Assembly of quantitative models of large complex networks brings about several challenges. One of them is combinatorial complexity, where relatively few signaling molecules can combine to form thousands or millions of distinct chemical species. A receptor that has several separate phosphorylation sites can exist in hundreds of different states, many of which must be accounted for individually when simulating the time course of signaling. When assembly of protein complexes is being included, the number of distinct molecular species can easily increase by a few orders of magnitude. Validation, visualization, and understanding the network can become intractable. Another challenge appears when the modeler needs to recast or grow a model. Keeping track of changes and adding new elements present a significant difficulty. We describe an approach to solve these challenges within the Virtual Cell (VCell). Using (i) automatic extraction from pathway databases of model components, and (ii) rules of interactions that serve as reaction network generators, we provide a way for semi-automatic generation of quantitative mathematical models that also facilitates the reuse of model elements. In this approach, kinetic models of large, complex networks can be assembled from separately constructed modules, either directly or via rules. To implement this approach, we have combined the strength of several related technologies: the BioPAX ontology, the BioNetGen rule-based description of molecular interactions, and the VCell modeling and simulation framework.
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