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Environmental Bioinformatics and Computational Toxicology Center
EPA Grant Number: R832721Center: New Jersey Research Center for Environmental Bioinformatics and Computational Toxicology
Center Director: Welsh, William J.
Title: Environmental Bioinformatics and Computational Toxicology Center
Investigators: Welsh, William J. , Georgopoulos, Panos G.
Current Investigators: Welsh, William J. , Androulakis, Ioannis , Floudas, Christodoulos , Georgopoulos, Panos G. , Ierapetritou, Marianthi , Rabitz, Herschel , Tong, Weida
Institution: University of Medicine and Dentistry of New Jersey , Princeton University , Rutgers University
Current Institution: Princeton University , Rutgers University , U.S. Food and Drug Administration , University of Medicine and Dentistry of New Jersey
EPA Project Officer: Mustra, David
Project Period: October 1, 2005 through September 30, 2010
Project Amount: $5,422,135
RFA: Computational Toxicology: Environmental Bioinformatics Research Center (2004)
Research Category: Health Effects , Computational Toxicology
Description:
Objective:The Research Center will bring together a team of computational scientists, with diverse backgrounds in bioinformatics, cheminformatics and enviroinformatics, from UMDNJ, Rutgers, and Princeton Universities, and the USFDA’s Center for Toxicoinformatics. This team will address, in a systematic and integrative manner, multiple elements of the toxicant Source-to-Outcome sequence ( Investigational Area 1, as identified in the RFA) as well as develop cheminformatics tools for toxicant characterization ( Investigational Area 2, Predictive Models for Hazard Identification ). The computational tools to be developed through this effort will be extensively evaluated and refined through collaborative applications involving Center scientists as well as colleagues from the three universities and USEPA; particular emphasis will be on methods that enhance current quantitative risk assessment practices and reduce uncertainties.
Approach:The proposed Center will address a wide range of issues in Investigational Areas 1 and 2 and, furthermore, will pursue complementary applications in risk assessment ( Investigational Area 3 ). This will be achieved with the requested resources, by building upon a variety of methods and software systems recently developed at UMDNJ, Rutgers, Princeton (with funding from USEPA, USDOE, NIH and NSF), and USFDA. Research activities over the proposed 5-year effort will be organized in five projects; each project will develop a set of “stand-alone" components addressing specific problems of computational toxicology. Furthermore, Research Project 1 will provide an integrative framework for Investigational Area 1 while Project 4 will address the core issues of Area 2. Extensive interaction as well as public outreach and training activities will constitute essential elements of the Center and will be tightly interwoven with the research activities.
Expected Results:Research Project 1 (Development and Application of a Dose-Response Information Analysis [DORIAN] System) will provide an integrative framework for the outcomes of the other projects. This framework will include the following components: a web-accessible Environmental Bioinformatics Knowledge Base (EBKB) that will provide a user-oriented interface to an extensive set of information and modeling resources; the ebTrack integrated analysis system that will include linkages to multiple (public and commercial) computational and database systems; Bayesian computational tools for characterizing and reducing uncertainties in mechanistic modeling of toxicity pathways; diagnostic computational tools for sensitivity and stability analysis of mechanistic models and statistical methods for data analysis; and enhanced tools for quantitative risk assessment (QRA) applications (e.g. for cross-species extrapolation, chemical mixtures, and dose-response).
Research Project 2 (Hepatocyte Metabolism Model for Xenobiotics) will develop tools for identifying maximally informative sets of toxicologically relevant genes; tools for analysis of toxicologically relevant regulatory networks; an expanded version of the Rutgers hepatocyte metabolism model that will incorporate transformations of xenobiotics; and tools for the analysis of transcriptional regulation that will allow assessing changes in hepatocyte phenotypic phase space.
Research Project 3 (Tools for Optimal Identification of Biological Networks) will develop efficient identification tools for inferring biological network structure from available laboratory data; optimization tools for extracting quantitative information of biological system parameters (rate constants, diffusion coefficients, binding affinities, etc.); global sensitivity analysis tools for identifying most effective molecular targets or pathways of biological networks and for guiding the design of laboratory experiments; and optimal feedback control tools for inferring networks with feedback loops.
Research Project 4 (Cheminformatics Tools for Toxicant Characterization) will develop an integrative hierarchical decision-forest framework for toxicant characterization that encompasses several novel technologies, including the Shape Signatures tool that rapidly matches organic and organometallic chemicals with each other or, alternatively, against target receptor sites/subsites; the Polynomial Neural Network (PNN) that automatically generates physically-intuitive linear or non-linear QSAR models; and virtual high-throughput screening (vHTS) methods that predict ligand binding affinity and provide mechanistic information (toxicity pathways).
Research Project 5 (Optimization Tools for In Silico Proteomics) will customize computational methods for protein structure prediction and de novo protein design, with specific focus on the important families of Glutathione Transferases (GST) (cytosolic, mitochondrial and microsomal GST); develop and implement computational methods for elucidating the topology of signal transduction networks and addressing uncertainties in experimental data and models; and develop de novo computational proteomics methods for peptide and protein identification via tandem mass spectroscopy.
Journal Articles: 20 Displayed | Download in RIS Format
| Other center views: | All 103 publications | 24 publications in selected types | All 20 journal articles |
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Androulakis IP, Yang E, Almon RR. Analysis of time-series gene expression data: methods, challenges, and opportunities. Annual Review of Biomedical Engineering 2007;9:205-228. |
R832721 (2007) |
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Davis E, Ierapetritou M. A kriging method for the solution of nonlinear programs with black-box functions. AIChE Journal 2007;53(8):2001-2012. |
R832721 (2007) |
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DiMaggio Jr PA, Floudas CA. A mixed-integer optimization framework for de novo peptide identification. AIChE Journal 2007;53(1):160-173. |
R832721 (2007) R832721C005 (2006) |
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DiMaggio Jr PA, Floudas CA. De novo peptide identification via tandem mass spectrometry and integer linear optimization. Analytical Chemistry 2007;79(4):1433-1446. |
R832721 (2007) |
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Ekins S, Chang C, Mani S, Krasowski MD, Reschly EJ, Iyer M, Kholodovych V, Ai N, Welsh WJ, Sinz M, Swaan PW, Patel R, Bachmann K. Human pregnane X receptor antagonists and agonists define molecular requirements for different binding sites. Molecular Pharmacology 2007;72(3):592-603. |
R832721 (2007) |
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Floudas CA. Computational methods in protein structure prediction. Biotechnology and Bioengineering 2007;97(2):207-213. |
R832721 (2007) |
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Foteinou PT, Calvano SE, Lowry SF, Androulakis IP. An indirect response model of endotoxin-induced systemic inflammation. Journal of Critical Care 2007;22(4):337-338. |
R832721 (2007) |
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Fung HK, Welsh WJ, Floudas CA. Computational de novo peptide and protein design: rigid templates versus flexible templates. Industrial & Engineering Chemistry Research 2008;47(4):993-1001. |
R832721 (2007) |
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Georgopoulos PG. A multiscale approach for assessing the interactions of environmental and biological systems in a holistic health risk assessment framework. Water, Air, & Soil Pollution: Focus 2008;8(1):1567-7230. |
R832721 (2007) |
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Jiao Y, Su M, Chen M, Jia W, Chou Y, Huang Z, Yang N, Tong W. LC/ESI-MS method for the determination of trimetazidine in human plasma: application to a bioequivalence study on Chinese volunteers. Journal of Pharmaceutical and Biomedical Analysis 2007;43(5):1804-1807. |
R832721 (2007) |
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Jornsten R, Ouyang M, Wang H-Y. A meta-data based method for DNA microarray imputation. BMC Bioinformatics 2007;8:109. |
R832721 (2007) |
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McAllister SR, Mickus BE, Klepeis JL, Floudas CA. Novel approach for α-helical topology prediction in globular proteins: generation of interhelical restraints. Proteins: Structure, Function, and Bioinformatics 2006;65(4):930-952. |
R832721 (2007) |
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Rajgaria R, McAllister SR, Floudas CA. A novel high resolution Cα—Cα distance dependent force field based on a high quality decoy set. Proteins: Structure, Function, and Bioinformatics 2006;65(3):726-741. |
R832721 (2007) R832721C005 (2006) |
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Wang CY, Ai N, Arora S, Erenrich E, Nagarajan K, Zauhar R, Young D, Welsh WJ. Identification of previously unrecognized antiestrogenic chemicals using a novel virtual screening approach. Chemical Research in Toxicology 2006;19(12):1595-1601. |
R832721C004 (2006) |
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Yang E, Foteinou PT, King KR, Yarmush ML, Androulakis IP. A novel non-overlapping bi-clustering algorithm for network generation using living cell array data. Bioinformatics 2007;23(17):2306-2313. |
R832721 (2007) |
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Yang E, Maguire T, Yarmush ML, Berthiaume F, Androulakis IP. Bioinformatics analysis of the early inflammatory response in a rat thermal injury model. BMC Bioinformatics 2007;8:10. |
R832721 (2007) |
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Yang E, Simcha D, Almon RR, Dubois DC, Jusko WJ, Androulakis IP. Context specific transcription factor prediction. Annals of Biomedical Engineering 2007;35(6):1053-1067. |
R832721 (2007) |
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Yang E, Almon RR, DuBois DC, Jusko WJ, Androulakis IP. Extracting global system dynamics of corticosteroid genomic effects in rat liver. Journal of Pharmacology and Experimental Therapeutics 2008;324(3):1243-1254. |
R832721 (2007) |
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Yang E, Maguire TJ, Yarmush ML, Berthiaume F, Androulakis IP. Identification of regulatory mechanisms of the hepatic response to thermal injury. Computers & Chemical Engineering 2008;32(1-2):356-369. |
R832721 (2007) |
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Yang E, Maguire T, Yarmush ML, Androulakis IP. Informative gene selection and design of regulatory networks using integer optimization. Computers & Chemical Engineering 2008;32(4-5):633-649. |
R832721 (2007) |
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computational toxicology, bioinformatics, proteomics, metabonomics, physiomics, cytomics, genomics, transcriptomics, enviroinformatics, cheminformatics, biologically based dose response-modeling, cross-species extrapolation, risk assessment,
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ENVIRONMENTAL MANAGEMENT, Geographic Area, Scientific Discipline, Health, Risk Assessment, Biology, Risk Assessments, Biochemistry, Environmental Monitoring, State, exposure assessment, biochemical research, chemical composition, ecological risk assessment, toxicologic assessment, bioinformatics, human health risk, biopollution, dose-response, toxicology, environmental risks, risk, outreach and training, computational toxicology, New Jersey (NJ)
Progress and Final Reports:
2006 Progress Report
2007 Progress Report
Subprojects under this Center:
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R832721C001 Development and Application of the DORIAN (Dose-Response Information Analysis) System
R832721C002 Hepatocyte Metabolism Model for Xenobiotics
R832721C003 Development of Computational Tools for Optimal Identification of Biological Networks
R832721C004 Cheminformatics Tools for Toxicant Characterization
R832721C005 Optimization Tools for In Silico Structural Proteomics