Carolina Center for Computational Toxicology

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The Carolina Center for Computational Toxicology will advance the field of computational toxicology through the development of new methods and tools, as well as through collaborative efforts. To balance the research needs detailed in the RFA 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).

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.

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