Discrete dynamic modelling of signal transduction networks

19 November 2010

Réka Albert
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.


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