19 November 2010
Departments of Physics and Biology
Pennsylvania State University
Modeling the dynamics of complex biological systems is challenging even when well-established biochemical frameworks are applicable. In the case of regulatory and signaling systems that include heterogeneous components and interactions, and/or are sparsely documented in terms of quantitative information, modeling is often thought impossible. This talk will argue for the usefulness of a discrete dynamic framework in incorporating qualitative interaction information into a predictive model. I will focus on a model of the signaling network responsible for the survival and long-term competence of cytotoxic T cells in the blood cancer T-LGL leukemia. Our model suggests that the persistence of IL-15 and PDGF is sufficient to reproduce all known deregulations in leukemic T-LGL. It also predicts the key nodes whose (in)activity is necessary to induce the apoptosis of T cells and reverse the disease. We experimentally validated several of these predictions. The success of this and other similar models indicates that network-based discrete dynamic modeling is a promising framework that allows system-level analysis and predictions that would not be possible using traditional methods.
current theory lunch schedule