Formal Model Reduction

Abstract

Modelers of molecular signaling networks must cope with the combinatorial explosion of protein states generated by post-translational modifications and complex formations. Rule-based models provide a powerful alternative to approaches that require an explicit enumeration of all possible molecular species of a system [1,2]. Such models consist of formal rules stipulating the (partial) contexts for specific protein-protein interactions to occur. The behavior of the models can be formally described by stochastic or differential semantics. Yet, the naive computation of these semantics does not scale to large systems, because it does not exploit the lower resolution at which rules specify interactions.