COLLEGE OF ARTS AND SCIENCES Department of Mathematics and Statistics


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Colloquium: Rule-based Modeling of Signaling by Epidermal Growth Factor Receptor


4:10pm, Neill Hall 5W

Michael Blinov

Abstract: A common feature of biochemical networks comprising protein-protein interactions is combinatorial complexity, which is present whenever a relatively small number of biomolecular interactions have the potential to generate a much larger number of distinct chemical species and reactions. For a system marked by combinatorial complexity, the conventional approach of manually specifying each term of a mathematical model is often impossible if the model is intended to account comprehensively for the consequences of biomolecular interactions. A solution to this problem is to specify a rule for each interaction and its effects, and then use the rules to automatically generate a logically consistent reaction network and corresponding models, which may take diverse forms. Proteins and protein complexes are represented as graphs; rules are represented as graph transformations. This graph-theoretic formalism allows for the abstraction of proteins, functional components of proteins, and protein complexes. It is implemented in BioNetGen software [Blinov et al. (2004) Bioinformatics; Blinov et al. (in press) LNCS] and used to model several systems, including early events in signaling by the epidermal growth factor receptor (EGFR) [Blinov et al. (2006) BioSystems]. The model predicts the dynamics of 356 chemical species connected through 3,749 reactions. Finding signaling patterns in such a large reaction network presents a mathematical challenge. The model yields new predictions, such as distinct temporal patterns of phosphorylation for different tyrosines of EGFR, distinct reaction paths for Sos activation, and signaling by receptor monomers. The model helps design experiments to test hypotheses, e.g., genetic mutation blocking Shc-dependent pathways helps to distinguish between competitive and non-competitive mechanisms of adapter proteins binding.