Grantee Research Project Results
Carolina Center for Computational Toxicology
EPA Grant Number: R833825Center: UT Center for Infrastructure Modeling and Management
Center Director: Hodges, Ben R.
Title: Carolina Center for Computational Toxicology
Investigators: Rusyn, Ivan , Wright, Fred A. , Tropsha, Alex , Nobel, Andrew , Gupta, Mayetri , Gomez, Shawn , Wang, Simon , Elston, Timothy , Sun, Wei
Current Investigators: Rusyn, Ivan , Wright, Fred A. , Tropsha, Alex , Nobel, Andrew , Gomez, Shawn , Elston, Timothy , Sun, Wei
Institution: University of North Carolina at Chapel Hill
Current Institution: University of North Carolina at Chapel Hill , North Carolina State University
EPA Project Officer: Hahn, Intaek
Project Period: April 1, 2008 through March 31, 2012
Project Amount: $3,400,000
RFA: Computational Toxicology Centers: Development Of Predictive Environmental And Biomedical Computer-Based Simulations And Models (2007) RFA Text | Recipients Lists
Research Category: Computational Toxicology , Human Health , Safer Chemicals
Objective:
The objective of this proposal is to create The Carolina Center for Computational Toxicology. We present a clear plan for an effective, broad and interdisciplinary effort to devise novel tools, methods and knowledge that will utilize publicly available data to assist the regulatory agencies and the greater environmental health sciences community in protecting the environment and human health.
Approach:
The Center will apply knowledge and expertise of the individual investigators and teams to develop complex predictive modeling solutions that span from mechanistic- to discovery-based efforts. The Center will be divided into three Research Projects and an Administrative Core Unit. To balance the research needs detailed in the Funding Opportunity EPA-G2007-STAR-D1 and maximize the interactions within the Center and between the Center and the larger environmental health community, the following sub-disciplines were recognized as critical to the Center: 1) Biomedical modeling of chemical-perturbed networks (Project 1, PIs Gomez and Elston), 2) Toxico-genetic modeling (Project 2, PIs Wright and Rusyn), and 3) Chem-informatics (Project 3, PI Tropsha). Overall, we chose a bottom-up approach to predictive computational modeling of adverse effects of toxic agents. Our emphasis spans from the fine-scale predictive simulations of the protein-protein/-chemical interactions in nuclear receptor networks (Project 1), to mapping chemical-perturbed networks and devising modeling tools that can predict the pathobiology of the test compounds based on a limited set of biological data (Project 1), to building tools that will enable toxicologists to understand the role of genetic diversity between individuals in responses to toxicants (Project 2), to unbiased discovery-driven prediction of adverse chronic in vivo outcomes based on statistical modeling of chemical structures, high-throughput screening and the genetic makeup of the organism (Project 3). The Administrative Core Unit provides administrative and programming staff in support of the entire Center, is responsible for ensuring that Center objectives and goals are being met, and provides oversight for each for the Projects. 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 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 will advance the field of computational toxicology through the development of new methods and tools, as well as through collaborative efforts. In each Project, new computer-based models will be developed and published that represent the state-of-the-art. The tools produced within each project will be widely disseminated, and the emphasis will be placed on their usability by the risk assessment community and the investigative toxicologists alike. 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.
Journal Articles: 63 Displayed | Download in RIS Format
| Other center views: | All 98 publications | 65 publications in selected types | All 63 journal articles |
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Abell AN, Jordan NV, Huang W, Prat A, Midland AA, Johnson NL, Granger DA, Mieczkowski PA, Perou CM, Gomez SM, Li L, Johnson GL. MAP3K4/CBP-regulated H2B acetylation controls epithelial-mesenchymal transition in trophoblast stem cells. Cell Stem Cell 2011;8(5):525-537. |
R833825 (Final) |
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Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicology and Applied Pharmacology 2015;284(2):262-272. |
R833825 (Final) R835166 (Final) |
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Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization. Toxicology and Applied Pharmacology 2015; 284(2):273-280. |
R833825 (Final) R835166 (Final) |
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Artemenko AG, Muratov EN, Kuz'min VE, Muratov NN, Varlamova EV, Kuz'mina AV, Gorb LG, Golius A, Hill FC, Leszczynski J, Tropsha A. QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action. SAR and QSAR in Environmental Research 2011;22(5-6):575-601. |
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Berginski ME, Vitriol EA, Hahn KM, Gomez SM. High-resolution quantification of focal adhesion spatiotemporal dynamics in living cells. PLoS ONE 2011;6(7):e22025 (13 pp.). |
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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. |
R833825 (Final) R832720 (2009) |
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Bradford BU, Lock EF, Kosyk O, Kim S, Uehara T, Harbourt D, DeSimone M, Threadgill DW, Tryndyak V, Pogribny IP, Bleyle L, Koop DR, Rusyn I. Interstrain differences in the liver effects of trichloroethylene in a multistrain panel of inbred mice. Toxicological Sciences 2011;120(1):206-217. |
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Chaki SP, Barhoumi R, Berginksi ME, Sreenivasappa H, Trache A, Gomez SM, Rivera GM. Nck enables directional cell migration through the coordination of polarized membrane protrusion with adhesion dynamics. Journal of Cell Science 2013;126(Pt 7):1637-1649. |
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Chen Z, Lessey E, Berginski ME, Cao L, Li J, Trepat X, Itano M, Gomez SM, Kapustina M, Huang C, Burridge K, Truskey G, Jacobson K. Gleevec, an Abl family inhibitor, produces a profound change in cell shape and migration. PLoS ONE 2013;8(1):e52233 (14 pp.). |
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Choi K, Gomez SM. Comparison of phylogenetic trees through alignment of embedded evolutionary distances. BMC Bioinformatics 2009;10:423. |
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Doolittle JM, Gomez SM. Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens. Virology Journal 2010;7:82 (15 pp.). |
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Doolittle JM, Gomez SM. Mapping protein interactions between Dengue virus and its human and insect hosts. PLoS Neglected Tropical Diseases 2011;5(2):e954 (15 pp.). |
R833825 (Final) |
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Duncan JS, Whittle MC, Nakamura K, Abell AN, Midland AA, Zawistowski JS, Johnson NL, Granger DA, Jordan NV, Darr DB, Usary J, Kuan P-F, Smalley DM, Major B, He X, Hoadley KA, Zhou B, Sharpless NE, Perou CM, Kim WY, Gomez SM, Chen X, Jin J, Frye SV, Earp HS, Graves LM, Johnson GL. Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple negative breast cancer. Cell 2012;149(2):307-321. |
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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. |
R833825 (Final) R832720 (2009) |
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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. |
R833825 (Final) R832720 (2009) |
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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. |
R833825 (Final) R832720 (2009) |
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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. |
R833825 (2008) R833825 (Final) R832720 (2008) |
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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. |
R833825 (Final) R832720 (2008) |
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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. |
R833825 (Final) R832720 (2009) |
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Gatti DM, Zhao N, Chesler EJ, Bradford BU, Shabalin AA, Yordanova R, Lu L, Rusyn I. Sex-specific gene expression in the BXD mouse liver. Physiological Genomics 2010;42(3):456-468. |
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Gatti DM, Barry WT, Nobel AB, Rusyn I, Wright FA. Heading down the wrong pathway: on the influence of correlation within gene sets. BMC Genomics 2010;11:574. |
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Gatti DM, Lu L, Williams RW, Sun W, Wright FA, Threadgill DW, Rusyn I. MicroRNA expression in the livers of inbred mice. Mutation Research 2011;714(1-2):126-133. |
R833825 (Final) |
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Golbraikh A, Muratov E, Fourches D, Tropsha A. Data set modelability by QSAR. Journal of Chemical Information and Modeling 2014;54(1):1-4. |
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Ha MJ, Sun W. Partial correlation matrix estimation using ridge penalty followed by thresholding and re-estimation. Biometrics 2014;70(3):765-773. |
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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. |
R833825 (2008) R833825 (Final) R832720 (2009) |
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Herschlag G, Garcia GJ, Button B, Tarran R, Lindley B, Reinhardt B, Elston TC, Forest MG. A mechanochemical model for auto-regulation of lung airway surface layer volume. Journal of Theoretical Biology 2013;325:42-51. |
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Kesseler KJ, Blinov ML, Elston TC, Kaufmann WK, Simpson DA. A predictive mathematical model of the DNA damage G2 checkpoint. Journal of Theoretical Biology 2013;320:159-169. |
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Lee S, Wright FA, Zou F. Control of population stratification by correlation-selected principal components. Biometrics 2011;67(3):967-974. |
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Lock EF, Abdo N, Huang R, Xia M, Kosyk O, O'Shea SH, Zhou YH, Sedykh A, Tropsha A, Austin CP, Tice RR, Wright FA, Rusyn I. Quantitative high-throughput screening for chemical toxicity in a population-based in vitro model. Toxicological Sciences 2012;126(2):578-588. |
R833825 (Final) R835166 (Final) |
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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. |
R833825 (Final) R832720 (2009) R834999 (Final) R835166 (2014) R835166 (2016) R835166 (Final) |
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Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Kuz’min V, Fourches D, Zhu H, Rusyn I, Tropsha A. Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chemical Research in Toxicology 2011;24(8):1251-1262. |
R833825 (Final) R834999 (Final) |
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Martinez SM, Bradford BU, Soldatow VY, Kosyk O, Sandot A, Witek R, Kaiser R, Stewart T, Amaral K, Freeman K, Black C, LeCluyse EL, Ferguson SS, Rusyn I. Evaluation of an in vitro toxicogenetic mouse model for hepatotoxicity. Toxicology and Applied Pharmacology 2010;249(3):208-216. |
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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. |
R833825 (Final) R832720 (2009) |
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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. |
R833825 (Final) R832720 (2009) |
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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. |
R833825 (Final) R832720 (2009) |
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O'Shea SH, Schwarz J, Kosyk O, Ross PK, Ha MJ, Wright FA, Rusyn I. In vitro screening for population variability in chemical toxicity. Toxicological Sciences 2011;119(2):398-407. |
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Politi R, Rusyn I, Tropsha A. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods. Toxicology and Applied Pharmacology 2014;280(1):177-189. |
R833825 (Final) R835166 (2014) R835166 (2016) R835166 (Final) |
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Rusyn I, Daston GP. Computational toxicology: realizing the promise of the toxicity testing in the 21st century. Environmental Health Perspectives 2010;118(8):1047-1050. |
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Rusyn I, Gatti DM, Wiltshire T, Kleeberger SR, Threadgill DW. Toxicogenetics: population-based testing of drug and chemical safety in mouse models. Pharmacogenomics 2010;11(8):1127-1136. |
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Rusyn I, Sedykh A, Low Y, Guyton KZ, Tropsha A. Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data. Toxicological Sciences 2012;127(1):1-9. |
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Sedykh A, Zhu H, Tang H, Zhang L, Richard A, Rusyn I, Tropsha A. Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity. Environmental Health Perspectives 2011;119(3):364-370. |
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Soldatow VY, LeCluyse EL, Griffith LG, Rusyn I. In vitro models for liver toxicity testing. Toxicology Research 2013;2(1):23-39. |
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Staab JM, O’Connell TM, Gomez SM. Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS). BMC Bioinformatics 2010;11:123. |
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Strychalski W, Adalsteinsson D, Elston TC. A cut-cell method for simulating spatial models of biochemical reaction networks in arbitrary geometries. Communications in Applied Mathematics and Computational Science 2010;5(1):31-53. |
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Sun W, Buck MJ, Patel M, Davis IJ. Improved ChIP-chip analysis by a mixture model approach. BMC Bioinformatics 2009;10:173 (13 pp.). |
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Sun W, Xie W, Xu F, Grunstein M, Li K-C. Dissecting nucleosome free regions by a segmental semi-Markov model. PLoS One 2009;4(3):e4721 (10 pp.). |
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Sun W, Wright FA, Tang Z, Nordgard SH, Van Loo P, Yu T, Kristensen VN, Perou CM. Integrated study of copy number states and genotype calls using high-density SNP arrays. Nucleic Acids Research 2009;37(16):5365-5377. |
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Sun W, Wright FA. A geometric interpretation of the permutation p-value and its application in eQTL studies. Annals of Applied Statistics 2010;4(2):1014-1033. |
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Sun W. A statistical framework for eQTL mapping using RNA-seq data. Biometrics 2012;68(1):1-11. |
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Sun W, Li L. Multiple loci mapping via model-free variable selection. Biometrics 2012;68(1):12-22. |
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Sun W, Hu Y. eQTL mapping using RNA-seq data. Statistics in Biosciences 2013;5(1):198-219. |
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Sushko I, Novotarskyi S, Korner R, Pandey AK, Cherkasov A, Li J, Gramatica P, Hansen K, Schroeter T, Muller KR, Xi L, Liu H, Yao X, Oberg T, Hormozdiari F, Dao P, Sahinalp C, Todeschini R, Polishchuk P, Artemenko A, Kuz’min V, Martin TM, Young DM, Fourches D, Muratov E, Tropsha A, Baskin I, Horvath D, Marcou G, Muller C, Varnek A, Prokopenko VV, Tetko IV. Applicability domains for classification problems: benchmarking of distance to models for Ames mutagenicity set. Journal of Chemical Information and Modeling 2010;50(12):2094-2111. |
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Wright FA, Shabalin AA, Rusyn I. Computational tools for discovery and interpretation of expression quantitative trait loci. Pharmacogenomics 2012;13(3):343-352. |
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Xia K, Shabalin AA, Huang S, Madar V, Zhou YH, Wang W, Zou F, Sun W, Sullivan PF, Wright FA. seeQTL: a searchable database for human eQTLs. Bioinformatics 2012;28(3):451-452. |
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Xu K, Morgan KT, Todd Gehris A, Elston TC, Gomez SM. A whole-body model for glycogen regulation reveals a critical role for substrate cycling in maintaining blood glucose homeostasis. PLoS Computational Biology 2011;7(12):e1002272 (13 pp.). |
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Zhang L, Sedykh A, Tripathi A, Zhu H, Afantitis A, Mouchlis VD, Melagraki G, Rusyn I, Tropsha A. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches. Toxicology and Applied Pharmacology 2013;272(1):67-76. |
R833825 (Final) R835166 (2013) R835166 (Final) |
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Zhang X, Huang S, Sun W, Wang W. Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study. Genetics 2012;190(4):1511-1520. |
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Zhou Y-H, Xia K, Wright FA. A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics 2011;27(19):2672-2678. |
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Zhou Y-H, Barry WT, Wright FA. Empirical pathway analysis, without permutation. Biostatistics 2013;14(3):573-585. |
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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. |
R833825 (2008) R833825 (Final) R832720 (2007) R832720 (2008) |
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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. |
R833825 (2008) R833825 (Final) R832720 (2009) |
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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. |
R833825 (Final) R832720 (2009) |
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Zou F, Lee S, Knowles MR, Wright FA. Quantification of population structure using correlated SNPs by shrinkage principal components. Human Heredity 2010;70(1):9-22. |
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Supplemental Keywords:
toxicogenomics, toxicants, chemicals, dose-response, QSAR, QSPR, modeling, quantitative risk assessment, public policy,Progress 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.