Rule-based modeling of signal-transduction systems

James R. Faeder (University of Pittsburgh School of Medicine)

Abstract

Binding interactions among proteins and other biomolecules occur at the level of domains and motifs and often follow phosphorylation or other posttranslational modifications. The catalog of these functional elements and their interactions is continually growing and poses a major barrier to the development of predictive mathematical models because of combinatorial complexity, the explosion in the number of possible chemical species and reactions that can occur in such networks. The BioNetGen (BNG) language uses graphs to represent proteins and other biomolecules, with nodes representing functional subunits of these molecules and edges representing binding interactions. Graph rewriting rules describe biochemical transformations, such the formation or dissociation of bonds or state changes. This language enables the construction of precise and comprehensive models and greatly expands the scope of information that can be incorporated into models of cellular networks. BNG also incorporates a wide range of analytical and simulation tools, and networks generated by BNG can be exported in the Systems Biology Markup Language and other formats allowing interoperability with other modeling platforms. Standard methods for simulating reaction networks, such as ODE’s and the Gillespie algorithm, are often not adequate to simulate networks arising from the rule-based description of realistic signaling cascades, requiring the development of simulation algorithms that avoid explicit generation of the reaction network. I will describe recent progress in the development of such algorithms and also present several applications to the modeling of signal transduction networks, including immune and growth factor receptors.

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