Grantee Research Project Results
Carolina Environmental Bioinformatics Center
EPA Grant Number: R832720Center: Center for Environmental Medicine, Asthma, lung biology
Center Director: Peden, David B
Title: Carolina Environmental Bioinformatics Center
Investigators: Wright, Fred A. , Stotts, David , Prins, Jan F. , Galluppi, Kenneth J. , Marron, J. Stephen , Threadgill, David W. , Tropsha, Alex , Rusyn, Ivan , Kupper, Lawrence
Current Investigators: Wright, Fred A. , Tropsha, Alex , Rusyn, Ivan , McMillan, Leonard , Farber, Rosann
Institution: University of North Carolina at Chapel Hill
EPA Project Officer: Hahn, Intaek
Project Period: October 1, 2005 through September 30, 2010
Project Amount: $4,494,117
RFA: Computational Toxicology: Environmental Bioinformatics Research Center (2004) RFA Text | Recipients Lists
Research Category: Safer Chemicals , Computational Toxicology , Human Health
Objective:
The Carolina Environmental Bioinformatics Research Center brings together multiple investigators and disciplines, combining expertise in biostatistics, computational biology, chem-informatics and computer science to advance the field of Computational Toxicology.
The objective of this proposal is to create an Environmental Bioinformatics Research Center with broad-ranging capability to enhance and advance the field of Computational Toxicology. The Center will develop novel analytic and computational methods, create efficient user-friendly tools to disseminate the methods to the wider community, and will apply the computational methods to data from molecular toxicology and other studies.
Approach:
Effort will be divided into three Research Projects and an Administrative Unit. Each Research Project is further divided into Functional Areas consisting of Analysis, Methods Development, and Tools Development. Project 1 (Biostatistics in Computational Biology) will provide biostatistical support to the Center, performing analysis and developing new methods in collaboration with EPA personnel and the computational toxicology community. Project 2 (Chem-informatics) will coordinate the compilation and mining of data from relevant external databases and perform analysis and methods development for investigating Quantitative Structure-Activity Relationships with burgeoning high-throughput chem-informatics data. In addition, Project 2 will develop computational tools to perform these tasks. Project 3 (Computational Infrastructure for Systems Toxicology) will create a framework for merging data from various –omic technologies in a systems biology approach. The investigation of rodent liver toxicity is used as a driving biological problem, inspiring new methods and architectures for data storage. Finally, Project 3 will provide programming support for the further development of tools arising from Projects 1 and 2. The Administration Core provides and staff and support to the Center, is responsible for ensuring that Center objectives and goals are being met, and provides oversight for each for the Functional Areas. A detailed Quality Management Plan ensures that the research and data management will be conducted with integrity and adhering to appropriate data interchange standards. The plans for Public Outreach and Translation Activity will ensure that the activities of the Center are translated into useable information and materials for the public and policy makers.
Expected Results:
The Center is expected to advance the field of computational toxicology through the development of new methods and tools, as well as through direct collaborative efforts with EPA and other environmental scientists. In each Project, we expect that new methods will be developed and published that represent the state-of-the-art. The tools developed within each project will be widely disseminated, and will be useful both to trained bioinformatics scientists and bench scientists. The synthesis of data from a variety of sources will move the field of computational toxicology from a hypothesis-driven science toward a predictive science. Each Project is goal-oriented, with criteria for success that will be reviewed by the Scientific Advisory Committee.
Journal Articles: 43 Displayed | Download in RIS Format
| Other center views: | All 115 publications | 44 publications in selected types | All 43 journal articles |
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Borysov P, Hannig J, Marron JS, Muratov E, Fourches D, Tropsha A. Activity prediction and identification of mis-annotated chemical compounds using extreme descriptors. Journal of Chemometrics 2016;30(3):99-108. |
R832720 (2009) R833825 (Final) |
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Bradford BU, O'Connell TM, Han J, Kosyk O, Shymonyak S, Ross PK, Winnike J, Kono H, Rusyn I. Metabolomic profiling of a modified alcohol liquid diet model for liver injury in the mouse uncovers new markers of disease. Toxicology and Applied Pharmacology 2008;232(2):236-243. |
R832720 (2008) |
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Chen M-H, Ibrahim JG, Chi Y-Y. A new class of mixture models for differential gene expression in DNA microarray data. Journal of Statistical Planning and Inference 2008;138(2):387-404. |
R832720 (2006) R832720 (2008) |
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Fourches D, Barnes JC, Day NC, Bradley P, Reed JZ, Tropsha A. Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. Chemical Research in Toxicology 2010;23(1):171-183. |
R832720 (2009) R833825 (Final) |
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Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of Chemical Information and Modeling 2010;50(7):1189-1204. |
R832720 (2009) R833825 (Final) |
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Fourches D, Muratov E, Ding F, Dokholyan NV, Tropsha A. Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches. Journal of Chemical Information and Modeling 2013;53(8):1915-1922. |
R832720 (2009) R833825 (Final) |
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Gatti DM, Shabalin AA, Lam T-C, Wright FA, Rusyn I, Nobel AB. FastMap: fast eQTL mapping in homozygous populations. Bioinformatics 2009;25(4):482-489. |
R832720 (2008) R833825 (2008) R833825 (Final) |
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Gatti DM, Sypa M, Rusyn I, Wright FA, Barry WT. SAFEGUI: resampling-based tests of categorical significance in gene expression data made easy. Bioinformatics 2009;25(4):541-542. |
R832720 (2008) R833825 (Final) |
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Gatti DM, Harrill AH, Wright FA, Threadgill DW, Rusyn I. Replication and narrowing of gene expression quantitative trait loci using inbred mice. Mammalian Genome 2009;20(7):437-446. |
R832720 (2009) R833825 (Final) |
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Gatti D, Maki A, Chesler EJ, Kirova R, Kosyk O, Lu L, Manly KF, Williams RW, Perkins A, Langston MA, Threadgill DW, Rusyn I. Genome-level analysis of genetic regulation of liver gene expression networks. Hepatology 2007;46(2):548-557. |
R832720 (2007) R832720 (2008) |
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Gelfond JAL, Ibrahim JG, Zou F. Proximity model for expression quantitative trait loci (eQTL) detection. Biometrics 2007;63(4):1108-1116. |
R832720 (2006) R832720 (2008) |
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Ghosh A, Zou F, Wright FA. Estimating odds ratios in genome scans: an approximate conditional likelihood approach. The American Journal of Human Genetics 2008;82(5):1064-1074. |
R832720 (2008) |
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Graham MR, Virtaneva K, Porcella SF, Gardner DJ, Long RD, Welty DM, Barry WT, Johnson CA, Parkins LD, Wright FA, Musser JM. Analysis of the transcriptome of Group A Streptococcus in mouse soft tissue infection. American Journal of Pathology 2006;169(3):927-942. |
R832720 (2007) R832720 (2008) |
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Gupta M, Ibrahim JG. Variable selection in regression mixture modeling for the discovery of gene regulatory networks. Journal of the American Statistical Association 2007;102(479):867-880. |
R832720 (2006) R832720 (2008) |
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Gupta M. Generalized hierarchical Markov models for the discovery of length-constrained sequence features from genome tiling arrays. Biometrics 2007;63(3):797-805. |
R832720 (2006) R832720 (2008) |
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Han J, Danell RM, Patel JR, Gumerov DR, Scarlett CO, Speir JP, Parker CE, Rusyn I, Zeisel S, Borchers CH. Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry. Metabolomics 2008;4(2):128-140. |
R832720 (2008) |
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Harrill AH, Rusyn I. Systems biology and functional genomics approaches for the identification of cellular responses to drug toxicity. Expert Opinion on Drug Metabolism & Toxicology 2008;4(11):1379-1389. |
R832720 (2008) |
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Harrill AH, Ross PK, Gatti DM, Threadgill DW, Rusyn I. Population-based discovery of toxicogenomics biomarkers for hepatotoxicity using a laboratory strain diversity panel. Toxicological Sciences 2009;110(1):235-243. |
R832720 (2009) R833825 (2008) R833825 (Final) |
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Harrill AH, Watkins PB, Su S, Ross PK, Harbourt DE, Stylianou IM, Boorman GA, Russo MW, Sackler RS, Harris SC, Smith PC, Tennant R, Bogue M, Paigen K, Harris C, Contractor T, Wiltshire T, Rusyn I, Threadgill DW. Mouse population-guided resequencing reveals that variants in CD44 contribute to acetaminophen-induced liver injury in humans. Genome Research 2009;19(9):1507-1515. |
R832720 (2009) |
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Harrill JA ,Li Z, Wright FA, Radio NM, Mundy WR, Tornero-Velez R, Crofton KM. Transcriptional response of rat frontal cortex following acute in vivo exposure to the pyrethroid insecticides permethrin and deltamethrin. BMC Genomics 2008;9:546. |
R832720 (2008) |
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Hu J, Wright FA, Zou F. Estimation of expression indexes for oligonucleotide arrays using the singular value decomposition. Journal of the American Statistical Association 2006;101(473):41-50. |
R832720 (2006) R832720 (2008) |
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Hu J, Wright FA. Assessing differential gene expression with small sample sizes in oligonucleotide arrays using a mean-variance model. Biometrics 2007;63(1):41-49. |
R832720 (2006) R832720 (2008) |
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Judson R, Elloumi F, Setzer RW, Li Z , Shah I. A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model. BMC Bioinformatics 2008;9:241-256. |
R832720 (2008) |
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Li Z, Wright FA, Royland J. Age-dependent variability in gene expression in male Fischer 344 rat retina. Toxicological Sciences 2009;107(1):281-292. |
R832720 (2008) |
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Liu Y, Hayes D, Nobel A, Marron J. Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2012;103(483):1281-1293. |
R832720 (2009) |
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Low YS, Sedykh AY, Rusyn I, Tropsha A. Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Current Topics in Medicinal Chemistry 2014;14(11):1356-1364. |
R832720 (2009) R833825 (Final) R835166 (2014) R835166 (2016) R835166 (Final) |
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Muratov EN, Varlamova EV, Artemenko AG, Khristova T, Kuz'min VE, Makarov VA, Riabova OB, Wutzler P, Schmidtke M. QSAR analysis of [(biphenyloxy)propyl] isoxazoles: agents against coxsackievirus B3. Future Medicinal Chemistry 2011;3(1):15-27. |
R832720 (2009) R833825 (Final) |
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Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Kuz'min VE. Existing and developing approaches for QSAR analysis of mixtures. Molecular Informatics 2012;31(3-4):202-221. |
R832720 (2009) R833825 (Final) |
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Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Nikolaeva-Glomb L, Galabov AS, Kuz'min VE. QSAR analysis of poliovirus inhibition by dual combinations of antivirals. Structural Chemistry 2013;24(5):1665-1679. |
R832720 (2009) R833825 (Final) |
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Nadler JJ, Zou F, Huang H, Moy SS, Lauder J, Crawley JN, Threadgill DW, Wright FA, Magnuson TR. Large-scale gene expression differences across brain regions and inbred strains correlate with a behavioral phenotype. Genetics 2006;174(3):1229-1236. |
R832720 (2006) R832720 (2008) |
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Pogribny IP, Tryndyak VP, Bagnyukova TV, Melnyk S, Montgomery B, Ross SA, Latendresse JR, Rusyn I, Beland FA. Hepatic epigenetic phenotype predetermines individual susceptibility to hepatic steatosis in mice fed a lipogenic methyl-deficient diet. Journal of Hepatology 2009;51(1):176-186. |
R832720 (2009) |
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Polishchuk PG, Kuz'min VE, Artemenko AG, Muratov EN. Universal approach for structural interpretation of QSAR/QSPR models. Molecular Informatics 2013;32(9-10):843-853. |
R832720 (2009) |
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Roberts A, Pardo-Manuel de Villena F, Wang W, McMillan L, Threadgill DW. The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics. Mammalian Genome 2007;18(6-7):473-481. |
R832720 (2007) R832720 (2008) |
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Scharph R, Tjelmeland H, Parmigiani G, Nobel A. A Bayesian Model for Cross-Study Differential Gene Expression. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2012;104(488):1295-1310 |
R832720 (2009) |
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Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E, Oberg T, Todeschini R, Fourches D, Varnek A. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. Journal of Chemical Information and Modeling 2008;48(9):1733-1746. |
R832720 (2008) |
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Tropsha A, Golbraikh A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Current Pharmaceutical Design 2007;13(34):3494-3504. |
R832720 (2007) R832720 (2008) |
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Woods CG, Kosyk O, Bradford BU, Ross PK, Burns AM, Cunningham ML, Qu P, Ibrahim JG, Rusyn I. Time course investigation of PPARα- and Kupffer cell-dependent effects of WY-14,643 in mouse liver using microarray gene expression. Toxicology and Applied Pharmacology 2007;225(3):267-277. |
R832720 (2007) R832720 (2008) |
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Woods CG, Vanden Heuvel JP, Rusyn I. Genomic profiling in nuclear receptor-mediated toxicity. Toxicologic Pathology 2007;35(4):474-494. |
R832720 (2007) R832720 (2008) |
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Wright FA, Huang H, Guan X, Gamiel K, Jeffries C, Barry WT, Pardo-Manuel de Villena F, Sullivan PF, Wilhelmsen KC, Zou F. Simulating association studies: a data-based resampling method for candidate regions or whole genome scans. Bioinformatics 2007;23(19):2581-2588. |
R832720 (2007) R832720 (2008) |
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Zhu H, Tropsha A, Fourches D, Varnek A, Papa E, Gramatica P, Oberg T, Dao P, Cherkasov A, Tetko IV. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. Journal of Chemical Information and Modeling 2008;48(4):766-784. |
R832720 (2008) |
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Zhu H, Rusyn I, Richard A, Tropsha A. Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity. Environmental Health Perspectives 2008;116(4):506-513. |
R832720 (2007) R832720 (2008) R833825 (2008) R833825 (Final) |
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Zhu H, Ye L, Richard A, Golbraikh A, Wright FA, Rusyn I, Tropsha A. A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. Environmental Health Perspectives 2009;117(8):1257-1264. |
R832720 (2009) R833825 (2008) R833825 (Final) |
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Zhu H, Martin TM, Ye L, Sedykh A, Young DM, Tropsha A. Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. Chemical Research in Toxicology 2009;22(12):1913-1921. |
R832720 (2009) R833825 (Final) |
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Supplemental Keywords:
Toxicogenomics, toxics, chemicals, dose-response, QSAR, QSPR, molecular biology, quantitative risk assessment, public policy,, Health, Scientific Discipline, ENVIRONMENTAL MANAGEMENT, Risk Assessments, Biochemistry, Environmental Monitoring, Biology, Risk Assessment, ecological risk assessment, computational toxicology, biostatistics, toxicology, dose-response, environmental risks, biopollution, outreach and training, chemical composition, human exposure, toxicologic assessment, bioinformatics, biochemical research, human health riskProgress and Final Reports:
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.