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We use tools from computational and mathematical modeling to solve a variety of problems in toxicology.


A major focus of our work is the application of computational methods to study the signaling and transcriptional regulatory networks that underlie the determination of cell fate, and the perturbation of these networks by environmental pollutants like dioxin. We are integrating diverse genomic data sets to map and model transcriptional regulatory networks and their environmental perturbation in the immune system and the liver. We are also interested in the extraction of predictive features from genomic data sets to model the toxic potential of chemical agents and pharmaceuticals, and spatial multi-scale modeling of tissue-level phenomena like toxin-induced liver injury. We rely primarily on mathematical and statistical modeling as research tools, and work in close collaboration with experimental scientists.