Jump to main content or area navigation.

Contact Us

Extramural Research

Carolina Center for Computational Toxicology

EPA Grant Number: R833825
Center: Carolina Center for Computational Toxicology
Center Director: Rusyn, Ivan
Title: Carolina Center for Computational Toxicology
Investigators: Rusyn, Ivan , Elston, Timothy , Gomez, Shawn , Gupta, Mayetri , Nobel, Andrew , Sun, Wei , Tropsha, Alex , Wang, Simon , Wright, Fred A.
Current Investigators: Rusyn, Ivan , Elston, Timothy , Gomez, Shawn , Tropsha, Alex , Wright, Fred A. , Yeatts, Karin
Institution: University of North Carolina at Chapel Hill
EPA Project Officer: Pascual, Pasky
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)
Research Category: Computational Toxicology

Description:

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: 6 Displayed | Download in RIS Format

Other center views: All 21 publications 6 publications in selected types All 6 journal articles

Type Citation Sub Project Document Sources
Journal Article 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)
R832720 (2008)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Oxford Journals-Full Text HTML
    Exit
  • Abstract: Oxford Journals
    Exit
  • Other: Oxford Journals-Full Text PDF
    Exit
  • Journal Article 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)
    R832720 (2009)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Toxicological Sciences - Full Text HTML
    Exit
  • Abstract: Toxicological Sciences - Abstract
    Exit
  • Other: Toxicological Sciences - Full Text PDF
    Exit
  • Journal Article Sun W, Buck MJ, Patel M, Davis IJ. Improved ChIP-chip analysis by a mixture model approach. BMC Bioinformatics 2009;10:173. R833825 (2008)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: BioMed Central-Full Text HTML
    Exit
  • Abstract: BioMed Central-Abstract
    Exit
  • Other: BioMed Central-PDF
    Exit
  • Journal Article 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. R833825 (2008)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: PLoS One-Full Text HTML & PDF Access Link
    Exit
  • Journal Article 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)
    R832720 (2007)
    R832720 (2008)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Environmental Health Perspectives-Full Text HTML
  • Abstract: Environmental Health Perspectives
  • Other: Environmental Health Perspectives-Full Text PDF
  • Journal Article 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)
    R832720 (2009)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Supplemental Keywords:

    toxicogenomics, toxicants, chemicals, dose-response, QSAR, QSPR, modeling, quantitative risk assessment, public policy,

    Progress and Final Reports:
    2008 Progress Report

    Top of Page

    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.

    Jump to main content.