales, from network to population levels, and to address dynamic interactions between plants and their environment. PMID:24143806

Improving Microbial Genome Annotations in an Integrated Database Context

PubMed Central

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken; Anderson, Iain; Mavromatis, Konstantinos; Kyrpides, Nikos C.; Ivanova, Natalia N.

2013-01-01

Effective comparative analysis of microbial genomes requires a consistent and complete view of biological data. Consistency regards the biological coherence of annotations, while completeness regards the extent and coverage of functional characterization for genomes. We have developed tools that allow scientists to assess and improve the consistency and completeness of microbial genome annotations in the context of the Integrated Microbial Genomes (IMG) family of systems. All publicly available microbial genomes are characterized in IMG using different functional annotation and pathway resources, thus providing a comprehensive framework for identifying and resolving annotation discrepancies. A rule based system for predicting phenotypes in IMG provides a powerful mechanism for validating functional annotations, whereby the phenotypic traits of an organism are inferred based on the presence of certain metabolic reactions and pathways and compared to experimentally observed phenotypes. The IMG family of systems are available at http://img.jgi.doe.gov/. PMID:23424620

Engineering plant metabolism into microbes: from systems biology to synthetic biology.

PubMed

Xu, Peng; Bhan, Namita; Koffas, Mattheos A G

2013-04-01

Plant metabolism represents an enormous repository of compounds that are of pharmaceutical and biotechnological importance. Engineering plant metabolism into microbes will provide sustainable solutions to produce pharmaceutical and fuel molecules that could one day replace substantial portions of the current fossil-fuel based economy. Metabolic engineering entails targeted manipulation of biosynthetic pathways to maximize yields of desired products. Recent advances in Systems Biology and the emergence of Synthetic Biology have accelerated our ability to design, construct and optimize cell factories for metabolic engineering applications. Progress in predicting and modeling genome-scale metabolic networks, versatile gene assembly platforms and delicate synthetic pathway optimization strategies has provided us exciting opportunities to exploit the full potential of cell metabolism. In this review, we will discuss how systems and synthetic biology tools can be integrated to create tailor-made cell factories for efficient production of natural products and fuel molecules in microorganisms. Copyright © 2012 Elsevier Ltd. All rights reserved.

Mechanisms of Severe Acute Respiratory Syndrome Coronavirus-Induced Acute Lung Injury

PubMed Central

Gralinski, Lisa E.; Bankhead, Armand; Jeng, Sophia; Menachery, Vineet D.; Proll, Sean; Belisle, Sarah E.; Matzke, Melissa; Webb-Robertson, Bobbie-Jo M.; Luna, Maria L.; Shukla, Anil K.; Ferris, Martin T.; Bolles, Meagan; Chang, Jean; Aicher, Lauri; Waters, Katrina M.; Smith, Richard D.; Metz, Thomas O.; Law, G. Lynn; Katze, Michael G.; McWeeney, Shannon; Baric, Ralph S.

2013-01-01

ABSTRACT Systems biology offers considerable promise in uncovering novel pathways by which viruses and other microbial pathogens interact with host signaling and expression networks to mediate disease severity. In this study, we have developed an unbiased modeling approach to identify new pathways and network connections mediating acute lung injury, using severe acute respiratory syndrome coronavirus (SARS-CoV) as a model pathogen. We utilized a time course of matched virologic, pathological, and transcriptomic data within a novel methodological framework that can detect pathway enrichment among key highly connected network genes. This unbiased approach produced a high-priority list of 4 genes in one pathway out of over 3,500 genes that were differentially expressed following SARS-CoV infection. With these data, we predicted that the urokinase and other wound repair pathways would regulate lethal versus sublethal disease following SARS-CoV infection in mice. We validated the importance of the urokinase pathway for SARS-CoV disease severity using genetically defined knockout mice, proteomic correlates of pathway activation, and pathological disease severity. The results of these studies demonstrate that a fine balance exists between host coagulation and fibrinolysin pathways regulating pathological disease outcomes, including diffuse alveolar damage and acute lung injury, following infection with highly pathogenic respiratory viruses, such as SARS-CoV. PMID:23919993

Prediction of Pathway Activation by Xenobiotic-Responsive Transcription Factors in the Mouse Liver

EPA Science Inventory

Many drugs and environmentally-relevant chemicals activate xenobioticresponsive transcription factors (TF). Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening. Starting with a large compendium of Affymet...

Detecting uber-operons in prokaryotic genomes.

PubMed

Che, Dongsheng; Li, Guojun; Mao, Fenglou; Wu, Hongwei; Xu, Ying

2006-01-01

We present a study on computational identification of uber-operons in a prokaryotic genome, each of which represents a group of operons that are evolutionarily or functionally associated through operons in other (reference) genomes. Uber-operons represent a rich set of footprints of operon evolution, whose full utilization could lead to new and more powerful tools for elucidation of biological pathways and networks than what operons have provided, and a better understanding of prokaryotic genome structures and evolution. Our prediction algorithm predicts uber-operons through identifying groups of functionally or transcriptionally related operons, whose gene sets are conserved across the target and multiple reference genomes. Using this algorithm, we have predicted uber-operons for each of a group of 91 genomes, using the other 90 genomes as references. In particular, we predicted 158 uber-operons in Escherichia coli K12 covering 1830 genes, and found that many of the uber-operons correspond to parts of known regulons or biological pathways or are involved in highly related biological processes based on their Gene Ontology (GO) assignments. For some of the predicted uber-operons that are not parts of known regulons or pathways, our analyses indicate that their genes are highly likely to work together in the same biological processes, suggesting the possibility of new regulons and pathways. We believe that our uber-operon prediction provides a highly useful capability and a rich information source for elucidation of complex biological processes, such as pathways in microbes. All the prediction results are available at our Uber-Operon Database: http://csbl.bmb.uga.edu/uber, the first of its kind.

Detecting uber-operons in prokaryotic genomes

PubMed Central

Che, Dongsheng; Li, Guojun; Mao, Fenglou; Wu, Hongwei; Xu, Ying

2006-01-01

We present a study on computational identification of uber-operons in a prokaryotic genome, each of which represents a group of operons that are evolutionarily or functionally associated through operons in other (reference) genomes. Uber-operons represent a rich set of footprints of operon evolution, whose full utilization could lead to new and more powerful tools for elucidation of biological pathways and networks than what operons have provided, and a better understanding of prokaryotic genome structures and evolution. Our prediction algorithm predicts uber-operons through identifying groups of functionally or transcriptionally related operons, whose gene sets are conserved across the target and multiple reference genomes. Using this algorithm, we have predicted uber-operons for each of a group of 91 genomes, using the other 90 genomes as references. In particular, we predicted 158 uber-operons in Escherichia coli K12 covering 1830 genes, and found that many of the uber-operons correspond to parts of known regulons or biological pathways or are involved in highly related biological processes based on their Gene Ontology (GO) assignments. For some of the predicted uber-operons that are not parts of known regulons or pathways, our analyses indicate that their genes are highly likely to work together in the same biological processes, suggesting the possibility of new regulons and pathways. We believe that our uber-operon prediction provides a highly useful capability and a rich information source for elucidation of complex biological processes, such as pathways in microbes. All the prediction results are available at our Uber-Operon Database: , the first of its kind. PMID:16682449

Survey of Genes Involved in Biosynthesis, Transport, and Signaling of Phytohormones with Focus on Solanum lycopersicum

PubMed Central

Simm, Stefan; Scharf, Klaus-Dieter; Jegadeesan, Sridharan; Chiusano, Maria Luisa; Firon, Nurit; Schleiff, Enrico

2016-01-01

Phytohormones control the development and growth of plants, as well as their response to biotic and abiotic stress. The seven most well-studied phytohormone classes defined today are as follows: auxins, ethylene, cytokinin, abscisic acid, jasmonic acid, gibberellins, and brassinosteroids. The basic principle of hormone regulation is conserved in all plants, but recent results suggest adaptations of synthesis, transport, or signaling pathways to the architecture and growth environment of different plant species. Thus, we aimed to define the extent to which information from the model plant Arabidopsis thaliana is transferable to other plants such as Solanum lycopersicum. We extracted the co-orthologues of genes coding for major pathway enzymes in A. thaliana from the translated genomes of 12 species from the clade Viridiplantae. Based on predicted domain architecture and localization of the identified proteins from all 13 species, we inspected the conservation of phytohormone pathways. The comparison was complemented by expression analysis of (co-) orthologous genes in S. lycopersicum. Altogether, this information allowed the assignment of putative functional equivalents between A. thaliana and S. lycopersicum but also pointed to some variations between the pathways in eudicots, monocots, mosses, and green algae. These results provide first insights into the conservation of the various phytohormone pathways between the model system A. thaliana and crop plants such as tomato. We conclude that orthologue prediction in combination with analysis of functional domain architecture and intracellular localization and expression studies are sufficient tools to transfer information from model plants to other plant species. Our results support the notion that hormone synthesis, transport, and response for most part of the pathways are conserved, and species-specific variations can be found. PMID:27695302

‘N-of-1-pathways’ unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine

PubMed Central

Gardeux, Vincent; Achour, Ikbel; Li, Jianrong; Maienschein-Cline, Mark; Li, Haiquan; Pesce, Lorenzo; Parinandi, Gurunadh; Bahroos, Neil; Winn, Robert; Foster, Ian; Garcia, Joe G N; Lussier, Yves A

2014-01-01

Background The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method ‘N-of-1-pathways’ is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. Results Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). Conclusions The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies. Software http://lussierlab.org/publications/N-of-1-pathways PMID:25301808

‘N-of-1- pathways ’ unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: Towards precision medicine

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

Gardeux, Vincent; Achour, Ikbel; Li, Jianrong

Background: The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. This research entails, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method: ‘N-of-1- pathways’ is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity betweenmore » genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1- pathways predicts the deregulated pathways of each patient. Results: Cross-patient N-of-1- pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). Conclusions: The N-of-1- pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies.« less

‘N-of-1- pathways ’ unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: Towards precision medicine

DOE PAGES

Gardeux, Vincent; Achour, Ikbel; Li, Jianrong; ...

2014-11-01

Background: The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. This research entails, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method: ‘N-of-1- pathways’ is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity betweenmore » genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1- pathways predicts the deregulated pathways of each patient. Results: Cross-patient N-of-1- pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). Conclusions: The N-of-1- pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies.« less

Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury.

PubMed

Wink, Steven; Hiemstra, Steven W; Huppelschoten, Suzanne; Klip, Janna E; van de Water, Bob

2018-05-01

Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-κB-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.

Expression profiling in canine osteosarcoma: identification of biomarkers and pathways associated with outcome

PubMed Central

2010-01-01

Background Osteosarcoma (OSA) spontaneously arises in the appendicular skeleton of large breed dogs and shares many physiological and molecular biological characteristics with human OSA. The standard treatment for OSA in both species is amputation or limb-sparing surgery, followed by chemotherapy. Unfortunately, OSA is an aggressive cancer with a high metastatic rate. Characterization of OSA with regard to its metastatic potential and chemotherapeutic resistance will improve both prognostic capabilities and treatment modalities. Methods We analyzed archived primary OSA tissue from dogs treated with limb amputation followed by doxorubicin or platinum-based drug chemotherapy. Samples were selected from two groups: dogs with disease free intervals (DFI) of less than 100 days (n = 8) and greater than 300 days (n = 7). Gene expression was assessed with Affymetrix Canine 2.0 microarrays and analyzed with a two-tailed t-test. A subset of genes was confirmed using qRT-PCR and used in classification analysis to predict prognosis. Systems-based gene ontology analysis was conducted on genes selected using a standard J5 metric. The genes identified using this approach were converted to their human homologues and assigned to functional pathways using the GeneGo MetaCore platform. Results Potential biomarkers were identified using gene expression microarray analysis and 11 differentially expressed (p < 0.05) genes were validated with qRT-PCR (n = 10/group). Statistical classification models using the qRT-PCR profiles predicted patient outcomes with 100% accuracy in the training set and up to 90% accuracy upon stratified cross validation. Pathway analysis revealed alterations in pathways associated with oxidative phosphorylation, hedgehog and parathyroid hormone signaling, cAMP/Protein Kinase A (PKA) signaling, immune responses, cytoskeletal remodeling and focal adhesion. Conclusions This profiling study has identified potential new biomarkers to predict patient outcome in OSA and new pathways that may be targeted for therapeutic intervention. PMID:20860831

In Silico Modelling of Transdermal and Systemic Kinetics of Topically Applied Solutes: Model Development and Initial Validation for Transdermal Nicotine.

PubMed

Chen, Tao; Lian, Guoping; Kattou, Panayiotis

2016-07-01

The purpose was to develop a mechanistic mathematical model for predicting the pharmacokinetics of topically applied solutes penetrating through the skin and into the blood circulation. The model could be used to support the design of transdermal drug delivery systems and skin care products, and risk assessment of occupational or consumer exposure. A recently reported skin penetration model [Pharm Res 32 (2015) 1779] was integrated with the kinetic equations for dermis-to-capillary transport and systemic circulation. All model parameters were determined separately from the molecular, microscopic and physiological bases, without fitting to the in vivo data to be predicted. Published clinical studies of nicotine were used for model demonstration. The predicted plasma kinetics is in good agreement with observed clinical data. The simulated two-dimensional concentration profile in the stratum corneum vividly illustrates the local sub-cellular disposition kinetics, including tortuous lipid pathway for diffusion and the "reservoir" effect of the corneocytes. A mechanistic model for predicting transdermal and systemic kinetics was developed and demonstrated with published clinical data. The integrated mechanistic approach has significantly extended the applicability of a recently reported microscopic skin penetration model by providing prediction of solute concentration in the blood.

Predictive toxicology using systemic biology and liver microfluidic “on chip” approaches: Application to acetaminophen injury

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

Prot, Jean-Matthieu; Bunescu, Andrei; Elena-Herrmann, Bénédicte

2012-03-15

We have analyzed transcriptomic, proteomic and metabolomic profiles of hepatoma cells cultivated inside a microfluidic biochip with or without acetaminophen (APAP). Without APAP, the results show an adaptive cellular response to the microfluidic environment, leading to the induction of anti-oxidative stress and cytoprotective pathways. In presence of APAP, calcium homeostasis perturbation, lipid peroxidation and cell death are observed. These effects can be attributed to APAP metabolism into its highly reactive metabolite, N-acetyl-p-benzoquinone imine (NAPQI). That toxicity pathway was confirmed by the detection of GSH-APAP, the large production of 2-hydroxybutyrate and 3-hydroxybutyrate, and methionine, cystine, and histidine consumption in the treatedmore » biochips. Those metabolites have been reported as specific biomarkers of hepatotoxicity and glutathione depletion in the literature. In addition, the integration of the metabolomic, transcriptomic and proteomic collected profiles allowed a more complete reconstruction of the APAP injury pathways. To our knowledge, this work is the first example of a global integration of microfluidic biochip data in toxicity assessment. Our results demonstrate the potential of that new approach to predictive toxicology. -- Highlights: ► We cultivated liver cells in microfluidic biochips ► We integrated transcriptomic, proteomic and metabolomics profiles ► Pathways reconstructions were proposed in control and acetaminophen treated cultures ► Biomarkers were identified ► Comparisons with in vivo studies were proposed.« less

Comprehensive investigations of kinetics of alkaline hydrolysis of TNT (2,4,6-trinitrotoluene), DNT (2,4-dinitrotoluene), and DNAN (2,4-dinitroanisole).

PubMed

Sviatenko, Liudmyla; Kinney, Chad; Gorb, Leonid; Hill, Frances C; Bednar, Anthony J; Okovytyy, Sergiy; Leszczynski, Jerzy

2014-09-02

Combined experimental and computational techniques were used to analyze multistep chemical reactions in the alkaline hydrolysis of three nitroaromatic compounds: 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (DNT), and 2,4-dinitroanisole (DNAN). The study reveals common features and differences in the kinetic behavior of these compounds. The analysis of the predicted pathways includes modeling of the reactions, along with simulation of UV-vis spectra, experimental monitoring of reactions using LC/MS techniques, development of the kinetic model by designing and solving the system of differential equations, and obtaining computationally predicted kinetics for decay and accumulation of reactants and products. Obtained results suggest that DNT and DNAN are more resistant to alkaline hydrolysis than TNT. The direct substitution of a nitro group by a hydroxide represents the most favorable pathway for all considered compounds. The formation of Meisenheimer complexes leads to the kinetic first-step intermediates in the hydrolysis of TNT. Janovsky complexes can also be formed during hydrolysis of TNT and DNT but in small quantities. Methyl group abstraction is one of the suggested pathways of DNAN transformation during alkaline hydrolysis.

The protein-protein interaction network of eyestalk, Y-organ and hepatopancreas in Chinese mitten crab Eriocheir sinensis.

PubMed

Hao, Tong; Zeng, Zheng; Wang, Bin; Zhang, Yichen; Liu, Yichen; Geng, Xuyun; Sun, Jinsheng

2014-03-27

The protein-protein interaction network (PIN) is an effective information tool for understanding the complex biological processes inside the cell and solving many biological problems such as signaling pathway identification and prediction of protein functions. Eriocheir sinensis is a highly-commercial aquaculture species with an unclear proteome background which hinders the construction and development of PIN for E. sinensis. However, in recent years, the development of next-generation deep-sequencing techniques makes it possible to get high throughput data of E. sinensis tanscriptome and subsequently obtain a systematic overview of the protein-protein interaction system. In this work we sequenced the transcriptional RNA of eyestalk, Y-organ and hepatopancreas in E. sinensis and generated a PIN of E. sinensis which included 3,223 proteins and 35,787 interactions. Each protein-protein interaction in the network was scored according to the homology and genetic relationship. The signaling sub-network, representing the signal transduction pathways in E. sinensis, was extracted from the global network, which depicted a global view of the signaling systems in E. sinensis. Seven basic signal transduction pathways were identified in E. sinensis. By investigating the evolution paths of the seven pathways, we found that these pathways got mature in different evolutionary stages. Moreover, the functions of unclassified proteins and unigenes in the PIN of E. sinensis were predicted. Specifically, the functions of 549 unclassified proteins related to 864 unclassified unigenes were assigned, which respectively covered 76% and 73% of all the unclassified proteins and unigenes in the network. The PIN generated in this work is the first large-scale PIN of aquatic crustacean, thereby providing a paradigmatic blueprint of the aquatic crustacean interactome. Signaling sub-network extracted from the global PIN depicts the interaction of different signaling proteins and the evolutionary paths of the identified signal transduction pathways. Furthermore, the function assignment of unclassified proteins based on the PIN offers a new reference in protein function exploration. More importantly, the construction of the E. sinensis PIN provides necessary experience for the exploration of PINs in other aquatic crustacean species.

Anomaly Detection in Host Signaling Pathways for the Early Prognosis of Acute Infection.

PubMed

Wang, Kun; Langevin, Stanley; O'Hern, Corey S; Shattuck, Mark D; Ogle, Serenity; Forero, Adriana; Morrison, Juliet; Slayden, Richard; Katze, Michael G; Kirby, Michael

2016-01-01

Clinical diagnosis of acute infectious diseases during the early stages of infection is critical to administering the appropriate treatment to improve the disease outcome. We present a data driven analysis of the human cellular response to respiratory viruses including influenza, respiratory syncytia virus, and human rhinovirus, and compared this with the response to the bacterial endotoxin, Lipopolysaccharides (LPS). Using an anomaly detection framework we identified pathways that clearly distinguish between asymptomatic and symptomatic patients infected with the four different respiratory viruses and that accurately diagnosed patients exposed to a bacterial infection. Connectivity pathway analysis comparing the viral and bacterial diagnostic signatures identified host cellular pathways that were unique to patients exposed to LPS endotoxin indicating this type of analysis could be used to identify host biomarkers that can differentiate clinical etiologies of acute infection. We applied the Multivariate State Estimation Technique (MSET) on two human influenza (H1N1 and H3N2) gene expression data sets to define host networks perturbed in the asymptomatic phase of infection. Our analysis identified pathways in the respiratory virus diagnostic signature as prognostic biomarkers that triggered prior to clinical presentation of acute symptoms. These early warning pathways correctly predicted that almost half of the subjects would become symptomatic in less than forty hours post-infection and that three of the 18 subjects would become symptomatic after only 8 hours. These results provide a proof-of-concept for utility of anomaly detection algorithms to classify host pathway signatures that can identify presymptomatic signatures of acute diseases and differentiate between etiologies of infection. On a global scale, acute respiratory infections cause a significant proportion of human co-morbidities and account for 4.25 million deaths annually. The development of clinical diagnostic tools to distinguish between acute viral and bacterial respiratory infections is critical to improve patient care and limit the overuse of antibiotics in the medical community. The identification of prognostic respiratory virus biomarkers provides an early warning system that is capable of predicting which subjects will become symptomatic to expand our medical diagnostic capabilities and treatment options for acute infectious diseases. The host response to acute infection may be viewed as a deterministic signaling network responsible for maintaining the health of the host organism. We identify pathway signatures that reflect the very earliest perturbations in the host response to acute infection. These pathways provide a monitor the health state of the host using anomaly detection to quantify and predict health outcomes to pathogens.

Anomaly Detection in Host Signaling Pathways for the Early Prognosis of Acute Infection

PubMed Central

O’Hern, Corey S.; Shattuck, Mark D.; Ogle, Serenity; Forero, Adriana; Morrison, Juliet; Slayden, Richard; Katze, Michael G.

2016-01-01

Clinical diagnosis of acute infectious diseases during the early stages of infection is critical to administering the appropriate treatment to improve the disease outcome. We present a data driven analysis of the human cellular response to respiratory viruses including influenza, respiratory syncytia virus, and human rhinovirus, and compared this with the response to the bacterial endotoxin, Lipopolysaccharides (LPS). Using an anomaly detection framework we identified pathways that clearly distinguish between asymptomatic and symptomatic patients infected with the four different respiratory viruses and that accurately diagnosed patients exposed to a bacterial infection. Connectivity pathway analysis comparing the viral and bacterial diagnostic signatures identified host cellular pathways that were unique to patients exposed to LPS endotoxin indicating this type of analysis could be used to identify host biomarkers that can differentiate clinical etiologies of acute infection. We applied the Multivariate State Estimation Technique (MSET) on two human influenza (H1N1 and H3N2) gene expression data sets to define host networks perturbed in the asymptomatic phase of infection. Our analysis identified pathways in the respiratory virus diagnostic signature as prognostic biomarkers that triggered prior to clinical presentation of acute symptoms. These early warning pathways correctly predicted that almost half of the subjects would become symptomatic in less than forty hours post-infection and that three of the 18 subjects would become symptomatic after only 8 hours. These results provide a proof-of-concept for utility of anomaly detection algorithms to classify host pathway signatures that can identify presymptomatic signatures of acute diseases and differentiate between etiologies of infection. On a global scale, acute respiratory infections cause a significant proportion of human co-morbidities and account for 4.25 million deaths annually. The development of clinical diagnostic tools to distinguish between acute viral and bacterial respiratory infections is critical to improve patient care and limit the overuse of antibiotics in the medical community. The identification of prognostic respiratory virus biomarkers provides an early warning system that is capable of predicting which subjects will become symptomatic to expand our medical diagnostic capabilities and treatment options for acute infectious diseases. The host response to acute infection may be viewed as a deterministic signaling network responsible for maintaining the health of the host organism. We identify pathway signatures that reflect the very earliest perturbations in the host response to acute infection. These pathways provide a monitor the health state of the host using anomaly detection to quantify and predict health outcomes to pathogens. PMID:27532264

An integrative systems biology approach to understanding pulmonary diseases.

PubMed

Auffray, Charles; Adcock, Ian M; Chung, Kian Fan; Djukanovic, Ratko; Pison, Christophe; Sterk, Peter J

2010-06-01

Chronic inflammatory pulmonary diseases such as COPD and asthma are highly prevalent and associated with a major health burden worldwide. Despite a wealth of biologic and clinical information on normal and pathologic airway structure and function, the primary causes and mechanisms of disease remain to a large extent unknown, preventing the development of more efficient diagnosis and treatment. We propose to overcome these limitations through an integrative systems biology research strategy designed to identify the functional and regulatory pathways that play central roles in respiratory pathophysiology, starting with severe asthma. This approach relies on global genome, transcriptome, proteome, and metabolome data sets collected in cross-sectional patient cohorts with high-throughput measurement platforms and integrated with biologic and clinical data to inform predictive multiscale models ranging from the molecular to the organ levels. Working hypotheses formulated on the mechanisms and pathways involved in various disease states are tested through perturbation experiments using model simulation combined with targeted and global technologies in cellular and animal models. The responses observed are compared with those predicted by the initial models, which are refined to account better for the results. Novel perturbation experiments are designed and tested both computationally and experimentally to arbitrate between competing hypotheses. The process is iterated until the derived knowledge allows a better classification and subphenotyping of severe asthma using complex biomarkers, which will facilitate the development of novel diagnostic and therapeutic interventions targeting multiple components of the molecular and cellular pathways involved. This can be tested and validated in prospective clinical trials.

Experimental animal models of encapsulating peritoneal sclerosis.

PubMed

Hoff, Catherine M

2005-04-01

Encapsulating peritoneal sclerosis (EPS) is an infrequent, but extremely serious complication of long-term peritoneal dialysis. The cause of EPS is unclear, but the low incidence suggests that it is most likely multifactorial. The elucidation of developmental pathways and predictive markers of EPS would facilitate the identification and management of high-risk patients. Animal models are often used to define pathways of disease progression and to test strategies for treatment and prevention in the patient population. Ideally such models could help to define the cause of EPS and its developmental pathways, to facilitate the identification of contributing factors and predictive markers, and to provide a system to test therapeutic strategies. Researchers have studied several rodent models of EPS that rely on chronic chemical irritation (for example, bleach, low-pH solution, chlorhexidine gluconate) to induce peritoneal sclerosis and abdominal encapsulation. Development in all models is progressive, with inflammation giving way to peritoneal fibrosis or sclerosis with accumulating membrane damage, culminating in cocoon formation. Microscopic findings are similar to those proposed as diagnostic criteria for clinical EPS: an initial inflammatory infiltrate and submesothelial thickening, collagen deposition, and activation and proliferation of peritoneal fibroblasts. The potential to block progression of peritoneal sclerosis in these models by anti-inflammatory, antifibrotic, and anti-angiogenic agents, and by inhibitors of the renin-angiotensin system have been demonstrated. Animal models based on clinically relevant risk factors (for example, uremia, peritonitis, and long-term exposure to dialysis solutions) now represent the next step in model development.

[Pharmacodynamic evaluation and molecular mechanism research of Huanshao capsule on irregular menstruation].

PubMed

Sun, Jian-Hui; Huo, Hai-Ru; Li, Xiao-Qin; Li, Hong-Mei; Qin, De-Huai; Wu, Chun

2018-04-01

Huanshao capsule is widely used in irregular menstruation and has achieved a good effect. Huanshao capsule can promote gonad development in mice, significantly improve the ovarian index in mice, increase estrogen level and reduce FSH level in rats, inhibit the pain response induced by oxytocin and estrogen, inhibit writhing reaction induced by acetic acid pain in mice. Due to the complexity of traditional Chinese medical formula, the pharmacological mechanism of the treatment on the irregular menstruation of the Huanshao capsule is unclear. In this study, the internet-based computation platform (www.tcmip.cn）was used to explore the molecular mechanism of Huanshao capsule on the menstrual. The aim of this study was to find the molecular mechanism of Huanshao capsule in treating menstrual. In the study of the molecular mechanism of Huanshao capsule in the treatment of menstrual by using the internet-based computation platform, Huanshao capsule maybe treat the menstrual by the pathway of endocrine system, GnRH signal transduction pathway, estrogen signal transduction pathway, oxytocin signaling pathway, thyroid hormone signaling pathway, VEGF signaling pathway, FCεRI signaling pathway and purine metabolism and nucleotide metabolism. The early pharmacological study confirmed Huanshao capsule could increase the serum estradiol level and decrease follicle stimulating hormone level and the traditional Chinese medicine pharmacology coincide with the prediction result of internet-based computation platform which roles as the pathway of GnRH signaling pathway and estrogen signal transduction pathway. Other pathway needs further experimental verification. Copyright© by the Chinese Pharmaceutical Association.

Evolution of a genome-encoded bias in amino acid biosynthetic pathways is a potential indicator of amino acid dynamics in the environment.

PubMed

Fasani, Rick A; Savageau, Michael A

2014-11-01

Overcoming the stress of starvation is one of an organism's most challenging phenotypic responses. Those organisms that frequently survive the challenge, by virtue of their fitness, will have evolved genomes that are shaped by their specific environments. Understanding this genotype-environment-phenotype relationship at a deep level will require quantitative predictive models of the complex molecular systems that link these aspects of an organism's existence. Here, we treat one of the most fundamental molecular systems, protein synthesis, and the amino acid biosynthetic pathways involved in the stringent response to starvation. These systems face an inherent logical dilemma: Building an amino acid biosynthetic pathway to synthesize its product-the cognate amino acid of the pathway-may require that very amino acid when it is no longer available. To study this potential "catch-22," we have created a generic model of amino acid biosynthesis in response to sudden starvation. Our mathematical analysis and computational results indicate that there are two distinctly different outcomes: Partial recovery to a new steady state, or full system failure. Moreover, the cell's fate is dictated by the cognate bias, the number of cognate amino acids in the corresponding biosynthetic pathway relative to the average number of that amino acid in the proteome. We test these implications by analyzing the proteomes of over 1,800 sequenced microbes, which reveals statistically significant evidence of low cognate bias, a genetic trait that would avoid the biosynthetic quandary. Furthermore, these results suggest that the pattern of cognate bias, which is readily derived by genome sequencing, may provide evolutionary clues to an organism's natural environment. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

PubMed

Wang, QuanQiu; Xu, Rong

2015-01-01

Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

Genomics and expression profiles of the Hedgehog and Notch signaling pathways in sea urchin development.

PubMed

Walton, Katherine D; Croce, Jenifer C; Glenn, Thomas D; Wu, Shu-Yu; McClay, David R

2006-12-01

The Hedgehog (Hh) and Notch signal transduction pathways control a variety of developmental processes including cell fate choice, differentiation, proliferation, patterning and boundary formation. Because many components of these pathways are conserved, it was predicted and confirmed that pathway components are largely intact in the sea urchin genome. Spatial and temporal location of these pathways in the embryo, and their function in development offer added insight into their mechanistic contributions. Accordingly, all major components of both pathways were identified and annotated in the sea urchin Strongylocentrotus purpuratus genome and the embryonic expression of key components was explored. Relationships of the pathway components, and modifiers predicted from the annotation of S. purpuratus, were compared against cnidarians, arthropods, urochordates, and vertebrates. These analyses support the prediction that the pathways are highly conserved through metazoan evolution. Further, the location of these two pathways appears to be conserved among deuterostomes, and in the case of Notch at least, display similar capacities in endomesoderm gene regulatory networks. RNA expression profiles by quantitative PCR and RNA in situ hybridization reveal that Hedgehog is produced by the endoderm beginning just prior to invagination, and signals to the secondary mesenchyme-derived tissues at least until the pluteus larva stage. RNA in situ hybridization of Notch pathway members confirms that Notch functions sequentially in the vegetal-most secondary mesenchyme cells and later in the endoderm. Functional analyses in future studies will embed these pathways into the growing knowledge of gene regulatory networks that govern early specification and morphogenesis.

Systems Biology for Organotypic Cell Cultures

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

Grego, Sonia; Dougherty, Edward R.; Alexander, Francis J.

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data. This consensus report summarizes the discussions held.« less

Workshop Report: Systems Biology for Organotypic Cell Cultures

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

Grego, Sonia; Dougherty, Edward R.; Alexander, Francis Joseph

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.« less

Workshop Report: Systems Biology for Organotypic Cell Cultures

DOE PAGES

Grego, Sonia; Dougherty, Edward R.; Alexander, Francis Joseph; ...

2016-11-14

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.« less

Systems biology for organotypic cell cultures.

PubMed

Grego, Sonia; Dougherty, Edward R; Alexander, Francis J; Auerbach, Scott S; Berridge, Brian R; Bittner, Michael L; Casey, Warren; Cooley, Philip C; Dash, Ajit; Ferguson, Stephen S; Fennell, Timothy R; Hawkins, Brian T; Hickey, Anthony J; Kleensang, Andre; Liebman, Michael N J; Martin, Florian; Maull, Elizabeth A; Paragas, Jason; Qiao, Guilin Gary; Ramaiahgari, Sreenivasa; Sumner, Susan J; Yoon, Miyoung

2017-01-01

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.

Manganese-Induced Neurotoxicity and Alterations in Gene Expression in Human Neuroblastoma SH-SY5Y Cells.

PubMed

Gandhi, Deepa; Sivanesan, Saravanadevi; Kannan, Krishnamurthi

2018-06-01

Manganese (Mn) is an essential trace element required for many physiological functions including proper biochemical and cellular functioning of the central nervous system (CNS). However, exposure to excess level of Mn through occupational settings or from environmental sources has been associated with neurotoxicity. The cellular and molecular mechanism of Mn-induced neurotoxicity remains unclear. In the current study, we investigated the effects of 30-day exposure to a sub-lethal concentration of Mn (100 μM) in human neuroblastoma cells (SH-SY5Y) using transcriptomic approach. Microarray analysis revealed differential expression of 1057 transcripts in Mn-exposed SH-SY5Y cells as compared to control cells. Gene functional annotation cluster analysis exhibited that the differentially expressed genes were associated with several biological pathways. Specifically, genes involved in neuronal pathways including neuron differentiation and development, regulation of neurogenesis, synaptic transmission, and neuronal cell death (apoptosis) were found to be significantly altered. KEGG pathway analysis showed upregulation of p53 signaling pathways and neuroactive ligand-receptor interaction pathways, and downregulation of neurotrophin signaling pathway. On the basis of the gene expression profile, possible molecular mechanisms underlying Mn-induced neuronal toxicity were predicted.

SolCyc: a database hub at the Sol Genomics Network (SGN) for the manual curation of metabolic networks in Solanum and Nicotiana specific databases

PubMed Central

Foerster, Hartmut; Bombarely, Aureliano; Battey, James N D; Sierro, Nicolas; Ivanov, Nikolai V; Mueller, Lukas A

2018-01-01

Abstract SolCyc is the entry portal to pathway/genome databases (PGDBs) for major species of the Solanaceae family hosted at the Sol Genomics Network. Currently, SolCyc comprises six organism-specific PGDBs for tomato, potato, pepper, petunia, tobacco and one Rubiaceae, coffee. The metabolic networks of those PGDBs have been computationally predicted by the pathologic component of the pathway tools software using the manually curated multi-domain database MetaCyc (http://www.metacyc.org/) as reference. SolCyc has been recently extended by taxon-specific databases, i.e. the family-specific SolanaCyc database, containing only curated data pertinent to species of the nightshade family, and NicotianaCyc, a genus-specific database that stores all relevant metabolic data of the Nicotiana genus. Through manual curation of the published literature, new metabolic pathways have been created in those databases, which are complemented by the continuously updated, relevant species-specific pathways from MetaCyc. At present, SolanaCyc comprises 199 pathways and 29 superpathways and NicotianaCyc accounts for 72 pathways and 13 superpathways. Curator-maintained, taxon-specific databases such as SolanaCyc and NicotianaCyc are characterized by an enrichment of data specific to these taxa and free of falsely predicted pathways. Both databases have been used to update recently created Nicotiana-specific databases for Nicotiana tabacum, Nicotiana benthamiana, Nicotiana sylvestris and Nicotiana tomentosiformis by propagating verifiable data into those PGDBs. In addition, in-depth curation of the pathways in N.tabacum has been carried out which resulted in the elimination of 156 pathways from the 569 pathways predicted by pathway tools. Together, in-depth curation of the predicted pathway network and the supplementation with curated data from taxon-specific databases has substantially improved the curation status of the species–specific N.tabacum PGDB. The implementation of this strategy will significantly advance the curation status of all organism-specific databases in SolCyc resulting in the improvement on database accuracy, data analysis and visualization of biochemical networks in those species. Database URL https://solgenomics.net/tools/solcyc/ PMID:29762652

Convergent evolution at the pathway level: predictable regulatory changes during flower color transitions.

PubMed

Larter, Maximilian; Dunbar-Wallis, Amy; Berardi, Andrea E; Smith, Stacey D

2018-06-07

The predictability of evolution, or whether lineages repeatedly follow the same evolutionary trajectories during phenotypic convergence remains an open question of evolutionary biology. In this study, we investigate evolutionary convergence at the biochemical pathway level and test the predictability of evolution using floral anthocyanin pigmentation, a trait with a well-understood genetic and regulatory basis. We reconstructed the evolution of floral anthocyanin content across 28 species of the Andean clade Iochrominae (Solanaceae) and investigated how shifts in pigmentation are related to changes in expression of 7 key anthocyanin pathway genes. We used phylogenetic multivariate analysis of gene expression to test for phenotypic and developmental convergence at a macroevolutionary scale. Our results show that the four independent losses of the ancestral pigment delphinidin involved convergent losses of expression of the three late pathway genes (F3'5'h, Dfr and Ans). Transitions between pigment types affecting floral hue (e.g. blue to red) involve changes to the expression of branching genes F3'h and F3'5'h, while the expression levels of early steps of the pathway are strongly conserved in all species. These patterns support the idea that the macroevolution of floral pigmentation follows predictable evolutionary trajectories to reach convergent phenotype space, repeatedly involving regulatory changes. This is likely driven by constraints at the pathway level, such as pleiotropy and regulatory structure.

The use of dermal fibroblasts as a predictive tool of the TLR4 response pathway and its development in Holstein heifers

USDA-ARS?s Scientific Manuscript database

The innate immune system comprises the host’s first line of defense against invading pathogens, and variation in the magnitude of this response between animals has been shown to affect susceptibility to mastitis. The toll-like receptor (TLR) family of proteins initiates the response to invading bact...

A network pharmacology approach to determine the synergetic mechanisms of herb couple for treating rheumatic arthritis.

PubMed

Xu, Xi-Xi; Bi, Jian-Ping; Ping, Li; Li, Ping; Li, Fei

2018-01-01

The purpose of this study was to investigate the therapeutic mechanism(s) of Clematis chinensis Osbeck/ Notopterygium incisum K.C. Ting ex H.T (CN). A network pharmacology approach integrating prediction of ingredients, target exploration, network construction, module partition and pathway analysis was used. This approach successfully helped to identify 12 active ingredients of CN, interacting with 13 key targets (Akt1, STAT3, TNFsf13, TP53, EPHB2, IL-10, IL-6, TNF, MAPK8, IL-8, RELA, ROS1 and STAT4). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that CN-regulated pathways were mainly classified into signal transduction and immune system. The present work may help to illustrate the mechanism(s) of action of CN, and it may provide a better understanding of antirheumatic effects.

Cancer stem cells: a systems biology view of their role in prognosis and therapy.

PubMed

Mertins, Susan D

2014-04-01

Evidence has accumulated that characterizes highly tumorigenic cancer cells residing in heterogeneous populations. The accepted term for such a subpopulation is cancer stem cells (CSCs). While many questions still remain about their precise role in the origin, progression, and drug resistance of tumors, it is clear they exist. In this review, a current understanding of the nature of CSC, their potential usefulness in prognosis, and the need to target them will be discussed. In particular, separate studies now suggest that the CSC is plastic in its phenotype, toggling between tumorigenic and nontumorigenic states depending on both intrinsic and extrinsic conditions. Because of this, a static view of gene and protein levels defined by correlations may not be sufficient to either predict disease progression or aid in the discovery and development of drugs to molecular targets leading to cures. Quantitative dynamic modeling, a bottom up systems biology approach whereby signal transduction pathways are described by differential equations, may offer a novel means to overcome the challenges of oncology today. In conclusion, the complexity of CSCs can be captured in mathematical models that may be useful for selecting molecular targets, defining drug action, and predicting sensitivity or resistance pathways for improved patient outcomes.

An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization.

PubMed

Halper, Sean M; Cetnar, Daniel P; Salis, Howard M

2018-01-01

Engineering many-enzyme metabolic pathways suffers from the design curse of dimensionality. There are an astronomical number of synonymous DNA sequence choices, though relatively few will express an evolutionary robust, maximally productive pathway without metabolic bottlenecks. To solve this challenge, we have developed an integrated, automated computational-experimental pipeline that identifies a pathway's optimal DNA sequence without high-throughput screening or many cycles of design-build-test. The first step applies our Operon Calculator algorithm to design a host-specific evolutionary robust bacterial operon sequence with maximally tunable enzyme expression levels. The second step applies our RBS Library Calculator algorithm to systematically vary enzyme expression levels with the smallest-sized library. After characterizing a small number of constructed pathway variants, measurements are supplied to our Pathway Map Calculator algorithm, which then parameterizes a kinetic metabolic model that ultimately predicts the pathway's optimal enzyme expression levels and DNA sequences. Altogether, our algorithms provide the ability to efficiently map the pathway's sequence-expression-activity space and predict DNA sequences with desired metabolic fluxes. Here, we provide a step-by-step guide to applying the Pathway Optimization Pipeline on a desired multi-enzyme pathway in a bacterial host.

Analyzing Data for Systems Biology: Working at the Intersection of Thermodynamics and Data Analytics

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

Cannon, William R.; Baxter, Douglas J.

2012-08-15

Many challenges in systems biology have to do with analyzing data within the framework of molecular phenomena and cellular pathways. How does this relate to thermodynamics that we know govern the behavior of molecules? Making progress in relating data analysis to thermodynamics is essential in systems biology if we are to build predictive models that enable the field of synthetic biology. This report discusses work at the crossroads of thermodynamics and data analysis, and demonstrates that statistical mechanical free energy is a multinomial log likelihood. Applications to systems biology are presented.

Mesoscale models for stacking faults, deformation twins and martensitic transformations: Linking atomistics to continuum

NASA Astrophysics Data System (ADS)

Kibey, Sandeep A.

We present a hierarchical approach that spans multiple length scales to describe defect formation---in particular, formation of stacking faults (SFs) and deformation twins---in fcc crystals. We link the energy pathways (calculated here via ab initio density functional theory, DFT) associated with formation of stacking faults and twins to corresponding heterogeneous defect nucleation models (described through mesoscale dislocation mechanics). Through the generalized Peieirls-Nabarro model, we first correlate the width of intrinsic SFs in fcc alloy systems to their nucleation pathways called generalized stacking fault energies (GSFE). We then establish a qualitative dependence of twinning tendency in fee metals and alloys---specifically, in pure Cu and dilute Cu-xAl (x= 5.0 and 8.3 at.%)---on their twin-energy pathways called the generalized planar fault energies (GPFE). We also link the twinning behavior of Cu-Al alloys to their electronic structure by determining the effect of solute Al on the valence charge density redistribution at the SF through ab initio DFT. Further, while several efforts have been undertaken to incorporate twinning for predicting stress-strain response of fcc materials, a fundamental law for critical twinning stress has not yet emerged. We resolve this long-standing issue by linking quantitatively the twin-energy pathways (GPFE) obtained via ab initio DFT to heterogeneous, dislocation-based twin nucleation models. We establish an analytical expression that quantitatively predicts the critical twinning stress in fcc metals in agreement with experiments without requiring any empiricism at any length scale. Our theory connects twinning stress to twin-energy pathways and predicts a monotonic relation between stress and unstable twin stacking fault energy revealing the physics of twinning. We further demonstrate that the theory holds for fcc alloys as well. Our theory inherently accounts for directional nature of twinning which available qualitative models do not necessarily account for. Finally, we extend the present work to martensitic transformations and determine the energy pathway for B2→B19 transformation in NiTi. Based on our ab initio DFT calculations, we propose a combined distortion-shuffle pathway for B2→B19 transformation in NiTi. Our results indicate that in NiTi, a barrier of 0.48 mRyd/atom (relative to B2 phase) must be overcome to transform the parent B2 into orthorhombic B19 phase.

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

DOE PAGES

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

2017-04-07

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

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

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

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

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

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

PubMed Central

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

2017-01-01

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

Bacterial fermentation platform for producing artificial aromatic amines

PubMed Central

Masuo, Shunsuke; Zhou, Shengmin; Kaneko, Tatsuo; Takaya, Naoki

2016-01-01

Aromatic amines containing an aminobenzene or an aniline moiety comprise versatile natural and artificial compounds including bioactive molecules and resources for advanced materials. However, a bio-production platform has not been implemented. Here we constructed a bacterial platform for para-substituted aminobenzene relatives of aromatic amines via enzymes in an alternate shikimate pathway predicted in a Pseudomonad bacterium. Optimization of the metabolic pathway in Escherichia coli cells converted biomass glucose to 4-aminophenylalanine with high efficiency (4.4 g L−1 in fed-batch cultivation). We designed and produced artificial pathways that mimicked the fungal Ehrlich pathway in E. coli and converted 4-aminophenylalanine into 4-aminophenylethanol and 4-aminophenylacetate at 90% molar yields. Combining these conversion systems or fungal phenylalanine decarboxylases, the 4-aminophenylalanine-producing platform fermented glucose to 4-aminophenylethanol, 4-aminophenylacetate, and 4-phenylethylamine. This original bacterial platform for producing artificial aromatic amines highlights their potential as heteroatoms containing bio-based materials that can replace those derived from petroleum. PMID:27167511

Studying Biology to Understand Risk: Dosimetry Models and Quantitative Adverse Outcome Pathways

EPA Science Inventory

Confidence in the quantitative prediction of risk is increased when the prediction is based to as great an extent as possible on the relevant biological factors that constitute the pathway from exposure to adverse outcome. With the first examples now over 40 years old, physiologi...

A Global Genomic and Genetic Strategy to Predict Pathway Activation of Xenobiotic Responsive Transcription Factors in the Mouse Liver

EPA Science Inventory

Many drugs and environmentally-relevant chemicals activate xenobiotic-responsive transcription factors(TF). Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening. Starting with a large compendium of Affymet...

Cancer Systems Biology Consortium | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

Cancer is a complex disease system involving multiple molecular, genetic, and cellular events. From its early initiation through progression and metastasis, cancer can adapt and evolve as a result of both internal and external signals. These properties make cancer difficult to predict, prevent, and treat. There has been significant progress in characterizing the genetics of cancer, as well as the downstream effects on the molecular and cellular pathways that are critical for the initiation and progression of cancer.

Pathway-related modules involved in the application of sevoflurane or propofol in off-pump coronary artery bypass graft surgery.

PubMed

Bu, Xiangmei; Wang, Bo; Wang, Yaoqi; Wang, Zhigang; Gong, Chunzhi; Qi, Feng; Zhang, Caixia

2017-07-01

Off-pump coronary artery bypass graft (CABG) surgery has recently emerged as a means to avoid the sequelae of extracorporeal circulation, including the whole-body inflammatory response, coagulation disorders and multiple organ dysfunction. At present, gas anesthesia, sevoflurane and intravenous anesthesia and propofol have been widely used during the CABG. To further understand the underlying mechanisms of these anesthetics on the gene level, the present study conducted pathway-related module analysis based on a co-expression network. This was performed in order to identify significant pathways in coronary artery disease patients who had undergone off-pump CABG surgery before and after applying sevoflurane or propofol. A total of 269 and 129 differentially expressed genes were obtained in the sevoflurane and propofol groups, respectively. In total, eight and seven pathways (P<0.05) in the sevoflurane and propofol groups were separately obtained via Kyoto Encyclopedia of Genes and Genome pathway analysis. Finally, eight and seven pathway-related modules in the sevoflurane and propofol groups were obtained, respectively. Furthermore, the mean degree of complement and coagulation cascades pathway-related module in both of the groups was the highest. It was predicted that during the CABG, the anesthetics might activate the complement and coagulation systems in order to possess some cardioprotective properties.

Bayesian parameter estimation for nonlinear modelling of biological pathways.

PubMed

Ghasemi, Omid; Lindsey, Merry L; Yang, Tianyi; Nguyen, Nguyen; Huang, Yufei; Jin, Yu-Fang

2011-01-01

The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC) method. We applied this approach to the biological pathways involved in the left ventricle (LV) response to myocardial infarction (MI) and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly parameterized dynamic systems. Our proposed Bayesian algorithm successfully estimated parameters in nonlinear mathematical models for biological pathways. This method can be further extended to high order systems and thus provides a useful tool to analyze biological dynamics and extract information using temporal data.

Molecular Pathways: Extracting Medical Knowledge from High Throughput Genomic Data

PubMed Central

Goldstein, Theodore; Paull, Evan O.; Ellis, Matthew J.; Stuart, Joshua M.

2013-01-01

High-throughput genomic data that measures RNA expression, DNA copy number, mutation status and protein levels provide us with insights into the molecular pathway structure of cancer. Genomic lesions (amplifications, deletions, mutations) and epigenetic modifications disrupt biochemical cellular pathways. While the number of possible lesions is vast, different genomic alterations may result in concordant expression and pathway activities, producing common tumor subtypes that share similar phenotypic outcomes. How can these data be translated into medical knowledge that provides prognostic and predictive information? First generation mRNA expression signatures such as Genomic Health's Oncotype DX already provide prognostic information, but do not provide therapeutic guidance beyond the current standard of care – which is often inadequate in high-risk patients. Rather than building molecular signatures based on gene expression levels, evidence is growing that signatures based on higher-level quantities such as from genetic pathways may provide important prognostic and diagnostic cues. We provide examples of how activities for molecular entities can be predicted from pathway analysis and how the composite of all such activities, referred to here as the “activitome,” help connect genomic events to clinical factors in order to predict the drivers of poor outcome. PMID:23430023

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

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

Counterbalancing Regulation in Response Memory of a Positively Autoregulated Two-Component System.

PubMed

Gao, Rong; Godfrey, Katherine A; Sufian, Mahir A; Stock, Ann M

2017-09-15

Fluctuations in nutrient availability often result in recurrent exposures to the same stimulus conditions. The ability to memorize the past event and use the "memory" to make adjustments to current behaviors can lead to a more efficient adaptation to the recurring stimulus. A short-term phenotypic memory can be conferred via carryover of the response proteins to facilitate the recurrent response, but the additional accumulation of response proteins can lead to a deviation from response homeostasis. We used the Escherichia coli PhoB/PhoR two-component system (TCS) as a model system to study how cells cope with the recurrence of environmental phosphate (Pi) starvation conditions. We discovered that "memory" of prior Pi starvation can exert distinct effects through two regulatory pathways, the TCS signaling pathway and the stress response pathway. Although carryover of TCS proteins can lead to higher initial levels of transcription factor PhoB and a faster initial response in prestarved cells than in cells not starved, the response enhancement can be overcome by an earlier and greater repression of promoter activity in prestarved cells due to the memory of the stress response. The repression counterbalances the carryover of the response proteins, leading to a homeostatic response whether or not cells are prestimulated. A computational model based on sigma factor competition was developed to understand the memory of stress response and to predict the homeostasis of other PhoB-regulated response proteins. Our insight into the history-dependent PhoBR response may provide a general understanding of how TCSs respond to recurring stimuli and adapt to fluctuating environmental conditions. IMPORTANCE Bacterial cells in their natural environments experience scenarios that are far more complex than are typically replicated in laboratory experiments. The architectures of signaling systems and the integration of multiple adaptive pathways have evolved to deal with such complexity. In this study, we examined the molecular "memory" that is generated by previous exposure to stimulus. Under our experimental conditions, activating effects of autoregulated two-component signaling and inhibitory effects of the stress response counterbalanced the transcriptional output to approach response homeostasis whether or not cells had been preexposed to stimulus. Modeling allows prediction of response behavior in different scenarios and demonstrates both the robustness of the system output and its sensitivity to historical parameters such as timing and levels of exposure to stimuli. Copyright © 2017 American Society for Microbiology.

Counterbalancing Regulation in Response Memory of a Positively Autoregulated Two-Component System

PubMed Central

Gao, Rong; Godfrey, Katherine A.; Sufian, Mahir A.

2017-01-01

ABSTRACT Fluctuations in nutrient availability often result in recurrent exposures to the same stimulus conditions. The ability to memorize the past event and use the “memory” to make adjustments to current behaviors can lead to a more efficient adaptation to the recurring stimulus. A short-term phenotypic memory can be conferred via carryover of the response proteins to facilitate the recurrent response, but the additional accumulation of response proteins can lead to a deviation from response homeostasis. We used the Escherichia coli PhoB/PhoR two-component system (TCS) as a model system to study how cells cope with the recurrence of environmental phosphate (Pi) starvation conditions. We discovered that “memory” of prior Pi starvation can exert distinct effects through two regulatory pathways, the TCS signaling pathway and the stress response pathway. Although carryover of TCS proteins can lead to higher initial levels of transcription factor PhoB and a faster initial response in prestarved cells than in cells not starved, the response enhancement can be overcome by an earlier and greater repression of promoter activity in prestarved cells due to the memory of the stress response. The repression counterbalances the carryover of the response proteins, leading to a homeostatic response whether or not cells are prestimulated. A computational model based on sigma factor competition was developed to understand the memory of stress response and to predict the homeostasis of other PhoB-regulated response proteins. Our insight into the history-dependent PhoBR response may provide a general understanding of how TCSs respond to recurring stimuli and adapt to fluctuating environmental conditions. IMPORTANCE Bacterial cells in their natural environments experience scenarios that are far more complex than are typically replicated in laboratory experiments. The architectures of signaling systems and the integration of multiple adaptive pathways have evolved to deal with such complexity. In this study, we examined the molecular “memory” that is generated by previous exposure to stimulus. Under our experimental conditions, activating effects of autoregulated two-component signaling and inhibitory effects of the stress response counterbalanced the transcriptional output to approach response homeostasis whether or not cells had been preexposed to stimulus. Modeling allows prediction of response behavior in different scenarios and demonstrates both the robustness of the system output and its sensitivity to historical parameters such as timing and levels of exposure to stimuli. PMID:28674072

NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

PubMed

Jurtz, Vanessa; Paul, Sinu; Andreatta, Massimo; Marcatili, Paolo; Peters, Bjoern; Nielsen, Morten

2017-11-01

Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes. Copyright © 2017 by The American Association of Immunologists, Inc.

Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

PubMed Central

Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie

2016-01-01

Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine. PMID:27774993

Developmental Pathways Linking Externalizing Symptoms, Internalizing Symptoms, and Academic Competence to Adolescent Substance Use

ERIC Educational Resources Information Center

Englund, Michelle M.; Siebenbruner, Jessica

2012-01-01

This study extends previous research investigating the developmental pathways predicting adolescent alcohol and marijuana use by examining the cascading effects of externalizing and internalizing symptoms and academic competence in the prediction of use and level of use of these substances in adolescence. Participants (N = 191) were drawn from a…

Computationally predicted Adverse Outcome Pathway networks for liver-related diseases using publicly available data sources: Case studies and lessons learned

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework summarizes key information about mechanistic events leading to an adverse health or ecological outcome. In recent years computationally predicted AOPs (cpAOP) making use of publicly available data have been proposed as a means of accele...

A genome-wide longitudinal transcriptome analysis of the aging model Podospora anserina.

PubMed

Philipp, Oliver; Hamann, Andrea; Servos, Jörg; Werner, Alexandra; Koch, Ina; Osiewacz, Heinz D

2013-01-01

Aging of biological systems is controlled by various processes which have a potential impact on gene expression. Here we report a genome-wide transcriptome analysis of the fungal aging model Podospora anserina. Total RNA of three individuals of defined age were pooled and analyzed by SuperSAGE (serial analysis of gene expression). A bioinformatics analysis identified different molecular pathways to be affected during aging. While the abundance of transcripts linked to ribosomes and to the proteasome quality control system were found to decrease during aging, those associated with autophagy increase, suggesting that autophagy may act as a compensatory quality control pathway. Transcript profiles associated with the energy metabolism including mitochondrial functions were identified to fluctuate during aging. Comparison of wild-type transcripts, which are continuously down-regulated during aging, with those down-regulated in the long-lived, copper-uptake mutant grisea, validated the relevance of age-related changes in cellular copper metabolism. Overall, we (i) present a unique age-related data set of a longitudinal study of the experimental aging model P. anserina which represents a reference resource for future investigations in a variety of organisms, (ii) suggest autophagy to be a key quality control pathway that becomes active once other pathways fail, and (iii) present testable predictions for subsequent experimental investigations.

Measures of Hindu Pathways: Development and Preliminary Evidence of Reliability and Validity.

ERIC Educational Resources Information Center

Tarakeshwar, Nalini; Pargament, Kenneth I.; Mahoney, Annette

2003-01-01

Examines religious practices of Hindus in the United States and develops measures of their religious pathways. Four religious pathways were identified: devotion, ethical action, knowledge, and physical restraint/yoga. Results indicate that the measures of the religious pathways possessed adequate psychometric properties and were predictive of…

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients.

PubMed

Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

2017-09-29

Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients.

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients

PubMed Central

Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

2017-01-01

Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients. PMID:29100405

ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning.

PubMed

Kayala, Matthew A; Baldi, Pierre

2012-10-22

Proposing reasonable mechanisms and predicting the course of chemical reactions is important to the practice of organic chemistry. Approaches to reaction prediction have historically used obfuscating representations and manually encoded patterns or rules. Here we present ReactionPredictor, a machine learning approach to reaction prediction that models elementary, mechanistic reactions as interactions between approximate molecular orbitals (MOs). A training data set of productive reactions known to occur at reasonable rates and yields and verified by inclusion in the literature or textbooks is derived from an existing rule-based system and expanded upon with manual curation from graduate level textbooks. Using this training data set of complex polar, hypervalent, radical, and pericyclic reactions, a two-stage machine learning prediction framework is trained and validated. In the first stage, filtering models trained at the level of individual MOs are used to reduce the space of possible reactions to consider. In the second stage, ranking models over the filtered space of possible reactions are used to order the reactions such that the productive reactions are the top ranked. The resulting model, ReactionPredictor, perfectly ranks polar reactions 78.1% of the time and recovers all productive reactions 95.7% of the time when allowing for small numbers of errors. Pericyclic and radical reactions are perfectly ranked 85.8% and 77.0% of the time, respectively, rising to >93% recovery for both reaction types with a small number of allowed errors. Decisions about which of the polar, pericyclic, or radical reaction type ranking models to use can be made with >99% accuracy. Finally, for multistep reaction pathways, we implement the first mechanistic pathway predictor using constrained tree-search to discover a set of reasonable mechanistic steps from given reactants to given products. Webserver implementations of both the single step and pathway versions of ReactionPredictor are available via the chemoinformatics portal http://cdb.ics.uci.edu/.

A hidden oncogenic positive feedback loop caused by crosstalk between Wnt and ERK pathways.

PubMed

Kim, D; Rath, O; Kolch, W; Cho, K-H

2007-07-05

The Wnt and the extracellular signal regulated-kinase (ERK) pathways are both involved in the pathogenesis of various kinds of cancers. Recently, the existence of crosstalk between Wnt and ERK pathways was reported. Gathering all reported results, we have discovered a positive feedback loop embedded in the crosstalk between the Wnt and ERK pathways. We have developed a plausible model that represents the role of this hidden positive feedback loop in the Wnt/ERK pathway crosstalk based on the integration of experimental reports and employing established basic mathematical models of each pathway. Our analysis shows that the positive feedback loop can generate bistability in both the Wnt and ERK signaling pathways, and this prediction was further validated by experiments. In particular, using the commonly accepted assumption that mutations in signaling proteins contribute to cancerogenesis, we have found two conditions through which mutations could evoke an irreversible response leading to a sustained activation of both pathways. One condition is enhanced production of beta-catenin, the other is a reduction of the velocity of MAP kinase phosphatase(s). This enables that high activities of Wnt and ERK pathways are maintained even without a persistent extracellular signal. Thus, our study adds a novel aspect to the molecular mechanisms of carcinogenesis by showing that mutational changes in individual proteins can cause fundamental functional changes well beyond the pathway they function in by a positive feedback loop embedded in crosstalk. Thus, crosstalk between signaling pathways provides a vehicle through which mutations of individual components can affect properties of the system at a larger scale.

Development and Use of Numerical and Factual Data Bases

DTIC Science & Technology

1983-10-01

the quantitative description of what has been accomplished by their scientific and technical endeavors. 1-3 overhead charge to the national treasury... Molecular properties calculated with the aid of quantum mechanics or the prediction of solar eclipses using celestial mechanics are examples of theoretical...system under study. Examples include phase diagrams, molecular models, geological maps, metabolic pathways. Symbolic data (F3) are data presented in

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

PubMed Central

Tabei, Yasuo; Yamanishi, Yoshihiro; Kotera, Masaaki

2016-01-01

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

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

PubMed

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

2017-06-07

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

Gut microbiome may contribute to insulin resistance and systemic inflammation in obese rodents: a meta-analysis.

PubMed

Jiao, Na; Baker, Susan S; Nugent, Colleen A; Tsompana, Maria; Cai, Liting; Wang, Yong; Buck, Michael J; Genco, Robert J; Baker, Robert D; Zhu, Ruixin; Zhu, Lixin

2018-04-01

A number of studies have associated obesity with altered gut microbiota, although results are discordant regarding compositional changes in the gut microbiota of obese animals. Herein we used a meta-analysis to obtain an unbiased evaluation of structural and functional changes of the gut microbiota in diet-induced obese rodents. The raw sequencing data of nine studies generated from high-fat diet (HFD)-induced obese rodent models were processed with QIIME to obtain gut microbiota compositions. Biological functions were predicted and annotated with KEGG pathways with PICRUSt. No significant difference was observed for alpha diversity and Bacteroidetes-to-Firmicutes ratio between obese and lean rodents. Bacteroidia, Clostridia, Bacilli, and Erysipelotrichi were dominant classes, but gut microbiota compositions varied among studies. Meta-analysis of the nine microbiome data sets identified 15 differential taxa and 57 differential pathways between obese and lean rodents. In obese rodents, increased abundance was observed for Dorea, Oscillospira, and Ruminococcus, known for fermenting polysaccharide into short chain fatty acids (SCFAs). Decreased Turicibacter and increased Lactococcus are consistent with elevated inflammation in the obese status. Differential functional pathways of the gut microbiome in obese rodents included enriched pyruvate metabolism, butanoate metabolism, propanoate metabolism, pentose phosphate pathway, fatty acid biosynthesis, and glycerolipid metabolism pathways. These pathways converge in the function of carbohydrate metabolism, SCFA metabolism, and biosynthesis of lipid. HFD-induced obesity results in structural and functional dysbiosis of gut microbiota. The altered gut microbiome may contribute to obesity development by promoting insulin resistance and systemic inflammation.

Transcriptome analysis reveals enrichment of genes associated with auditory system in swimbladder of channel catfish.

PubMed

Yang, Yujia; Wang, Xiaozhu; Liu, Yang; Fu, Qiang; Tian, Changxu; Wu, Chenglong; Shi, Huitong; Yuan, Zihao; Tan, Suxu; Liu, Shikai; Gao, Dongya; Dunham, Rex; Liu, Zhanjiang

2018-04-30

In aquatic organisms, hearing is an important sense for acoustic communications and detection of sound-emitting predators and prey. Channel catfish is a dominant aquaculture species in the United States. As channel catfish can hear sounds of relatively high frequency, it serves as a good model for study auditory mechanisms. In catfishes, Weberian ossicles connect the swimbladder to the inner ear to transfer the forced vibrations and improve hearing ability. In this study, we examined the transcriptional profiles of channel catfish swimbladder and other four tissues (gill, liver, skin, and intestine). We identified a total of 1777 genes that exhibited preferential expression pattern in swimbladder of channel catfish. Based on Gene Ontology enrichment analysis, many of swimbladder-enriched genes were categorized into sensory perception of sound, auditory behavior, response to auditory stimulus, or detection of mechanical stimulus involved in sensory perception of sound, such as coch, kcnq4, sptbn1, sptbn4, dnm1, ush2a, and col11a1. Six signaling pathways associated with hearing (Glutamatergic synapse, GABAergic synapse pathways, Axon guidance, cAMP signaling pathway, Ionotropic glutamate receptor pathway, and Metabotropic glutamate receptor group III pathway) were over-represented in KEGG and PANTHER databases. Protein interaction prediction revealed an interactive relationship among the swimbladder-enriched genes and genes involved in sensory perception of sound. This study identified a set of genes and signaling pathways associated with auditory system in the swimbladder of channel catfish and provide resources for further study on the biological and physiological roles in catfish swimbladder. Copyright © 2018 Elsevier Inc. All rights reserved.

VisANT 3.0: new modules for pathway visualization, editing, prediction and construction.

PubMed

Hu, Zhenjun; Ng, David M; Yamada, Takuji; Chen, Chunnuan; Kawashima, Shuichi; Mellor, Joe; Linghu, Bolan; Kanehisa, Minoru; Stuart, Joshua M; DeLisi, Charles

2007-07-01

With the integration of the KEGG and Predictome databases as well as two search engines for coexpressed genes/proteins using data sets obtained from the Stanford Microarray Database (SMD) and Gene Expression Omnibus (GEO) database, VisANT 3.0 supports exploratory pathway analysis, which includes multi-scale visualization of multiple pathways, editing and annotating pathways using a KEGG compatible visual notation and visualization of expression data in the context of pathways. Expression levels are represented either by color intensity or by nodes with an embedded expression profile. Multiple experiments can be navigated or animated. Known KEGG pathways can be enriched by querying either coexpressed components of known pathway members or proteins with known physical interactions. Predicted pathways for genes/proteins with unknown functions can be inferred from coexpression or physical interaction data. Pathways produced in VisANT can be saved as computer-readable XML format (VisML), graphic images or high-resolution Scalable Vector Graphics (SVG). Pathways in the format of VisML can be securely shared within an interested group or published online using a simple Web link. VisANT is freely available at http://visant.bu.edu.

Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds.

PubMed

Chen, Lei; Zhang, Yu-Hang; Zheng, Mingyue; Huang, Tao; Cai, Yu-Dong

2016-12-01

Compound-protein interactions play important roles in every cell via the recognition and regulation of specific functional proteins. The correct identification of compound-protein interactions can lead to a good comprehension of this complicated system and provide useful input for the investigation of various attributes of compounds and proteins. In this study, we attempted to understand this system by extracting properties from both proteins and compounds, in which proteins were represented by gene ontology and KEGG pathway enrichment scores and compounds were represented by molecular fragments. Advanced feature selection methods, including minimum redundancy maximum relevance, incremental feature selection, and the basic machine learning algorithm random forest, were used to analyze these properties and extract core factors for the determination of actual compound-protein interactions. Compound-protein interactions reported in The Binding Databases were used as positive samples. To improve the reliability of the results, the analytic procedure was executed five times using different negative samples. Simultaneously, five optimal prediction methods based on a random forest and yielding maximum MCCs of approximately 77.55 % were constructed and may be useful tools for the prediction of compound-protein interactions. This work provides new clues to understanding the system of compound-protein interactions by analyzing extracted core features. Our results indicate that compound-protein interactions are related to biological processes involving immune, developmental and hormone-associated pathways.

Development of a Physiologically-Based Pharmacokinetic Model of the Rat Central Nervous System

PubMed Central

Badhan, Raj K. Singh; Chenel, Marylore; Penny, Jeffrey I.

2014-01-01

Central nervous system (CNS) drug disposition is dictated by a drug’s physicochemical properties and its ability to permeate physiological barriers. The blood–brain barrier (BBB), blood-cerebrospinal fluid barrier and centrally located drug transporter proteins influence drug disposition within the central nervous system. Attainment of adequate brain-to-plasma and cerebrospinal fluid-to-plasma partitioning is important in determining the efficacy of centrally acting therapeutics. We have developed a physiologically-based pharmacokinetic model of the rat CNS which incorporates brain interstitial fluid (ISF), choroidal epithelial and total cerebrospinal fluid (CSF) compartments and accurately predicts CNS pharmacokinetics. The model yielded reasonable predictions of unbound brain-to-plasma partition ratio (Kpuu,brain) and CSF:plasma ratio (CSF:Plasmau) using a series of in vitro permeability and unbound fraction parameters. When using in vitro permeability data obtained from L-mdr1a cells to estimate rat in vivo permeability, the model successfully predicted, to within 4-fold, Kpuu,brain and CSF:Plasmau for 81.5% of compounds simulated. The model presented allows for simultaneous simulation and analysis of both brain biophase and CSF to accurately predict CNS pharmacokinetics from preclinical drug parameters routinely available during discovery and development pathways. PMID:24647103

Characterizing Protein Interactions Employing a Genome-Wide siRNA Cellular Phenotyping Screen

PubMed Central

Suratanee, Apichat; Schaefer, Martin H.; Betts, Matthew J.; Soons, Zita; Mannsperger, Heiko; Harder, Nathalie; Oswald, Marcus; Gipp, Markus; Ramminger, Ellen; Marcus, Guillermo; Männer, Reinhard; Rohr, Karl; Wanker, Erich; Russell, Robert B.; Andrade-Navarro, Miguel A.; Eils, Roland; König, Rainer

2014-01-01

Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted de novo unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest. PMID:25255318

Starling forces drive intracranial water exchange during normal and pathological states.

PubMed

Linninger, Andreas A; Xu, Colin; Tangen, Kevin; Hartung, Grant

2017-12-31

To quantify the exchange of water between cerebral compartments, specifically blood, tissue, perivascular pathways, and cerebrospinal fluid-filled spaces, on the basis of experimental data and to propose a dynamic global model of water flux through the entire brain to elucidate functionally relevant fluid exchange phenomena. The mechanistic computer model to predict brain water shifts is discretized by cerebral compartments into nodes. Water and species flux is calculated between these nodes across a network of arcs driven by Hagen-Poiseuille flow (blood), Darcy flow (interstitial fluid transport), and Starling's Law (transmembrane fluid exchange). Compartment compliance is accounted for using a pressure-volume relationship to enforce the Monro-Kellie doctrine. This nonlinear system of differential equations is solved implicitly using MATLAB software. The model predictions of intraventricular osmotic injection caused a pressure rise from 10 to 22 mmHg, followed by a taper to 14 mmHg over 100 minutes. The computational results are compared to experimental data with R2=0.929. Moreover, simulated osmotic therapy of systemic (blood) injection reduced intracranial pressure from 25 to 10 mmHg. The modeled volume and intracranial pressure changes following cerebral edema agree with experimental trends observed in animal models with R2=0.997. The model successfully predicted time course and the efficacy of osmotic therapy for clearing cerebral edema. Furthermore, the mathematical model implicated the perivascular pathways as a possible conduit for water and solute exchange. This was a first step to quantify fluid exchange throughout the brain.

Comparative proteomics of cerebrospinal fluid reveals a predictive model for differential diagnosis of pneumococcal, meningococcal, and enteroviral meningitis, and novel putative therapeutic targets

PubMed Central

2015-01-01

Background Meningitis is the inflammation of the meninges in response to infection or chemical agents. While aseptic meningitis, most frequently caused by enteroviruses, is usually benign with a self-limiting course, bacterial meningitis remains associated with high morbidity and mortality rates, despite advances in antimicrobial therapy and intensive care. Fast and accurate differential diagnosis is crucial for assertive choice of the appropriate therapeutic approach for each form of meningitis. Methods We used 2D-PAGE and mass spectrometry to identify the cerebrospinal fluid proteome specifically related to the host response to pneumococcal, meningococcal, and enteroviral meningitis. The disease-specific proteome signatures were inspected by pathway analysis. Results Unique cerebrospinal fluid proteome signatures were found to the three aetiological forms of meningitis investigated, and a qualitative predictive model with four protein markers was developed for the differential diagnosis of these diseases. Nevertheless, pathway analysis of the disease-specific proteomes unveiled that Kallikrein-kinin system may play a crucial role in the pathophysiological mechanisms leading to brain damage in bacterial meningitis. Proteins taking part in this cellular process are proposed as putative targets to novel adjunctive therapies. Conclusions Comparative proteomics of cerebrospinal fluid disclosed candidate biomarkers, which were combined in a qualitative and sequential predictive model with potential to improve the differential diagnosis of pneumococcal, meningococcal and enteroviral meningitis. Moreover, we present the first evidence of the possible implication of Kallikrein-kinin system in the pathophysiology of bacterial meningitis. PMID:26040285

Comparative proteomics of cerebrospinal fluid reveals a predictive model for differential diagnosis of pneumococcal, meningococcal, and enteroviral meningitis, and novel putative therapeutic targets.

PubMed

Cordeiro, Ana Paula; Silva Pereira, Rosiane Aparecida; Chapeaurouge, Alex; Coimbra, Clarice Semião; Perales, Jonas; Oliveira, Guilherme; Sanchez Candiani, Talitah Michel; Coimbra, Roney Santos

2015-01-01

Meningitis is the inflammation of the meninges in response to infection or chemical agents. While aseptic meningitis, most frequently caused by enteroviruses, is usually benign with a self-limiting course, bacterial meningitis remains associated with high morbidity and mortality rates, despite advances in antimicrobial therapy and intensive care. Fast and accurate differential diagnosis is crucial for assertive choice of the appropriate therapeutic approach for each form of meningitis. We used 2D-PAGE and mass spectrometry to identify the cerebrospinal fluid proteome specifically related to the host response to pneumococcal, meningococcal, and enteroviral meningitis. The disease-specific proteome signatures were inspected by pathway analysis. Unique cerebrospinal fluid proteome signatures were found to the three aetiological forms of meningitis investigated, and a qualitative predictive model with four protein markers was developed for the differential diagnosis of these diseases. Nevertheless, pathway analysis of the disease-specific proteomes unveiled that Kallikrein-kinin system may play a crucial role in the pathophysiological mechanisms leading to brain damage in bacterial meningitis. Proteins taking part in this cellular process are proposed as putative targets to novel adjunctive therapies. Comparative proteomics of cerebrospinal fluid disclosed candidate biomarkers, which were combined in a qualitative and sequential predictive model with potential to improve the differential diagnosis of pneumococcal, meningococcal and enteroviral meningitis. Moreover, we present the first evidence of the possible implication of Kallikrein-kinin system in the pathophysiology of bacterial meningitis.

Reaction pathways in atomistic models of thin film growth

NASA Astrophysics Data System (ADS)

Lloyd, Adam L.; Zhou, Ying; Yu, Miao; Scott, Chris; Smith, Roger; Kenny, Steven D.

2017-10-01

The atomistic processes that form the basis of thin film growth often involve complex multi-atom movements of atoms or groups of atoms on or close to the surface of a substrate. These transitions and their pathways are often difficult to predict in advance. By using an adaptive kinetic Monte Carlo (AKMC) approach, many complex mechanisms can be identified so that the growth processes can be understood and ultimately controlled. Here the AKMC technique is briefly described along with some special adaptions that can speed up the simulations when, for example, the transition barriers are small. Examples are given of such complex processes that occur in different material systems especially for the growth of metals and metallic oxides.

Predicting kinetics of polymorphic transformations from structure mapping and coordination analysis

NASA Astrophysics Data System (ADS)

Stevanović, Vladan; Trottier, Ryan; Musgrave, Charles; Therrien, Félix; Holder, Aaron; Graf, Peter

2018-03-01

To extend materials design and discovery into the space of metastable polymorphs, rapid and reliable assessment of transformation kinetics to lower energy structures is essential. Herein we focus on diffusionless polymorphic transformations and investigate routes to assess their kinetics using solely crystallographic arguments. As part of this investigation we developed a general algorithm to map crystal structures onto each other, and ascertain the low-energy (fast-kinetics) transformation pathways between them. Pathways with minimal dissociation of chemical bonds, along which the number of bonds (in ionic systems the first-shell coordination) does not decrease below that in the end structures, are shown to always be the fast-kinetics pathways. These findings enable the rapid assessment of the kinetics of polymorphic transformation and the identification of long-lived metastable structures. The utility is demonstrated on a number of transformations including those between high-pressure SnO2 phases, which lack a detailed atomic-level understanding.

Is Phosphoproteomics Ready for Clinical Research?

PubMed Central

Iliuk, Anton B.; Tao, W. Andy

2012-01-01

Background For many diseases such as cancer where phosphorylation-dependent signaling is the foundation of disease onset and progression, single-gene testing and genomic profiling alone are not sufficient in providing most critical information. The reason for this is that in these activated pathways the signaling changes and drug resistance are often not directly correlated with changes in protein expression levels. In order to obtain the essential information needed to evaluate pathway activation or the effects of certain drugs and therapies on the molecular level, the analysis of changes in protein phosphorylation is critical. Methods Existing approaches do not differentiate clinical disease subtypes on the protein and signaling pathway level, and therefore hamper the predictive management of the disease and the selection of therapeutic targets. Conclusions The mini-review examines the impact of emerging systems biology tools and the possibility of applying phosphoproteomics to clinical research. PMID:23159844

Testing for ontological errors in probabilistic forecasting models of natural systems

PubMed Central

Marzocchi, Warner; Jordan, Thomas H.

2014-01-01

Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the forecasting problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic forecasting. Pessimistic views of system predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not. PMID:25097265

Early-Stage Visual Processing and Cortical Amplification Deficits in Schizophrenia

PubMed Central

Butler, Pamela D.; Zemon, Vance; Schechter, Isaac; Saperstein, Alice M.; Hoptman, Matthew J.; Lim, Kelvin O.; Revheim, Nadine; Silipo, Gail; Javitt, Daniel C.

2005-01-01

Background Patients with schizophrenia show deficits in early-stage visual processing, potentially reflecting dysfunction of the magnocellular visual pathway. The magnocellular system operates normally in a nonlinear amplification mode mediated by glutamatergic (N-methyl-d-aspartate) receptors. Investigating magnocellular dysfunction in schizophrenia therefore permits evaluation of underlying etiologic hypotheses. Objectives To evaluate magnocellular dysfunction in schizophrenia, relative to known neurochemical and neuroanatomical substrates, and to examine relationships between electrophysiological and behavioral measures of visual pathway dysfunction and relationships with higher cognitive deficits. Design, Setting, and Participants Between-group study at an inpatient state psychiatric hospital and out-patient county psychiatric facilities. Thirty-three patients met DSM-IV criteria for schizophrenia or schizoaffective disorder, and 21 nonpsychiatric volunteers of similar ages composed the control group. Main Outcome Measures (1) Magnocellular and parvocellular evoked potentials, analyzed using nonlinear (Michaelis-Menten) and linear contrast gain approaches; (2) behavioral contrast sensitivity measures; (3) white matter integrity; (4) visual and nonvisual neuropsychological measures, and (5) clinical symptom and community functioning measures. Results Patients generated evoked potentials that were significantly reduced in response to magnocellular-biased, but not parvocellular-biased, stimuli (P=.001). Michaelis-Menten analyses demonstrated reduced contrast gain of the magnocellular system (P=.001). Patients showed decreased contrast sensitivity to magnocellular-biased stimuli (P<.001). Evoked potential deficits were significantly related to decreased white matter integrity in the optic radiations (P<.03). Evoked potential deficits predicted impaired contrast sensitivity (P=.002), which was in turn related to deficits in complex visual processing (P≤.04). Both evoked potential (P≤.04) and contrast sensitivity (P=.01) measures significantly predicted community functioning. Conclusions These findings confirm the existence of early-stage visual processing dysfunction in schizophrenia and provide the first evidence that such deficits are due to decreased nonlinear signal amplification, consistent with glutamatergic theories. Neuroimaging studies support the hypothesis of dysfunction within low-level visual pathways involving thalamocortical radiations. Deficits in early-stage visual processing significantly predict higher cognitive deficits. PMID:15867102

Identification of ageing-associated naturally occurring peptides in human urine

PubMed Central

Nkuipou-Kenfack, Esther; Bhat, Akshay; Klein, Julie; Jankowski, Vera; Mullen, William; Vlahou, Antonia; Dakna, Mohammed; Koeck, Thomas; Schanstra, Joost P.; Zürbig, Petra; Rudolph, Karl L.; Schumacher, Björn; Pich, Andreas; Mischak, Harald

2015-01-01

To assess normal and pathological peptidomic changes that may lead to an improved understanding of molecular mechanisms underlying ageing, urinary peptidomes of 1227 healthy and 10333 diseased individuals between 20 and 86 years of age were investigated. The diseases thereby comprised diabetes mellitus, renal and cardiovascular diseases. Using age as a continuous variable, 116 peptides were identified that significantly (p < 0.05; |ρ|≥0.2) correlated with age in the healthy cohort. The same approach was applied to the diseased cohort. Upon comparison of the peptide patterns of the two cohorts 112 common age-correlated peptides were identified. These 112 peptides predominantly originated from collagen, uromodulin and fibrinogen. While most fibrillar and basement membrane collagen fragments showed a decreased age-related excretion, uromodulin, beta-2-microglobulin and fibrinogen fragments showed an increase. Peptide-based in silico protease analysis was performed and 32 proteases, including matrix metalloproteinases and cathepsins, were predicted to be involved in ageing. Identified peptides, predicted proteases and patient information were combined in a systems biology pathway analysis to identify molecular pathways associated with normal and/or pathological ageing. While perturbations in collagen homeostasis, trafficking of toll-like receptors and endosomal pathways were commonly identified, degradation of insulin-like growth factor-binding proteins was uniquely identified in pathological ageing. PMID:26431327

The Dual Role of Media Internalization in Adolescent Sexual Behavior.

PubMed

Rousseau, Ann; Beyens, Ine; Eggermont, Steven; Vandenbosch, Laura

2017-08-01

Sexualizing media content is prevalent in various media types. Sexualizing media messages and portrayals emphasize unattainable body and appearance ideals as the primary components of sexual desirability. The internalization of these ideals is positively related to self-objectification and sexual body consciousness. In turn, self-objectification and sexual body consciousness affect adolescents' sexual behavior, albeit in opposing directions. While objectifying self-perceptions are linked to higher levels of sexual behavior, body consciousness during physical intimacy is linked to lower levels of sexual behavior. Based on this knowledge, the present three-wave panel study of 824 Belgian, predominant heterosexual adolescents (M age  = 15.33; SD = 1.45) proposes a dual-pathway model that investigates two different pathways through which the internalization of media ideals may impact adolescents' sexual behavior. An inhibitory pathway links media internalization to lower levels of sexual behavior through sexual body consciousness, and a supportive pathway links media internalization to higher levels of sexual behavior through self-objectification. Structural equation analyses supported the proposed dual-pathway, showing that the impact of media internalization on adolescents' sexual behavior proceeds through an inhibitory pathway and a supportive pathway. Regarding the supportive pathway, media internalization (W1) positively predicted sexual behavior (W3), through valuing appearance over competence (W2). Regarding the inhibitory pathway, media internalization (W1) positively predicted body surveillance, which, in turn, positively predicted sexual body consciousness (all W2). Sexual body consciousness (W2) is negatively related to sexual behavior (W3). From a sexual developmental perspective, these findings emphasize the importance of guiding adolescents in interpreting and processing sexualizing media messages.

Modeling the flux of metabolites in the juvenile hormone biosynthesis pathway using generalized additive models and ordinary differential equations.

PubMed

Martínez-Rincón, Raúl O; Rivera-Pérez, Crisalejandra; Diambra, Luis; Noriega, Fernando G

2017-01-01

Juvenile hormone (JH) regulates development and reproductive maturation in insects. The corpora allata (CA) from female adult mosquitoes synthesize fluctuating levels of JH, which have been linked to the ovarian development and are influenced by nutritional signals. The rate of JH biosynthesis is controlled by the rate of flux of isoprenoids in the pathway, which is the outcome of a complex interplay of changes in precursor pools and enzyme levels. A comprehensive study of the changes in enzymatic activities and precursor pool sizes have been previously reported for the mosquito Aedes aegypti JH biosynthesis pathway. In the present studies, we used two different quantitative approaches to describe and predict how changes in the individual metabolic reactions in the pathway affect JH synthesis. First, we constructed generalized additive models (GAMs) that described the association between changes in specific metabolite concentrations with changes in enzymatic activities and substrate concentrations. Changes in substrate concentrations explained 50% or more of the model deviances in 7 of the 13 metabolic steps analyzed. Addition of information on enzymatic activities almost always improved the fitness of GAMs built solely based on substrate concentrations. GAMs were validated using experimental data that were not included when the model was built. In addition, a system of ordinary differential equations (ODE) was developed to describe the instantaneous changes in metabolites as a function of the levels of enzymatic catalytic activities. The results demonstrated the ability of the models to predict changes in the flux of metabolites in the JH pathway, and can be used in the future to design and validate experimental manipulations of JH synthesis.

Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output

DOE PAGES

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

2017-09-10

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

Validation of a novel air toxic risk model with air monitoring.

PubMed

Pratt, Gregory C; Dymond, Mary; Ellickson, Kristie; Thé, Jesse

2012-01-01

Three modeling systems were used to estimate human health risks from air pollution: two versions of MNRiskS (for Minnesota Risk Screening), and the USEPA National Air Toxics Assessment (NATA). MNRiskS is a unique cumulative risk modeling system used to assess risks from multiple air toxics, sources, and pathways on a local to a state-wide scale. In addition, ambient outdoor air monitoring data were available for estimation of risks and comparison with the modeled estimates of air concentrations. Highest air concentrations and estimated risks were generally found in the Minneapolis-St. Paul metropolitan area and lowest risks in undeveloped rural areas. Emissions from mobile and area (nonpoint) sources created greater estimated risks than emissions from point sources. Highest cancer risks were via ingestion pathway exposures to dioxins and related compounds. Diesel particles, acrolein, and formaldehyde created the highest estimated inhalation health impacts. Model-estimated air concentrations were generally highest for NATA and lowest for the AERMOD version of MNRiskS. This validation study showed reasonable agreement between available measurements and model predictions, although results varied among pollutants, and predictions were often lower than measurements. The results increased confidence in identifying pollutants, pathways, geographic areas, sources, and receptors of potential concern, and thus provide a basis for informing pollution reduction strategies and focusing efforts on specific pollutants (diesel particles, acrolein, and formaldehyde), geographic areas (urban centers), and source categories (nonpoint sources). The results heighten concerns about risks from food chain exposures to dioxins and PAHs. Risk estimates were sensitive to variations in methodologies for treating emissions, dispersion, deposition, exposure, and toxicity. © 2011 Society for Risk Analysis.

Dopamine and the Creative Mind: Individual Differences in Creativity Are Predicted by Interactions between Dopamine Genes DAT and COMT.

PubMed

Zabelina, Darya L; Colzato, Lorenza; Beeman, Mark; Hommel, Bernhard

2016-01-01

The dopaminergic (DA) system may be involved in creativity, however results of past studies are mixed. We attempted to clarify this putative relation by considering the mediofrontal and the nigrostriatal DA pathways, uniquely and in combination, and their contribution to two different measures of creativity--an abbreviated version of the Torrance Test of Creative Thinking, assessing divergent thinking, and a real-world creative achievement index. We found that creativity can be predicted from interactions between genetic polymorphisms related to frontal (COMT) and striatal (DAT) DA pathways. Importantly, the Torrance test and the real-world creative achievement index related to different genetic patterns, suggesting that these two measures tap into different aspects of creativity, and depend on distinct, but interacting, DA sub-systems. Specifically, we report that successful performance on the Torrance test is linked with dopaminergic polymorphisms associated with good cognitive flexibility and medium top-down control, or with weak cognitive flexibility and strong top-down control. The latter is particularly true for the originality factor of divergent thinking. High real-world creative achievement, on the other hand, as assessed by the Creative Achievement Questionnaire, is linked with dopaminergic polymorphisms associated with weak cognitive flexibility and weak top-down control. Taken altogether, our findings support the idea that human creativity relies on dopamine, and on the interaction between frontal and striatal dopaminergic pathways in particular. This interaction may help clarify some apparent inconsistencies in the prior literature, especially if the genes and/or creativity measures were analyzed separately.

Dopamine and the Creative Mind: Individual Differences in Creativity Are Predicted by Interactions between Dopamine Genes DAT and COMT

PubMed Central

Zabelina, Darya L.; Colzato, Lorenza; Beeman, Mark; Hommel, Bernhard

2016-01-01

The dopaminergic (DA) system may be involved in creativity, however results of past studies are mixed. We attempted to clarify this putative relation by considering the mediofrontal and the nigrostriatal DA pathways, uniquely and in combination, and their contribution to two different measures of creativity–an abbreviated version of the Torrance Test of Creative Thinking, assessing divergent thinking, and a real-world creative achievement index. We found that creativity can be predicted from interactions between genetic polymorphisms related to frontal (COMT) and striatal (DAT) DA pathways. Importantly, the Torrance test and the real-world creative achievement index related to different genetic patterns, suggesting that these two measures tap into different aspects of creativity, and depend on distinct, but interacting, DA sub-systems. Specifically, we report that successful performance on the Torrance test is linked with dopaminergic polymorphisms associated with good cognitive flexibility and medium top-down control, or with weak cognitive flexibility and strong top-down control. The latter is particularly true for the originality factor of divergent thinking. High real-world creative achievement, on the other hand, as assessed by the Creative Achievement Questionnaire, is linked with dopaminergic polymorphisms associated with weak cognitive flexibility and weak top-down control. Taken altogether, our findings support the idea that human creativity relies on dopamine, and on the interaction between frontal and striatal dopaminergic pathways in particular. This interaction may help clarify some apparent inconsistencies in the prior literature, especially if the genes and/or creativity measures were analyzed separately. PMID:26783754

Comparative systems pharmacology of HIF stabilization in the prevention of retinopathy of prematurity

PubMed Central

Hoppe, George; Yoon, Suzy; Gopalan, Banu; Savage, Alexandria R.; Brown, Rebecca; Case, Kelsey; Vasanji, Amit; Chan, E. Ricky; Silver, Randi B.; Sears, Jonathan E.

2016-01-01

Retinopathy of prematurity (ROP) causes 100,000 new cases of childhood blindness each year. ROP is initiated by oxygen supplementation necessary to prevent neonatal death. We used organ systems pharmacology to define the transcriptomes of mice that were cured of oxygen-induced retinopathy (OIR, ROP model) by hypoxia-inducible factor (HIF) stabilization via HIF prolyl hydroxylase inhibition using the isoquinolone Roxadustat or the 2-oxoglutarate analog dimethyloxalylglycine (DMOG). Although both molecules conferred a protective phenotype, gene expression analysis by RNA sequencing found that Roxadustat can prevent OIR by two pathways: direct retinal HIF stabilization and induction of aerobic glycolysis or indirect hepatic HIF-1 stabilization and increased serum angiokines. As predicted by pathway analysis, Roxadustat rescued the hepatic HIF-1 knockout mouse from retinal oxygen toxicity, whereas DMOG could not. The simplicity of systemic treatment that targets both the liver and the eye provides a rationale for protecting the severely premature infant from oxygen toxicity. PMID:27091985

Watershed erosion modeling using the probability of sediment connectivity in a gently rolling system

NASA Astrophysics Data System (ADS)

Mahoney, David Tyler; Fox, James Forrest; Al Aamery, Nabil

2018-06-01

Sediment connectivity has been shown in recent years to explain how the watershed configuration controls sediment transport. However, we find no studies develop a watershed erosion modeling framework based on sediment connectivity, and few, if any, studies have quantified sediment connectivity for gently rolling systems. We develop a new predictive sediment connectivity model that relies on the intersecting probabilities for sediment supply, detachment, transport, and buffers to sediment transport, which is integrated in a watershed erosion model framework. The model predicts sediment flux temporally and spatially across a watershed using field reconnaissance results, a high-resolution digital elevation models, a hydrologic model, and shear-based erosion formulae. Model results validate the capability of the model to predict erosion pathways causing sediment connectivity. More notably, disconnectivity dominates the gently rolling watershed across all morphologic levels of the uplands, including, microtopography from low energy undulating surfaces across the landscape, swales and gullies only active in the highest events, karst sinkholes that disconnect drainage areas, and floodplains that de-couple the hillslopes from the stream corridor. Results show that sediment connectivity is predicted for about 2% or more the watershed's area 37 days of the year, with the remaining days showing very little or no connectivity. Only 12.8 ± 0.7% of the gently rolling watershed shows sediment connectivity on the wettest day of the study year. Results also highlight the importance of urban/suburban sediment pathways in gently rolling watersheds, and dynamic and longitudinal distributions of sediment connectivity might be further investigated in future work. We suggest the method herein provides the modeler with an added tool to account for sediment transport criteria and has the potential to reduce computational costs in watershed erosion modeling.

A Global Genomic and Genetic Strategy to Identify, Validate and Use Gene Signatures of Xenobiotic-Responsive Transcription Factors in Prediction of Pathway Activation in the Mouse Liver

EPA Science Inventory

Many drugs and environmentally-relevant chemicals activate xenobiotic-responsive transcription factors. Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening as well as their involvement in disease states. ...

Chemical-agnostic hazard prediction: statistical inference of in vitro toxicity pathways from proteomics responses to chemical mixtures

EPA Science Inventory

Toxicity pathways have been defined as normal cellular pathways that, when sufficiently perturbed as a consequence of chemical exposure, lead to an adverse outcome. If an exposure alters one or more normal biological pathways to an extent that leads to an adverse toxicity outcome...

Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach

NASA Astrophysics Data System (ADS)

Liu, Wenyang; Sawant, Amit; Ruan, Dan

2016-07-01

The development of high-dimensional imaging systems in image-guided radiotherapy provides important pathways to the ultimate goal of real-time full volumetric motion monitoring. Effective motion management during radiation treatment usually requires prediction to account for system latency and extra signal/image processing time. It is challenging to predict high-dimensional respiratory motion due to the complexity of the motion pattern combined with the curse of dimensionality. Linear dimension reduction methods such as PCA have been used to construct a linear subspace from the high-dimensional data, followed by efficient predictions on the lower-dimensional subspace. In this study, we extend such rationale to a more general manifold and propose a framework for high-dimensional motion prediction with manifold learning, which allows one to learn more descriptive features compared to linear methods with comparable dimensions. Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where accurate and efficient prediction can be performed. A fixed-point iterative pre-image estimation method is used to recover the predicted value in the original state space. We evaluated and compared the proposed method with a PCA-based approach on level-set surfaces reconstructed from point clouds captured by a 3D photogrammetry system. The prediction accuracy was evaluated in terms of root-mean-squared-error. Our proposed method achieved consistent higher prediction accuracy (sub-millimeter) for both 200 ms and 600 ms lookahead lengths compared to the PCA-based approach, and the performance gain was statistically significant.

Simulation of dual carbon-bromine stable isotope fractionation during 1,2-dibromoethane degradation.

PubMed

Jin, Biao; Nijenhuis, Ivonne; Rolle, Massimo

2018-06-01

We performed a model-based investigation to simultaneously predict the evolution of concentration, as well as stable carbon and bromine isotope fractionation during 1,2-dibromoethane (EDB, ethylene dibromide) transformation in a closed system. The modelling approach considers bond-cleavage mechanisms during different reactions and allows evaluating dual carbon-bromine isotopic signals for chemical and biotic reactions, including aerobic and anaerobic biological transformation, dibromoelimination by Zn(0) and alkaline hydrolysis. The proposed model allowed us to accurately simulate the evolution of concentrations and isotope data observed in a previous laboratory study and to successfully identify different reaction pathways. Furthermore, we illustrated the model capabilities in degradation scenarios involving complex reaction systems. Specifically, we examined (i) the case of sequential multistep transformation of EDB and the isotopic evolution of the parent compound, the intermediate and the reaction product and (ii) the case of parallel competing abiotic pathways of EDB transformation in alkaline solution.

Engineering the Bacterial Microcompartment Domain for Molecular Scaffolding Applications

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

Young, Eric J.; Burton, Rodney; Mahalik, Jyoti P.

As synthetic biology advances the intricacy of engineered biological systems, the importance of spatial organization within the cellular environment must not be marginalized. Increasingly, biological engineers are investigating means to control spatial organization within the cell, mimicking strategies used by natural pathways to increase flux and reduce cross-talk. A modular platform for constructing a diverse set of defined, programmable architectures would greatly assist in improving yields from introduced metabolic pathways and increasing insulation of other heterologous systems. Here, we review recent research on the shell proteins of bacterial microcompartments and discuss their potential application as “building blocks” for a rangemore » of customized intracellular scaffolds. As a result, we summarize the state of knowledge on the self-assembly of BMC shell proteins and discuss future avenues of research that will be important to realize the potential of BMC shell proteins as predictively assembling and programmable biological materials for bioengineering.« less

Engineering the Bacterial Microcompartment Domain for Molecular Scaffolding Applications

DOE PAGES

Young, Eric J.; Burton, Rodney; Mahalik, Jyoti P.; ...

2017-07-31

As synthetic biology advances the intricacy of engineered biological systems, the importance of spatial organization within the cellular environment must not be marginalized. Increasingly, biological engineers are investigating means to control spatial organization within the cell, mimicking strategies used by natural pathways to increase flux and reduce cross-talk. A modular platform for constructing a diverse set of defined, programmable architectures would greatly assist in improving yields from introduced metabolic pathways and increasing insulation of other heterologous systems. Here, we review recent research on the shell proteins of bacterial microcompartments and discuss their potential application as “building blocks” for a rangemore » of customized intracellular scaffolds. As a result, we summarize the state of knowledge on the self-assembly of BMC shell proteins and discuss future avenues of research that will be important to realize the potential of BMC shell proteins as predictively assembling and programmable biological materials for bioengineering.« less

Engineering the Bacterial Microcompartment Domain for Molecular Scaffolding Applications

PubMed Central

Young, Eric J.; Burton, Rodney; Mahalik, Jyoti P.; Sumpter, Bobby G.; Fuentes-Cabrera, Miguel; Kerfeld, Cheryl A.; Ducat, Daniel C.

2017-01-01

As synthetic biology advances the intricacy of engineered biological systems, the importance of spatial organization within the cellular environment must not be marginalized. Increasingly, biological engineers are investigating means to control spatial organization within the cell, mimicking strategies used by natural pathways to increase flux and reduce cross-talk. A modular platform for constructing a diverse set of defined, programmable architectures would greatly assist in improving yields from introduced metabolic pathways and increasing insulation of other heterologous systems. Here, we review recent research on the shell proteins of bacterial microcompartments and discuss their potential application as “building blocks” for a range of customized intracellular scaffolds. We summarize the state of knowledge on the self-assembly of BMC shell proteins and discuss future avenues of research that will be important to realize the potential of BMC shell proteins as predictively assembling and programmable biological materials for bioengineering. PMID:28824573

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

PubMed Central

2015-01-01

Background Enzymes are known as the molecular machines that drive the metabolism of an organism; hence identification of the full enzyme complement of an organism is essential to build the metabolic blueprint of that species as well as to understand the interplay of multiple species in an ecosystem. Experimental characterization of the enzymatic reactions of all enzymes in a genome is a tedious and expensive task. The problem is more pronounced in the metagenomic samples where even the species are not adequately cultured or characterized. Enzymes encoded by the gut microbiota play an essential role in the host metabolism; thus, warranting the need to accurately identify and annotate the full enzyme complements of species in the genomic and metagenomic projects. To fulfill this need, we develop and apply a method called ECemble, an ensemble approach to identify enzymes and enzyme classes and study the human gut metabolic pathways. Results ECemble method uses an ensemble of machine-learning methods to accurately model and predict enzymes from protein sequences and also identifies the enzyme classes and subclasses at the finest resolution. A tenfold cross-validation result shows accuracy between 97 and 99% at different levels in the hierarchy of enzyme classification, which is superior to comparable methods. We applied ECemble to predict the entire complements of enzymes from ten sequenced proteomes including the human proteome. We also applied this method to predict enzymes encoded by the human gut microbiome from gut metagenomic samples, and to study the role played by the microbe-derived enzymes in the human metabolism. After mapping the known and predicted enzymes to canonical human pathways, we identified 48 pathways that have at least one bacteria-encoded enzyme, which demonstrates the complementary role of gut microbiome in human gut metabolism. These pathways are primarily involved in metabolizing dietary nutrients such as carbohydrates, amino acids, lipids, cofactors and vitamins. Conclusions The ECemble method is able to hierarchically assign high quality enzyme annotations to genomic and metagenomic data. This study demonstrated the real application of ECemble to understand the indispensable role played by microbe-encoded enzymes in the healthy functioning of human metabolic systems. PMID:26099921

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

PubMed

Mohammed, Akram; Guda, Chittibabu

2015-01-01

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

Genetic variants in the vitamin D pathway and breast cancer disease-free survival

PubMed Central

Brewster, Abenaa M.

2013-01-01

Epidemiological studies have investigated the association between vitamin D pathway genes and breast cancer risk; however, little is known about the association between vitamin D pathway genes and breast cancer prognosis. In a retrospective cohort of 1029 patients with early-stage breast cancer, we analyzed the association between 106 tagging single nucleotide polymorphisms (SNPs) in eight vitamin D pathway genes and breast cancer disease-free survival (DFS) using Cox regression analysis adjusted for known prognostic variables. Using a false discovery rate of 10%, six intronic SNPs were significantly associated with poorer DFS: retinoid-X receptor alpha (RXRA) SNPs (rs881658, rs11185659, rs10881583, rs881657 and rs7864987) and plasminogen activator and urokinase receptor (PLAUR) SNP (rs4251864). Treatment received (no systemic therapy, hormone therapy alone or chemotherapy) was an effect modifier of the RXRA SNPs association with DFS (P < 0.05); therefore, we stratified further analysis by treatment group. Among patients who did not receive systemic therapy, RXRA SNP [rs10881583 (P = 0.02)] was associated with poorer DFS, and among patients who received chemotherapy, RXRA SNPs (rs881658, rs11185659, rs10881583, rs881657 and rs7864987) were associated with poorer DFS (P < 0.001 for all SNPs). However, RXRA SNPs: rs10881583 (P < 0.001) and rs881657 (P = 0.02) were associated with improved DFS in patients treated with hormone therapy alone. Our results suggest that SNPs in the RXRA and PLAUR genes in the vitamin D pathway may contribute to breast cancer DFS. In particular, SNPs in RXRA may predict for poorer or improved DFS in patients, according to type of systemic treatment received. If validated, these markers could be used for risk stratification of breast cancer patients. PMID:23180655

ROCK inhibition abolishes the establishment of the aquiferous system in Ephydatia muelleri (Porifera, Demospongiae).

PubMed

Schenkelaars, Quentin; Quintero, Omar; Hall, Chelsea; Fierro-Constain, Laura; Renard, Emmanuelle; Borchiellini, Carole; Hill, April L

2016-04-15

The Rho associated coiled-coil protein kinase (ROCK) plays crucial roles in development across bilaterian animals. The fact that the Rho/Rock pathway is required to initiate epithelial morphogenesis and thus to establish body plans in bilaterians makes this conserved signaling pathway key for studying the molecular mechanisms that may control early development of basally branching metazoans. The purpose of this study was to evaluate whether or not the main components of this signaling pathway exist in sponges, and if present, to investigate the possible role of the regulatory network in an early branching non-bilaterian species by evaluating ROCK function during Ephydatia muelleri development. Molecular phylogenetic analyses and protein domain predictions revealed the existence of Rho/Rock components in all studied poriferan lineages. Binding assays revealed that both Y-27632 and GSK429286A are capable of inhibiting Em-ROCK activity in vitro. Treatment with both drugs leads to impairment of growth and formation of the basal pinacoderm layer in the developing sponge. Furthermore, inhibition of Em-Rock prevents the establishment of a functional aquiferous system, including the absence of an osculum. In contrast, no effect of ROCK inhibition was observed in juvenile sponges that already possess a fully developed and functional aquiferous system. Thus, the Rho/Rock pathway appears to be essential for the proper development of the freshwater sponge, and may play a role in various cell behaviors (e.g. cell proliferation, cell adhesion and cell motility). Taken together, these data are consistent with an ancestral function of Rho/Rock signaling in playing roles in early developmental processes and may provide a new framework to study the interaction between Wnt signaling and the Rho/Rock pathway. Copyright © 2016 Elsevier Inc. All rights reserved.

Excitatory and inhibitory STDP jointly tune feedforward neural circuits to selectively propagate correlated spiking activity

PubMed Central

Kleberg, Florence I.; Fukai, Tomoki; Gilson, Matthieu

2014-01-01

Spike-timing-dependent plasticity (STDP) has been well established between excitatory neurons and several computational functions have been proposed in various neural systems. Despite some recent efforts, however, there is a significant lack of functional understanding of inhibitory STDP (iSTDP) and its interplay with excitatory STDP (eSTDP). Here, we demonstrate by analytical and numerical methods that iSTDP contributes crucially to the balance of excitatory and inhibitory weights for the selection of a specific signaling pathway among other pathways in a feedforward circuit. This pathway selection is based on the high sensitivity of STDP to correlations in spike times, which complements a recent proposal for the role of iSTDP in firing-rate based selection. Our model predicts that asymmetric anti-Hebbian iSTDP exceeds asymmetric Hebbian iSTDP for supporting pathway-specific balance, which we show is useful for propagating transient neuronal responses. Furthermore, we demonstrate how STDPs at excitatory–excitatory, excitatory–inhibitory, and inhibitory–excitatory synapses cooperate to improve the pathway selection. We propose that iSTDP is crucial for shaping the network structure that achieves efficient processing of synchronous spikes. PMID:24847242

Fine-grained temporal coding of visually-similar categories in the ventral visual pathway and prefrontal cortex

PubMed Central

Xu, Yang; D'Lauro, Christopher; Pyles, John A.; Kass, Robert E.; Tarr, Michael J.

2013-01-01

Humans are remarkably proficient at categorizing visually-similar objects. To better understand the cortical basis of this categorization process, we used magnetoencephalography (MEG) to record neural activity while participants learned–with feedback–to discriminate two highly-similar, novel visual categories. We hypothesized that although prefrontal regions would mediate early category learning, this role would diminish with increasing category familiarity and that regions within the ventral visual pathway would come to play a more prominent role in encoding category-relevant information as learning progressed. Early in learning we observed some degree of categorical discriminability and predictability in both prefrontal cortex and the ventral visual pathway. Predictability improved significantly above chance in the ventral visual pathway over the course of learning with the left inferior temporal and fusiform gyri showing the greatest improvement in predictability between 150 and 250 ms (M200) during category learning. In contrast, there was no comparable increase in discriminability in prefrontal cortex with the only significant post-learning effect being a decrease in predictability in the inferior frontal gyrus between 250 and 350 ms (M300). Thus, the ventral visual pathway appears to encode learned visual categories over the long term. At the same time these results add to our understanding of the cortical origins of previously reported signature temporal components associated with perceptual learning. PMID:24146656

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.

Maturation of biomass-to-biofuels conversion technology pathways for rapid expansion of biofuels production: A system dynamics perspective

DOE PAGES

Vimmerstedt, Laura J.; Bush, Brian W.; Hsu, Dave D.; ...

2014-08-12

The Biomass Scenario Model (BSM) is a system-dynamics simulation model intended to explore the potential for rapid expansion of the biofuels industry. The model is not predictive — it uses scenario assumptions based on various types of data to simulate industry development, emphasizing how incentives and technological learning-by-doing might accelerate industry growth. The BSM simulates major sectors of the biofuels industry, including feedstock production and logistics, conversion, distribution, and end uses, as well as interactions among sectors. The model represents conversion of biomass to biofuels as a set of technology pathways, each of which has allowable feedstocks, capital and operatingmore » costs, allowable products, and other defined characteristics. This study and the BSM address bioenergy modeling analytic needs that were identified in recent literature reviews. Simulations indicate that investments are most effective at expanding biofuels production through learning-by-doing when they are coordinated with respect to timing, pathway, and target sector within the biofuels industry. Effectiveness metrics include timing and magnitude of increased production, incentive cost and cost effectiveness, and avoidance of windfall profits. Investment costs and optimal investment targets have inherent risks and uncertainties, such as the relative value of investment in more-mature versus less mature pathways. These can be explored through scenarios, but cannot be precisely predicted. Dynamic competition, including competition for cellulosic feedstocks and ethanol market shares, intensifies during times of rapid growth. Ethanol production increases rapidly, even up to Renewable Fuel Standards-targeted volumes of biofuel, in simulations that allow higher blending proportions of ethanol in gasoline-fueled vehicles. Published 2014. This document is a U.S. Government work and is in the public domain in the USA. Biofuels, Bioproducts, Biorefining published by John Wiley & Sons, Ltd on behalf of Society of Chemical Industry.« less

Longitudinal examination of peer and partner influences on gender-specific pathways from child abuse to adult crime.

PubMed

Lee, Jungeun Olivia; Herrenkohl, Todd I; Jung, Hyunzee; Skinner, Martie L; Klika, J Bart

2015-09-01

Research provides increasing evidence of the association of child abuse with adult antisocial behavior. However, less is known about the developmental pathways that underlie this association. Building on the life course model of antisocial behavior, the present study examined possible developmental pathways linking various forms of child abuse (physical, emotional, sexual) to adult antisocial behavior. These pathways include child and adolescent antisocial behavior, as well as adulthood measures of partner risk taking, warmth, and antisocial peer influences. Data are from the Lehigh Longitudinal Study, a prospective longitudinal study examining long-term developmental outcomes subsequent to child maltreatment. Participant families in the Lehigh Longitudinal Study were followed from preschool age into adulthood. Analyses of gender differences addressed the consistency of path coefficients across genders. Results for 297 adult participants followed from early childhood showed that, for both genders, physical and emotional child abuse predicted adult crime indirectly through child and adolescent antisocial behavior, as well as adult partner and antisocial peer influences. However, for females, having an antisocial partner predicted an affiliation with antisocial peers, and that in turn predicted adult crime. For males, having an antisocial partner was associated with less partner warmth, which in turn predicted an affiliation with antisocial peers, itself a proximal predictor of adult crime. Sexual abuse also predicted adolescent antisocial behavior, but only for males, supporting what some have called "a delayed-onset pathway" for females, whereby the exposure to early risks produce much later developmental outcomes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Perturbation Biology: Inferring Signaling Networks in Cellular Systems

PubMed Central

Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2013-01-01

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

Reversal of Increasing Tropical Ocean Hypoxia Trends With Sustained Climate Warming

NASA Astrophysics Data System (ADS)

Fu, Weiwei; Primeau, Francois; Keith Moore, J.; Lindsay, Keith; Randerson, James T.

2018-04-01

Dissolved oxygen (O2) is essential for the survival of marine animals. Climate change impacts on future oxygen distributions could modify species biogeography, trophic interactions, biodiversity, and biogeochemistry. The Coupled Model Intercomparison Project Phase 5 models predict a decreasing trend in marine O2 over the 21st century. Here we show that this increasing hypoxia trend reverses in the tropics after 2100 in the Community Earth System Model forced by atmospheric CO2 from the Representative Concentration Pathway 8.5 and Extended Concentration Pathway 8.5. In tropical intermediate waters between 200 and 1,000 m, the model predicts a steady decline of O2 and an expansion of oxygen minimum zones (OMZs) during the 21st century. By 2150, however, the trend reverses with oxygen concentration increasing and OMZ volume shrinking through 2300. A novel five-box model approach in conjunction with output from the full Earth system model is used to separate the contributions of biological and physical processes to the trends in tropical oxygen. The tropical O2 recovery is caused mainly by reductions in tropical biological export, coupled with a modest increase in ventilation after 2200. The time-evolving oxygen distribution impacts marine nitrogen cycling, with potentially important climate feedbacks.

Systems biology from micro-organisms to human metabolic diseases: the role of detailed kinetic models.

PubMed

Bakker, Barbara M; van Eunen, Karen; Jeneson, Jeroen A L; van Riel, Natal A W; Bruggeman, Frank J; Teusink, Bas

2010-10-01

Human metabolic diseases are typically network diseases. This holds not only for multifactorial diseases, such as metabolic syndrome or Type 2 diabetes, but even when a single gene defect is the primary cause, where the adaptive response of the entire network determines the severity of disease. The latter may differ between individuals carrying the same mutation. Understanding the adaptive responses of human metabolism naturally requires a systems biology approach. Modelling of metabolic pathways in micro-organisms and some mammalian tissues has yielded many insights, qualitative as well as quantitative, into their control and regulation. Yet, even for a well-known pathway such as glycolysis, precise predictions of metabolite dynamics from experimentally determined enzyme kinetics have been only moderately successful. In the present review, we compare kinetic models of glycolysis in three cell types (African trypanosomes, yeast and skeletal muscle), evaluate their predictive power and identify limitations in our understanding. Although each of these models has its own merits and shortcomings, they also share common features. For example, in each case independently measured enzyme kinetic parameters were used as input. Based on these 'lessons from glycolysis', we will discuss how to make best use of kinetic computer models to advance our understanding of human metabolic diseases.

The impact of neighborhood disadvantage and exposure to violence on self-report of antisocial behavior among girls in the juvenile justice system.

PubMed

Chauhan, Preeti; Reppucci, N Dickon

2009-03-01

The current study extended previous research with adults and boys to girls in the juvenile justice system (N = 122; M = 16.7; SD = 1.3). Using a longitudinal research design, neighborhood disadvantage and exposure to violence (i.e., physical abuse by parents, physical abuse by peers, and witnessing violence) were assessed during incarceration. These risk factors were used to predict violent and delinquent behavior post-release. Furthermore, race specific pathways were examined to determine if the impact of these risk factors varied among Black (n = 69) and White girls (n = 53). Results indicated that Black girls were more likely than White girls to live in disadvantaged neighborhoods, but both reported similar levels of exposure to violence and self-report of antisocial behavior. Physical abuse by parents, time at risk, and age were related to violent behavior, while witnessing violence and time at risk were related to delinquent behavior. Multiple group analyses indicated the existence of race specific pathways. Specifically, physical abuse by parents was related to violent behavior for White girls while witnessing violence was related to violent and delinquent behaviors for Black girls. Results suggest that contextual processes play an important role in predicting antisocial behavior for Black girls.

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

Experimental Approaches to Systematic Discovery and Development of Reproductive Adverse Outcome Pathways in Fish

EPA Science Inventory

Adverse outcome pathways (AOPs) are conceptual frameworks that portray causal and predictive linkages between key events at multiple scales of biological organization that connect molecular initiating events and early cellular perturbations (e.g., initiation of toxicity pathways)...

Pathway index models for construction of patient-specific risk profiles.

PubMed

Eng, Kevin H; Wang, Sijian; Bradley, William H; Rader, Janet S; Kendziorski, Christina

2013-04-30

Statistical methods for variable selection, prediction, and classification have proven extremely useful in moving personalized genomics medicine forward, in particular, leading to a number of genomic-based assays now in clinical use for predicting cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, a patient is classified into one of very few groups (e.g., recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival. Copyright © 2012 John Wiley & Sons, Ltd.

Computer-assisted engineering of the synthetic pathway for biodegradation of a toxic persistent pollutant.

PubMed

Kurumbang, Nagendra Prasad; Dvorak, Pavel; Bendl, Jaroslav; Brezovsky, Jan; Prokop, Zbynek; Damborsky, Jiri

2014-03-21

Anthropogenic halogenated compounds were unknown to nature until the industrial revolution, and microorganisms have not had sufficient time to evolve enzymes for their degradation. The lack of efficient enzymes and natural pathways can be addressed through a combination of protein and metabolic engineering. We have assembled a synthetic route for conversion of the highly toxic and recalcitrant 1,2,3-trichloropropane to glycerol in Escherichia coli, and used it for a systematic study of pathway bottlenecks. Optimal ratios of enzymes for the maximal production of glycerol, and minimal toxicity of metabolites were predicted using a mathematical model. The strains containing the expected optimal ratios of enzymes were constructed and characterized for their viability and degradation efficiency. Excellent agreement between predicted and experimental data was observed. The validated model was used to quantitatively describe the kinetic limitations of currently available enzyme variants and predict improvements required for further pathway optimization. This highlights the potential of forward engineering of microorganisms for the degradation of toxic anthropogenic compounds.

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations.

PubMed

Tang, Jing

2017-01-01

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.

Virtual Institute of Microbial Stress and Survival: Deduction of Stress Response Pathways in Metal and Radionuclide Reducing Microorganisms

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

None

2004-04-17

The projects application goals are to: (1) To understand bacterial stress-response to the unique stressors in metal/radionuclide contamination sites; (2) To turn this understanding into a quantitative, data-driven model for exploring policies for natural and biostimulatory bioremediation; (3) To implement proposed policies in the field and compare results to model predictions; and (4) Close the experimental/computation cycle by using discrepancies between models and predictions to drive new measurements and construction of new models. The projects science goals are to: (1) Compare physiological and molecular response of three target microorganisms to environmental perturbation; (2) Deduce the underlying regulatory pathways that controlmore » these responses through analysis of phenotype, functional genomic, and molecular interaction data; (3) Use differences in the cellular responses among the target organisms to understand niche specific adaptations of the stress and metal reduction pathways; (4) From this analysis derive an understanding of the mechanisms of pathway evolution in the environment; and (5) Ultimately, derive dynamical models for the control of these pathways to predict how natural stimulation can optimize growth and metal reduction efficiency at field sites.« less

Integrating publicly-available data to generate computationally-predicted adverse outcome pathways for hepatic steatosis

EPA Science Inventory

The adverse outcome pathway (AOP) framework provides a way of organizing knowledge related to the key biological events that result in a particular health outcome. For the majority of environmental chemicals, the availability of curated pathways characterizing potential toxicity ...

Kinase Pathway Dependence in Primary Human Leukemias Determined by Rapid Inhibitor Screening

PubMed Central

Tyner, Jeffrey W.; Yang, Wayne F.; Bankhead, Armand; Fan, Guang; Fletcher, Luke B.; Bryant, Jade; Glover, Jason M.; Chang, Bill H.; Spurgeon, Stephen E.; Fleming, William H.; Kovacsovics, Tibor; Gotlib, Jason R.; Oh, Stephen T.; Deininger, Michael W.; Zwaan, C. Michel; Den Boer, Monique L.; van den Heuvel-Eibrink, Marry M.; O’Hare, Thomas; Druker, Brian J.; Loriaux, Marc M.

2012-01-01

Kinases are dysregulated in most cancer but the frequency of specific kinase mutations is low, indicating a complex etiology in kinase dysregulation. Here we report a strategy to rapidly identify functionally important kinase targets, irrespective of the etiology of kinase pathway dysregulation, ultimately enabling a correlation of patient genetic profiles to clinically effective kinase inhibitors. Our methodology assessed the sensitivity of primary leukemia patient samples to a panel of 66 small-molecule kinase inhibitors over 3 days. Screening of 151 leukemia patient samples revealed a wide diversity of drug sensitivities, with 70% of the clinical specimens exhibiting hypersensitivity to one or more drugs. From this data set, we developed an algorithm to predict kinase pathway dependence based on analysis of inhibitor sensitivity patterns. Applying this algorithm correctly identified pathway dependence in proof-of-principle specimens with known oncogenes, including a rare FLT3 mutation outside regions covered by standard molecular diagnostic tests. Interrogation of all 151 patient specimens with this algorithm identified a diversity of gene targets and signaling pathways that could aid prioritization of deep sequencing data sets, permitting a cumulative analysis to understand kinase pathway dependence within leukemia subsets. In a proof-of-principle case, we showed that in vitro drug sensitivity could predict both a clinical response and the development of drug resistance. Taken together, our results suggested that drug target scores derived from a comprehensive kinase inhibitor panel could predict pathway dependence in cancer cells while simultaneously identifying potential therapeutic options. PMID:23087056

Insulation of a synthetic hydrogen metabolism circuit in bacteria

PubMed Central

2010-01-01

Background The engineering of metabolism holds tremendous promise for the production of desirable metabolites, particularly alternative fuels and other highly reduced molecules. Engineering approaches must redirect the transfer of chemical reducing equivalents, preventing these electrons from being lost to general cellular metabolism. This is especially the case for high energy electrons stored in iron-sulfur clusters within proteins, which are readily transferred when two such clusters are brought in close proximity. Iron sulfur proteins therefore require mechanisms to ensure interaction between proper partners, analogous to many signal transduction proteins. While there has been progress in the isolation of engineered metabolic pathways in recent years, the design of insulated electron metabolism circuits in vivo has not been pursued. Results Here we show that a synthetic hydrogen-producing electron transfer circuit in Escherichia coli can be insulated from existing cellular metabolism via multiple approaches, in many cases improving the function of the pathway. Our circuit is composed of heterologously expressed [Fe-Fe]-hydrogenase, ferredoxin, and pyruvate-ferredoxin oxidoreductase (PFOR), allowing the production of hydrogen gas to be coupled to the breakdown of glucose. We show that this synthetic pathway can be insulated through the deletion of competing reactions, rational engineering of protein interaction surfaces, direct protein fusion of interacting partners, and co-localization of pathway components on heterologous protein scaffolds. Conclusions Through the construction and characterization of a synthetic metabolic circuit in vivo, we demonstrate a novel system that allows for predictable engineering of an insulated electron transfer pathway. The development of this system demonstrates working principles for the optimization of engineered pathways for alternative energy production, as well as for understanding how electron transfer between proteins is controlled. PMID:20184755

Direct Observation of Parallel Folding Pathways Revealed Using a Symmetric Repeat Protein System

PubMed Central

Aksel, Tural; Barrick, Doug

2014-01-01

Although progress has been made to determine the native fold of a polypeptide from its primary structure, the diversity of pathways that connect the unfolded and folded states has not been adequately explored. Theoretical and computational studies predict that proteins fold through parallel pathways on funneled energy landscapes, although experimental detection of pathway diversity has been challenging. Here, we exploit the high translational symmetry and the direct length variation afforded by linear repeat proteins to directly detect folding through parallel pathways. By comparing folding rates of consensus ankyrin repeat proteins (CARPs), we find a clear increase in folding rates with increasing size and repeat number, although the size of the transition states (estimated from denaturant sensitivity) remains unchanged. The increase in folding rate with chain length, as opposed to a decrease expected from typical models for globular proteins, is a clear demonstration of parallel pathways. This conclusion is not dependent on extensive curve-fitting or structural perturbation of protein structure. By globally fitting a simple parallel-Ising pathway model, we have directly measured nucleation and propagation rates in protein folding, and have quantified the fluxes along each path, providing a detailed energy landscape for folding. This finding of parallel pathways differs from results from kinetic studies of repeat-proteins composed of sequence-variable repeats, where modest repeat-to-repeat energy variation coalesces folding into a single, dominant channel. Thus, for globular proteins, which have much higher variation in local structure and topology, parallel pathways are expected to be the exception rather than the rule. PMID:24988356

Evolution of a Genome-Encoded Bias in Amino Acid Biosynthetic Pathways Is a Potential Indicator of Amino Acid Dynamics in the Environment

PubMed Central

Fasani, Rick A.; Savageau, Michael A.

2014-01-01

Overcoming the stress of starvation is one of an organism’s most challenging phenotypic responses. Those organisms that frequently survive the challenge, by virtue of their fitness, will have evolved genomes that are shaped by their specific environments. Understanding this genotype–environment–phenotype relationship at a deep level will require quantitative predictive models of the complex molecular systems that link these aspects of an organism’s existence. Here, we treat one of the most fundamental molecular systems, protein synthesis, and the amino acid biosynthetic pathways involved in the stringent response to starvation. These systems face an inherent logical dilemma: Building an amino acid biosynthetic pathway to synthesize its product—the cognate amino acid of the pathway—may require that very amino acid when it is no longer available. To study this potential “catch-22,” we have created a generic model of amino acid biosynthesis in response to sudden starvation. Our mathematical analysis and computational results indicate that there are two distinctly different outcomes: Partial recovery to a new steady state, or full system failure. Moreover, the cell’s fate is dictated by the cognate bias, the number of cognate amino acids in the corresponding biosynthetic pathway relative to the average number of that amino acid in the proteome. We test these implications by analyzing the proteomes of over 1,800 sequenced microbes, which reveals statistically significant evidence of low cognate bias, a genetic trait that would avoid the biosynthetic quandary. Furthermore, these results suggest that the pattern of cognate bias, which is readily derived by genome sequencing, may provide evolutionary clues to an organism’s natural environment. PMID:25118252

20170312 - Adverse Outcome Pathway (AOP) framework for ...

EPA Pesticide Factsheets

Vascular development commences with de novo assembly of a primary capillary plexus (vasculogenesis) followed by its expansion (angiogenesis) and maturation (angio-adaptation) into a hierarchical system of arteries and veins. These processes are tightly regulated by genetic signals and environmental factors linked to morphogenesis and microphysiology. Gestational exposure to some chemicals disrupts vascular development leading to adverse outcomes. To broadly assess consequences of gestational toxicant exposure on vascular development, an Adverse Outcome Pathway (AOP) framework was constructed that integrates data from ToxCast high-throughput screening (HTS) assays with pathway-level information from the literature and public databases. The AOP-based model resolved the ToxCast library (1065 compounds) into a matrix based on several dozen molecular functions critical for developmental angiogenesis. A sample of 38 ToxCast chemicals selected across the matrix tested model performance. Putative vascular disrupting chemical (pVDC) bioactivity was assessed by multiple laboratories utilizing diverse angiogenesis assays, including: transgenic zebrafish, complex human cell co-cultures, engineered microscale systems, and human-synthetic models. The ToxCast pVDC signature predicted vascular disruption in a manner that was chemical-specific and assay-dependent. An AOP for developmental vascular toxicity was constructed that focuses on inhibition of VEGF receptor (VEGFR2). Thi

What do monoamines do in pain modulation?

PubMed

Bannister, Kirsty; Dickenson, Anthony H

2016-06-01

Here, we give a topical overview of the ways in which brain processing can alter spinal pain transmission through descending control pathways, and how these change in pain states. We link preclinical findings on the transmitter systems involved and discuss how the monoamines, noradrenaline, 5-hydroxytryptamine (5-HT), and dopamine, can interact through inhibitory and excitatory pathways. Descending pathways control sensory events and the actions of the neurotransmitters noradrenaline and 5-HT in the dorsal horn of the spinal cord are chiefly implicated in nociception or antinociception according to the receptor that is activated. Abnormalities in descending controls effect central pain processing. Following nerve injury a noradrenaline-mediated control of spinal excitability is lost, whereas its restoration reduces neuropathic hypersensitivity. The story with 5-HT remains more complex because of the myriad of receptors that it can act upon; however the most recent findings support that facilitations may dominate over inhibitions. The monoaminergic system can be manipulated to great effect in the clinic resulting in improved treatment outcomes and is the basis for the actions of the antidepressant drugs in pain. Looking to the future, prediction of treatment responses will possible by monitoring a form of inhibitory descending control for optimized pain relief.

Adverse Outcome Pathway (AOP) framework for embryonic ...

EPA Pesticide Factsheets

Vascular development commences with de novo assembly of a primary capillary plexus (vasculogenesis) followed by its expansion (angiogenesis) and maturation (angio-adaptation) into a hierarchical system of arteries and veins. These processes are tightly regulated by genetic signals and environmental factors linked to morphogenesis and microphysiology. Gestational exposure to some chemicals disrupts vascular development leading to adverse outcomes. To broadly assess consequences of gestational toxicant exposure on vascular development, an Adverse Outcome Pathway (AOP) framework was constructed that integrates data from ToxCast high-throughput screening (HTS) assays with pathway-level information from the literature and public databases. The AOP-based model resolved the ToxCast library (1065 compounds) into a matrix based on several dozen molecular functions critical for developmental angiogenesis. A sample of 38 ToxCast chemicals selected across the matrix tested model performance. Putative vascular disrupting chemical (pVDC) bioactivity was assessed by multiple laboratories utilizing diverse angiogenesis assays, including: transgenic zebrafish, complex human cell co-cultures, engineered microscale systems, and human-synthetic models. The ToxCast pVDC signature predicted vascular disruption in a manner that was chemical-specific and assay-dependent. An AOP for developmental vascular toxicity was constructed that focuses on inhibition of VEGF receptor (VEGFR2). Thi

A systems biology approach uncovers cellular strategies used by Methylobacterium extorquens AM1 during the switch from multi- to single-carbon growth.

PubMed

Skovran, Elizabeth; Crowther, Gregory J; Guo, Xiaofeng; Yang, Song; Lidstrom, Mary E

2010-11-24

When organisms experience environmental change, how does their metabolic network reset and adapt to the new condition? Methylobacterium extorquens is a bacterium capable of growth on both multi- and single-carbon compounds. These different modes of growth utilize dramatically different central metabolic pathways with limited pathway overlap. This study focused on the mechanisms of metabolic adaptation occurring during the transition from succinate growth (predicted to be energy-limited) to methanol growth (predicted to be reducing-power-limited), analyzing changes in carbon flux, gene expression, metabolites and enzymatic activities over time. Initially, cells experienced metabolic imbalance with excretion of metabolites, changes in nucleotide levels and cessation of cell growth. Though assimilatory pathways were induced rapidly, a transient block in carbon flow to biomass synthesis occurred, and enzymatic assays suggested methylene tetrahydrofolate dehydrogenase as one control point. This "downstream priming" mechanism ensures that significant carbon flux through these pathways does not occur until they are fully induced, precluding the buildup of toxic intermediates. Most metabolites that are required for growth on both carbon sources did not change significantly, even though transcripts and enzymatic activities required for their production changed radically, underscoring the concept of metabolic setpoints. This multi-level approach has resulted in new insights into the metabolic strategies carried out to effect this shift between two dramatically different modes of growth and identified a number of potential flux control and regulatory check points as a further step toward understanding metabolic adaptation and the cellular strategies employed to maintain metabolic setpoints.

Thermal stability comparison of nanocrystalline Fe-based binary alloy pairs

DOE PAGES

Clark, Blythe G.; Hattar, Khalid Mikhiel; Marshall, Michael Thomas; ...

2016-03-24

Here, the widely recognized property improvements of nanocrystalline (NC) materials have generated significant interest, yet have been difficult to realize in engineering applications due to the propensity for grain growth in these interface-dense systems. While traditional pathways to thermal stabilization can slow the mobility of grain boundaries, recent theories suggest that solute segregation in NC alloy can reduce the grain boundary energy such that thermodynamic stabilization is achieved. Following the predictions of Murdock et al., here we compare for the first time the thermal stability of a predicted NC stable alloy (Fe-10at.% Mg) with a predicted non-NC stable alloy (Fe-10at.%more » Cu) using the same processing and characterization methodologies. Results indicate improved thermal stability of the Fe-Mg alloy in comparison to the Fe-Cu, and observed microstructures are consistent with those predicted by Monte Carlo simulations.« less

Toward Engineering Synthetic Microbial Metabolism

PubMed Central

McArthur, George H.; Fong, Stephen S.

2010-01-01

The generation of well-characterized parts and the formulation of biological design principles in synthetic biology are laying the foundation for more complex and advanced microbial metabolic engineering. Improvements in de novo DNA synthesis and codon-optimization alone are already contributing to the manufacturing of pathway enzymes with improved or novel function. Further development of analytical and computer-aided design tools should accelerate the forward engineering of precisely regulated synthetic pathways by providing a standard framework for the predictable design of biological systems from well-characterized parts. In this review we discuss the current state of synthetic biology within a four-stage framework (design, modeling, synthesis, analysis) and highlight areas requiring further advancement to facilitate true engineering of synthetic microbial metabolism. PMID:20037734

Knowledge-driven genomic interactions: an application in ovarian cancer.

PubMed

Kim, Dokyoon; Li, Ruowang; Dudek, Scott M; Frase, Alex T; Pendergrass, Sarah A; Ritchie, Marylyn D

2014-01-01

Effective cancer clinical outcome prediction for understanding of the mechanism of various types of cancer has been pursued using molecular-based data such as gene expression profiles, an approach that has promise for providing better diagnostics and supporting further therapies. However, clinical outcome prediction based on gene expression profiles varies between independent data sets. Further, single-gene expression outcome prediction is limited for cancer evaluation since genes do not act in isolation, but rather interact with other genes in complex signaling or regulatory networks. In addition, since pathways are more likely to co-operate together, it would be desirable to incorporate expert knowledge to combine pathways in a useful and informative manner. Thus, we propose a novel approach for identifying knowledge-driven genomic interactions and applying it to discover models associated with cancer clinical phenotypes using grammatical evolution neural networks (GENN). In order to demonstrate the utility of the proposed approach, an ovarian cancer data from the Cancer Genome Atlas (TCGA) was used for predicting clinical stage as a pilot project. We identified knowledge-driven genomic interactions associated with cancer stage from single knowledge bases such as sources of pathway-pathway interaction, but also knowledge-driven genomic interactions across different sets of knowledge bases such as pathway-protein family interactions by integrating different types of information. Notably, an integration model from different sources of biological knowledge achieved 78.82% balanced accuracy and outperformed the top models with gene expression or single knowledge-based data types alone. Furthermore, the results from the models are more interpretable because they are framed in the context of specific biological pathways or other expert knowledge. The success of the pilot study we have presented herein will allow us to pursue further identification of models predictive of clinical cancer survival and recurrence. Understanding the underlying tumorigenesis and progression in ovarian cancer through the global view of interactions within/between different biological knowledge sources has the potential for providing more effective screening strategies and therapeutic targets for many types of cancer.

Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics

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

Kleinstreuer, N.C., E-mail: kleinstreuer.nicole@epa.gov; Smith, A.M.; West, P.R.

2011-11-15

Metabolomics analysis was performed on the supernatant of human embryonic stem (hES) cell cultures exposed to a blinded subset of 11 chemicals selected from the chemical library of EPA's ToxCast Trade-Mark-Sign chemical screening and prioritization research project. Metabolites from hES cultures were evaluated for known and novel signatures that may be indicative of developmental toxicity. Significant fold changes in endogenous metabolites were detected for 83 putatively annotated mass features in response to the subset of ToxCast chemicals. The annotations were mapped to specific human metabolic pathways. This revealed strong effects on pathways for nicotinate and nicotinamide metabolism, pantothenate and CoAmore » biosynthesis, glutathione metabolism, and arginine and proline metabolism pathways. Predictivity for adverse outcomes in mammalian prenatal developmental toxicity studies used ToxRefDB and other sources of information, including Stemina Biomarker Discovery's predictive DevTox Registered-Sign model trained on 23 pharmaceutical agents of known developmental toxicity and differing potency. The model initially predicted developmental toxicity from the blinded ToxCast compounds in concordance with animal data with 73% accuracy. Retraining the model with data from the unblinded test compounds at one concentration level increased the predictive accuracy for the remaining concentrations to 83%. These preliminary results on a 11-chemical subset of the ToxCast chemical library indicate that metabolomics analysis of the hES secretome provides information valuable for predictive modeling and mechanistic understanding of mammalian developmental toxicity. -- Highlights: Black-Right-Pointing-Pointer We tested 11 environmental compounds in a hESC metabolomics platform. Black-Right-Pointing-Pointer Significant changes in secreted small molecule metabolites were observed. Black-Right-Pointing-Pointer Perturbed mass features map to pathways critical for normal development and pregnancy. Black-Right-Pointing-Pointer Arginine, proline, nicotinate, nicotinamide and glutathione pathways were affected.« less

Use of the adverse outcome pathway framework to represent cross-species consequences of specific pathway perturbations

EPA Science Inventory

The adverse outcome pathway (AOP) framework has been developed as a means for assembling scientifically defensible descriptions of how particular molecular perturbations, termed molecular initiating events (MIEs), can evoke a set of predictable responses at different levels of bi...

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

PubMed Central

Pathak, Rajesh Kumar; Gupta, Sanjay Mohan; Gaur, Vikram Singh; Pandey, Dinesh

2015-01-01

Abstract In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use well-organized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes. PMID:26484978

Starling forces drive intracranial water exchange during normal and pathological states

PubMed Central

Linninger, Andreas A.; Xu, Colin; Tangen, Kevin; Hartung, Grant

2017-01-01

Aim To quantify the exchange of water between cerebral compartments, specifically blood, tissue, perivascular pathways, and cerebrospinal fluid-filled spaces, on the basis of experimental data and to propose a dynamic global model of water flux through the entire brain to elucidate functionally relevant fluid exchange phenomena. Methods The mechanistic computer model to predict brain water shifts is discretized by cerebral compartments into nodes. Water and species flux is calculated between these nodes across a network of arcs driven by Hagen-Poiseuille flow (blood), Darcy flow (interstitial fluid transport), and Starling’s Law (transmembrane fluid exchange). Compartment compliance is accounted for using a pressure-volume relationship to enforce the Monro-Kellie doctrine. This nonlinear system of differential equations is solved implicitly using MATLAB software. Results The model predictions of intraventricular osmotic injection caused a pressure rise from 10 to 22 mmHg, followed by a taper to 14 mmHg over 100 minutes. The computational results are compared to experimental data with R2 = 0.929. Moreover, simulated osmotic therapy of systemic (blood) injection reduced intracranial pressure from 25 to 10 mmHg. The modeled volume and intracranial pressure changes following cerebral edema agree with experimental trends observed in animal models with R2 = 0.997. Conclusion The model successfully predicted time course and the efficacy of osmotic therapy for clearing cerebral edema. Furthermore, the mathematical model implicated the perivascular pathways as a possible conduit for water and solute exchange. This was a first step to quantify fluid exchange throughout the brain. PMID:29308830

Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction

PubMed Central

Hu, Jianfei; Neiswinger, Johnathan; Zhang, Jin; Zhu, Heng; Qian, Jiang

2015-01-01

Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components. In this study, we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks. We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways. The computational prediction was validated using a protein microarray-based approach. The predicted scaffold proteins showed several interesting characteristics, as we expected from the functionality of scaffold proteins. We found that the scaffold proteins are likely to interact with each other, which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers. Interestingly, a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners. Furthermore, we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process. PMID:26393507

Symmetries of a generic utricular projection: neural connectivity and the distribution of utricular information.

PubMed

Chartrand, Thomas; McCollum, Gin; Hanes, Douglas A; Boyle, Richard D

2016-02-01

Sensory contribution to perception and action depends on both sensory receptors and the organization of pathways (or projections) reaching the central nervous system. Unlike the semicircular canals that are divided into three discrete sensitivity directions, the utricle has a relatively complicated anatomical structure, including sensitivity directions over essentially 360° of a curved, two-dimensional disk. The utricle is not flat, and we do not assume it to be. Directional sensitivity of individual utricular afferents decreases in a cosine-like fashion from peak excitation for movement in one direction to a null or near null response for a movement in an orthogonal direction. Directional sensitivity varies slowly between neighboring cells except within the striolar region that separates the medial from the lateral zone, where the directional selectivity abruptly reverses along the reversal line. Utricular primary afferent pathways reach the vestibular nuclei and cerebellum and, in many cases, converge on target cells with semicircular canal primary afferents and afference from other sources. Mathematically, some canal pathways are known to be characterized by symmetry groups related to physical space. These groups structure rotational information and movement. They divide the target neural center into distinct populations according to the innervation patterns they receive. Like canal pathways, utricular pathways combine symmetries from the utricle with those from target neural centers. This study presents a generic set of transformations drawn from the known structure of the utricle and therefore likely to be found in utricular pathways, but not exhaustive of utricular pathway symmetries. This generic set of transformations forms a 32-element group that is a semi-direct product of two simple abelian groups. Subgroups of the group include order-four elements corresponding to discrete rotations. Evaluation of subgroups allows us to functionally identify the spatial implications of otolith and canal symmetries regarding action and perception. Our results are discussed in relation to observed utricular pathways, including those convergent with canal pathways. Oculomotor and other sensorimotor systems are organized according to canal planes. However, the utricle is evolutionarily prior to the canals and may provide a more fundamental spatial framework for canal pathways as well as for movement. The fullest purely otolithic pathway is likely that which reaches the lumbar spine via Deiters' cells in the lateral vestibular nucleus. It will be of great interest to see whether symmetries predicted from the utricle are identified within this pathway.

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

PubMed Central

Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J.; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M. E. (Bette); Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M.; Whelan, Maurice

2017-01-01

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24–25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment. PMID:27994170

Visual pathways from the perspective of cost functions and multi-task deep neural networks.

PubMed

Scholte, H Steven; Losch, Max M; Ramakrishnan, Kandan; de Haan, Edward H F; Bohte, Sander M

2018-01-01

Vision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals. In this paper, we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks. Machine learning has shown that tasks become easier to solve when they are decomposed into subtasks with their own cost function. We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception, and a dorsal pathway dedicated to vision for action. To evaluate the functional organization in multi-task deep neural networks, we propose a method that measures the contribution of a unit towards each task, applying it to two networks that have been trained on either two related or two unrelated tasks, using an identical stimulus set. Results show that the network trained on the unrelated tasks shows a decreasing degree of feature representation sharing towards higher-tier layers while the network trained on related tasks uniformly shows high degree of sharing. We conjecture that the method we propose can be used to analyze the anatomical and functional organization of the visual system and beyond. We predict that the degree to which tasks are related is a good descriptor of the degree to which they share downstream cortical-units. Copyright © 2017 Elsevier Ltd. All rights reserved.

Systems pharmacology - Towards the modeling of network interactions.

PubMed

Danhof, Meindert

2016-10-30

Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior. Copyright © 2016 The Author. Published by Elsevier B.V. All rights reserved.

Modeling Drug- and Chemical-Induced Hepatotoxicity with Systems Biology Approaches

PubMed Central

Bhattacharya, Sudin; Shoda, Lisl K.M.; Zhang, Qiang; Woods, Courtney G.; Howell, Brett A.; Siler, Scott Q.; Woodhead, Jeffrey L.; Yang, Yuching; McMullen, Patrick; Watkins, Paul B.; Andersen, Melvin E.

2012-01-01

We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales. PMID:23248599

Metabolomic Quantitative Trait Loci (mQTL) Mapping Implicates the Ubiquitin Proteasome System in Cardiovascular Disease Pathogenesis.

PubMed

Kraus, William E; Muoio, Deborah M; Stevens, Robert; Craig, Damian; Bain, James R; Grass, Elizabeth; Haynes, Carol; Kwee, Lydia; Qin, Xuejun; Slentz, Dorothy H; Krupp, Deidre; Muehlbauer, Michael; Hauser, Elizabeth R; Gregory, Simon G; Newgard, Christopher B; Shah, Svati H

2015-11-01

Levels of certain circulating short-chain dicarboxylacylcarnitine (SCDA), long-chain dicarboxylacylcarnitine (LCDA) and medium chain acylcarnitine (MCA) metabolites are heritable and predict cardiovascular disease (CVD) events. Little is known about the biological pathways that influence levels of most of these metabolites. Here, we analyzed genetics, epigenetics, and transcriptomics with metabolomics in samples from a large CVD cohort to identify novel genetic markers for CVD and to better understand the role of metabolites in CVD pathogenesis. Using genomewide association in the CATHGEN cohort (N = 1490), we observed associations of several metabolites with genetic loci. Our strongest findings were for SCDA metabolite levels with variants in genes that regulate components of endoplasmic reticulum (ER) stress (USP3, HERC1, STIM1, SEL1L, FBXO25, SUGT1) These findings were validated in a second cohort of CATHGEN subjects (N = 2022, combined p = 8.4x10-6-2.3x10-10). Importantly, variants in these genes independently predicted CVD events. Association of genomewide methylation profiles with SCDA metabolites identified two ER stress genes as differentially methylated (BRSK2 and HOOK2). Expression quantitative trait loci (eQTL) pathway analyses driven by gene variants and SCDA metabolites corroborated perturbations in ER stress and highlighted the ubiquitin proteasome system (UPS) arm. Moreover, culture of human kidney cells in the presence of levels of fatty acids found in individuals with cardiometabolic disease, induced accumulation of SCDA metabolites in parallel with increases in the ER stress marker BiP. Thus, our integrative strategy implicates the UPS arm of the ER stress pathway in CVD pathogenesis, and identifies novel genetic loci associated with CVD event risk.

Metabolomic Quantitative Trait Loci (mQTL) Mapping Implicates the Ubiquitin Proteasome System in Cardiovascular Disease Pathogenesis

PubMed Central

Kraus, William E.; Muoio, Deborah M.; Stevens, Robert; Craig, Damian; Bain, James R.; Grass, Elizabeth; Haynes, Carol; Kwee, Lydia; Qin, Xuejun; Slentz, Dorothy H.; Krupp, Deidre; Muehlbauer, Michael; Hauser, Elizabeth R.; Gregory, Simon G.; Newgard, Christopher B.; Shah, Svati H.

2015-01-01

Levels of certain circulating short-chain dicarboxylacylcarnitine (SCDA), long-chain dicarboxylacylcarnitine (LCDA) and medium chain acylcarnitine (MCA) metabolites are heritable and predict cardiovascular disease (CVD) events. Little is known about the biological pathways that influence levels of most of these metabolites. Here, we analyzed genetics, epigenetics, and transcriptomics with metabolomics in samples from a large CVD cohort to identify novel genetic markers for CVD and to better understand the role of metabolites in CVD pathogenesis. Using genomewide association in the CATHGEN cohort (N = 1490), we observed associations of several metabolites with genetic loci. Our strongest findings were for SCDA metabolite levels with variants in genes that regulate components of endoplasmic reticulum (ER) stress (USP3, HERC1, STIM1, SEL1L, FBXO25, SUGT1) These findings were validated in a second cohort of CATHGEN subjects (N = 2022, combined p = 8.4x10-6–2.3x10-10). Importantly, variants in these genes independently predicted CVD events. Association of genomewide methylation profiles with SCDA metabolites identified two ER stress genes as differentially methylated (BRSK2 and HOOK2). Expression quantitative trait loci (eQTL) pathway analyses driven by gene variants and SCDA metabolites corroborated perturbations in ER stress and highlighted the ubiquitin proteasome system (UPS) arm. Moreover, culture of human kidney cells in the presence of levels of fatty acids found in individuals with cardiometabolic disease, induced accumulation of SCDA metabolites in parallel with increases in the ER stress marker BiP. Thus, our integrative strategy implicates the UPS arm of the ER stress pathway in CVD pathogenesis, and identifies novel genetic loci associated with CVD event risk. PMID:26540294

Prediction of target genes for miR-140-5p in pulmonary arterial hypertension using bioinformatics methods.

PubMed

Li, Fangwei; Shi, Wenhua; Wan, Yixin; Wang, Qingting; Feng, Wei; Yan, Xin; Wang, Jian; Chai, Limin; Zhang, Qianqian; Li, Manxiang

2017-12-01

The expression of microRNA (miR)-140-5p is known to be reduced in both pulmonary arterial hypertension (PAH) patients and monocrotaline-induced PAH models in rat. Identification of target genes for miR-140-5p with bioinformatics analysis may reveal new pathways and connections in PAH. This study aimed to explore downstream target genes and relevant signaling pathways regulated by miR-140-5p to provide theoretical evidences for further researches on role of miR-140-5p in PAH. Multiple downstream target genes and upstream transcription factors (TFs) of miR-140-5p were predicted in the analysis. Gene ontology (GO) enrichment analysis indicated that downstream target genes of miR-140-5p were enriched in many biological processes, such as biological regulation, signal transduction, response to chemical stimulus, stem cell proliferation, cell surface receptor signaling pathways. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis found that downstream target genes were mainly located in Notch, TGF-beta, PI3K/Akt, and Hippo signaling pathway. According to TF-miRNA-mRNA network, the important downstream target genes of miR-140-5p were PPI, TGF-betaR1, smad4, JAG1, ADAM10, FGF9, PDGFRA, VEGFA, LAMC1, TLR4, and CREB. After thoroughly reviewing published literature, we found that 23 target genes and seven signaling pathways were truly inhibited by miR-140-5p in various tissues or cells; most of these verified targets were in accordance with our present prediction. Other predicted targets still need further verification in vivo and in vitro .

Simulating Metabolism with Statistical Thermodynamics

PubMed Central

Cannon, William R.

2014-01-01

New methods are needed for large scale modeling of metabolism that predict metabolite levels and characterize the thermodynamics of individual reactions and pathways. Current approaches use either kinetic simulations, which are difficult to extend to large networks of reactions because of the need for rate constants, or flux-based methods, which have a large number of feasible solutions because they are unconstrained by the law of mass action. This report presents an alternative modeling approach based on statistical thermodynamics. The principles of this approach are demonstrated using a simple set of coupled reactions, and then the system is characterized with respect to the changes in energy, entropy, free energy, and entropy production. Finally, the physical and biochemical insights that this approach can provide for metabolism are demonstrated by application to the tricarboxylic acid (TCA) cycle of Escherichia coli. The reaction and pathway thermodynamics are evaluated and predictions are made regarding changes in concentration of TCA cycle intermediates due to 10- and 100-fold changes in the ratio of NAD+:NADH concentrations. Finally, the assumptions and caveats regarding the use of statistical thermodynamics to model non-equilibrium reactions are discussed. PMID:25089525

Simulating metabolism with statistical thermodynamics.

PubMed

Cannon, William R

2014-01-01

New methods are needed for large scale modeling of metabolism that predict metabolite levels and characterize the thermodynamics of individual reactions and pathways. Current approaches use either kinetic simulations, which are difficult to extend to large networks of reactions because of the need for rate constants, or flux-based methods, which have a large number of feasible solutions because they are unconstrained by the law of mass action. This report presents an alternative modeling approach based on statistical thermodynamics. The principles of this approach are demonstrated using a simple set of coupled reactions, and then the system is characterized with respect to the changes in energy, entropy, free energy, and entropy production. Finally, the physical and biochemical insights that this approach can provide for metabolism are demonstrated by application to the tricarboxylic acid (TCA) cycle of Escherichia coli. The reaction and pathway thermodynamics are evaluated and predictions are made regarding changes in concentration of TCA cycle intermediates due to 10- and 100-fold changes in the ratio of NAD+:NADH concentrations. Finally, the assumptions and caveats regarding the use of statistical thermodynamics to model non-equilibrium reactions are discussed.

[ARIA 2016 executive summary: Integrated care pathways for predictive medicine throughout the life cycle in Argentina].

PubMed

Ivancevich, Juan Carlos; Neffen, Hugo; Zernotti, Mario E; Asayag, Estrella; Blua, Ariel; Cicerán, Alberto; Jares, Edgardo J; Lavrut, Alberto J; Máspero, Jorge F; Agache, Ioana; Bachert, Claus; Bedbrook, Anna; Canonica, Giorgio W; Casale, Thomas B; Cruz, Álvaro A; Fokkens, Wytske J; Hellings, Peter W; Samolinski, Boleslaw; Bousquet, Jean

2017-01-01

The ARIA initiative was started during a World Health Organization workshop in 1999. The initial goals were to propose a new classification for allergic rhinitis, to promote the concept of multi-morbidity in asthma and rhinitis and to develop guidelines with stakeholders for world-wide use. ARIA is now focused on the implementation of emerging technologies for individualized and predictive medicine. MASK: MACVIA-Aria Sentinel Network uses mobile technology to develop care pathways that enable management by a multidisciplinary group or by patients themselves. An App for iOS and Android uses a visual analogue scale to assess symptom control and work productivity, as well as a clinical decision support system; it is associated with an interoperable tablet for health professionals. The escalation strategy uses recommendations of the European Innovation Partnership on Active and Healthy Ageing. The aim of ARIA's new approach is to provide an active and healthy life to people affected by rhinitis, regardless of age, gender or socioeconomic status, in order to reduce social and health inequalities caused by the disease.

Systems approach for the selection of micro-RNAs as therapeutic biomarkers of anti-EGFR monoclonal antibody treatment in colorectal cancer

NASA Astrophysics Data System (ADS)

Deyati, Avisek; Bagewadi, Shweta; Senger, Philipp; Hofmann-Apitius, Martin; Novac, Natalia

2015-01-01

miRNA plays an important role in tumourgenesis by regulating expression of oncogenes and tumour suppressors. Thus affects cell proliferation and differentiation, apoptosis, invasion and angiogenesis. miRNAs are potential biomarkers for diagnosis, prognosis and therapies of different forms of cancer. However, relationship between response of cancer patients towards targeted therapy and the resulting modifications of the miRNA transcriptome in the context of pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have produced an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. Efficient translation of -omics data and accumulated knowledge to clinical decision-making are of paramount scientific and public health interest. Well-structured translational algorithms are needed to bridge the gap from databases to decisions. Herein, we present a novel SMARTmiR algorithm to prospectively predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer.

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

PubMed Central

Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

2015-01-01

Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

Stoichiometry of Nck-dependent actin polymerization in living cells

PubMed Central

Ditlev, Jonathon A.; Michalski, Paul J.; Huber, Greg; Rivera, Gonzalo M.; Mohler, William A.

2012-01-01

Regulation of actin dynamics through the Nck/N-WASp (neural Wiskott–Aldrich syndrome protein)/Arp2/3 pathway is essential for organogenesis, cell invasiveness, and pathogen infection. Although many of the proteins involved in this pathway are known, the detailed mechanism by which it functions remains undetermined. To examine the signaling mechanism, we used a two-pronged strategy involving computational modeling and quantitative experimentation. We developed predictions for Nck-dependent actin polymerization using the Virtual Cell software system. In addition, we used antibody-induced aggregation of membrane-targeted Nck SH3 domains to test these predictions and to determine how the number of molecules in Nck aggregates and the density of aggregates affected localized actin polymerization in living cells. Our results indicate that the density of Nck molecules in aggregates is a critical determinant of actin polymerization. Furthermore, results from both computational simulations and experimentation support a model in which the Nck/N-WASp/Arp2/3 stoichiometry is 4:2:1. These results provide new insight into activities involving localized actin polymerization, including tumor cell invasion, microbial pathogenesis, and T cell activation. PMID:22613834

Functional characterization of EI24-induced autophagy in the degradation of RING-domain E3 ligases

PubMed Central

Devkota, Sushil; Jeong, Hyobin; Kim, Yunmi; Ali, Muhammad; Roh, Jae-il; Hwang, Daehee; Lee, Han-Woong

2016-01-01

ABSTRACT Historically, the ubiquitin-proteasome system (UPS) and autophagy pathways were believed to be independent; however, recent data indicate that these pathways engage in crosstalk. To date, the players mediating this crosstalk have been elusive. Here, we show experimentally that EI24 (EI24, autophagy associated transmembrane protein), a key component of basal macroautophagy/autophagy, degrades 14 physiologically important E3 ligases with a RING (really interesting new gene) domain, whereas 5 other ligases were not degraded. Based on the degradation results, we built a statistical model that predicts the RING E3 ligases targeted by EI24 using partial least squares discriminant analysis. Of 381 RING E3 ligases examined computationally, our model predicted 161 EI24 targets. Those targets are primarily involved in transcription, proteolysis, cellular bioenergetics, and apoptosis and regulated by TP53 and MTOR signaling. Collectively, our work demonstrates that EI24 is an essential player in UPS-autophagy crosstalk via degradation of RING E3 ligases. These results indicate a paradigm shift regarding the fate of E3 ligases. PMID:27541728

Dynamics of tropical oxygen minium zones (OMZ): The role of vertical mixing and eddy stirring in ventilating the OMZ in the tropical Atlantic

NASA Astrophysics Data System (ADS)

Visbeck, M.; Banyte, D.; Brandt, P.; Dengler, M.; Fischer, T.; Karstensen, J.; Krahmann, G.; Tanhua, T. S.; Stramma, L.

2013-12-01

Equatorial Dynamics provide an essential influence on the ventilation pathways of well oxygenated surface water on their route to tropical oxygen minimum zones (OMZ). The large scale wind driven circulation shield OMZs from the direct ventilation pathways. They are located in the so called ';shadow zones' equator ward of the subtropical gyres. From what is known most of the oxygen is supplied via pathways from the western boundary modulated by the complex zonal equatorial current system and marginally by vertical mixing. What was less clear is which of the possible pathways are most effective in transporting dissolved oxygen towards the OMZ. A collaborative research program focused on the dynamics of oxygen minimum zones, called SFB754 "Climate - Biogeochemistry Interactions in the Tropical Ocean", allowed us to conduct two ocean tracer release experiments to investigate the vertical and horizontal mixing rates and associated oxygen transports. Specifically we report on the first deliberate tracer release experiment (GUTRE, Guinea Upwelling Tracer Release Experiment) in the tropical northeast Atlantic carried out in order to determine the diapycnal diffusivity coefficient in the upper layer of the OMZ. A tracer (CF3SF5) was injected in spring of 2008 and subsequently measured during three designated tracer survey cruises until the end of 2010. We found that, generally, the diffusivity is larger than expected for low latitudes and similar in magnitude to what has previously been experimentally determined in the Canary Basin. When combining the tracer study with estimates of diapycnal mixing based on microstructure profiling and a newly developed method using ship board ADCPs we were able to compute the vertical oxygen flux and its divergence for the OMZ. To our surprise, the vertical flux of oxygen by diapycnal mixing provides about 30% of the total ventilation. The estimate was derived from the simple advection-diffusion model taking into account moored and ship based velocity observations of the equatorial current systems along 23°W in the tropical Atlantic. However, the advective pathways are less certain and possibly more variable. Firstly, the strength of lateral eddy stirring and the role in oxygen transport is less well known, and is the focus of the ongoing second tracer release experiment (OSTRE, Oxygen Supply Tracer Release Experiment). Secondly, the analysis of historical data from the equatorial regime suggests that the observed decline in dissolved oxygen in the tropical North Atlantic might in part be a consequence of reduced horizontal ventilation by equatorial intermediate current systems. The uncertainty of the long-term variability of the circulation in the equatorial systems and additional uncertainty in the biogeochemical consumption rates provide a challenge for estimates of the future of the OMZ regimes. Model prediction of future oxygen changes depend on the models ability to reproduce the observed oxygen ventilation pathways and processes, which might limit the prediction's accuracy.

Generation of computationally predicted Adverse Outcome Pathway networks through integration of publicly available in vivo, in vitro, phenotype, and biological pathway data.

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework is becoming a widely used tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse ecological and human health outcomes. However, the conventional process...

USE OF PHARMACOKINETIC MODELING TO DESIGN STUDIES FOR PATHWAY-SPECIFIC EXPOSURE MODEL EVALUATION

EPA Science Inventory

Validating an exposure pathway model is difficult because the biomarker, which is often used to evaluate the model prediction, is an integrated measure for exposures from all the exposure routes/pathways. The purpose of this paper is to demonstrate a method to use pharmacokeneti...

A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma

PubMed Central

Rao, Soumya Alige Mahabala; Srinivasan, Sujaya; Patric, Irene Rosita Pia; Hegde, Alangar Sathyaranjandas; Chandramouli, Bangalore Ashwathnarayanara; Arimappamagan, Arivazhagan; Santosh, Vani; Kondaiah, Paturu; Rao, Manchanahalli R. Sathyanarayana; Somasundaram, Kumaravel

2014-01-01

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma. PMID:24475040

Comparative transcriptome analysis of Methylibium petroleiphilum PM1 exposed to the fuel oxygenates methyl tert-butyl ether and ethanol.

PubMed

Hristova, Krassimira R; Schmidt, Radomir; Chakicherla, Anu Y; Legler, Tina C; Wu, Janice; Chain, Patrick S; Scow, Kate M; Kane, Staci R

2007-11-01

High-density whole-genome cDNA microarrays were used to investigate substrate-dependent gene expression of Methylibium petroleiphilum PM1, one of the best-characterized aerobic methyl tert-butyl ether (MTBE)-degrading bacteria. Differential gene expression profiling was conducted with PM1 grown on MTBE and ethanol as sole carbon sources. Based on microarray high scores and protein similarity analysis, an MTBE regulon located on the megaplasmid was identified for further investigation. Putative functions for enzymes encoded in this regulon are described with relevance to the predicted MTBE degradation pathway. A new unique dioxygenase enzyme system that carries out the hydroxylation of tert-butyl alcohol to 2-methyl-2-hydroxy-1-propanol in M. petroleiphilum PM1 was discovered. Hypotheses regarding the acquisition and evolution of MTBE genes as well as the involvement of IS elements in these complex processes were formulated. The pathways for toluene, phenol, and alkane oxidation via toluene monooxygenase, phenol hydroxylase, and propane monooxygenase, respectively, were upregulated in MTBE-grown cells compared to ethanol-grown cells. Four out of nine putative cyclohexanone monooxygenases were also upregulated in MTBE-grown cells. The expression data allowed prediction of several hitherto-unknown enzymes of the upper MTBE degradation pathway in M. petroleiphilum PM1 and aided our understanding of the regulation of metabolic processes that may occur in response to pollutant mixtures and perturbations in the environment.

Glassy nature and glass-to-crystal transition in the binary metallic glass CuZr

NASA Astrophysics Data System (ADS)

Wei, Zi-Yang; Shang, Cheng; Zhang, Xiao-Jie; Liu, Zhi-Pan

2017-06-01

The prediction for the stability of glassy material is a key challenge in physical science. Here, we report a theoretical framework to predict the glass stability based on stochastic surface walking global optimization and reaction pathway sampling. This is demonstrated by revealing for the first time the global potential energy surface (PES) of two systems, CuZr binary metallic glass and nonglassy pure Cu systems, and establishing the lowest energy pathways linking glassy/amorphous structures with crystalline structures. The CuZr system has a significant number of glassy structures on PES that are ˜0.045 eV /atom above the crystal structure. Two clear trends are identified from global PES in the glass-to-crystal transition of the CuZr system: (i) the local Zr-Cu coordination (nearest neighbor) increases, and (ii) the local Zr bonding environment becomes homogeneous. This allows us to introduce quantitative structural and energetics conditions to distinguish the glassy structures from the crystalline structures. Because of the local Zr-Cu exchange in the glass-to-crystal transition, a high reaction barrier (>0.048 eV /atom ) is present to separate the glassy structures and the crystals in CuZr. By contrast, the Cu system, although it does possess amorphous structures that appear at much higher energy (˜0.075 eV /atom ) with respect to the crystal structure, has very low reaction barriers for the crystallization of amorphous structures, i.e. <0.011 eV /atom . The quantitative data on PES now available from global optimization techniques deepens our understanding on the microscopic nature of glassy material and might eventually facilitate the design of stable glassy materials.

Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM.

PubMed

Tuncbag, Nurcan; Gursoy, Attila; Nussinov, Ruth; Keskin, Ozlem

2011-08-11

Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.

A network approach to predict pathogenic genes for Fusarium graminearum.

PubMed

Liu, Xiaoping; Tang, Wei-Hua; Zhao, Xing-Ming; Chen, Luonan

2010-10-04

Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which demonstrate the effectiveness of the proposed method. The results presented in this paper not only can provide guidelines for future experimental verification, but also shed light on the pathogenesis of the destructive fungus F. graminearum.

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation

EPA Science Inventory

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation Adverse outcome pathways (AOPs) are a framework for predicting quantitative relationships between molecular initiatin...

Creation of a Genome-Wide Metabolic Pathway Database for Populus trichocarpa Using a New Approach for Reconstruction and Curation of Metabolic Pathways for Plants1[W][OA

PubMed Central

Zhang, Peifen; Dreher, Kate; Karthikeyan, A.; Chi, Anjo; Pujar, Anuradha; Caspi, Ron; Karp, Peter; Kirkup, Vanessa; Latendresse, Mario; Lee, Cynthia; Mueller, Lukas A.; Muller, Robert; Rhee, Seung Yon

2010-01-01

Metabolic networks reconstructed from sequenced genomes or transcriptomes can help visualize and analyze large-scale experimental data, predict metabolic phenotypes, discover enzymes, engineer metabolic pathways, and study metabolic pathway evolution. We developed a general approach for reconstructing metabolic pathway complements of plant genomes. Two new reference databases were created and added to the core of the infrastructure: a comprehensive, all-plant reference pathway database, PlantCyc, and a reference enzyme sequence database, RESD, for annotating metabolic functions of protein sequences. PlantCyc (version 3.0) includes 714 metabolic pathways and 2,619 reactions from over 300 species. RESD (version 1.0) contains 14,187 literature-supported enzyme sequences from across all kingdoms. We used RESD, PlantCyc, and MetaCyc (an all-species reference metabolic pathway database), in conjunction with the pathway prediction software Pathway Tools, to reconstruct a metabolic pathway database, PoplarCyc, from the recently sequenced genome of Populus trichocarpa. PoplarCyc (version 1.0) contains 321 pathways with 1,807 assigned enzymes. Comparing PoplarCyc (version 1.0) with AraCyc (version 6.0, Arabidopsis [Arabidopsis thaliana]) showed comparable numbers of pathways distributed across all domains of metabolism in both databases, except for a higher number of AraCyc pathways in secondary metabolism and a 1.5-fold increase in carbohydrate metabolic enzymes in PoplarCyc. Here, we introduce these new resources and demonstrate the feasibility of using them to identify candidate enzymes for specific pathways and to analyze metabolite profiling data through concrete examples. These resources can be searched by text or BLAST, browsed, and downloaded from our project Web site (http://plantcyc.org). PMID:20522724

An Integrated Human/Murine Transcriptome and Pathway Approach To Identify Prenatal Treatments For Down Syndrome.

PubMed

Guedj, Faycal; Pennings, Jeroen LA; Massingham, Lauren J; Wick, Heather C; Siegel, Ashley E; Tantravahi, Umadevi; Bianchi, Diana W

2016-09-02

Anatomical and functional brain abnormalities begin during fetal life in Down syndrome (DS). We hypothesize that novel prenatal treatments can be identified by targeting signaling pathways that are consistently perturbed in cell types/tissues obtained from human fetuses with DS and mouse embryos. We analyzed transcriptome data from fetuses with trisomy 21, age and sex-matched euploid controls, and embryonic day 15.5 forebrains from Ts1Cje, Ts65Dn, and Dp16 mice. The new datasets were compared to other publicly available datasets from humans with DS. We used the human Connectivity Map (CMap) database and created a murine adaptation to identify FDA-approved drugs that can rescue affected pathways. USP16 and TTC3 were dysregulated in all affected human cells and two mouse models. DS-associated pathway abnormalities were either the result of gene dosage specific effects or the consequence of a global cell stress response with activation of compensatory mechanisms. CMap analyses identified 56 molecules with high predictive scores to rescue abnormal gene expression in both species. Our novel integrated human/murine systems biology approach identified commonly dysregulated genes and pathways. This can help to prioritize therapeutic molecules on which to further test safety and efficacy. Additional studies in human cells are ongoing prior to pre-clinical prenatal treatment in mice.

Incorporating Information of microRNAs into Pathway Analysis in a Genome-Wide Association Study of Bipolar Disorder

PubMed Central

Shih, Wei-Liang; Kao, Chung-Feng; Chuang, Li-Chung; Kuo, Po-Hsiu

2012-01-01

MicroRNAs (miRNAs) are known to be important post-transcriptional regulators that are involved in the etiology of complex psychiatric traits. The present study aimed to incorporate miRNAs information into pathway analysis using a genome-wide association dataset to identify relevant biological pathways for bipolar disorder (BPD). We selected psychiatric- and neurological-associated miRNAs (N = 157) from PhenomiR database. The miRNA target genes (miTG) predictions were obtained from microRNA.org. Canonical pathways (N = 4,051) were downloaded from the Molecule Signature Database. We employed a novel weighting scheme for miTGs in pathway analysis using methods of gene set enrichment analysis and sum-statistic. Under four statistical scenarios, 38 significantly enriched pathways (P-value < 0.01 after multiple testing correction) were identified for the risk of developing BPD, including pathways of ion channels associated (e.g., gated channel activity, ion transmembrane transporter activity, and ion channel activity) and nervous related biological processes (e.g., nervous system development, cytoskeleton, and neuroactive ligand receptor interaction). Among them, 19 were identified only when the weighting scheme was applied. Many miRNA-targeted genes were functionally related to ion channels, collagen, and axonal growth and guidance that have been suggested to be associated with BPD previously. Some of these genes are linked to the regulation of miRNA machinery in the literature. Our findings provide support for the potential involvement of miRNAs in the psychopathology of BPD. Further investigations to elucidate the functions and mechanisms of identified candidate pathways are needed. PMID:23264780

N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes.

PubMed

Li, Qike; Schissler, A Grant; Gardeux, Vincent; Achour, Ikbel; Kenost, Colleen; Berghout, Joanne; Li, Haiquan; Zhang, Hao Helen; Lussier, Yves A

2017-05-24

Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.

Modeling central metabolism and energy biosynthesis across microbial life

DOE PAGES

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

2016-08-08

Here, automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. As a result, to overcome this challenge, we developed methods and tools to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of modelmore » organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. In conclusion, we predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.« less

Modeling central metabolism and energy biosynthesis across microbial life

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

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

Here, automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. As a result, to overcome this challenge, we developed methods and tools to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of modelmore » organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. In conclusion, we predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.« less

Modeling central metabolism and energy biosynthesis across microbial life.

PubMed

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

2016-08-08

Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.

Meta-learning framework applied in bioinformatics inference system design.

PubMed

Arredondo, Tomás; Ormazábal, Wladimir

2015-01-01

This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions

PubMed Central

Hucka, Michael; Bergmann, Frank T.; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M.; Le Novére, Nicolas; Myers, Chris J.; Olivier, Brett G.; Sahle, Sven; Schaff, James C.; Smith, Lucian P.; Waltemath, Dagmar; Wilkinson, Darren J.

2017-01-01

Summary Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/. PMID:26528569

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core

PubMed Central

Hucka, Michael; Bergmann, Frank T.; Hoops, Stefan; Keating, Sarah M.; Sahle, Sven; Schaff, James C.; Smith, Lucian P.; Wilkinson, Darren J.

2017-01-01

Summary Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/. PMID:26528564

Information content and cross-talk in biological signal transduction: An information theory study

NASA Astrophysics Data System (ADS)

Prasad, Ashok; Lyons, Samanthe

2014-03-01

Biological cells respond to chemical cues provided by extra-cellular chemical signals, but many of these chemical signals and the pathways they activate interfere and overlap with one another. How well cells can distinguish between interfering extra-cellular signals is thus an important question in cellular signal transduction. Here we use information theory with stochastic simulations of networks to address the question of what happens to total information content when signals interfere. We find that both total information transmitted by the biological pathway, as well as its theoretical capacity to discriminate between overlapping signals, are relatively insensitive to cross-talk between the extracellular signals, until significantly high levels of cross-talk have been reached. This robustness of information content against cross-talk requires that the average amplitude of the signals are large. We predict that smaller systems, as exemplified by simple phosphorylation relays (two-component systems) in bacteria, should be significantly much less robust against cross-talk. Our results suggest that mammalian signal transduction can tolerate a high amount of cross-talk without degrading information content, while smaller bacterial systems cannot.

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

PubMed

Hucka, Michael; Bergmann, Frank T; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M; Le Novère, Nicolas; Myers, Chris J; Olivier, Brett G; Sahle, Sven; Schaff, James C; Smith, Lucian P; Waltemath, Dagmar; Wilkinson, Darren J

2015-09-04

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core.

PubMed

Hucka, Michael; Bergmann, Frank T; Hoops, Stefan; Keating, Sarah M; Sahle, Sven; Schaff, James C; Smith, Lucian P; Wilkinson, Darren J

2015-09-04

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core.

PubMed

Hucka, Michael; Bergmann, Frank T; Hoops, Stefan; Keating, Sarah M; Sahle, Sven; Schaff, James C; Smith, Lucian P; Wilkinson, Darren J

2015-06-01

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

PubMed

Hucka, Michael; Bergmann, Frank T; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M; Le Novère, Nicolas; Myers, Chris J; Olivier, Brett G; Sahle, Sven; Schaff, James C; Smith, Lucian P; Waltemath, Dagmar; Wilkinson, Darren J

2015-06-01

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.

Writing with ring currents: selectively hydrogenated polycyclic aromatics as finite models of graphene and graphane

PubMed Central

Fowler, Patrick W.; Gibson, Christopher M.; Bean, David E.

2014-01-01

Alternating partial hydrogenation of the interior region of a polycyclic aromatic hydrocarbon gives a finite model system representing systems on the pathway from graphene to the graphane modification of the graphene sheet. Calculations at the DFT and coupled Hartree–Fock levels confirm that sp2 cycles of bare carbon centres isolated by selective hydrogenation retain the essentially planar geometry and electron delocalization of the annulene that they mimic. Delocalization is diagnosed by the presence of ring currents, as detected by ipsocentric calculation and visualization of the current density induced in the π system by a perpendicular external magnetic field. These induced ‘ring’ currents have essentially the same sense, strength and orbital origin as in the free hydrocarbon. Subjected to the important experimental proviso of the need for atomic-scale control of hydrogenation, this finding predicts the possibility of writing single, multiple and concentric diatropic and/or paratropic ring currents on the graphene/graphane sheet. The implication is that pathways for free flow of ballistic current can be modelled in the same way. PMID:24611026

Complete Proteomic-Based Enzyme Reaction and Inhibition Kinetics Reveal How Monolignol Biosynthetic Enzyme Families Affect Metabolic Flux and Lignin in Populus trichocarpa[W

PubMed Central

Wang, Jack P.; Naik, Punith P.; Chen, Hsi-Chuan; Shi, Rui; Lin, Chien-Yuan; Liu, Jie; Shuford, Christopher M.; Li, Quanzi; Sun, Ying-Hsuan; Tunlaya-Anukit, Sermsawat; Williams, Cranos M.; Muddiman, David C.; Ducoste, Joel J.; Sederoff, Ronald R.; Chiang, Vincent L.

2014-01-01

We established a predictive kinetic metabolic-flux model for the 21 enzymes and 24 metabolites of the monolignol biosynthetic pathway using Populus trichocarpa secondary differentiating xylem. To establish this model, a comprehensive study was performed to obtain the reaction and inhibition kinetic parameters of all 21 enzymes based on functional recombinant proteins. A total of 104 Michaelis-Menten kinetic parameters and 85 inhibition kinetic parameters were derived from these enzymes. Through mass spectrometry, we obtained the absolute quantities of all 21 pathway enzymes in the secondary differentiating xylem. This extensive experimental data set, generated from a single tissue specialized in wood formation, was used to construct the predictive kinetic metabolic-flux model to provide a comprehensive mathematical description of the monolignol biosynthetic pathway. The model was validated using experimental data from transgenic P. trichocarpa plants. The model predicts how pathway enzymes affect lignin content and composition, explains a long-standing paradox regarding the regulation of monolignol subunit ratios in lignin, and reveals novel mechanisms involved in the regulation of lignin biosynthesis. This model provides an explanation of the effects of genetic and transgenic perturbations of the monolignol biosynthetic pathway in flowering plants. PMID:24619611

Longitudinal Examination of Peer and Partner Influences on Gender-specific Pathways From Child Abuse to Adult Crime

PubMed Central

Lee, Jungeun Olivia; Herrenkohl, Todd I.; Jung, Hyunzee; Skinner, Martie L.; Klika, J. Bart

2015-01-01

Research provides increasing evidence of the association of child abuse with adult antisocial behavior. However, less is known about the developmental pathways that underlie this association. Building on the life course model of antisocial behavior, the present study examined possible developmental pathways linking various forms of child abuse (physical, emotional, sexual) to adult antisocial behavior. These pathways include child and adolescent antisocial behavior, as well as adulthood measures of partner risk taking, warmth, and antisocial peer influences. Data are from the Lehigh Longitudinal Study, a prospective longitudinal study examining long-term developmental outcomes subsequent to child maltreatment. Participant families in the Lehigh Longitudinal Study were followed from preschool age into adulthood. Analyses of gender differences addressed the consistency of path coefficients across genders. Results for 297 adult participants followed from early childhood showed that, for both genders, physical and emotional child abuse predicted adult crime indirectly through child and adolescent antisocial behavior, as well as adult partner and antisocial peer influences. However, for females, having an antisocial partner predicted an affiliation with antisocial peers, and that in turn predicted adult crime. For males, having an antisocial partner was associated with less partner warmth, which in turn predicted an affiliation with antisocial peers, itself a proximal predictor of adult crime. Sexual abuse also predicted adolescent antisocial behavior, but only for males, supporting what some have called “a delayed-onset pathway” for females, whereby the exposure to early risks produce much later developmental outcomes. PMID:26271556

Development of computationally predicted Adverse Outcome Pathway (AOP) networks through data mining and integration of publicly available in vivo, in vitro, phenotype, and biological pathway data

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework is increasingly being adopted as a tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse outcomes relevant for ecological and human health outcomes. Ho...

Proteome Profiling Reveals Potential Toxicity and Detoxification Pathways Following Exposure of BEAS-2B Cells to Engineered Nanoparticle Titanium Dioxide

EPA Science Inventory

Identification of toxicity pathways linked to chemical -exposure is critical for a better understanding of biological effects of the exposure, toxic mechanisms, and for enhancement of the prediction of chemical toxicity and adverse health outcomes. To identify toxicity pathways a...

[Transcriptome analysis of Dunaliella viridis].

PubMed

Zhu, Shuai-qi; Gong, Yi-fu; Hang, Yu-qing; Liu, Hao; Wang, He-yu

2015-08-01

In order to understand the gene information, function, haloduric pathway (glycerolipid metabolism) and related key genes for Dunaliella viridis, we used Illumina HiSeqTM 2000 high-throughput sequencing technology to sequence its transcriptome. Trinity soft was used to assemble the data to form transcripts. Based on the Clusters of Orthologous Groups (COG), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG ) databases, we carried out functional annotation and classification, pathway annotation, and the opening reading fragment (ORF) sequence prediction of transcripts. The key genes in the glycerolipid metabolism were analyzed. The results suggested that 81,593 transcripts were found, and 77,117 ORF sequences were predicted, accounting for 94.50% of all transcripts. COG classification results showed that 16,569 transcripts were assigned to 24 categories. GO classification annotated 76,436 transcripts. The number of transcripts for biologcial processes was 30,678, accounting for 40.14% of all transcripts. KEGG pathway analysis showed that 26,428 transcripts were annotated to 317 pathways, and 131 pathways were related to metabolism, accounting for 41.32% of all annotated pathways. Only one transcript was annotated as coding the key enzyme dihydroxyacetone kinase involved in the glycerolipid pathway. This enzyme could be related to glycerol biosynthesis under salt stress. This study further improved the gene information and laid the foundation of metabolic pathway research for Dunaliella viridis.

Dependence of prevalence of contiguous pathways in proteins on structural complexity.

PubMed

Thayer, Kelly M; Galganov, Jesse C; Stein, Avram J

2017-01-01

Allostery is a regulatory mechanism in proteins where an effector molecule binds distal from an active site to modulate its activity. Allosteric signaling may occur via a continuous path of residues linking the active and allosteric sites, which has been suggested by large conformational changes evident in crystal structures. An alternate possibility is that the signal occurs in the realm of ensemble dynamics via an energy landscape change. While the latter was first proposed on theoretical grounds, increasing evidence suggests that such a control mechanism is plausible. A major difficulty for testing the two methods is the ability to definitively determine that a residue is directly involved in allosteric signal transduction. Statistical Coupling Analysis (SCA) is a method that has been successful at predicting pathways, and experimental tests involving mutagenesis or domain substitution provide the best available evidence of signaling pathways. However, ascertaining energetic pathways which need not be contiguous is far more difficult. To date, simple estimates of the statistical significance of a pathway in a protein remain to be established. The focus of this work is to estimate such benchmarks for the statistical significance of contiguous pathways for the null model of selecting residues at random. We found that when 20% of residues in proteins are randomly selected, contiguous pathways at the 6 Å cutoff level were found with success rates of 51% in PDZ, 30% in p53, and 3% in MutS. The results suggest that the significance of pathways may have system specific factors involved. Furthermore, the possible existence of false positives for contiguous pathways implies that signaling could be occurring via alternate routes including those consistent with the energetic landscape model.

Predicting 3D structure and stability of RNA pseudoknots in monovalent and divalent ion solutions.

PubMed

Shi, Ya-Zhou; Jin, Lei; Feng, Chen-Jie; Tan, Ya-Lan; Tan, Zhi-Jie

2018-06-01

RNA pseudoknots are a kind of minimal RNA tertiary structural motifs, and their three-dimensional (3D) structures and stability play essential roles in a variety of biological functions. Therefore, to predict 3D structures and stability of RNA pseudoknots is essential for understanding their functions. In the work, we employed our previously developed coarse-grained model with implicit salt to make extensive predictions and comprehensive analyses on the 3D structures and stability for RNA pseudoknots in monovalent/divalent ion solutions. The comparisons with available experimental data show that our model can successfully predict the 3D structures of RNA pseudoknots from their sequences, and can also make reliable predictions for the stability of RNA pseudoknots with different lengths and sequences over a wide range of monovalent/divalent ion concentrations. Furthermore, we made comprehensive analyses on the unfolding pathway for various RNA pseudoknots in ion solutions. Our analyses for extensive pseudokonts and the wide range of monovalent/divalent ion concentrations verify that the unfolding pathway of RNA pseudoknots is mainly dependent on the relative stability of unfolded intermediate states, and show that the unfolding pathway of RNA pseudoknots can be significantly modulated by their sequences and solution ion conditions.

Route prediction model of infectious diseases for 2018 Winter Olympics in Korea

NASA Astrophysics Data System (ADS)

Kim, Eungyeong; Lee, Seok; Byun, Young Tae; Kim, Jae Hun; Lee, Hyuk-jae; Lee, Taikjin

2014-03-01

There are many types of respiratory infectious diseases caused by germs, virus, mycetes and parasites. Researchers recently have tried to develop mathematical models to predict the epidemic of infectious diseases. However, with the development of ground transportation system in modern society, the spread of infectious diseases became faster and more complicated in terms of the speed and the pathways. The route of infectious diseases during Vancouver Olympics was predicted based on the Susceptible-Infectious-Recovered (SIR) model. In this model only the air traffic as an essential factor for the intercity migration of infectious diseases was involved. Here, we propose a multi-city transmission model to predict the infection route during 2018 Winter Olympics in Korea based on the pre-existing SIR model. Various types of transportation system such as a train, a car, a bus, and an airplane for the interpersonal contact in both inter- and intra-city are considered. Simulation is performed with assumptions and scenarios based on realistic factors including demographic, transportation and diseases data in Korea. Finally, we analyze an economic profit and loss caused by the variation of the number of tourists during the Olympics.

Rate and extent of protein localization is controlled by peptide-binding domain association kinetics and morphology.

PubMed

Mills, Evan; Truong, Kevin

2009-06-01

Protein localization is an important regulatory mechanism in many cell signaling pathways such as cytoskeletal organization and genetic regulation. The specific mechanism of protein localization determines the kinetics and morphological constraints of protein translocation, and thus affects the rate and extent of localization. To investigate the affect of localization kinetics and morphology on protein localization, we designed a protein localization system based on Ca(2+)-calmodulin and Src homology 3 domain binding peptides that can translocate between specific localizations in response to a Ca(2+) signal. We used a stochastic biomolecular simulator to predict that such a protein localization system will exhibit slower and less complete translocations when the association kinetics of a binding domain and peptide are reduced. As well, we predicted that increasing the diffusion resistance by manipulating the morphology of the system would similarly impair translocation speed and completeness. We then constructed a network of synthetic fusion proteins and showed that these predictions could be qualitatively confirmed in vitro. This work provides a basis for explaining the different characteristics (rate and extent) of protein transport and localization in cells as a consequence of the kinetics and morphology of the transport mechanism.

Computer Simulation of Embryonic Systems: What can a ...

EPA Pesticide Factsheets

(1) Standard practice for assessing developmental toxicity is the observation of apical endpoints (intrauterine death, fetal growth retardation, structural malformations) in pregnant rats/rabbits following exposure during organogenesis. EPA’s computational toxicology research program (ToxCast) generated vast in vitro cellular and molecular effects data on >1858 chemicals in >600 high-throughput screening (HTS) assays. The diversity of assays has been increased for developmental toxicity with several HTS platforms, including the devTOX-quickPredict assay from Stemina Biomarker Discovery utilizing the human embryonic stem cell line (H9). Translating these HTS data into higher order-predictions of developmental toxicity is a significant challenge. Here, we address the application of computational systems models that recapitulate the kinematics of dynamical cell signaling networks (e.g., SHH, FGF, BMP, retinoids) in a CompuCell3D.org modeling environment. Examples include angiogenesis (angiodysplasia) and dysmorphogenesis. Being numerically responsive to perturbation, these models are amenable to data integration for systems Toxicology and Adverse Outcome Pathways (AOPs). The AOP simulation outputs predict potential phenotypes based on the in vitro HTS data ToxCast. A heuristic computational intelligence framework that recapitulates the kinematics of dynamical cell signaling networks in the embryo, together with the in vitro profiling data, produce quantitative pr

In silico Study of the Pharmacologic Properties and Cytotoxicity Pathways in Cancer Cells of Various Indolylquinone Analogues of Perezone.

PubMed

Escobedo-González, René; Vargas-Requena, Claudia Lucia; Moyers-Montoya, Edgar; Aceves-Hernández, Juan Manuel; Nicolás-Vázquez, María Inés; Miranda-Ruvalcaba, René

2017-06-25

Several indolylquinone analogues of perezone, a natural sesquiterpene quinone, were characterized in this work by theoretical methods. In addition, some physicochemical, toxicological and metabolic properties were predicted using bioinformatics software. The predicted physicochemical properties are in agreement with the solubility and cLogP values, the penetration across the cell membrane, and absorption values, as well as with a possible apoptosis-activated mechanism of cytotoxic action. The toxicological predictions suggest no mutagenic, tumorigenic or reproductive effects of the four target molecules. Complementarily, the results of a performed docking study show high scoring values and hydrogen bonding values in agreement with the cytotoxicity IC 50 value ranking, i.e: indolylmenadione > indolylperezone > indolylplumbagine > indolylisoperezone. Consequently, it is possible to suggest an appropriate apoptotic pathway for each compound. Finally, potential metabolic pathways of the molecules were proposed.

Prediction of type III secretion signals in genomes of gram-negative bacteria.

PubMed

Löwer, Martin; Schneider, Gisbert

2009-06-15

Pathogenic bacteria infecting both animals as well as plants use various mechanisms to transport virulence factors across their cell membranes and channel these proteins into the infected host cell. The type III secretion system represents such a mechanism. Proteins transported via this pathway ("effector proteins") have to be distinguished from all other proteins that are not exported from the bacterial cell. Although a special targeting signal at the N-terminal end of effector proteins has been proposed in literature its exact characteristics remain unknown. In this study, we demonstrate that the signals encoded in the sequences of type III secretion system effectors can be consistently recognized and predicted by machine learning techniques. Known protein effectors were compiled from the literature and sequence databases, and served as training data for artificial neural networks and support vector machine classifiers. Common sequence features were most pronounced in the first 30 amino acids of the effector sequences. Classification accuracy yielded a cross-validated Matthews correlation of 0.63 and allowed for genome-wide prediction of potential type III secretion system effectors in 705 proteobacterial genomes (12% predicted candidates protein), their chromosomes (11%) and plasmids (13%), as well as 213 Firmicute genomes (7%). We present a signal prediction method together with comprehensive survey of potential type III secretion system effectors extracted from 918 published bacterial genomes. Our study demonstrates that the analyzed signal features are common across a wide range of species, and provides a substantial basis for the identification of exported pathogenic proteins as targets for future therapeutic intervention. The prediction software is publicly accessible from our web server (www.modlab.org).

Identification of a 5‑lncRNA signature‑based risk scoring system for survival prediction in colorectal cancer.

PubMed

Gu, Liqiang; Yu, Jun; Wang, Qing; Xu, Bin; Ji, Liechen; Yu, Lin; Zhang, Xipeng; Cai, Hui

2018-05-03

The present study aimed to investigate potential prognostic long noncoding RNAs (lncRNAs) associated with colorectal cancer (CRC). An mRNA‑seq dataset obtained from The Cancer Genome Atlas was employed to identify the differentially expressed lncRNAs (DELs) between CRC patients with good and poor prognoses. Subsequently, univariate and multivariate Cox regression analyses were conducted to analyze the prognosis‑associated lncRNAs among all DELs. In addition, a risk scoring system was developed according to the expression levels of the prognostic lncRNAs, which was then applied to a training set and an independent testing set. Furthermore, the co‑expressed genes of prognostic lncRNAs were screened using a Multi‑Experiment Matrix online tool for construction of lncRNA‑gene networks. Finally, Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology (GO) function enrichment analyses were performed on genes in the lncRNA‑gene networks using KOBAS, GOATOOLS and ClusterProfiler. The present study identified 82 DELs, of which long intergenic nonprotein coding RNA 2159, RP11‑452L6.6, RP11‑894P9.1 and RP11‑69M1.6, and whey acidic protein four‑disulfide core domain 21 (WFDC21P) were reported to be independently associated with the prognosis of patients with CRC. A 5‑lncRNA signature‑based risk scoring system was developed, which may be used to classify patients into low‑ and high‑risk groups with significantly different recurrence‑free survival times in the training and testing sets (P<0.05). Co‑expressed genes of WFDC21P or RP11‑69M1.6 were utilized to construct the lncRNA‑gene networks. Genes in the networks were significantly enriched in 'tight junction', 'focal adhesion' and 'regulation of actin cytoskeleton' pathways, and numerous GO terms associated with 'reactive oxygen species metabolism' and 'nitric oxide metabolism'. The present study proposed a 5‑lncRNA signature‑based risk scoring system for predicting the prognosis of patients with CRC, and revealed the associated signaling pathways and biological processes. The results of the present study may help improve prognostic evaluation in clinical practice.

Pathways, Contributors, and Correlates of Functional Limitation across Specialties: Workshop Summary.

PubMed

Kritchevsky, Stephen B; Forman, Daniel E; Callahan, Kathryn; Ely, E Wesley; High, Kevin P; McFarland, Frances; Pérez-Stable, Eliseo J; Schmader, Kenneth; Studenski, Stephanie; Williams, John; Zieman, Susan; Guralnik, Jack M

2018-04-25

Traditional clinical care models focus on the measurement and normalization of individual organ systems and de-emphasize aspects of health related to the integration of physiologic systems. Measures of physical, cognitive and sensory, and psychosocial or emotional function predict important health outcomes like death and disability independently from the severity of a specific disease, cumulative co-morbidity, or disease severity measures. A growing number of clinical scientists in several subspecialties are exploring the utility of functional assessment to predict complication risk, indicate stress resistance, inform disease screening approaches and risk factor interpretation, and evaluate care. Because a substantial number of older adults in the community have some form of functional limitation, integrating functional assessment into clinical medicine could have a large impact. Although interest in functional implications for health and disease management is growing, the science underlying functional capacity, functional limitation, physical frailty, and functional metrics is often siloed among different clinicians and researchers, with fragmented concepts and methods. On August 25-26, 2016, participants at a trans-disciplinary workshop, supported by the National Institute on Aging and the John A. Hartford Foundation, explored what is known about the pathways, contributors, and correlates of physical, cognitive and sensory functional measures across conditions and disease states; considered social determinants and health disparities; identified knowledge gaps, and suggested priorities for future research. This paper summarizes those discussions.

Systematic profiling of Caenorhabditis elegans locomotive behaviors reveals additional components in G-protein Gαq signaling.

PubMed

Yu, Hui; Aleman-Meza, Boanerges; Gharib, Shahla; Labocha, Marta K; Cronin, Christopher J; Sternberg, Paul W; Zhong, Weiwei

2013-07-16

Genetic screens have been widely applied to uncover genetic mechanisms of movement disorders. However, most screens rely on human observations of qualitative differences. Here we demonstrate the application of an automatic imaging system to conduct a quantitative screen for genes regulating the locomotive behavior in Caenorhabditis elegans. Two hundred twenty-seven neuronal signaling genes with viable homozygous mutants were selected for this study. We tracked and recorded each animal for 4 min and analyzed over 4,400 animals of 239 genotypes to obtain a quantitative, 10-parameter behavioral profile for each genotype. We discovered 87 genes whose inactivation causes movement defects, including 50 genes that had never been associated with locomotive defects. Computational analysis of the high-content behavioral profiles predicted 370 genetic interactions among these genes. Network partition revealed several functional modules regulating locomotive behaviors, including sensory genes that detect environmental conditions, genes that function in multiple types of excitable cells, and genes in the signaling pathway of the G protein Gαq, a protein that is essential for animal life and behavior. We developed quantitative epistasis analysis methods to analyze the locomotive profiles and validated the prediction of the γ isoform of phospholipase C as a component in the Gαq pathway. These results provided a system-level understanding of how neuronal signaling genes coordinate locomotive behaviors. This study also demonstrated the power of quantitative approaches in genetic studies.

Limit of validity of Ostwald's rule of stages in a statistical mechanical model of crystallization.

PubMed

Hedges, Lester O; Whitelam, Stephen

2011-10-28

We have only rules of thumb with which to predict how a material will crystallize, chief among which is Ostwald's rule of stages. It states that the first phase to appear upon transformation of a parent phase is the one closest to it in free energy. Although sometimes upheld, the rule is without theoretical foundation and is not universally obeyed, highlighting the need for microscopic understanding of crystallization controls. Here we study in detail the crystallization pathways of a prototypical model of patchy particles. The range of crystallization pathways it exhibits is richer than can be predicted by Ostwald's rule, but a combination of simulation and analytic theory reveals clearly how these pathways are selected by microscopic parameters. Our results suggest strategies for controlling self-assembly pathways in simulation and experiment.

AOP: An R Package For Sufficient Causal Analysis in Pathway ...

EPA Pesticide Factsheets

Summary: How can I quickly find the key events in a pathway that I need to monitor to predict that a/an beneficial/adverse event/outcome will occur? This is a key question when using signaling pathways for drug/chemical screening in pharma-cology, toxicology and risk assessment. By identifying these sufficient causal key events, we have fewer events to monitor for a pathway, thereby decreasing assay costs and time, while maximizing the value of the information. I have developed the “aop” package which uses backdoor analysis of causal net-works to identify these minimal sets of key events that are suf-ficient for making causal predictions. Availability and Implementation: The source and binary are available online through the Bioconductor project (http://www.bioconductor.org/) as an R package titled “aop”. The R/Bioconductor package runs within the R statistical envi-ronment. The package has functions that can take pathways (as directed graphs) formatted as a Cytoscape JSON file as input, or pathways can be represented as directed graphs us-ing the R/Bioconductor “graph” package. The “aop” package has functions that can perform backdoor analysis to identify the minimal set of key events for making causal predictions.Contact: burgoon.lyle@epa.gov This paper describes an R/Bioconductor package that was developed to facilitate the identification of key events within an AOP that are the minimal set of sufficient key events that need to be tested/monit

Different Roles of Direct and Indirect Frontoparietal Pathways for Individual Working Memory Capacity.

PubMed

Ekman, Matthias; Fiebach, Christian J; Melzer, Corina; Tittgemeyer, Marc; Derrfuss, Jan

2016-03-09

The ability to temporarily store and manipulate information in working memory is a hallmark of human intelligence and differs considerably across individuals, but the structural brain correlates underlying these differences in working memory capacity (WMC) are only poorly understood. In two separate studies, diffusion MRI data and WMC scores were collected for 70 and 109 healthy individuals. Using a combination of probabilistic tractography and network analysis of the white matter tracts, we examined whether structural brain network properties were predictive of individual WMC. Converging evidence from both studies showed that lateral prefrontal cortex and posterior parietal cortex of high-capacity individuals are more densely connected compared with low-capacity individuals. Importantly, our network approach was further able to dissociate putative functional roles associated with two different pathways connecting frontal and parietal regions: a corticocortical pathway and a subcortical pathway. In Study 1, where participants were required to maintain and update working memory items, the connectivity of the direct and indirect pathway was predictive of WMC. In contrast, in Study 2, where participants were required to maintain working memory items without updating, only the connectivity of the direct pathway was predictive of individual WMC. Our results suggest an important dissociation in the circuitry connecting frontal and parietal regions, where direct frontoparietal connections might support storage and maintenance, whereas subcortically mediated connections support the flexible updating of working memory content. Copyright © 2016 the authors 0270-6474/16/362894-10$15.00/0.

Differentiating Human Multipotent Mesenchymal Stromal Cells Regulate microRNAs: Prediction of microRNA Regulation by PDGF During Osteogenesis

PubMed Central

Goff, Loyal A.; Boucher, Shayne; Ricupero, Christopher L.; Fenstermacher, Sara; Swerdel, Mavis; Chase, Lucas; Adams, Christopher; Chesnut, Jonathan; Lakshmipathy, Uma; Hart, Ronald P.

2009-01-01

Objective Human multipotent mesenchymal stromal cells (MSC) have the potential to differentiate into multiple cell types, although little is known about factors that control their fate. Differentiation-specific microRNAs may play a key role in stem cell self renewal and differentiation. We propose that specific intracellular signalling pathways modulate gene expression during differentiation by regulating microRNA expression. Methods Illumina mRNA and NCode microRNA expression analyses were performed on MSC and their differentiated progeny. A combination of bioinformatic prediction and pathway inhibition was used to identify microRNAs associated with PDGF signalling. Results The pattern of microRNA expression in MSC is distinct from that in pluripotent stem cells such as human embryonic stem cells. Specific populations of microRNAs are regulated in MSC during differentiation targeted towards specific cell types. Complementary mRNA expression analysis increases the pool of markers characteristic of MSC or differentiated progeny. To identify microRNA expression patterns affected by signalling pathways, we examined the PDGF pathway found to be regulated during osteogenesis by microarray studies. A set of microRNAs bioinformatically predicted to respond to PDGF signalling was experimentally confirmed by direct PDGF inhibition. Conclusion Our results demonstrate that a subset of microRNAs regulated during osteogenic differentiation of MSCs is responsive to perturbation of the PDGF pathway. This approach not only identifies characteristic classes of differentiation-specific mRNAs and microRNAs, but begins to link regulated molecules with specific cellular pathways. PMID:18657893

A systems-level approach for investigating organophosphorus pesticide toxicity.

PubMed

Zhu, Jingbo; Wang, Jing; Ding, Yan; Liu, Baoyue; Xiao, Wei

2018-03-01

The full understanding of the single and joint toxicity of a variety of organophosphorus (OP) pesticides is still unavailable, because of the extreme complex mechanism of action. This study established a systems-level approach based on systems toxicology to investigate OP pesticide toxicity by incorporating ADME/T properties, protein prediction, and network and pathway analysis. The results showed that most OP pesticides are highly toxic according to the ADME/T parameters, and can interact with significant receptor proteins to cooperatively lead to various diseases by the established OP pesticide -protein and protein-disease networks. Furthermore, the studies that multiple OP pesticides potentially act on the same receptor proteins and/or the functionally diverse proteins explained that multiple OP pesticides could mutually enhance toxicological synergy or additive on a molecular/systematic level. To the end, the integrated pathways revealed the mechanism of toxicity of the interaction of OP pesticides and elucidated the pathogenesis induced by OP pesticides. This study demonstrates a systems-level approach for investigating OP pesticide toxicity that can be further applied to risk assessments of various toxins, which is of significant interest to food security and environmental protection. Copyright © 2017 Elsevier Inc. All rights reserved.

Framework for computationally-predicted AOPs

EPA Science Inventory

Framework for computationally-predicted AOPs Given that there are a vast number of existing and new chemicals in the commercial pipeline, emphasis is placed on developing high throughput screening (HTS) methods for hazard prediction. Adverse Outcome Pathways (AOPs) represent a...

Crystallization by Particle Attachment in Synthetic, Biogenic, and Geologic Environments

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

De Yoreo, James J.; Gilbert, Pupa U.; Sommerdijk, Nico

Field and laboratory observations show that crystals commonly form by the addition and attachment of particles that range from multi-ion complexes to fully formed nanoparticles. These non-classical pathways to crystallization are diverse, in contrast to classical models that consider the addition of monomeric chemical species. We review progress toward understanding crystal growth by particle attachment processes and show that multiple pathways result from the interplay of free energy landscapes and reaction dynamics. Much remains unknown about the fundamental aspects; particularly the relationships between solution structure, interfacial forces, and particle motion. Developing a predictive description that connects molecular details to ensemblemore » behavior will require revisiting long-standing interpretations of crystal formation in synthetic systems and patterns of mineralization in natural environments.« less

Heritable temperament pathways to early callous-unemotional behaviour.

PubMed

Waller, Rebecca; Trentacosta, Christopher J; Shaw, Daniel S; Neiderhiser, Jenae M; Ganiban, Jody M; Reiss, David; Leve, Leslie D; Hyde, Luke W

2016-12-01

Early callous-unemotional behaviours identify children at risk for antisocial behaviour. Recent work suggests that the high heritability of callous-unemotional behaviours is qualified by interactions with positive parenting. To examine whether heritable temperament dimensions of fearlessness and low affiliative behaviour are associated with early callous-unemotional behaviours and whether parenting moderates these associations. Using an adoption sample (n = 561), we examined pathways from biological mother self-reported fearlessness and affiliative behaviour to child callous-unemotional behaviours via observed child fearlessness and affiliative behaviour, and whether adoptive parent observed positive parenting moderated pathways. Biological mother fearlessness predicted child callous-unemotional behaviours via earlier child fearlessness. Biological mother low affiliative behaviour predicted child callous-unemotional behaviours, although not via child affiliative behaviours. Adoptive mother positive parenting moderated the fearlessness to callous-unemotional behaviour pathway. Heritable fearlessness and low interpersonal affiliation traits contribute to the development of callous-unemotional behaviours. Positive parenting can buffer these risky pathways. © The Royal College of Psychiatrists 2016.

Heritable temperament pathways to early callous–unemotional behaviour

PubMed Central

Waller, Rebecca; Trentacosta, Christopher J.; Shaw, Daniel S.; Neiderhiser, Jenae M.; Ganiban, Jody M.; Reiss, David; Leve, Leslie D.; Hyde, Luke W.

2016-01-01

Background Early callous–unemotional behaviours identify children at risk for antisocial behaviour. Recent work suggests that the high heritability of callous–unemotional behaviours is qualified by interactions with positive parenting. Aims To examine whether heritable temperament dimensions of fearlessness and low affiliative behaviour are associated with early callous–unemotional behaviours and whether parenting moderates these associations. Method Using an adoption sample (n = 561), we examined pathways from biological mother self-reported fearlessness and affiliative behaviour to child callous–unemotional behaviours via observed child fearlessness and affiliative behaviour, and whether adoptive parent observed positive parenting moderated pathways. Results Biological mother fearlessness predicted child callous–unemotional behaviours via earlier child fearlessness. Biological mother low affiliative behaviour predicted child callous–unemotional behaviours, although not via child affiliative behaviours. Adoptive mother positive parenting moderated the fearlessness to callous–unemotional behaviour pathway. Conclusions Heritable fearlessness and low interpersonal affiliation traits contribute to the development of callous–unemotional behaviours. Positive parenting can buffer these risky pathways. PMID:27765772

A Systems Biology Approach Uncovers Cellular Strategies Used by Methylobacterium extorquens AM1 During the Switch from Multi- to Single-Carbon Growth

PubMed Central

Skovran, Elizabeth; Crowther, Gregory J.; Guo, Xiaofeng; Yang, Song; Lidstrom, Mary E.

2010-01-01

Background When organisms experience environmental change, how does their metabolic network reset and adapt to the new condition? Methylobacterium extorquens is a bacterium capable of growth on both multi- and single-carbon compounds. These different modes of growth utilize dramatically different central metabolic pathways with limited pathway overlap. Methodology/Principal Findings This study focused on the mechanisms of metabolic adaptation occurring during the transition from succinate growth (predicted to be energy-limited) to methanol growth (predicted to be reducing-power-limited), analyzing changes in carbon flux, gene expression, metabolites and enzymatic activities over time. Initially, cells experienced metabolic imbalance with excretion of metabolites, changes in nucleotide levels and cessation of cell growth. Though assimilatory pathways were induced rapidly, a transient block in carbon flow to biomass synthesis occurred, and enzymatic assays suggested methylene tetrahydrofolate dehydrogenase as one control point. This “downstream priming” mechanism ensures that significant carbon flux through these pathways does not occur until they are fully induced, precluding the buildup of toxic intermediates. Most metabolites that are required for growth on both carbon sources did not change significantly, even though transcripts and enzymatic activities required for their production changed radically, underscoring the concept of metabolic setpoints. Conclusions/Significance This multi-level approach has resulted in new insights into the metabolic strategies carried out to effect this shift between two dramatically different modes of growth and identified a number of potential flux control and regulatory check points as a further step toward understanding metabolic adaptation and the cellular strategies employed to maintain metabolic setpoints. PMID:21124828

Automated genomic context analysis and experimental validation platform for discovery of prokaryote transcriptional regulator functions

DOE PAGES

Martí-Arbona, Ricardo; Mu, Fangping; Nowak-Lovato, Kristy L.; ...

2014-12-18

In this study, the clustering of genes in a pathway and the co-location of functionally related genes is widely recognized in prokaryotes. We used these characteristics to predict the metabolic involvement for a Transcriptional Regulator (TR) of unknown function, identified and confirmed its biological activity. software tool that identifies the genes encoded within a defined genomic neighborhood for the subject TR and its homologs was developed. The output lists of genes in the genetic neighborhoods, their annotated functions, the reactants/products, and identifies the metabolic pathway in which the encoded-proteins function. When a set of TRs of known function was analyzed,more » we observed that their homologs frequently had conserved genomic neighborhoods that co-located the metabolically related genes regulated by the subject TR. We postulate that TR effectors are metabolites in the identified pathways; indeed the known effectors were present. We analyzed Bxe_B3018 from Burkholderia xenovorans, a TR of unknown function and predicted that this TR was related to the glycine, threonine and serine degradation. We tested the binding of metabolites in these pathways and for those that bound, their ability to modulate TR binding to its specific DNA operator sequence. Using rtPCR, we confirmed that methylglyoxal was an effector of Bxe_3018. These studies provide the proof of concept and validation of a systematic approach to the discovery of the biological activity for proteins of unknown function, in this case a TR. Bxe_B3018 is a methylglyoxal responsive TR that controls the expression of an operon composed of a putative efflux system.« less

Self-catalyzed growth of S layers via an amorphous-to-crystalline transition limited by folding kinetics.

PubMed

Chung, Sungwook; Shin, Seong-Ho; Bertozzi, Carolyn R; De Yoreo, James J

2010-09-21

The importance of nonclassical, multistage crystallization pathways is increasingly evident from theoretical studies on colloidal systems and experimental investigations of proteins and biomineral phases. Although theoretical predictions suggest that proteins follow these pathways as a result of fluctuations that create unstable dense-liquid states, microscopic studies indicate these states are long-lived. Using in situ atomic force microscopy to follow 2D assembly of S-layer proteins on supported lipid bilayers, we have obtained a molecular-scale picture of multistage protein crystallization that reveals the importance of conformational transformations in directing the pathway of assembly. We find that monomers with an extended conformation first form a mobile adsorbed phase, from which they condense into amorphous clusters. These clusters undergo a phase transition through S-layer folding into crystalline clusters composed of compact tetramers. Growth then proceeds by formation of new tetramers exclusively at cluster edges, implying tetramer formation is autocatalytic. Analysis of the growth kinetics leads to a quantitative model in which tetramer creation is rate limiting. However, the estimated barrier is much smaller than expected for folding of isolated S-layer proteins, suggesting an energetic rationale for this multistage pathway.

Transcriptome analysis reveals candidate genes involved in luciferin metabolism in Luciola aquatilis (Coleoptera: Lampyridae)

PubMed Central

Vongsangnak, Wanwipa; Chumnanpuen, Pramote

2016-01-01

Bioluminescence, which living organisms such as fireflies emit light, has been studied extensively for over half a century. This intriguing reaction, having its origins in nature where glowing insects can signal things such as attraction or defense, is now widely used in biotechnology with applications of bioluminescence and chemiluminescence. Luciferase, a key enzyme in this reaction, has been well characterized; however, the enzymes involved in the biosynthetic pathway of its substrate, luciferin, remains unsolved at present. To elucidate the luciferin metabolism, we performed a de novo transcriptome analysis using larvae of the firefly species, Luciola aquatilis. Here, a comparative analysis is performed with the model coleopteran insect Tribolium casteneum to elucidate the metabolic pathways in L. aquatilis. Based on a template luciferin biosynthetic pathway, combined with a range of protein and pathway databases, and various prediction tools for functional annotation, the candidate genes, enzymes, and biochemical reactions involved in luciferin metabolism are proposed for L. aquatilis. The candidate gene expression is validated in the adult L. aquatilis using reverse transcription PCR (RT-PCR). This study provides useful information on the bio-production of luciferin in the firefly and will benefit to future applications of the valuable firefly bioluminescence system. PMID:27761329

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.

PubMed

Ferreira da Costa, Joana; Silva, David; Caamaño, Olga; Brea, José M; Loza, Maria Isabel; Munteanu, Cristian R; Pazos, Alejandro; García-Mera, Xerardo; González-Díaz, Humbert

2018-06-25

Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC 50 , EC 50 , K i , etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.

River Mixing in the Amazon as a Driver of Concentration-Discharge Relationships

NASA Astrophysics Data System (ADS)

Bouchez, Julien; Moquet, Jean-Sébastien; Espinoza, Jhan Carlo; Martinez, Jean-Michel; Guyot, Jean-Loup; Lagane, Christelle; Filizola, Naziano; Noriega, Luis; Hidalgo Sanchez, Liz; Pombosa, Rodrigo

2017-11-01

Large hydrological systems aggregate compositionally different waters derived from a variety of pathways. In the case of continental-scale rivers, such aggregation occurs noticeably at confluences between tributaries. Here we explore how such aggregation can affect solute concentration-discharge (C-Q) relationships and thus obscure the message carried by these relationships in terms of weathering properties of the Critical Zone. We build up a simple model for tributary mixing to predict the behavior of C-Q relationships during aggregation. We test a set of predictions made in the context of the largest world's river, the Amazon. In particular, we predict that the C-Q relationships of the rivers draining heterogeneous catchments should be the most "dilutional" and should display the widest hysteresis loops. To check these predictions, we compute 10 day-periodicity time series of Q and major solute (Si, Ca2+, Mg2+, K+, Na+, Cl-, SO42-) C and fluxes (F) for 13 gauging stations located throughout the Amazon basin. In agreement with the model predictions, C-Q relationships of most solutes shift from a fairly "chemostatic" behavior (nearly constant C) at the Andean mountain front and in pure lowland areas, to more "dilutional" patterns (negative C-Q relationship) toward the system mouth. More prominent C-Q hysteresis loops are also observed at the most downstream stations. Altogether, this study suggests that mixing of water and solutes between different flowpaths exerts a strong control on C-Q relationships of large-scale hydrological systems.

PrePhyloPro: phylogenetic profile-based prediction of whole proteome linkages

PubMed Central

Niu, Yulong; Liu, Chengcheng; Moghimyfiroozabad, Shayan; Yang, Yi

2017-01-01

Direct and indirect functional links between proteins as well as their interactions as part of larger protein complexes or common signaling pathways may be predicted by analyzing the correlation of their evolutionary patterns. Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at http://prephylopro.org/phyloprofile/. PMID:28875072

AOP: An R Package For Sufficient Causal Analysis in Pathway-based Screening of Drugs and Chemicals for Adversity

EPA Science Inventory

Summary: How can I quickly find the key events in a pathway that I need to monitor to predict that a/an beneficial/adverse event/outcome will occur? This is a key question when using signaling pathways for drug/chemical screening in pharma-cology, toxicology and risk assessment. ...

Structural characterization of the PTS IIA and IIB proteins associated with pneumococcal fucose utilization.

PubMed

Higgins, Melanie A; Hamilton, Aileen M; Boraston, Alisdair B

2017-05-01

Streptococcus pneumoniae harbors a significant number of transporters, including phosphotransferase (PTS) systems, allowing the bacterium to utilize a number of different carbohydrates for metabolic and other purposes. The genes encoding for one PTS transport system in particular (EII fuc ) are found within a fucose utilization operon in S. pneumoniae TIGR4. Here, we report the three-dimensional structures of IIA fuc and IIB fuc providing evidence that this PTS system belongs to the EII man family. Additionally, the predicted metabolic pathway for this distinctive fucose utilization system suggests that EII fuc transports the H-disaccharide blood group antigen, which would represent a novel PTS transporter specificity. Proteins 2017; 85:963-968. © 2016 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Multiscale modeling of mucosal immune responses

PubMed Central

2015-01-01

Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM. Background Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation. Implementation Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed. Conclusion We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation. PMID:26329787

Multiscale modeling of mucosal immune responses.

PubMed

Mei, Yongguo; Abedi, Vida; Carbo, Adria; Zhang, Xiaoying; Lu, Pinyi; Philipson, Casandra; Hontecillas, Raquel; Hoops, Stefan; Liles, Nathan; Bassaganya-Riera, Josep

2015-01-01

Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation. Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed. We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation.Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM.

Exploring the Conformational Transitions of Biomolecular Systems Using a Simple Two-State Anisotropic Network Model

PubMed Central

Jo, Sunhwan; Bahar, Ivet; Roux, Benoît

2014-01-01

Biomolecular conformational transitions are essential to biological functions. Most experimental methods report on the long-lived functional states of biomolecules, but information about the transition pathways between these stable states is generally scarce. Such transitions involve short-lived conformational states that are difficult to detect experimentally. For this reason, computational methods are needed to produce plausible hypothetical transition pathways that can then be probed experimentally. Here we propose a simple and computationally efficient method, called ANMPathway, for constructing a physically reasonable pathway between two endpoints of a conformational transition. We adopt a coarse-grained representation of the protein and construct a two-state potential by combining two elastic network models (ENMs) representative of the experimental structures resolved for the endpoints. The two-state potential has a cusp hypersurface in the configuration space where the energies from both the ENMs are equal. We first search for the minimum energy structure on the cusp hypersurface and then treat it as the transition state. The continuous pathway is subsequently constructed by following the steepest descent energy minimization trajectories starting from the transition state on each side of the cusp hypersurface. Application to several systems of broad biological interest such as adenylate kinase, ATP-driven calcium pump SERCA, leucine transporter and glutamate transporter shows that ANMPathway yields results in good agreement with those from other similar methods and with data obtained from all-atom molecular dynamics simulations, in support of the utility of this simple and efficient approach. Notably the method provides experimentally testable predictions, including the formation of non-native contacts during the transition which we were able to detect in two of the systems we studied. An open-access web server has been created to deliver ANMPathway results. PMID:24699246

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction

PubMed Central

Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han

2015-01-01

Objective Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Methods Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Results Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Conclusions Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. PMID:25002459

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.

PubMed

Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han

2015-01-01

Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology.

PubMed

Wittwehr, Clemens; Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M E Bette; Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M; Whelan, Maurice

2017-02-01

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment. © The Author 2016. Published by Oxford University Press on behalf of the Society of Toxicology.

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

Karp, Peter D.

Pathway Tools is a systems-biology software package written by SRI International (SRI) that produces Pathway/Genome Databases (PGDBs) for organisms with a sequenced genome. Pathway Tools also provides a wide range of capabilities for analyzing predicted metabolic networks and user-generated omics data. More than 5,000 academic, industrial, and government groups have licensed Pathway Tools. This user community includes researchers at all three DOE bioenergy centers, as well as academic and industrial metabolic engineering (ME) groups. An integral part of the Pathway Tools software is MetaCyc, a large, multiorganism database of metabolic pathways and enzymes that SRI and its academic collaborators manuallymore » curate. This project included two main goals: I. Enhance the MetaCyc content of bioenergy-related enzymes and pathways. II. Develop computational tools for engineering metabolic pathways that satisfy specified design goals, in particular for bioenergy-related pathways. In part I, SRI proposed to significantly expand the coverage of bioenergy-related metabolic information in MetaCyc, followed by the generation of organism-specific PGDBs for all energy-relevant organisms sequenced at the DOE Joint Genome Institute (JGI). Part I objectives included: 1: Expand the content of MetaCyc to include bioenergy-related enzymes and pathways. 2: Enhance the Pathway Tools software to enable display of complex polymer degradation processes. 3: Create new PGDBs for the energy-related organisms sequenced by JGI, update existing PGDBs with new MetaCyc content, and make these data available to JBEI via the BioCyc website. In part II, SRI proposed to develop an efficient computational tool for the engineering of metabolic pathways. Part II objectives included: 4: Develop computational tools for generating metabolic pathways that satisfy specified design goals, enabling users to specify parameters such as starting and ending compounds, and preferred or disallowed intermediate compounds. The pathways were to be generated using metabolic reactions from a reference database (DB). 5: Develop computational tools for ranking the pathways generated in objective (4) according to their optimality. The ranking criteria include stoichiometric yield, the number and cost of additional inputs and the cofactor compounds required by the pathway, pathway length, and pathway energetics. 6: Develop tools for visualizing generated pathways to facilitate the evaluation of a large space of generated pathways.« less

A Method of Accounting for Enzyme Costs in Flux Balance Analysis Reveals Alternative Pathways and Metabolite Stores in an Illuminated Arabidopsis Leaf.

PubMed

Cheung, C Y Maurice; Ratcliffe, R George; Sweetlove, Lee J

2015-11-01

Flux balance analysis of plant metabolism is an established method for predicting metabolic flux phenotypes and for exploring the way in which the plant metabolic network delivers specific outcomes in different cell types, tissues, and temporal phases. A recurring theme is the need to explore the flexibility of the network in meeting its objectives and, in particular, to establish the extent to which alternative pathways can contribute to achieving specific outcomes. Unfortunately, predictions from conventional flux balance analysis minimize the simultaneous operation of alternative pathways, but by introducing flux-weighting factors to allow for the variable intrinsic cost of supporting each flux, it is possible to activate different pathways in individual simulations and, thus, to explore alternative pathways by averaging thousands of simulations. This new method has been applied to a diel genome-scale model of Arabidopsis (Arabidopsis thaliana) leaf metabolism to explore the flexibility of the network in meeting the metabolic requirements of the leaf in the light. This identified alternative flux modes in the Calvin-Benson cycle revealed the potential for alternative transitory carbon stores in leaves and led to predictions about the light-dependent contribution of alternative electron flow pathways and futile cycles in energy rebalancing. Notable features of the analysis include the light-dependent tradeoff between the use of carbohydrates and four-carbon organic acids as transitory storage forms and the way in which multiple pathways for the consumption of ATP and NADPH can contribute to the balancing of the requirements of photosynthetic metabolism with the energy available from photon capture. © 2015 American Society of Plant Biologists. All Rights Reserved.

Biological interpretation of genome-wide association studies using predicted gene functions.

PubMed

Pers, Tune H; Karjalainen, Juha M; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R; Yang, Jian; Lui, Julian C; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S N; Hirschhorn, Joel N; Franke, Lude

2015-01-19

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

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

PubMed

Imam, Saheed; Schäuble, Sascha; Valenzuela, Jacob; López García de Lomana, Adrián; Carter, Warren; Price, Nathan D; Baliga, Nitin S

2015-12-01

Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems-level understanding of their metabolism, which can be fundamentally achieved through large-scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome-scale metabolic model for the widely-studied microalga, Chlamydomonas reinhardtii. The model, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 encompasses a broad range of metabolic functions encoded across the nuclear, chloroplast and mitochondrial genomes accounting for 1355 genes (1460 transcripts), 2394 and 1133 metabolites. We found improved performance over the previous metabolic model based on comparisons of predictive accuracy across 306 phenotypes (from 81 mutants), lipid yield analysis and growth rates derived from chemostat-grown cells (under three conditions). Measurement of macronutrient uptake revealed carbon and phosphate to be good predictors of growth rate, while nitrogen consumption appeared to be in excess. We analyzed high-resolution time series transcriptomics data using iCre1355 to uncover dynamic pathway-level changes that occur in response to nitrogen starvation and changes in light intensity. This approach enabled accurate prediction of growth rates, the cessation of growth and accumulation of triacylglycerols during nitrogen starvation, and the temporal response of different growth-associated pathways to increased light intensity. Thus, iCre1355 represents an experimentally validated genome-scale reconstruction of C. reinhardtii metabolism that should serve as a useful resource for studying the metabolic processes of this and related microalgae. © 2015 The Authors The Plant Journal © 2015 John Wiley & Sons Ltd.

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

DOE PAGES

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

2015-10-20

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

A Computational Model Predicting Disruption of Blood Vessel Development

PubMed Central

Kleinstreuer, Nicole; Dix, David; Rountree, Michael; Baker, Nancy; Sipes, Nisha; Reif, David; Spencer, Richard; Knudsen, Thomas

2013-01-01

Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis) and remodeling (angiogenesis) come from a variety of biological pathways linked to endothelial cell (EC) behavior, extracellular matrix (ECM) remodeling and the local generation of chemokines and growth factors. Simulating these interactions at a systems level requires sufficient biological detail about the relevant molecular pathways and associated cellular behaviors, and tractable computational models that offset mathematical and biological complexity. Here, we describe a novel multicellular agent-based model of vasculogenesis using the CompuCell3D (http://www.compucell3d.org/) modeling environment supplemented with semi-automatic knowledgebase creation. The model incorporates vascular endothelial growth factor signals, pro- and anti-angiogenic inflammatory chemokine signals, and the plasminogen activating system of enzymes and proteases linked to ECM interactions, to simulate nascent EC organization, growth and remodeling. The model was shown to recapitulate stereotypical capillary plexus formation and structural emergence of non-coded cellular behaviors, such as a heterologous bridging phenomenon linking endothelial tip cells together during formation of polygonal endothelial cords. Molecular targets in the computational model were mapped to signatures of vascular disruption derived from in vitro chemical profiling using the EPA's ToxCast high-throughput screening (HTS) dataset. Simulating the HTS data with the cell-agent based model of vascular development predicted adverse effects of a reference anti-angiogenic thalidomide analog, 5HPP-33, on in vitro angiogenesis with respect to both concentration-response and morphological consequences. These findings support the utility of cell agent-based models for simulating a morphogenetic series of events and for the first time demonstrate the applicability of these models for predictive toxicology. PMID:23592958

Endocrine disrupting chemicals in fish: developing exposure indicators and predictive models of effects based on mechanism of action.

PubMed

Ankley, Gerald T; Bencic, David C; Breen, Michael S; Collette, Timothy W; Conolly, Rory B; Denslow, Nancy D; Edwards, Stephen W; Ekman, Drew R; Garcia-Reyero, Natalia; Jensen, Kathleen M; Lazorchak, James M; Martinović, Dalma; Miller, David H; Perkins, Edward J; Orlando, Edward F; Villeneuve, Daniel L; Wang, Rong-Lin; Watanabe, Karen H

2009-05-05

Knowledge of possible toxic mechanisms (or modes) of action (MOA) of chemicals can provide valuable insights as to appropriate methods for assessing exposure and effects, thereby reducing uncertainties related to extrapolation across species, endpoints and chemical structure. However, MOA-based testing seldom has been used for assessing the ecological risk of chemicals. This is in part because past regulatory mandates have focused more on adverse effects of chemicals (reductions in survival, growth or reproduction) than the pathways through which these effects are elicited. A recent departure from this involves endocrine-disrupting chemicals (EDCs), where there is a need to understand both MOA and adverse outcomes. To achieve this understanding, advances in predictive approaches are required whereby mechanistic changes caused by chemicals at the molecular level can be translated into apical responses meaningful to ecological risk assessment. In this paper we provide an overview and illustrative results from a large, integrated project that assesses the effects of EDCs on two small fish models, the fathead minnow (Pimephales promelas) and zebrafish (Danio rerio). For this work a systems-based approach is being used to delineate toxicity pathways for 12 model EDCs with different known or hypothesized toxic MOA. The studies employ a combination of state-of-the-art genomic (transcriptomic, proteomic, metabolomic), bioinformatic and modeling approaches, in conjunction with whole animal testing, to develop response linkages across biological levels of organization. This understanding forms the basis for predictive approaches for species, endpoint and chemical extrapolation. Although our project is focused specifically on EDCs in fish, we believe that the basic conceptual approach has utility for systematically assessing exposure and effects of chemicals with other MOA across a variety of biological systems.

Systemic Inflammatory Response Syndrome After Major Abdominal Surgery Predicted by Early Upregulation of TLR4 and TLR5.

PubMed

Lahiri, Rajiv; Derwa, Yannick; Bashir, Zora; Giles, Edward; Torrance, Hew D T; Owen, Helen C; O'Dwyer, Michael J; O'Brien, Alastair; Stagg, Andrew J; Bhattacharya, Satyajit; Foster, Graham R; Alazawi, William

2016-05-01

To study innate immune pathways in patients undergoing hepatopancreaticobiliary surgery to understand mechanisms leading to enhanced inflammatory responses and identifying biomarkers of adverse clinical consequences. Patients undergoing major abdominal surgery are at risk of life-threatening systemic inflammatory response syndrome (SIRS) and sepsis. Early identification of at-risk patients would allow tailored postoperative care and improve survival. Two separate cohorts of patients undergoing major hepatopancreaticobiliary surgery were studied (combined n = 69). Bloods were taken preoperatively, on day 1 and day 2 postoperatively. Peripheral blood mononuclear cells and serum were separated and immune phenotype and function assessed ex vivo. Early innate immune dysfunction was evident in 12 patients who subsequently developed SIRS (postoperative day 6) compared with 27 who did not, when no clinical evidence of SIRS was apparent (preoperatively or days 1 and 2). Serum interleukin (IL)-6 concentration and monocyte Toll-like receptor (TLR)/NF-κB/IL-6 functional pathways were significantly upregulated and overactive in patients who developed SIRS (P < 0.0001). Interferon α-mediated STAT1 phosphorylation was higher preoperatively in patients who developed SIRS. Increased TLR4 and TLR5 gene expression in whole blood was demonstrated in a separate validation cohort of 30 patients undergoing similar surgery. Expression of TLR4/5 on monocytes, particularly intermediate CD14CD16 monocytes, on day 1 or 2 predicted SIRS with accuracy 0.89 to 1.0 (areas under receiver operator curves). These data demonstrate the mechanism for IL-6 overproduction in patients who develop postoperative SIRS and identify markers that predict patients at risk of SIRS 5 days before the onset of clinical signs.

Annotation of the Asian Citrus Psyllid Genome Reveals a Reduced Innate Immune System

PubMed Central

Arp, Alex P.; Hunter, Wayne B.; Pelz-Stelinski, Kirsten S.

2016-01-01

Citrus production worldwide is currently facing significant losses due to citrus greening disease, also known as Huanglongbing. The citrus greening bacteria, Candidatus Liberibacter asiaticus (CLas), is a persistent propagative pathogen transmitted by the Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Liviidae). Hemipterans characterized to date lack a number of insect immune genes, including those associated with the Imd pathway targeting Gram-negative bacteria. The D. citri draft genome was used to characterize the immune defense genes present in D. citri. Predicted mRNAs identified by screening the published D. citri annotated draft genome were manually searched using a custom database of immune genes from previously annotated insect genomes. Toll and JAK/STAT pathways, general defense genes Dual oxidase, Nitric oxide synthase, prophenoloxidase, and cellular immune defense genes were present in D. citri. In contrast, D. citri lacked genes for the Imd pathway, most antimicrobial peptides, 1,3-β-glucan recognition proteins (GNBPs), and complete peptidoglycan recognition proteins. These data suggest that D. citri has a reduced immune capability similar to that observed in A. pisum, P. humanus, and R. prolixus. The absence of immune system genes from the D. citri genome may facilitate CLas infections, and is possibly compensated for by their relationship with their microbial endosymbionts. PMID:27965582

A Methodology for Cancer Therapeutics by Systems Pharmacology-Based Analysis: A Case Study on Breast Cancer-Related Traditional Chinese Medicines.

PubMed

Li, Yan; Wang, Jinghui; Lin, Feng; Yang, Yinfeng; Chen, Su-Shing

2017-01-01

Breast cancer is the most common carcinoma in women. Comprehensive therapy on breast cancer including surgical operation, chemotherapy, radiotherapy, endocrinotherapy, etc. could help, but still has serious side effect and resistance against anticancer drugs. Complementary and alternative medicine (CAM) may avoid these problems, in which traditional Chinese medicine (TCM) has been highlighted. In this section, to analyze the mechanism through which TCM act on breast cancer, we have built a virtual model consisting of the construction of database, oral bioavailability prediction, drug-likeness evaluation, target prediction, network construction. The 20 commonly employed herbs for the treatment of breast cancer were used as a database to carry out research. As a result, 150 ingredient compounds were screened out as active molecules for the herbs, with 33 target proteins predicted. Our analysis indicates that these herbs 1) takes a 'Jun-Chen-Zuo-Shi" as rule of prescription, 2) which function mainly through perturbing three pathways involving the epidermal growth factor receptor, estrogen receptor, and inflammatory pathways, to 3) display the breast cancer-related anti-estrogen, anti-inflammatory, regulation of cell metabolism and proliferation activities. To sum it up, by providing a novel in silico strategy for investigation of the botanical drugs, this work may be of some help for understanding the action mechanisms of herbal medicines and for discovery of new drugs from plants.

Systemic Chromosome Instability Resulted in Colonic Transcriptomic Changes in Metabolic, Proliferation, and Stem Cell Regulators in Sgo1-/+ Mice.

PubMed

Rao, Chinthalapally V; Sanghera, Saira; Zhang, Yuting; Biddick, Laura; Reddy, Arun; Lightfoot, Stan; Janakiram, Naveena B; Mohammed, Altaf; Dai, Wei; Yamada, Hiroshi Y

2016-02-01

Colon cancer is the second most lethal cancer and is predicted to claim 49,700 lives in the United States this year. Chromosome instability (CIN) is observed in 80% to 90% of colon cancers and is thought to contribute to colon cancer progression and recurrence. To investigate the impact of CIN on colon cancer development, we developed shugoshin-1 (Sgo1) haploinsufficient (-/+) mice, an animal model focusing on mitotic error-induced CIN. In this study, we analyzed signature changes in the colonic transcriptome of Sgo1(-/+) mice to examine the molecular events underlying the altered carcinogenesis profiles in Sgo1(-/+) mice. We performed next-generation sequencing of normal-looking colonic mucosal tissue from mice treated with the carcinogen azoxymethane after 24 weeks. Transcriptome profiling revealed 349 hits with a 2-fold expression difference threshold (217 upregulated genes, 132 downregulated genes, P < 0.05). Pathway analyses indicated that the Sgo1-CIN tissues upregulated pathways known to be activated in colon cancer, including lipid metabolism (z score 4.47), Notch signaling (4.47), insulin signaling (3.81), and PPAR pathways (3.75), and downregulated pathways involved in immune responses including allograft rejection (6.69) and graft-versus-host disease (6.54). Notably, stem cell markers were also misregulated. Collectively, our findings demonstrate that systemic CIN results in transcriptomic changes in metabolism, proliferation, cell fate, and immune responses in the colon, which may foster a microenvironment amenable to cancer development. Therefore, therapeutic approaches focusing on these identified pathways may be valuable for colon cancer prevention and treatment. ©2016 American Association for Cancer Research.

A Combined Pathway and Regional Heritability Analysis Indicates NETRIN1 Pathway Is Associated With Major Depressive Disorder.

PubMed

Zeng, Yanni; Navarro, Pau; Fernandez-Pujals, Ana M; Hall, Lynsey S; Clarke, Toni-Kim; Thomson, Pippa A; Smith, Blair H; Hocking, Lynne J; Padmanabhan, Sandosh; Hayward, Caroline; MacIntyre, Donald J; Wray, Naomi R; Deary, Ian J; Porteous, David J; Haley, Chris S; McIntosh, Andrew M

2017-02-15

Genome-wide association studies (GWASs) of major depressive disorder (MDD) have identified few significant associations. Testing the aggregation of genetic variants, in particular biological pathways, may be more powerful. Regional heritability analysis can be used to detect genomic regions that contribute to disease risk. We integrated pathway analysis and multilevel regional heritability analyses in a pipeline designed to identify MDD-associated pathways. The pipeline was applied to two independent GWAS samples [Generation Scotland: The Scottish Family Health Study (GS:SFHS, N = 6455) and Psychiatric Genomics Consortium (PGC:MDD) (N = 18,759)]. A polygenic risk score (PRS) composed of single nucleotide polymorphisms from the pathway most consistently associated with MDD was created, and its accuracy to predict MDD, using area under the curve, logistic regression, and linear mixed model analyses, was tested. In GS:SFHS, four pathways were significantly associated with MDD, and two of these explained a significant amount of pathway-level regional heritability. In PGC:MDD, one pathway was significantly associated with MDD. Pathway-level regional heritability was significant in this pathway in one subset of PGC:MDD. For both samples the regional heritabilities were further localized to the gene and subregion levels. The NETRIN1 signaling pathway showed the most consistent association with MDD across the two samples. PRSs from this pathway showed competitive predictive accuracy compared with the whole-genome PRSs when using area under the curve statistics, logistic regression, and linear mixed model. These post-GWAS analyses highlight the value of combining multiple methods on multiple GWAS data for the identification of risk pathways for MDD. The NETRIN1 signaling pathway is identified as a candidate pathway for MDD and should be explored in further large population studies. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases.

PubMed

Berger, Seth I; Posner, Jeremy M; Ma'ayan, Avi

2007-10-04

In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

Conceptualising and mapping coupled estuary, coast and inner shelf sediment systems

NASA Astrophysics Data System (ADS)

French, Jon; Burningham, Helene; Thornhill, Gillian; Whitehouse, Richard; Nicholls, Robert J.

2016-03-01

Whilst understanding and predicting the effects of coastal change are primarily modelling problems, it is essential that we have appropriate conceptual frameworks for (1) the formalisation of existing knowledge; (2) the formulation of relevant scientific questions and management issues; (3) the implementation and deployment of predictive models; and (4) meaningful engagement involvement of stakeholders. Important progress continues to be made on the modelling front, but our conceptual frameworks have not evolved at a similar pace. Accordingly, this paper presents a new approach that re-engages with formal systems analysis and provides a mesoscale geomorphological context within which the coastal management challenges of the 21st century can be more effectively addressed. Coastal and Estuarine System Mapping (CESM) is founded on an ontology of landforms and human interventions that is partly inspired by the coastal tract concept and its temporal hierarchy of sediment sharing systems, but places greater emphasis on a hierarchy of spatial scales. This extends from coastal regions, through landform complexes, to landforms, the morphological adjustment of which is constrained by diverse forms of human intervention. Crucially, CESM integrates open coastal environments with estuaries and relevant portions of the inner shelf that have previously been treated separately. In contrast to the nesting of littoral cells that has hitherto framed shoreline management planning, CESM charts a complex web of interactions, of which a sub-set of mass transfer pathways defines the sediment budget, and a multitude of human interventions constrains natural landform behaviour. Conducted within a geospatial framework, CESM constitutes a form of knowledge formalisation in which disparate sources of information (published research, imagery, mapping, raw data etc.) are generalised into usable knowledge. The resulting system maps provide a framework for the development and application of predictive models and a repository for the outputs they generate (not least, flux estimates for the major sediment system pathways). They also permit comparative analyses of the relative abundance of landforms and the multi-scale interactions between them. Finally, they articulate scientific understanding of the structure and function of complex geomorphological systems in a way that is transparent and accessible to diverse stakeholder audiences. As our models of mesoscale landform evolution increase in sophistication, CESM provides a platform for a more participatory approach to their application to coastal and estuarine management.

1-CMDb: A Curated Database of Genomic Variations of the One-Carbon Metabolism Pathway.

PubMed

Bhat, Manoj K; Gadekar, Veerendra P; Jain, Aditya; Paul, Bobby; Rai, Padmalatha S; Satyamoorthy, Kapaettu

2017-01-01

The one-carbon metabolism pathway is vital in maintaining tissue homeostasis by driving the critical reactions of folate and methionine cycles. A myriad of genetic and epigenetic events mark the rate of reactions in a tissue-specific manner. Integration of these to predict and provide personalized health management requires robust computational tools that can process multiomics data. The DNA sequences that may determine the chain of biological events and the endpoint reactions within one-carbon metabolism genes remain to be comprehensively recorded. Hence, we designed the one-carbon metabolism database (1-CMDb) as a platform to interrogate its association with a host of human disorders. DNA sequence and network information of a total of 48 genes were extracted from a literature survey and KEGG pathway that are involved in the one-carbon folate-mediated pathway. The information generated, collected, and compiled for all these genes from the UCSC genome browser included the single nucleotide polymorphisms (SNPs), CpGs, copy number variations (CNVs), and miRNAs, and a comprehensive database was created. Furthermore, a significant correlation analysis was performed for SNPs in the pathway genes. Detailed data of SNPs, CNVs, CpG islands, and miRNAs for 48 folate pathway genes were compiled. The SNPs in CNVs (9670), CpGs (984), and miRNAs (14) were also compiled for all pathway genes. The SIFT score, the prediction and PolyPhen score, as well as the prediction for each of the SNPs were tabulated and represented for folate pathway genes. Also included in the database for folate pathway genes were the links to 124 various phenotypes and disease associations as reported in the literature and from publicly available information. A comprehensive database was generated consisting of genomic elements within and among SNPs, CNVs, CpGs, and miRNAs of one-carbon metabolism pathways to facilitate (a) single source of information and (b) integration into large-genome scale network analysis to be developed in the future by the scientific community. The database can be accessed at http://slsdb.manipal.edu/ocm/. © 2017 S. Karger AG, Basel.

Terrestrial and marine perspectives on modeling organic matter degradation pathways.

PubMed

Burd, Adrian B; Frey, Serita; Cabre, Anna; Ito, Takamitsu; Levine, Naomi M; Lønborg, Christian; Long, Matthew; Mauritz, Marguerite; Thomas, R Quinn; Stephens, Brandon M; Vanwalleghem, Tom; Zeng, Ning

2016-01-01

Organic matter (OM) plays a major role in both terrestrial and oceanic biogeochemical cycles. The amount of carbon stored in these systems is far greater than that of carbon dioxide (CO2 ) in the atmosphere, and annual fluxes of CO2 from these pools to the atmosphere exceed those from fossil fuel combustion. Understanding the processes that determine the fate of detrital material is important for predicting the effects that climate change will have on feedbacks to the global carbon cycle. However, Earth System Models (ESMs) typically utilize very simple formulations of processes affecting the mineralization and storage of detrital OM. Recent changes in our view of the nature of this material and the factors controlling its transformation have yet to find their way into models. In this review, we highlight the current understanding of the role and cycling of detrital OM in terrestrial and marine systems and examine how this pool of material is represented in ESMs. We include a discussion of the different mineralization pathways available as organic matter moves from soils, through inland waters to coastal systems and ultimately into open ocean environments. We argue that there is strong commonality between aspects of OM transformation in both terrestrial and marine systems and that our respective scientific communities would benefit from closer collaboration. © 2015 John Wiley & Sons Ltd.

Hedgehog signaling mediates adaptive variation in a dynamic functional system in the cichlid feeding apparatus.

PubMed

Hu, Yinan; Albertson, R Craig

2014-06-10

Adaptive variation in the craniofacial skeleton is a key component of resource specialization and habitat divergence in vertebrates, but the proximate genetic mechanisms that underlie complex patterns of craniofacial variation are largely unknown. Here we demonstrate that the Hedgehog (Hh) signaling pathway mediates widespread variation across a complex functional system that affects the kinematics of lower jaw depression--the opercular four-bar linkage apparatus--among Lake Malawi cichlids. By using a combined quantitative trait locus mapping and population genetics approach, we show that allelic variation in the Hh receptor, ptch1, affects the development of distinct bony elements in the head that represent two of three movable links in this functional system. The evolutionarily derived allele is found in species that feed from the water column, and is associated with shifts in anatomy that translate to a four-bar system capable of faster jaw rotation. Alternatively, the ancestral allele is found in species that feed on attached algae, and is associated with the development of a four-bar system that predicts slower jaw movement. Experimental manipulation of the Hh pathway during cichlid development recapitulates functionally salient natural variation in craniofacial geometry. In all, these results significantly extend our understanding of the mechanisms that fine-tune the craniofacial skeletal complex during adaptation to new foraging niches.

Identification of the active compounds and significant pathways of yinchenhao decoction based on network pharmacology

PubMed Central

Huang, Jihan; Cheung, Fan; Tan, Hor-Yue; Hong, Ming; Wang, Ning; Yang, Juan; Feng, Yibin; Zheng, Qingshan

2017-01-01

Yinchenhao decoction (YCHD) is a traditional Chinese medicine formulation, which has been widely used for the treatment of jaundice for 2,000 years. Currently, YCHD is used to treat various liver disorders and metabolic diseases, however its chemical/pharmacologic profiles remain to be elucidated. The present study identified the active compounds and significant pathways of YCHD based on network pharmacology. All of the chemical ingredients of YCHD were retrieved from the Traditional Chinese Medicine Systems Pharmacology database. Absorption, distribution, metabolism and excretion screening with oral bioavailability (OB) screening, drug-likeness (DL) and intestinal epithelial permeability (Caco-2) evaluation were applied to discover the bioactive compounds in YCHD. Following this, target prediction, pathway identification and network construction were employed to clarify the mechanism of action of YCHD. Following OB screening, and evaluation of DL and Caco-2, 34 compounds in YCHD were identified as potential active ingredients, of which 30 compounds were associated with 217 protein targets. A total of 31 significant pathways were obtained by performing enrichment analyses of 217 proteins using the JEPETTO 3.x plugin, and 16 classes of gene-associated diseases were revealed by performing enrichment analyses using Database for Annotation, Visualization and Integrated Discovery v6.7. The present study identified potential active compounds and significant pathways in YCHD. In addition, the mechanism of action of YCHD in the treatment of various diseases through multiple pathways was clarified. PMID:28791364

The Use of Signal-Transduction and Metabolic Pathways to Predict Human Disease Targets from Electric and Magnetic Fields Using in vitro Data in Human Cell Lines

PubMed Central

Parham, Fred; Portier, Christopher J.; Chang, Xiaoqing; Mevissen, Meike

2016-01-01

Using in vitro data in human cell lines, several research groups have investigated changes in gene expression in cellular systems following exposure to extremely low frequency (ELF) and radiofrequency (RF) electromagnetic fields (EMF). For ELF EMF, we obtained five studies with complete microarray data and three studies with only lists of significantly altered genes. Likewise, for RF EMF, we obtained 13 complete microarray datasets and 5 limited datasets. Plausible linkages between exposure to ELF and RF EMF and human diseases were identified using a three-step process: (a) linking genes associated with classes of human diseases to molecular pathways, (b) linking pathways to ELF and RF EMF microarray data, and (c) identifying associations between human disease and EMF exposures where the pathways are significantly similar. A total of 60 pathways were associated with human diseases, mostly focused on basic cellular functions like JAK–STAT signaling or metabolic functions like xenobiotic metabolism by cytochrome P450 enzymes. ELF EMF datasets were sporadically linked to human diseases, but no clear pattern emerged. Individual datasets showed some linkage to cancer, chemical dependency, metabolic disorders, and neurological disorders. RF EMF datasets were not strongly linked to any disorders but strongly linked to changes in several pathways. Based on these analyses, the most promising area for further research would be to focus on EMF and neurological function and disorders. PMID:27656641

Evaluation of protein-ligand affinity prediction using steered molecular dynamics simulations.

PubMed

Okimoto, Noriaki; Suenaga, Atsushi; Taiji, Makoto

2017-11-01

In computational drug design, ranking a series of compound analogs in a manner that is consistent with experimental affinities remains a challenge. In this study, we evaluated the prediction of protein-ligand binding affinities using steered molecular dynamics simulations. First, we investigated the appropriate conditions for accurate predictions in these simulations. A conic harmonic restraint was applied to the system for efficient sampling of work values on the ligand unbinding pathway. We found that pulling velocity significantly influenced affinity predictions, but that the number of collectable trajectories was less influential. We identified the appropriate pulling velocity and collectable trajectories for binding affinity predictions as 1.25 Å/ns and 100, respectively, and these parameters were used to evaluate three target proteins (FK506 binding protein, trypsin, and cyclin-dependent kinase 2). For these proteins using our parameters, the accuracy of affinity prediction was higher and more stable when Jarzynski's equality was employed compared with the second-order cumulant expansion equation of Jarzynski's equality. Our results showed that steered molecular dynamics simulations are effective for predicting the rank order of ligands; thus, they are a potential tool for compound selection in hit-to-lead and lead optimization processes.

Resting state functional connectivity of the ventral auditory pathway in musicians with absolute pitch.

PubMed

Kim, Seung-Goo; Knösche, Thomas R

2017-08-01

Absolute pitch (AP) is the ability to recognize pitch chroma of tonal sound without external references, providing a unique model of the human auditory system (Zatorre: Nat Neurosci 6 () 692-695). In a previous study (Kim and Knösche: Hum Brain Mapp () 3486-3501), we identified enhanced intracortical myelination in the right planum polare (PP) in musicians with AP, which could be a potential site for perceptional processing of pitch chroma information. We speculated that this area, which initiates the ventral auditory pathway, might be crucially involved in the perceptual stage of the AP process in the context of the "dual pathway hypothesis" that suggests the role of the ventral pathway in processing nonspatial information related to the identity of an auditory object (Rauschecker: Eur J Neurosci 41 () 579-585). To test our conjecture on the ventral pathway, we investigated resting state functional connectivity (RSFC) using functional magnetic resonance imaging (fMRI) from musicians with varying degrees of AP. Should our hypothesis be correct, RSFC via the ventral pathway is expected to be stronger in musicians with AP, whereas such group effect is not predicted in the RSFC via the dorsal pathway. In the current data, we found greater RSFC between the right PP and bilateral anteroventral auditory cortices in musicians with AP. In contrast, we did not find any group difference in the RSFC of the planum temporale (PT) between musicians with and without AP. We believe that these findings support our conjecture on the critical role of the ventral pathway in AP recognition. Hum Brain Mapp 38:3899-3916, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Metabolic pathway analysis and kinetic studies for production of nattokinase in Bacillus subtilis.

PubMed

Unrean, Pornkamol; Nguyen, Nhung H A

2013-01-01

We have constructed a reaction network model of Bacillus subtilis. The model was analyzed using a pathway analysis tool called elementary mode analysis (EMA). The analysis tool was used to study the network capabilities and the possible effects of altered culturing conditions on the production of a fibrinolytic enzyme, nattokinase (NK) by B. subtilis. Based on all existing metabolic pathways, the maximum theoretical yield for NK synthesis in B. subtilis under different substrates and oxygen availability was predicted and the optimal culturing condition for NK production was identified. To confirm model predictions, experiments were conducted by testing these culture conditions for their influence on NK activity. The optimal culturing conditions were then applied to batch fermentation, resulting in high NK activity. The EMA approach was also applied for engineering B. subtilis metabolism towards the most efficient pathway for NK synthesis by identifying target genes for deletion and overexpression that enable the cell to produce NK at the maximum theoretical yield. The consistency between experiments and model predictions proves the feasibility of EMA being used to rationally design culture conditions and genetic manipulations for the efficient production of desired products.

Differential Expression of MicroRNA and Predicted Targets in Pulmonary Sarcoidosis

PubMed Central

Crouser, Elliott D.; Julian, Mark W.; Crawford, Melissa; Shao, Guohong; Yu, Lianbo; Planck, Stephen R.; Rosenbaum, James T.; Nana-Sinkam, S. Patrick

2014-01-01

Background Recent studies show that various inflammatory diseases are regulated at the level of RNA translation by small non-coding RNAs, termed microRNAs (miRNAs). We sought to determine whether sarcoidosis tissues harbor a distinct pattern of miRNA expression and then considered their potential molecular targets. Methods and Results Genome-wide microarray analysis of miRNA expression in lung tissue and peripheral blood mononuclear cells (PBMCs) was performed and differentially expressed (DE)-miRNAs were then validated by real-time PCR. A distinct pattern of DE-miRNA expression was identified in both lung tissue and PBMCs of sarcoidosis patients. A subgroup of DE-miRNAs common to lung and lymph node tissues were predicted to target transforming growth factor (TGFβ)-regulated pathways. Likewise, the DE-miRNAs identified in PBMCs of sarcoidosis patients were predicted to target the TGFβ-regulated “wingless and integrase-1” (WNT) pathway. Conclusions This study is the first to profile miRNAs in sarcoidosis tissues and to consider their possible roles in disease pathogenesis. Our results suggest that miRNA regulate TGFβ and related WNT pathways in sarcoidosis tissues, pathways previously incriminated in the pathogenesis of sarcoidosis. PMID:22209793

Development of linear free energy relationships for aqueous phase radical-involved chemical reactions.

PubMed

Minakata, Daisuke; Mezyk, Stephen P; Jones, Jace W; Daws, Brittany R; Crittenden, John C

2014-12-02

Aqueous phase advanced oxidation processes (AOPs) produce hydroxyl radicals (HO•) which can completely oxidize electron rich organic compounds. The proper design and operation of AOPs require that we predict the formation and fate of the byproducts and their associated toxicity. Accordingly, there is a need to develop a first-principles kinetic model that can predict the dominant reaction pathways that potentially produce toxic byproducts. We have published some of our efforts on predicting the elementary reaction pathways and the HO• rate constants. Here we develop linear free energy relationships (LFERs) that predict the rate constants for aqueous phase radical reactions. The LFERs relate experimentally obtained kinetic rate constants to quantum mechanically calculated aqueous phase free energies of activation. The LFERs have been applied to 101 reactions, including (1) HO• addition to 15 aromatic compounds; (2) addition of molecular oxygen to 65 carbon-centered aliphatic and cyclohexadienyl radicals; (3) disproportionation of 10 peroxyl radicals, and (4) unimolecular decay of nine peroxyl radicals. The LFERs correlations predict the rate constants within a factor of 2 from the experimental values for HO• reactions and molecular oxygen addition, and a factor of 5 for peroxyl radical reactions. The LFERs and the elementary reaction pathways will enable us to predict the formation and initial fate of the byproducts in AOPs. Furthermore, our methodology can be applied to other environmental processes in which aqueous phase radical-involved reactions occur.

On the phase space structure of IP3 induced Ca2+ signalling and concepts for predictive modeling

NASA Astrophysics Data System (ADS)

Falcke, Martin; Moein, Mahsa; TilÅ«naitÄ--, Agne; Thul, Rüdiger; Skupin, Alexander

2018-04-01

The correspondence between mathematical structures and experimental systems is the basis of the generalizability of results found with specific systems and is the basis of the predictive power of theoretical physics. While physicists have confidence in this correspondence, it is less recognized in cellular biophysics. On the one hand, the complex organization of cellular dynamics involving a plethora of interacting molecules and the basic observation of cell variability seem to question its possibility. The practical difficulties of deriving the equations describing cellular behaviour from first principles support these doubts. On the other hand, ignoring such a correspondence would severely limit the possibility of predictive quantitative theory in biophysics. Additionally, the existence of functional modules (like pathways) across cell types suggests also the existence of mathematical structures with comparable universality. Only a few cellular systems have been sufficiently investigated in a variety of cell types to follow up these basic questions. IP3 induced Ca2+signalling is one of them, and the mathematical structure corresponding to it is subject of ongoing discussion. We review the system's general properties observed in a variety of cell types. They are captured by a reaction diffusion system. We discuss the phase space structure of its local dynamics. The spiking regime corresponds to noisy excitability. Models focussing on different aspects can be derived starting from this phase space structure. We discuss how the initial assumptions on the set of stochastic variables and phase space structure shape the predictions of parameter dependencies of the mathematical models resulting from the derivation.

Environmental Impact on Vascular Development Predicted by High-Throughput Screening

PubMed Central

Judson, Richard S.; Reif, David M.; Sipes, Nisha S.; Singh, Amar V.; Chandler, Kelly J.; DeWoskin, Rob; Dix, David J.; Kavlock, Robert J.; Knudsen, Thomas B.

2011-01-01

Background: Understanding health risks to embryonic development from exposure to environmental chemicals is a significant challenge given the diverse chemical landscape and paucity of data for most of these compounds. High-throughput screening (HTS) in the U.S. Environmental Protection Agency (EPA) ToxCast™ project provides vast data on an expanding chemical library currently consisting of > 1,000 unique compounds across > 500 in vitro assays in phase I (complete) and Phase II (under way). This public data set can be used to evaluate concentration-dependent effects on many diverse biological targets and build predictive models of prototypical toxicity pathways that can aid decision making for assessments of human developmental health and disease. Objective: We mined the ToxCast phase I data set to identify signatures for potential chemical disruption of blood vessel formation and remodeling. Methods: ToxCast phase I screened 309 chemicals using 467 HTS assays across nine assay technology platforms. The assays measured direct interactions between chemicals and molecular targets (receptors, enzymes), as well as downstream effects on reporter gene activity or cellular consequences. We ranked the chemicals according to individual vascular bioactivity score and visualized the ranking using ToxPi (Toxicological Priority Index) profiles. Results: Targets in inflammatory chemokine signaling, the vascular endothelial growth factor pathway, and the plasminogen-activating system were strongly perturbed by some chemicals, and we found positive correlations with developmental effects from the U.S. EPA ToxRefDB (Toxicological Reference Database) in vivo database containing prenatal rat and rabbit guideline studies. We observed distinctly different correlative patterns for chemicals with effects in rabbits versus rats, despite derivation of in vitro signatures based on human cells and cell-free biochemical targets, implying conservation but potentially differential contributions of developmental pathways among species. Follow-up analysis with antiangiogenic thalidomide analogs and additional in vitro vascular targets showed in vitro activity consistent with the most active environmental chemicals tested here. Conclusions: We predicted that blood vessel development is a target for environmental chemicals acting as putative vascular disruptor compounds (pVDCs) and identified potential species differences in sensitive vascular developmental pathways. PMID:21788198

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

PubMed Central

Ander, Bradley P.; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R.; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed. PMID:23844055

An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.

PubMed

Peng, Bin; Zhu, Dianwen; Ander, Bradley P; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.

Nanoparticle transport and delivery in a heterogeneous pulmonary vasculature.

PubMed

Sohrabi, Salman; Wang, Shunqiang; Tan, Jifu; Xu, Jiang; Yang, Jie; Liu, Yaling

2017-01-04

Quantitative understanding of nanoparticles delivery in a complex vascular networks is very challenging because it involves interplay of transport, hydrodynamic force, and multivalent interactions across different scales. Heterogeneous pulmonary network includes up to 16 generations of vessels in its arterial tree. Modeling the complete pulmonary vascular system in 3D is computationally unrealistic. To save computational cost, a model reconstructed from MRI scanned images is cut into an arbitrary pathway consisting of the upper 4-generations. The remaining generations are represented by an artificially rebuilt pathway. Physiological data such as branch information and connectivity matrix are used for geometry reconstruction. A lumped model is used to model the flow resistance of the branches that are cut off from the truncated pathway. Moreover, since the nanoparticle binding process is stochastic in nature, a binding probability function is used to simplify the carrier attachment and detachment processes. The stitched realistic and artificial geometries coupled with the lumped model at the unresolved outlets are used to resolve the flow field within the truncated arterial tree. Then, the biodistribution of 200nm, 700nm and 2µm particles at different vessel generations is studied. At the end, 0.2-0.5% nanocarrier deposition is predicted during one time passage of drug carriers through pulmonary vascular tree. Our truncated approach enabled us to efficiently model hemodynamics and accordingly particle distribution in a complex 3D vasculature providing a simple, yet efficient predictive tool to study drug delivery at organ level. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multiple Biomarker Panels for Early Detection of Breast Cancer in Peripheral Blood

PubMed Central

Zhang, Fan; Deng, Youping; Drabier, Renee

2013-01-01

Detecting breast cancer at early stages can be challenging. Traditional mammography and tissue microarray that have been studied for early breast cancer detection and prediction have many drawbacks. Therefore, there is a need for more reliable diagnostic tools for early detection of breast cancer due to a number of factors and challenges. In the paper, we presented a five-marker panel approach based on SVM for early detection of breast cancer in peripheral blood and show how to use SVM to model the classification and prediction problem of early detection of breast cancer in peripheral blood. We found that the five-marker panel can improve the prediction performance (area under curve) in the testing data set from 0.5826 to 0.7879. Further pathway analysis showed that the top four five-marker panels are associated with signaling, steroid hormones, metabolism, immune system, and hemostasis, which are consistent with previous findings. Our prediction model can serve as a general model for multibiomarker panel discovery in early detection of other cancers. PMID:24371830

Translational systems pharmacology‐based predictive assessment of drug‐induced cardiomyopathy

PubMed Central

Messinis, Dimitris E.; Melas, Ioannis N.; Hur, Junguk; Varshney, Navya; Alexopoulos, Leonidas G.

2018-01-01

Drug‐induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug's signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs' signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP‐augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave‐one‐out cross validation. The ILP‐constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin‐induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers. PMID:29341478

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

PubMed

Simm, Jaak; Klambauer, Günter; Arany, Adam; Steijaert, Marvin; Wegner, Jörg Kurt; Gustin, Emmanuel; Chupakhin, Vladimir; Chong, Yolanda T; Vialard, Jorge; Buijnsters, Peter; Velter, Ingrid; Vapirev, Alexander; Singh, Shantanu; Carpenter, Anne E; Wuyts, Roel; Hochreiter, Sepp; Moreau, Yves; Ceulemans, Hugo

2018-05-17

In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.

PubMed

Kell, Alexander J E; Yamins, Daniel L K; Shook, Erica N; Norman-Haignere, Sam V; McDermott, Josh H

2018-05-02

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy-primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems. Copyright © 2018 Elsevier Inc. All rights reserved.

Multiple biomarker panels for early detection of breast cancer in peripheral blood.

PubMed

Zhang, Fan; Deng, Youping; Drabier, Renee

2013-01-01

Detecting breast cancer at early stages can be challenging. Traditional mammography and tissue microarray that have been studied for early breast cancer detection and prediction have many drawbacks. Therefore, there is a need for more reliable diagnostic tools for early detection of breast cancer due to a number of factors and challenges. In the paper, we presented a five-marker panel approach based on SVM for early detection of breast cancer in peripheral blood and show how to use SVM to model the classification and prediction problem of early detection of breast cancer in peripheral blood. We found that the five-marker panel can improve the prediction performance (area under curve) in the testing data set from 0.5826 to 0.7879. Further pathway analysis showed that the top four five-marker panels are associated with signaling, steroid hormones, metabolism, immune system, and hemostasis, which are consistent with previous findings. Our prediction model can serve as a general model for multibiomarker panel discovery in early detection of other cancers.

Enhancing microbial production of biofuels by expanding microbial metabolic pathways.

PubMed

Yu, Ping; Chen, Xingge; Li, Peng

2017-09-01

Fatty acid, isoprenoid, and alcohol pathways have been successfully engineered to produce biofuels. By introducing three genes, atfA, adhE, and pdc, into Escherichia coli to expand fatty acid pathway, up to 1.28 g/L of fatty acid ethyl esters can be achieved. The isoprenoid pathway can be expanded to produce bisabolene with a high titer of 900 mg/L in Saccharomyces cerevisiae. Short- and long-chain alcohols can also be effectively biosynthesized by extending the carbon chain of ketoacids with an engineered "+1" alcohol pathway. Thus, it can be concluded that expanding microbial metabolic pathways has enormous potential for enhancing microbial production of biofuels for future industrial applications. However, some major challenges for microbial production of biofuels should be overcome to compete with traditional fossil fuels: lowering production costs, reducing the time required to construct genetic elements and to increase their predictability and reliability, and creating reusable parts with useful and predictable behavior. To address these challenges, several aspects should be further considered in future: mining and transformation of genetic elements related to metabolic pathways, assembling biofuel elements and coordinating their functions, enhancing the tolerance of host cells to biofuels, and creating modular subpathways that can be easily interconnected. © 2016 International Union of Biochemistry and Molecular Biology, Inc.

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

PubMed Central

Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

PubMed

Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

Fundamental Escherichia coli biochemical pathways for biomass and energy production: creation of overall flux states.

PubMed

Carlson, Ross; Srienc, Friedrich

2004-04-20

We have previously shown that the metabolism for most efficient cell growth can be realized by a combination of two types of elementary modes. One mode produces biomass while the second mode generates only energy. The identity of the four most efficient biomass and energy pathway pairs changes, depending on the degree of oxygen limitation. The identification of such pathway pairs for different growth conditions offers a pathway-based explanation of maintenance energy generation. For a given growth rate, experimental aerobic glucose consumption rates can be used to estimate the contribution of each pathway type to the overall metabolic flux pattern. All metabolic fluxes are then completely determined by the stoichiometries of involved pathways defining all nutrient consumption and metabolite secretion rates. We present here equations that permit computation of network fluxes on the basis of unique pathways for the case of optimal, glucose-limited Escherichia coli growth under varying levels of oxygen stress. Predicted glucose and oxygen uptake rates and some metabolite secretion rates are in remarkable agreement with experimental observations supporting the validity of the presented approach. The entire most efficient, steady-state, metabolic rate structure is explicitly defined by the developed equations without need for additional computer simulations. The approach should be generally useful for analyzing and interpreting genomic data by predicting concise, pathway-based metabolic rate structures. Copyright 2004 Wiley Periodicals, Inc.

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

PubMed

Voyle, Nicola; Keohane, Aoife; Newhouse, Stephen; Lunnon, Katie; Johnston, Caroline; Soininen, Hilkka; Kloszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon; Hodges, Angela; Kiddle, Steven; Dobson, Richard Jb

2016-01-01

Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer's disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust. This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts. Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis. Gene and pathway level models performed similarly to each other and to a model based on demographic information only. Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

Evaluation of two high through-put (HTP) androgen receptor based assays: Utility of data for prioritization for further testing versus prediction of adverse effects.

EPA Science Inventory

The androgen signaling pathway plays a critical role in sexual differentiation during development in mammals and is one of the better understood pathways in human development. Thus it was chosen as a model pathway to evaluate the potential of HTP in vitro assays as risk assessmen...

The Relationship between Vocational Interests, Self-Efficacy, and Achievement in the Prediction of Educational Pathways

ERIC Educational Resources Information Center

Patrick, Lyn; Care, Esther; Ainley, Mary

2011-01-01

The influence of vocational interest, self-efficacy beliefs, and academic achievement on choice of educational pathway is described for a cohort of Australian students. Participants were 189 students aged 14-15 years, who were considering either academic or applied learning pathways and subject choices for the final 3 years of secondary school.…

Genome-wide analysis of long non-coding RNAs and their role in postnatal porcine testis development.

PubMed

Weng, Bo; Ran, Maoliang; Chen, Bin; He, Changqing; Dong, Lianhua; Peng, Fuzhi

2017-10-01

A comprehensive and systematic understanding of the roles of lncRNAs in the postnatal development of the pig testis has still not been achieved. In the present study, we obtained more than one billion clean reads and identified 15,528 lncRNA transcripts; these transcripts included 5032 known and 10,496 novel porcine lncRNA transcripts and corresponded to 10,041 lncRNA genes. Pairwise comparisons identified 449 known and 324 novel lncRNAs that showed differential expression patterns. GO and KEGG pathway enrichment analyses revealed that the targeted genes were involved in metabolic pathways regulating testis development and spermatogenesis, such as the TGF-beta pathway, the PI3K-Akt pathway, the Wnt/β-catenin pathway, and the AMPK pathway. Using this information, we predicted some lncRNAs and coding gene pairs were predicted that may function in testis development and spermatogenesis; these are listed in detail. This study has provided the most comprehensive catalog to date of lncRNAs in the postnatal pig testis and will aid our understanding of their functional roles in testis development and spermatogenesis. Copyright © 2017. Published by Elsevier Inc.

Motor pathway convergence predicts syllable repertoire size in oscine birds

PubMed Central

Moore, Jordan M.; Székely, Tamás; Büki, József; DeVoogd, Timothy J.

2011-01-01

Behavioral specializations are frequently associated with expansions of the brain regions controlling them. This principle of proper mass spans sensory, motor, and cognitive abilities and has been observed in a wide variety of vertebrate species. Yet, it is unknown if this concept extrapolates to entire neural pathways or how selection on a behavioral capacity might otherwise shape circuit structure. We investigate these questions by comparing the songs and neuroanatomy of 49 species from 17 families of songbirds, which vary immensely in the number of unique song components they produce and possess a conserved neural network dedicated to this behavior. We find that syllable repertoire size is strongly related to the degree of song motor pathway convergence. Repertoire size is more accurately predicted by the number of neurons in higher motor areas relative to that in their downstream targets than by the overall number of neurons in the song motor pathway. Additionally, the convergence values along serial premotor and primary motor projections account for distinct portions of the behavioral variation. These findings suggest that selection on song has independently shaped different components of this hierarchical pathway, and they elucidate how changes in pathway structure could have underlain elaborations of this learned motor behavior. PMID:21918109

Validation and Interrogation of Differentially Expressed and Alternatively Spliced Genes in African American Prostate Cancer

DTIC Science & Technology

2016-10-01

These analyses have led to two submitted manuscripts. The first manuscript, “Variants of stemness -related genes predicted to regulate RNA splicing...and Table 1-3 at the end of this progress report. The second manuscript, “Single nucleotide polymorphisms of stemness pathway genes predicted to...cancer and support a contribution of the stemness pathway to prostate cancer patient outcome. Please see Figure 5-7 and Table 4-6 at the end of this

Biological interpretation of genome-wide association studies using predicted gene functions

PubMed Central

Pers, Tune H.; Karjalainen, Juha M.; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R.; Yang, Jian; Lui, Julian C.; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K.; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S.N.; Hirschhorn, Joel N.; Franke, Lude

2015-01-01

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes. PMID:25597830

Mechanism-based classification of PAH mixtures to predict carcinogenic potential

DOE PAGES

Tilton, Susan C.; Siddens, Lisbeth K.; Krueger, Sharon K.; ...

2015-04-22

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[ a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[ def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profilesmore » measured in skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. As a result, these data further provide a ‘source-to outcome’ model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.« less

Mechanism-Based Classification of PAH Mixtures to Predict Carcinogenic Potential.

PubMed

Tilton, Susan C; Siddens, Lisbeth K; Krueger, Sharon K; Larkin, Andrew J; Löhr, Christiane V; Williams, David E; Baird, William M; Waters, Katrina M

2015-07-01

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway-based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP, or environmental PAH mixtures (Mix 1-3) following a 2-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC > BaP = Mix2 = Mix3 > Mix1 = Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared with tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (P < .05) for DNA damage, apoptosis, response to chemical stimulus, and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. These data further provide a 'source-to-outcome' model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action-based risk assessment could be employed for environmental PAH mixtures. © The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Inferring interventional predictions from observational learning data.

PubMed

Meder, Bjorn; Hagmayer, York; Waldmann, Michael R

2008-02-01

Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann & Hagmayer, 2005). However, these studies were limited, since learning data were presented as tabulated data only, which may have turned the task more into a reasoning rather than a learning task. In two experiments, we therefore presented learners with trial-by-trial observational learning input referring to a complex causal model consisting of four events. To test the robustness of the capacity to derive correct observational and interventional inferences, we pitted causal order against the temporal order of learning events. The results show that people are, in principle, capable of deriving correct predictions after purely observational trial-by-trial learning, even with relatively complex causal models. However, conflicting temporal information can impair performance, particularly when the inferences require taking alternative causal pathways into account.

SalmonDB: a bioinformatics resource for Salmo salar and Oncorhynchus mykiss

PubMed Central

Di Génova, Alex; Aravena, Andrés; Zapata, Luis; González, Mauricio; Maass, Alejandro; Iturra, Patricia

2011-01-01

SalmonDB is a new multiorganism database containing EST sequences from Salmo salar, Oncorhynchus mykiss and the whole genome sequence of Danio rerio, Gasterosteus aculeatus, Tetraodon nigroviridis, Oryzias latipes and Takifugu rubripes, built with core components from GMOD project, GOPArc system and the BioMart project. The information provided by this resource includes Gene Ontology terms, metabolic pathways, SNP prediction, CDS prediction, orthologs prediction, several precalculated BLAST searches and domains. It also provides a BLAST server for matching user-provided sequences to any of the databases and an advanced query tool (BioMart) that allows easy browsing of EST databases with user-defined criteria. These tools make SalmonDB database a valuable resource for researchers searching for transcripts and genomic information regarding S. salar and other salmonid species. The database is expected to grow in the near feature, particularly with the S. salar genome sequencing project. Database URL: http://genomicasalmones.dim.uchile.cl/ PMID:22120661

SalmonDB: a bioinformatics resource for Salmo salar and Oncorhynchus mykiss.

PubMed

Di Génova, Alex; Aravena, Andrés; Zapata, Luis; González, Mauricio; Maass, Alejandro; Iturra, Patricia

2011-01-01

SalmonDB is a new multiorganism database containing EST sequences from Salmo salar, Oncorhynchus mykiss and the whole genome sequence of Danio rerio, Gasterosteus aculeatus, Tetraodon nigroviridis, Oryzias latipes and Takifugu rubripes, built with core components from GMOD project, GOPArc system and the BioMart project. The information provided by this resource includes Gene Ontology terms, metabolic pathways, SNP prediction, CDS prediction, orthologs prediction, several precalculated BLAST searches and domains. It also provides a BLAST server for matching user-provided sequences to any of the databases and an advanced query tool (BioMart) that allows easy browsing of EST databases with user-defined criteria. These tools make SalmonDB database a valuable resource for researchers searching for transcripts and genomic information regarding S. salar and other salmonid species. The database is expected to grow in the near feature, particularly with the S. salar genome sequencing project. Database URL: http://genomicasalmones.dim.uchile.cl/

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

PubMed

Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

2017-11-24

Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

Peak oil and health in low- and middle-income countries: impacts and potential responses.

PubMed

Winch, Peter; Stepnitz, Rebecca

2011-09-01

Peak oil refers to the predicted peak and subsequent decline in global production of petroleum products over the coming decades. We describe how peak oil will affect health, nutrition, and health systems in low- and middle-income countries along 5 pathways. The negative effects of peak oil on health and nutrition will be felt most acutely in the 58 low-income countries experiencing minimal or negative economic growth because of their patterns of sociopolitical, geographic, and economic vulnerability. The global health community needs to take additional steps to build resilience among the residents of low- and middle-income countries and maintain access to maternal and other health services in the face of predicted changes in availability and price of fossil fuels.

Resveratrol Interferes with an Early Step in the Fibrillization Pathway of Human Lysozyme and Modulates it towards Less-Toxic, Off-Pathway Aggregates.

PubMed

Kamal Zaidi, Fatima; Bhat, Rajiv

2018-01-18

The effect of resveratrol, a polyphenol in red wine, on the amyloid fibril formation of human lysozyme (HuL) was investigated, towards elucidating the mechanism of resveratrol action and probing its role as a possible modulator of lysozyme aggregation and toxicity. By using a number of biophysical tools, resveratrol was observed to alter the fibrillization kinetics of HuL and inhibit its fibrillization by binding with weak to moderate affinity to the conformations populated at the early stages of the pathway with concomitant stabilization of these initial conformations. The marginal decrease in the lifetime of HuL in the presence of resveratrol by time-resolved fluorescence measurements indicated the involvement of a static quenching mechanism in the interaction between HuL and resveratrol. Docking studies predicted the binding of resveratrol to aggregation-prone regions in HuL, and structure and activity analyses demonstrated the retention of much of the α-helical structure and activity of HuL in the presence of resveratrol. Resveratrol modulated the fibrillization pathway towards less-hydrophobic, less-toxic, off-pathway aggregates. These results demonstrate that binding of resveratrol to HuL could protect against the formation of pathogenic, cytotoxic aggregates formed in amyloidogenic disorders, such as systemic amyloidosis; thus suggesting its potential as a plausible therapeutic agent against lysozyme amyloidosis. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Overview of an internationally-harmonized program for adverse outcome pathway development

EPA Science Inventory

Adverse outcome pathways (AOPs) are critical frameworks for organizing knowledge concerning the scientifically-credible predictive linkages between toxicological observations made at molecular and cellular levels (e.g., via molecular screening assays, biomarker responses, or chem...

Computational Approaches for Identifying Adverse Outcome Pathways

EPA Science Inventory

Adverse Outcome Pathways (AOPs) provide a framework for organizing toxicity information to improve predictions of the potential adverse impact of environment stressors on humans or wildlife populations, but these benefits are currently limited by the small number of AOPs currentl...

Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets.

PubMed

Ng, Hui Wen; Doughty, Stephen W; Luo, Heng; Ye, Hao; Ge, Weigong; Tong, Weida; Hong, Huixiao

2015-12-21

Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration's Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency's ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼ 70-89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals.

Chlorite, Biotite, Illite, Muscovite, and Feldspar Dissolution Kinetics at Variable pH and Temperatures up to 280 C

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

Carroll, S.; Smith, M.; Lammers, K.

2016-10-05

Summary Sheet silicates and clays are ubiquitous in geothermal environments. Their dissolution is of interest because this process contributes to scaling reactions along fluid pathways and alteration of fracture surfaces, which could affect reservoir permeability. In order to better predict the geochemical impacts on long-term performance of engineered geothermal systems, we have measured chlorite, biotite, illite, and muscovite dissolution and developed generalized kinetic rate laws that are applicable over an expanded range of solution pH and temperature for each mineral. This report summarizes the rate equations for layered silicates where data were lacking for geothermal systems.

Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment

NASA Astrophysics Data System (ADS)

Zell, Wesley O.; Culver, Teresa B.; Sanford, Ward E.

2018-06-01

Uncertainties about the age of base-flow discharge can have serious implications for the management of degraded environmental systems where subsurface pathways, and the ongoing release of pollutants that accumulated in the subsurface during past decades, dominate the water quality signal. Numerical groundwater models may be used to estimate groundwater return times and base-flow ages and thus predict the time required for stakeholders to see the results of improved agricultural management practices. However, the uncertainty inherent in the relationship between (i) the observations of atmospherically-derived tracers that are required to calibrate such models and (ii) the predictions of system age that the observations inform have not been investigated. For example, few if any studies have assessed the uncertainty of numerically-simulated system ages or evaluated the uncertainty reductions that may result from the expense of collecting additional subsurface tracer data. In this study we combine numerical flow and transport modeling of atmospherically-derived tracers with prediction uncertainty methods to accomplish four objectives. First, we show the relative importance of head, discharge, and tracer information for characterizing response times in a uniquely data rich catchment that includes 266 age-tracer measurements (SF6, CFCs, and 3H) in addition to long term monitoring of water levels and stream discharge. Second, we calculate uncertainty intervals for model-simulated base-flow ages using both linear and non-linear methods, and find that the prediction sensitivity vector used by linear first-order second-moment methods results in much larger uncertainties than non-linear Monte Carlo methods operating on the same parameter uncertainty. Third, by combining prediction uncertainty analysis with multiple models of the system, we show that data-worth calculations and monitoring network design are sensitive to variations in the amount of water leaving the system via stream discharge and irrigation withdrawals. Finally, we demonstrate a novel model-averaged computation of potential data worth that can account for these uncertainties in model structure.

The Global Food System as a Transport Pathway for Hazardous Chemicals: The Missing Link between Emissions and Exposure.

PubMed

Ng, Carla A; von Goetz, Natalie

2017-01-01

Food is a major pathway for human exposure to hazardous chemicals. The modern food system is becoming increasingly complex and globalized, but models for food-borne exposure typically assume locally derived diets or use concentrations directly measured in foods without accounting for food origin. Such approaches may not reflect actual chemical intakes because concentrations depend on food origin, and representative analysis is seldom available. Processing, packaging, storage, and transportation also impart different chemicals to food and are not yet adequately addressed. Thus, the link between environmental emissions and realistic human exposure is effectively broken. We discuss the need for a fully integrated treatment of the modern industrialized food system, and we propose strategies for using existing models and relevant supporting data sources to track chemicals during production, processing, packaging, storage, and transport. Fate and bioaccumulation models describe how chemicals distribute in the environment and accumulate through local food webs. Human exposure models can use concentrations in food to determine body burdens based on individual or population characteristics. New models now include the impacts of processing and packaging but are far from comprehensive. We propose to close the gap between emissions and exposure by utilizing a wider variety of models and data sources, including global food trade data, processing, and packaging models. A comprehensive approach that takes into account the complexity of the modern global food system is essential to enable better prediction of human exposure to chemicals in food, sound risk assessments, and more focused risk abatement strategies. Citation: Ng CA, von Goetz N. 2017. The global food system as a transport pathway for hazardous chemicals: the missing link between emissions and exposure. Environ Health Perspect 125:1-7; http://dx.doi.org/10.1289/EHP168.

Prediction of novel stable compounds in the Mg-Si-O system under exoplanet pressures

PubMed Central

Niu, Haiyang; Oganov, Artem R.; Chen, Xing-Qiu; Li, Dianzhong

2015-01-01

The Mg-Si-O system is the major Earth and rocky planet-forming system. Here, through quantum variable-composition evolutionary structure explorations, we have discovered several unexpected stable binary and ternary compounds in the Mg-Si-O system. Besides the well-known SiO2 phases, we have found two extraordinary silicon oxides, SiO3 and SiO, which become stable at pressures above 0.51 TPa and 1.89 TPa, respectively. In the Mg-O system, we have found one new compound, MgO3, which becomes stable at 0.89 TPa. We find that not only the (MgO)x·(SiO2)y compounds, but also two (MgO3)x·(SiO3)y compounds, MgSi3O12 and MgSiO6, have stability fields above 2.41 TPa and 2.95 TPa, respectively. The highly oxidized MgSi3O12 can form in deep mantles of mega-Earths with masses above 20 M⊕ (M⊕:Earth’s mass). Furthermore, the dissociation pathways of pPv-MgSiO3 are also clarified, and found to be different at low and high temperatures. The low-temperature pathway is MgSiO3 ⇒ Mg2SiO4 + MgSi2O5 ⇒ SiO2 + Mg2SiO4 ⇒ MgO + SiO2, while the high-temperature pathway is MgSiO3 ⇒ Mg2SiO4 + MgSi2O5 ⇒ MgO + MgSi2O5 ⇒ MgO + SiO2. Present results are relevant for models of the internal structure of giant exoplanets, and for understanding the high-pressure behavior of materials. PMID:26691903

Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME.

PubMed

Godoy, Patricio; Hewitt, Nicola J; Albrecht, Ute; Andersen, Melvin E; Ansari, Nariman; Bhattacharya, Sudin; Bode, Johannes Georg; Bolleyn, Jennifer; Borner, Christoph; Böttger, Jan; Braeuning, Albert; Budinsky, Robert A; Burkhardt, Britta; Cameron, Neil R; Camussi, Giovanni; Cho, Chong-Su; Choi, Yun-Jaie; Craig Rowlands, J; Dahmen, Uta; Damm, Georg; Dirsch, Olaf; Donato, María Teresa; Dong, Jian; Dooley, Steven; Drasdo, Dirk; Eakins, Rowena; Ferreira, Karine Sá; Fonsato, Valentina; Fraczek, Joanna; Gebhardt, Rolf; Gibson, Andrew; Glanemann, Matthias; Goldring, Chris E P; Gómez-Lechón, María José; Groothuis, Geny M M; Gustavsson, Lena; Guyot, Christelle; Hallifax, David; Hammad, Seddik; Hayward, Adam; Häussinger, Dieter; Hellerbrand, Claus; Hewitt, Philip; Hoehme, Stefan; Holzhütter, Hermann-Georg; Houston, J Brian; Hrach, Jens; Ito, Kiyomi; Jaeschke, Hartmut; Keitel, Verena; Kelm, Jens M; Kevin Park, B; Kordes, Claus; Kullak-Ublick, Gerd A; LeCluyse, Edward L; Lu, Peng; Luebke-Wheeler, Jennifer; Lutz, Anna; Maltman, Daniel J; Matz-Soja, Madlen; McMullen, Patrick; Merfort, Irmgard; Messner, Simon; Meyer, Christoph; Mwinyi, Jessica; Naisbitt, Dean J; Nussler, Andreas K; Olinga, Peter; Pampaloni, Francesco; Pi, Jingbo; Pluta, Linda; Przyborski, Stefan A; Ramachandran, Anup; Rogiers, Vera; Rowe, Cliff; Schelcher, Celine; Schmich, Kathrin; Schwarz, Michael; Singh, Bijay; Stelzer, Ernst H K; Stieger, Bruno; Stöber, Regina; Sugiyama, Yuichi; Tetta, Ciro; Thasler, Wolfgang E; Vanhaecke, Tamara; Vinken, Mathieu; Weiss, Thomas S; Widera, Agata; Woods, Courtney G; Xu, Jinghai James; Yarborough, Kathy M; Hengstler, Jan G

2013-08-01

This review encompasses the most important advances in liver functions and hepatotoxicity and analyzes which mechanisms can be studied in vitro. In a complex architecture of nested, zonated lobules, the liver consists of approximately 80 % hepatocytes and 20 % non-parenchymal cells, the latter being involved in a secondary phase that may dramatically aggravate the initial damage. Hepatotoxicity, as well as hepatic metabolism, is controlled by a set of nuclear receptors (including PXR, CAR, HNF-4α, FXR, LXR, SHP, VDR and PPAR) and signaling pathways. When isolating liver cells, some pathways are activated, e.g., the RAS/MEK/ERK pathway, whereas others are silenced (e.g. HNF-4α), resulting in up- and downregulation of hundreds of genes. An understanding of these changes is crucial for a correct interpretation of in vitro data. The possibilities and limitations of the most useful liver in vitro systems are summarized, including three-dimensional culture techniques, co-cultures with non-parenchymal cells, hepatospheres, precision cut liver slices and the isolated perfused liver. Also discussed is how closely hepatoma, stem cell and iPS cell-derived hepatocyte-like-cells resemble real hepatocytes. Finally, a summary is given of the state of the art of liver in vitro and mathematical modeling systems that are currently used in the pharmaceutical industry with an emphasis on drug metabolism, prediction of clearance, drug interaction, transporter studies and hepatotoxicity. One key message is that despite our enthusiasm for in vitro systems, we must never lose sight of the in vivo situation. Although hepatocytes have been isolated for decades, the hunt for relevant alternative systems has only just begun.

MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.

PubMed

Hayashi, Takanori; Matsuzaki, Yuri; Yanagisawa, Keisuke; Ohue, Masahito; Akiyama, Yutaka

2018-05-08

Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on docking calculations with biochemical pathways and enables users to easily and quickly assess PPI feasibilities by archiving PPI predictions. MEGADOCK-Web also promotes the discovery of new PPIs and protein functions and is freely available for use at http://www.bi.cs.titech.ac.jp/megadock-web/ .

Global Sources and Pathways of Mercury in the Context of Human Health.

PubMed

Sundseth, Kyrre; Pacyna, Jozef M; Pacyna, Elisabeth G; Pirrone, Nicola; Thorne, Rebecca J

2017-01-22

This paper reviews information from the existing literature and the EU GMOS (Global Mercury Observation System) project to assess the current scientific knowledge on global mercury releases into the atmosphere, on global atmospheric transport and deposition, and on the linkage between environmental contamination and potential impacts on human health. The review concludes that assessment of global sources and pathways of mercury in the context of human health is important for being able to monitor the effects from implementation of the Minamata Convention targets, although new research is needed on the improvement of emission inventory data, the chemical and physical behaviour of mercury in the atmosphere, the improvement of monitoring network data, predictions of future emissions and speciation, and on the subsequent effects on the environment, human health, as well as the economic costs and benefits of reducing these aspects.

The mechanism of ureido-pyrimidinone:2,7-diamido-naphthyridine complexation and the presence of kinetically controlled pathways in multicomponent hydrogen-bonded systems.

PubMed

de Greef, Tom F A; Ligthart, G B W L; Lutz, Martin; Spek, Anthony L; Meijer, E W; Sijbesma, Rint P

2008-04-23

The kinetics of association of ureido-pyrimidinone (U) dimers, present either in the 4[1H]-keto form or in the pyrimidin-4-ol form, with 2,7-diamido-1,8-naphthyridine (N) into a complementary heterodimer have been investigated. The formation of heterodimers with 2,7-diamido-1,8-naphthyridine from pyrimidin-4-ol dimers is much faster than from 4[1H]-pyrimidinone dimers. Using a combination of simple measurements and simulations, evidence for a bimolecular tautomerization step is presented. Finally, the acquired kinetic knowledge of the different pathways leading from ureido-pyrimidinone homodimers to ureido-pyrimidinone:diamido-naphthyridine (U:N) heterodimers allows the prediction and observation of kinetically determined ureido-pyrimidinone heterodimers which slowly convert back to the corresponding homodimers.

CRYSTAL GROWTH. Crystallization by particle attachment in synthetic, biogenic, and geologic environments.

PubMed

De Yoreo, James J; Gilbert, Pupa U P A; Sommerdijk, Nico A J M; Penn, R Lee; Whitelam, Stephen; Joester, Derk; Zhang, Hengzhong; Rimer, Jeffrey D; Navrotsky, Alexandra; Banfield, Jillian F; Wallace, Adam F; Michel, F Marc; Meldrum, Fiona C; Cölfen, Helmut; Dove, Patricia M

2015-07-31

Field and laboratory observations show that crystals commonly form by the addition and attachment of particles that range from multi-ion complexes to fully formed nanoparticles. The particles involved in these nonclassical pathways to crystallization are diverse, in contrast to classical models that consider only the addition of monomeric chemical species. We review progress toward understanding crystal growth by particle-attachment processes and show that multiple pathways result from the interplay of free-energy landscapes and reaction dynamics. Much remains unknown about the fundamental aspects, particularly the relationships between solution structure, interfacial forces, and particle motion. Developing a predictive description that connects molecular details to ensemble behavior will require revisiting long-standing interpretations of crystal formation in synthetic systems, biominerals, and patterns of mineralization in natural environments. Copyright © 2015, American Association for the Advancement of Science.

Exploring the formation pathways of donor-acceptor catenanes in aqueous dynamic combinatorial libraries.

PubMed

Cougnon, Fabien B L; Au-Yeung, Ho Yu; Pantoş, G Dan; Sanders, Jeremy K M

2011-03-09

The discovery through dynamic combinatorial chemistry (DCC) of a new generation of donor-acceptor [2]catenanes highlights the power of DCC to access unprecedented structures. While conventional thinking has limited the scope of donor-acceptor catenanes to strictly alternating stacks of donor (D) and acceptor (A) aromatic units, DCC is demonstrated in this paper to give access to unusual DAAD, DADD, and ADAA stacks. Each of these catenanes has specific structural requirements, allowing control of their formation. On the basis of these results, and on the observation that the catenanes represent kinetic bottlenecks in the reaction pathway, we propose a mechanism that explains and predicts the structures formed. Furthermore, the spontaneous assembly of catenanes in aqueous dynamic systems gives a fundamental insight into the role played by hydrophobic effect and donor-acceptor interactions when building such complex architectures.

Assessing the utility of the willingness/prototype model in predicting help-seeking decisions.

PubMed

Hammer, Joseph H; Vogel, David L

2013-01-01

Prior research on professional psychological help-seeking behavior has operated on the assumption that the decision to seek help is based on intentional and reasoned processes. However, research on the dual-process prototype/willingness model (PWM; Gerrard, Gibbons, Houlihan, Stock, & Pomery, 2008) suggests health-related decisions may also involve social reaction processes that influence one's spontaneous willingness (rather than planned intention) to seek help, given conducive circumstances. The present study used structural equation modeling to evaluate the ability of these 2 information-processing pathways (i.e., the reasoned pathway and the social reaction pathway) to predict help-seeking decisions among 182 college students currently experiencing clinical levels of psychological distress. Results indicated that when both pathways were modeled simultaneously, only the social reaction pathway independently accounted for significant variance in help-seeking decisions. These findings argue for the utility of the PWM framework in the context of professional psychological help seeking and hold implications for future counseling psychology research, prevention, and practice. PsycINFO Database Record (c) 2013 APA, all rights reserved.

A combined computational-experimental analyses of selected metabolic enzymes in Pseudomonas species.

PubMed

Perumal, Deepak; Lim, Chu Sing; Chow, Vincent T K; Sakharkar, Kishore R; Sakharkar, Meena K

2008-09-10

Comparative genomic analysis has revolutionized our ability to predict the metabolic subsystems that occur in newly sequenced genomes, and to explore the functional roles of the set of genes within each subsystem. These computational predictions can considerably reduce the volume of experimental studies required to assess basic metabolic properties of multiple bacterial species. However, experimental validations are still required to resolve the apparent inconsistencies in the predictions by multiple resources. Here, we present combined computational-experimental analyses on eight completely sequenced Pseudomonas species. Comparative pathway analyses reveal that several pathways within the Pseudomonas species show high plasticity and versatility. Potential bypasses in 11 metabolic pathways were identified. We further confirmed the presence of the enzyme O-acetyl homoserine (thiol) lyase (EC: 2.5.1.49) in P. syringae pv. tomato that revealed inconsistent annotations in KEGG and in the recently published SYSTOMONAS database. These analyses connect and integrate systematic data generation, computational data interpretation, and experimental validation and represent a synergistic and powerful means for conducting biological research.

Genome sequence analysis of a flocculant-producing bacterium, Paenibacillus shenyangensis.

PubMed

Fu, Lili; Jiang, Binhui; Liu, Jinliang; Zhao, Xin; Liu, Qian; Hu, Xiaomin

2016-03-01

To explore the metabolic process of Paenibacillus shenyangensis that is an efficient bioflocculant-producing bacterium. The biosynthesis mechanism of bioflocculation was used to enrich the genome of Paenibacillus shenyangensis and provide a basis for molecular genetics and functional genomics analyses. According to the analysis of de novo assembly, a total of 5,501,467 bp clean reads were generated, and were assembled into 92 contigs. 4800 unigenes were predicted of which 4393 were annotated showing a specific gene function in the NCBI-Nr database. 3423 genes were found in the database of cluster of orthologous groups. Among the 168 Kyoto Encyclopedia of Genes and Genomes database, cell growth and metabolism were the main biological processes, and a potential metabolic pathway was predicted from glucose to exopolysaccharide within the starch and sucrose metabolism pathway. By using the high-throughput sequencing technology, we provide a genome analysis of Paenibacillus shenyangensis that predicts the main metabolic processes and a potential pathway of exopolysaccharide biosynthesis.

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

Tribble, G.W.

A combination of field and theoretical work is used to study controls on the saturation state of aragonite inside a coral-reef framework. A closed-system ion-speciation model is used to evaluate the effect of organic-matter oxidation on the saturation state of aragonite. The aragonite saturation state initially drops below 1 but becomes oversaturated during sulfate reduction. The C:N ratio of the organic matter affects the degree of oversaturation with N-poor organic material resulting in a system more corrosive to aragonite. Precipitation of sulfide as FeS strongly affects the aragonite saturation state, and systems with much FeS formation will have a strongermore » tendency to become oversaturated with respect to aragonite. Both precipitation and dissolution of aragonite are predicted at different stages of the organic reaction pathway if the model system is maintained at aragonite saturation. Field data from a coral-reef framework indicate that the system maintains itself at aragonite saturation, and model-predicted changes in dissolved calcium follow those observed in the interstitial waters of the reef. Aragonite probably acts as a solid-phase buffer in regulating the pH of interstitial waters. Because interstitial water in the reef has a short residence time, the observed equilibration suggests rapid kinetics.« less

Goal striving within agentic and communal roles: separate but functionally similar pathways to enhanced well-being.

PubMed

Sheldon, Kennon M; Cooper, M Lynne

2008-06-01

Do agency and communion strivings provide functionally similar but predictively independent pathways to enhanced well-being? We tested this idea via a year-long study of 493 diverse community adults. Our process model, based on self-determination and motive disposition theories, fit the data well. First, the need for achievement predicted initial autonomous motivation for agentic (work and school) role-goals and the need for intimacy predicted felt autonomy for communal (relationship and parenting) goals. For both agentic and communal goals, autonomous motivation predicted corresponding initial expectancies that predicted later goal attainment. Finally, each type of attainment predicted improved adjustment or role-satisfaction over the year. Besides being similar across agency and communion, the model was also similar across race and gender, except that the beneficial effects of communal goal attainment were stronger for high need for intimacy women and Blacks. Implications for agency/communion theories, motivation theories, and theories of well-being are discussed.

Accelerating Adverse Outcome Pathway (AOP) development via computationally predicted AOP networks

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework is increasingly being adopted as a tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse outcomes relevant for ecological and human health outcomes. Ho...

Adverse Outcome Pathways and Extrapolation Tools to Advance the Three Rs in Ecotoxicology

EPA Science Inventory

Adverse outcome pathways (AOPs) are conceptual frameworks for identifying and organizing predictive and causal linkages between cellular-level responses and endpoints conventionally considered in ecological risk assessment (e.g., effects on survival, growth/development, and repro...

Synthetic Biology Outside the Cell: Linking Computational Tools to Cell-Free Systems

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

Lewis, Daniel D.; Department of Biomedical Engineering, University of California Davis, Davis, CA; Villarreal, Fernando D.

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with amore » special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.« less

Synthetic Biology Outside the Cell: Linking Computational Tools to Cell-Free Systems

PubMed Central

Lewis, Daniel D.; Villarreal, Fernando D.; Wu, Fan; Tan, Cheemeng

2014-01-01

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems. PMID:25538941

Synthetic biology outside the cell: linking computational tools to cell-free systems.

PubMed

Lewis, Daniel D; Villarreal, Fernando D; Wu, Fan; Tan, Cheemeng

2014-01-01

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.

A network of molecular switches controls the activation of the two-component response regulator NtrC

NASA Astrophysics Data System (ADS)

Vanatta, Dan K.; Shukla, Diwakar; Lawrenz, Morgan; Pande, Vijay S.

2015-06-01

Recent successes in simulating protein structure and folding dynamics have demonstrated the power of molecular dynamics to predict the long timescale behaviour of proteins. Here, we extend and improve these methods to predict molecular switches that characterize conformational change pathways between the active and inactive state of nitrogen regulatory protein C (NtrC). By employing unbiased Markov state model-based molecular dynamics simulations, we construct a dynamic picture of the activation pathways of this key bacterial signalling protein that is consistent with experimental observations and predicts new mutants that could be used for validation of the mechanism. Moreover, these results suggest a novel mechanistic paradigm for conformational switching.

Non-invasively predicting differentiation of pancreatic cancer through comparative serum metabonomic profiling.

PubMed

Wen, Shi; Zhan, Bohan; Feng, Jianghua; Hu, Weize; Lin, Xianchao; Bai, Jianxi; Huang, Heguang

2017-11-02

The differentiation of pancreatic ductal adenocarcinoma (PDAC) could be associated with prognosis and may influence the choices of clinical management. No applicable methods could reliably predict the tumor differentiation preoperatively. Thus, the aim of this study was to compare the metabonomic profiling of pancreatic ductal adenocarcinoma with different differentiations and assess the feasibility of predicting tumor differentiations through metabonomic strategy based on nuclear magnetic resonance spectroscopy. By implanting pancreatic cancer cell strains Panc-1, Bxpc-3 and SW1990 in nude mice in situ, we successfully established the orthotopic xenograft models of PDAC with different differentiations. The metabonomic profiling of serum from different PDAC was achieved and analyzed by using 1 H nuclear magnetic resonance (NMR) spectroscopy combined with the multivariate statistical analysis. Then, the differential metabolites acquired were used for enrichment analysis of metabolic pathways to get a deep insight. An obvious metabonomic difference was demonstrated between all groups and the pattern recognition models were established successfully. The higher concentrations of amino acids, glycolytic and glutaminolytic participators in SW1990 and choline-contain metabolites in Panc-1 relative to other PDAC cells were demonstrated, which may be served as potential indicators for tumor differentiation. The metabolic pathways and differential metabolites identified in current study may be associated with specific pathways such as serine-glycine-one-carbon and glutaminolytic pathways, which can regulate tumorous proliferation and epigenetic regulation. The NMR-based metabonomic strategy may be served as a non-invasive detection method for predicting tumor differentiation preoperatively.

Transmission of Predictable Sensory Signals to the Cerebellum via Climbing Fiber Pathways Is Gated during Exploratory Behavior

PubMed Central

Lawrenson, Charlotte L.; Watson, Thomas C.

2016-01-01

Pathways arising from the periphery that target the inferior olive [spino-olivocerebellar pathways (SOCPs)] are a vital source of information to the cerebellum and are modulated (gated) during active movements. This limits their ability to forward signals to climbing fibers in the cerebellar cortex. We tested the hypothesis that the temporal pattern of gating is related to the predictability of a sensory signal. Low-intensity electrical stimulation of the ipsilateral hindlimb in awake rats evoked field potentials in the C1 zone in the copula pyramidis of the cerebellar cortex. Responses had an onset latency of 12.5 ± 0.3 ms and were either short or long duration (8.7 ± 0.1 vs 31.2 ± 0.3 ms, respectively). Both types of response were shown to be mainly climbing fiber in origin and therefore evoked by transmission in hindlimb SOCPs. Changes in response size (area of field, millivolts per millisecond) were used to monitor differences in transmission during rest and three phases of rearing: phase 1, rearing up; phase 2, upright; and phase 3, rearing down. Responses evoked during phase 2 were similar in size to rest but were smaller during phases 1 and 3, i.e., transmission was reduced during active movement when self-generated (predictable) sensory signals from the hindlimbs are likely to occur. To test whether the pattern of gating was related to the predictability of the sensory signal, some animals received the hindlimb stimulation only during phase 2. Over ∼10 d, the responses became progressively smaller in size, consistent with gating-out transmission of predictable sensory signals relayed via SOCPs. SIGNIFICANCE STATEMENT A major route for peripheral information to gain access to the cerebellum is via ascending climbing fiber pathways. During active movements, gating of transmission in these pathways controls when climbing fiber signals can modify cerebellar activity. We investigated this phenomenon in rats during their exploratory behavior of rearing. During rearing up and down, transmission was reduced at a time when self-generated, behaviorally irrelevant (predictable) signals occur. However, during the upright phase of rearing, transmission was increased when behaviorally relevant (unpredictable) signals may occur. When the peripheral stimulation was delivered only during the upright phase, so its occurrence became predictable over time, transmission was reduced. Therefore, the results indicate that the gating is related to the level of predictability of a sensory signal. PMID:27466330

Transmission of Predictable Sensory Signals to the Cerebellum via Climbing Fiber Pathways Is Gated during Exploratory Behavior.

PubMed

Lawrenson, Charlotte L; Watson, Thomas C; Apps, Richard

2016-07-27

Pathways arising from the periphery that target the inferior olive [spino-olivocerebellar pathways (SOCPs)] are a vital source of information to the cerebellum and are modulated (gated) during active movements. This limits their ability to forward signals to climbing fibers in the cerebellar cortex. We tested the hypothesis that the temporal pattern of gating is related to the predictability of a sensory signal. Low-intensity electrical stimulation of the ipsilateral hindlimb in awake rats evoked field potentials in the C1 zone in the copula pyramidis of the cerebellar cortex. Responses had an onset latency of 12.5 ± 0.3 ms and were either short or long duration (8.7 ± 0.1 vs 31.2 ± 0.3 ms, respectively). Both types of response were shown to be mainly climbing fiber in origin and therefore evoked by transmission in hindlimb SOCPs. Changes in response size (area of field, millivolts per millisecond) were used to monitor differences in transmission during rest and three phases of rearing: phase 1, rearing up; phase 2, upright; and phase 3, rearing down. Responses evoked during phase 2 were similar in size to rest but were smaller during phases 1 and 3, i.e., transmission was reduced during active movement when self-generated (predictable) sensory signals from the hindlimbs are likely to occur. To test whether the pattern of gating was related to the predictability of the sensory signal, some animals received the hindlimb stimulation only during phase 2. Over ∼10 d, the responses became progressively smaller in size, consistent with gating-out transmission of predictable sensory signals relayed via SOCPs. A major route for peripheral information to gain access to the cerebellum is via ascending climbing fiber pathways. During active movements, gating of transmission in these pathways controls when climbing fiber signals can modify cerebellar activity. We investigated this phenomenon in rats during their exploratory behavior of rearing. During rearing up and down, transmission was reduced at a time when self-generated, behaviorally irrelevant (predictable) signals occur. However, during the upright phase of rearing, transmission was increased when behaviorally relevant (unpredictable) signals may occur. When the peripheral stimulation was delivered only during the upright phase, so its occurrence became predictable over time, transmission was reduced. Therefore, the results indicate that the gating is related to the level of predictability of a sensory signal. Copyright © 2016 Lawrenson et al.

Proteomics show antigen presentation processes in human immune cells after AS03-H5N1 vaccination.

PubMed

Galassie, Allison C; Goll, Johannes B; Samir, Parimal; Jensen, Travis L; Hoek, Kristen L; Howard, Leigh M; Allos, Tara M; Niu, Xinnan; Gordy, Laura E; Creech, C Buddy; Hill, Heather; Joyce, Sebastian; Edwards, Kathryn M; Link, Andrew J

2017-06-01

Adjuvants enhance immunity elicited by vaccines through mechanisms that are poorly understood. Using a systems biology approach, we investigated temporal protein expression changes in five primary human immune cell populations: neutrophils, monocytes, natural killer cells, T cells, and B cells after administration of either an Adjuvant System 03 adjuvanted or unadjuvanted split-virus H5N1 influenza vaccine. Monocytes demonstrated the strongest differential signal between vaccine groups. On day 3 post-vaccination, several antigen presentation-related pathways, including MHC class I-mediated antigen processing and presentation, were enriched in monocytes and neutrophils and expression of HLA class I proteins was increased in the Adjuvant System 03 group. We identified several protein families whose proteomic responses predicted seroprotective antibody responses (>1:40 hemagglutination inhibition titer), including inflammation and oxidative stress proteins at day 1 as well as immunoproteasome subunit (PSME1 and PSME2) and HLA class I proteins at day 3 in monocytes. While comparison between temporal proteomic and transcriptomic results showed little overlap overall, enrichment of the MHC class I antigen processing and presentation pathway in monocytes and neutrophils was confirmed by both approaches. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core.

PubMed

Hucka, Michael; Bergmann, Frank T; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M; Le Novère, Nicolas; Myers, Chris J; Olivier, Brett G; Sahle, Sven; Schaff, James C; Smith, Lucian P; Waltemath, Dagmar; Wilkinson, Darren J

2018-03-09

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. The design of Version 2 differs from Version 1 principally in allowing new MathML constructs, making more child elements optional, and adding identifiers to all SBML elements instead of only selected elements. Other materials and software are available from the SBML project website at http://sbml.org/.

Glycolysis Is Governed by Growth Regime and Simple Enzyme Regulation in Adherent MDCK Cells

PubMed Central

Rehberg, Markus; Ritter, Joachim B.; Reichl, Udo

2014-01-01

Due to its vital importance in the supply of cellular pathways with energy and precursors, glycolysis has been studied for several decades regarding its capacity and regulation. For a systems-level understanding of the Madin-Darby canine kidney (MDCK) cell metabolism, we couple a segregated cell growth model published earlier with a structured model of glycolysis, which is based on relatively simple kinetics for enzymatic reactions of glycolysis, to explain the pathway dynamics under various cultivation conditions. The structured model takes into account in vitro enzyme activities, and links glycolysis with pentose phosphate pathway and glycogenesis. Using a single parameterization, metabolite pool dynamics during cell cultivation, glucose limitation and glucose pulse experiments can be consistently reproduced by considering the cultivation history of the cells. Growth phase-dependent glucose uptake together with cell-specific volume changes generate high intracellular metabolite pools and flux rates to satisfy the cellular demand during growth. Under glucose limitation, the coordinated control of glycolytic enzymes re-adjusts the glycolytic flux to prevent the depletion of glycolytic intermediates. Finally, the model's predictive power supports the design of more efficient bioprocesses. PMID:25329309

Glycolysis is governed by growth regime and simple enzyme regulation in adherent MDCK cells.

PubMed

Rehberg, Markus; Ritter, Joachim B; Reichl, Udo

2014-10-01

Due to its vital importance in the supply of cellular pathways with energy and precursors, glycolysis has been studied for several decades regarding its capacity and regulation. For a systems-level understanding of the Madin-Darby canine kidney (MDCK) cell metabolism, we couple a segregated cell growth model published earlier with a structured model of glycolysis, which is based on relatively simple kinetics for enzymatic reactions of glycolysis, to explain the pathway dynamics under various cultivation conditions. The structured model takes into account in vitro enzyme activities, and links glycolysis with pentose phosphate pathway and glycogenesis. Using a single parameterization, metabolite pool dynamics during cell cultivation, glucose limitation and glucose pulse experiments can be consistently reproduced by considering the cultivation history of the cells. Growth phase-dependent glucose uptake together with cell-specific volume changes generate high intracellular metabolite pools and flux rates to satisfy the cellular demand during growth. Under glucose limitation, the coordinated control of glycolytic enzymes re-adjusts the glycolytic flux to prevent the depletion of glycolytic intermediates. Finally, the model's predictive power supports the design of more efficient bioprocesses.

Simplified Protein Models: Predicting Folding Pathways and Structure Using Amino Acid Sequences

NASA Astrophysics Data System (ADS)

Adhikari, Aashish N.; Freed, Karl F.; Sosnick, Tobin R.

2013-07-01

We demonstrate the ability of simultaneously determining a protein’s folding pathway and structure using a properly formulated model without prior knowledge of the native structure. Our model employs a natural coordinate system for describing proteins and a search strategy inspired by the observation that real proteins fold in a sequential fashion by incrementally stabilizing nativelike substructures or “foldons.” Comparable folding pathways and structures are obtained for the twelve proteins recently studied using atomistic molecular dynamics simulations [K. Lindorff-Larsen, S. Piana, R. O. Dror, D. E. Shaw, Science 334, 517 (2011)], with our calculations running several orders of magnitude faster. We find that nativelike propensities in the unfolded state do not necessarily determine the order of structure formation, a departure from a major conclusion of the molecular dynamics study. Instead, our results support a more expansive view wherein intrinsic local structural propensities may be enhanced or overridden in the folding process by environmental context. The success of our search strategy validates it as an expedient mechanism for folding both in silico and in vivo.

LWRS ATR Irradiation Testing Readiness Status

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

Kristine Barrett

2012-09-01

The Light Water Reactor Sustainability (LWRS) Program was established by the U.S. Department of Energy Office of Nuclear Energy (DOE-NE) to develop technologies and other solutions that can improve the reliability, sustain the safety, and extend the life of the current reactors. The LWRS Program is divided into four R&D Pathways: (1) Materials Aging and Degradation; (2) Advanced Light Water Reactor Nuclear Fuels; (3) Advanced Instrumentation, Information and Control Systems; and (4) Risk-Informed Safety Margin Characterization. This report describes an irradiation testing readiness analysis in preparation of LWRS experiments for irradiation testing at the Idaho National Laboratory (INL) Advanced Testmore » Reactor (ATR) under Pathway (2). The focus of the Advanced LWR Nuclear Fuels Pathway is to improve the scientific knowledge basis for understanding and predicting fundamental performance of advanced nuclear fuel and cladding in nuclear power plants during both nominal and off-nominal conditions. This information will be applied in the design and development of high-performance, high burn-up fuels with improved safety, cladding integrity, and improved nuclear fuel cycle economics« less

Dynamic Redox Regulation of IL-4 Signaling.

PubMed

Dwivedi, Gaurav; Gran, Margaret A; Bagchi, Pritha; Kemp, Melissa L

2015-11-01

Quantifying the magnitude and dynamics of protein oxidation during cell signaling is technically challenging. Computational modeling provides tractable, quantitative methods to test hypotheses of redox mechanisms that may be simultaneously operative during signal transduction. The interleukin-4 (IL-4) pathway, which has previously been reported to induce reactive oxygen species and oxidation of PTP1B, may be controlled by several other putative mechanisms of redox regulation; widespread proteomic thiol oxidation observed via 2D redox differential gel electrophoresis upon IL-4 treatment suggests more than one redox-sensitive protein implicated in this pathway. Through computational modeling and a model selection strategy that relied on characteristic STAT6 phosphorylation dynamics of IL-4 signaling, we identified reversible protein tyrosine phosphatase (PTP) oxidation as the primary redox regulatory mechanism in the pathway. A systems-level model of IL-4 signaling was developed that integrates synchronous pan-PTP oxidation with ROS-independent mechanisms. The model quantitatively predicts the dynamics of IL-4 signaling over a broad range of new redox conditions, offers novel hypotheses about regulation of JAK/STAT signaling, and provides a framework for interrogating putative mechanisms involving receptor-initiated oxidation.

Dynamic Redox Regulation of IL-4 Signaling

PubMed Central

Dwivedi, Gaurav; Gran, Margaret A.; Bagchi, Pritha; Kemp, Melissa L.

2015-01-01

Quantifying the magnitude and dynamics of protein oxidation during cell signaling is technically challenging. Computational modeling provides tractable, quantitative methods to test hypotheses of redox mechanisms that may be simultaneously operative during signal transduction. The interleukin-4 (IL-4) pathway, which has previously been reported to induce reactive oxygen species and oxidation of PTP1B, may be controlled by several other putative mechanisms of redox regulation; widespread proteomic thiol oxidation observed via 2D redox differential gel electrophoresis upon IL-4 treatment suggests more than one redox-sensitive protein implicated in this pathway. Through computational modeling and a model selection strategy that relied on characteristic STAT6 phosphorylation dynamics of IL-4 signaling, we identified reversible protein tyrosine phosphatase (PTP) oxidation as the primary redox regulatory mechanism in the pathway. A systems-level model of IL-4 signaling was developed that integrates synchronous pan-PTP oxidation with ROS-independent mechanisms. The model quantitatively predicts the dynamics of IL-4 signaling over a broad range of new redox conditions, offers novel hypotheses about regulation of JAK/STAT signaling, and provides a framework for interrogating putative mechanisms involving receptor-initiated oxidation. PMID:26562652

Engineering in complex systems.

PubMed

Bujara, Matthias; Panke, Sven

2010-10-01

The implementation of the engineering design cycle of measure, model, manipulate would drastically enhance the success rate of biotechnological designs. Recent progress for the three elements suggests that the scope of the traditional engineering paradigm in biotechnology is expanding. Substantial advances were made in dynamic in vivo analysis of metabolism, which is essential for the accurate prediction of metabolic pathway behavior. Novel methods that require variable degrees of system knowledge facilitate metabolic system manipulation. The combinatorial testing of pre-characterized parts is particularly promising, because it can profit from automation and limits the search space. Finally, conceptual advances in orthogonalizing cells should enhance the reliability of engineering designs in the future. Coupled to improved in silico models of metabolism, these advances should allow a more rational design of metabolic systems. Copyright © 2010 Elsevier Ltd. All rights reserved.

Prediction of thermodynamically reversible hydrogen storage reactions utilizing Ca-M(M = Li, Na, K)-B-H systems: a first-principles study.

PubMed

Guo, Yajuan; Ren, Ying; Wu, Haishun; Jia, Jianfeng

2013-12-01

Calcium borohydride is a potential candidate for onboard hydrogen storage because it has a high gravimetric capacity (11.5 wt.%) and a high volumetric hydrogen content (∼130 kg m(-3)). Unfortunately, calcium borohydride suffers from the drawback of having very strongly bound hydrogen. In this study, Ca(BH₄)₂ was predicted to form a destabilized system when it was mixed with LiBH₄, NaBH₄, or KBH₄. The release of hydrogen from Ca(BH₄)₂ was predicted to proceed via two competing reaction pathways (leading to CaB₆ and CaH₂ or CaB₁₂H₁₂ and CaH₂) that were found to have almost equal free energies. Using a set of recently developed theoretical methods derived from first principles, we predicted five new hydrogen storage reactions that are among the most attractive of those presently known. These combine high gravimetric densities (>6.0 wt.% H₂) with have low enthalpies [approximately 35 kJ/(mol(-1) H₂)] and are thermodynamically reversible at low pressure within the target window for onboard storage that is actively being considered for hydrogen storage applications. Thus, the first-principles theoretical design of new materials for energy storage in future research appears to be possible.

Comparison of Optimal Thermodynamic Models of the Tricarboxylic Acid Cycle from Heterotrophs, Cyanobacteria, and Green Sulfur Bacteria.

PubMed

Thomas, Dennis G; Jaramillo-Riveri, Sebastian; Baxter, Douglas J; Cannon, William R

2014-12-26

We have applied a new stochastic simulation approach to predict the metabolite levels, material flux, and thermodynamic profiles of the oxidative TCA cycles found in E. coli and Synechococcus sp. PCC 7002, and in the reductive TCA cycle typical of chemolithoautotrophs and phototrophic green sulfur bacteria such as Chlorobaculum tepidum. The simulation approach is based on modeling states using statistical thermodynamics and employs an assumption similar to that used in transition state theory. The ability to evaluate the thermodynamics of metabolic pathways allows one to understand the relationship between coupling of energy and material gradients in the environment and the self-organization of stable biological systems, and it is shown that each cycle operates in the direction expected due to its environmental niche. The simulations predict changes in metabolite levels and flux in response to changes in cofactor concentrations that would be hard to predict without an elaborate model based on the law of mass action. In fact, we show that a thermodynamically unfavorable reaction can still have flux in the forward direction when it is part of a reaction network. The ability to predict metabolite levels, energy flow, and material flux should be significant for understanding the dynamics of natural systems and for understanding principles for engineering organisms for production of specialty chemicals.

Global motions exhibited by proteins in micro- to milliseconds simulations concur with anisotropic network model predictions

NASA Astrophysics Data System (ADS)

Gur, M.; Zomot, E.; Bahar, I.

2013-09-01

The Anton supercomputing technology recently developed for efficient molecular dynamics simulations permits us to examine micro- to milli-second events at full atomic resolution for proteins in explicit water and lipid bilayer. It also permits us to investigate to what extent the collective motions predicted by network models (that have found broad use in molecular biophysics) agree with those exhibited by full-atomic long simulations. The present study focuses on Anton trajectories generated for two systems: the bovine pancreatic trypsin inhibitor, and an archaeal aspartate transporter, GltPh. The former, a thoroughly studied system, helps benchmark the method of comparative analysis, and the latter provides new insights into the mechanism of function of glutamate transporters. The principal modes of motion derived from both simulations closely overlap with those predicted for each system by the anisotropic network model (ANM). Notably, the ANM modes define the collective mechanisms, or the pathways on conformational energy landscape, that underlie the passage between the crystal structure and substates visited in simulations. In particular, the lowest frequency ANM modes facilitate the conversion between the most probable substates, lending support to the view that easy access to functional substates is a robust determinant of evolutionarily selected native contact topology.

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

PubMed Central

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

2016-01-01

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

Atomic switch networks as complex adaptive systems

NASA Astrophysics Data System (ADS)

Scharnhorst, Kelsey S.; Carbajal, Juan P.; Aguilera, Renato C.; Sandouk, Eric J.; Aono, Masakazu; Stieg, Adam Z.; Gimzewski, James K.

2018-03-01

Complexity is an increasingly crucial aspect of societal, environmental and biological phenomena. Using a dense unorganized network of synthetic synapses it is shown that a complex adaptive system can be physically created on a microchip built especially for complex problems. These neuro-inspired atomic switch networks (ASNs) are a dynamic system with inherent and distributed memory, recurrent pathways, and up to a billion interacting elements. We demonstrate key parameters describing self-organized behavior such as non-linearity, power law dynamics, and multistate switching regimes. Device dynamics are then investigated using a feedback loop which provides control over current and voltage power-law behavior. Wide ranging prospective applications include understanding and eventually predicting future events that display complex emergent behavior in the critical regime.

Evolutionary trends and functional anatomy of the human expanded autophagy network

PubMed Central

Till, Andreas; Saito, Rintaro; Merkurjev, Daria; Liu, Jing-Jing; Syed, Gulam Hussain; Kolnik, Martin; Siddiqui, Aleem; Glas, Martin; Scheffler, Björn; Ideker, Trey; Subramani, Suresh

2015-01-01

All eukaryotic cells utilize autophagy for protein and organelle turnover, thus assuring subcellular quality control, homeostasis, and survival. In order to address recent advances in identification of human autophagy associated genes, and to describe autophagy on a system-wide level, we established an autophagy-centered gene interaction network by merging various primary data sets and by retrieving respective interaction data. The resulting network (‘AXAN’) was analyzed with respect to subnetworks, e.g. the prime gene subnetwork (including the core machinery, signaling pathways and autophagy receptors) and the transcription subnetwork. To describe aspects of evolution within this network, we assessed the presence of protein orthologs across 99 eukaryotic model organisms. We visualized evolutionary trends for prime gene categories and evolutionary tracks for selected AXAN genes. This analysis confirms the eukaryotic origin of autophagy core genes while it points to a diverse evolutionary history of autophagy receptors. Next, we used module identification to describe the functional anatomy of the network at the level of pathway modules. In addition to obvious pathways (e.g., lysosomal degradation, insulin signaling) our data unveil the existence of context-related modules such as Rho GTPase signaling. Last, we used a tripartite, image-based RNAi – screen to test candidate genes predicted to play a role in regulation of autophagy. We verified the Rho GTPase, CDC42, as a novel regulator of autophagy-related signaling. This study emphasizes the applicability of system-wide approaches to gain novel insights into a complex biological process and to describe the human autophagy pathway at a hitherto unprecedented level of detail. PMID:26103419

Minimal metabolic pathway structure is consistent with associated biomolecular interactions

PubMed Central

Bordbar, Aarash; Nagarajan, Harish; Lewis, Nathan E; Latif, Haythem; Ebrahim, Ali; Federowicz, Stephen; Schellenberger, Jan; Palsson, Bernhard O

2014-01-01

Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high-throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human-defined pathways. Here, we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based on principles of parsimony that do not mimic canonical human-defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway-associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated. PMID:24987116

Predicted Responses of Vegetation to Climate Change: A Global Analysis of Changes in Primary Productivity and Water Use Efficiency in the 21st Century

NASA Astrophysics Data System (ADS)

Bernardes, S.

2016-12-01

Global coupled carbon-climate simulations show considerable variability in outputs for atmospheric and land fields over the 21st century. This variability includes changes in temperature and in the quantity and spatiotemporal distribution of precipitation for large regions on the planet. Studies have considered that reductions in water availability due to decreased precipitation and increased water demand by the atmosphere may negatively affect plant metabolism and reduce carbon uptake. Future increases in carbon dioxide concentrations are expected to affect those interactions and potentially offset reductions in productivity. It is uncertain how plants will adjust their water use efficiency (WUE, plant production per water loss by evapotranspiration) in response to changing environmental conditions. This work investigates predicted changes in WUE in the 21st century by analyzing an ensemble of Earth System Models from the Coupled Model Intercomparison Project 5 (CMIP5), together with flux tower data and products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Two representative concentration pathways were selected to describe possible climate futures (RCP4.5 and RCP8.5). Periods of analysis included 2006-2099 (predicted) and 1850-2005 (reference). Comparisons between modeled, flux and satellite data for IPCC SREX regions were used to address the significant intermodel variability observed for the CMIP5 ensemble (larger variability for RCP8.5, higher intermodel agreement in Southeast Asia, lower intermodel agreement in arid areas). Model skill was evaluated in support of model selection and the spatiotemporal analysis of changes in WUE. Global, regional and latitudinal distributions of departures of projected conditions in relation to historical values are presented for both concentration pathways. Results showed high model sensitivity to different concentration pathways and increase in GPP and WUE for most of the planet (increases consistently higher for RCP8.5). Higher increases in GPP and WUE are predicted to occur over higher latitudes in the northern hemisphere (boreal region), with WUE usually following GPP in changes. Decreases in productivity and WUE occur mostly in the tropics, affecting tropical forests in Central America and in the Amazon.

A Multimodel Ensemble Analysis of Global Changes in Plant Water Use Efficiency and Primary Productivity in the 21st Century

NASA Astrophysics Data System (ADS)

Bernardes, S.

2017-12-01

Outputs from coupled carbon-climate models show considerable variability in atmospheric and land fields over the 21st century, including changes in temperature and in the spatiotemporal distribution and quantity of precipitation over the planet. Reductions in water availability due to decreased precipitation and increased water demand by the atmosphere may reduce carbon uptake by critical ecosystems. Conversely, increases in atmospheric carbon dioxide have the potential to offset reductions in productivity. This work focuses on predicted responses of plants to environmental changes and on how plants will adjust their water use efficiency (WUE, plant production per water loss by evapotranspiration) in the 21st century. Predicted changes in WUE were investigated using an ensemble of Earth System Models from the Coupled Model Intercomparison Project 5 (CMIP5), flux tower data and products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Scenarios for climate futures used two representative concentration pathways, including carbon concentration peak in 2040 (RCP4.5) and rising emissions throughout the 21st century (RCP8.5). Model results included the periods 2006-2009 (predicted) and 1850-2005 (reference). IPCC SREX regions were used to compare modeled, flux and satellite data and to address the significant intermodel variability observed for the CMIP5 ensemble (larger variability for RCP8.5, higher intermodel agreement in Southeast Asia, lower intermodel agreement in arid areas). An evaluation of model skill at the regional level supported model selection and the spatiotemporal analysis of changes in WUE. Departures of projected conditions in relation to historical values are presented for both concentration pathways at global, regional levels, including latitudinal distributions. High model sensitivity to different concentration pathways and increase in GPP and WUE was observed for most of the planet (increases consistently higher for RCP8.5). Higher latitudes in the northern hemisphere (boreal region) are predicted to experience higher increases in GPP and WUE, with WUE usually following GPP in changes. Models point to decreases in productivity and WUE mostly in the tropics, affecting tropical forests in the Amazon and in Central America.

Literature-based condition-specific miRNA-mRNA target prediction.

PubMed

Oh, Minsik; Rhee, Sungmin; Moon, Ji Hwan; Chae, Heejoon; Lee, Sunwon; Kang, Jaewoo; Kim, Sun

2017-01-01

miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets.