The development of mathematical tools to simulate signaling dynamics is extremely important. Based on our previous experimental data and simulation results, we are convinced that to accurately and appropriately model signaling networks, we cannot neglect the spatial organization of the cell membrane (and ultimately the cytosol as well). We have extensive experience in developing spatially realistic simulations of the cell membrane and studied the initiation of signaling. However, while these simulations are extremely computationally intense, crucial aspects of cell signaling can only be correctly represented in the context of the entire cell membrane, or at least a significant portion of it. Th Fc?RI system presents an additional challenge relative to other commonly studied receptor systems (i.e. EGFR, VEGFR), in that the Fc?RI/IgE complex essentially behaves as a bivalent receptor, while the antigens are typically multivalent (valency from 3 to ?24). As a result, arbitrarily large aggregates may emerge through the multivalent cross-linking of FceRI/IgE complexes by the antigen. Due to the combinatorial explosion of the number of possible aggregate types, this problem is eminently suited for the rule-based approach to bio-molecular network modeling. Thus, the nature of the FceRI/IgE system requires the integration of the rule-based approach with a spatial modeling framework. We will develop a coarse grained methodology for integration of detailed (microscopic) and cell-level (mesoscopic) simulations and experimental results. This framework will help address both challenges described above, namely (a) integrate detailed, microscopic simulations and experimental data into a mesoscopic model that captures a significant portion of the cell membrane and (b) provide a mechanism to include spatial mobility and steric constraints for large molecular aggregates in a rule-based, stochastic model of the Fc epsilon RI system.
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|Pryor, Meghan McCabe; Low-Nam, Shalini T; Halasz, Adam M et al. (2013) Dynamic transition states of ErbB1 phosphorylation predicted by spatial stochastic modeling. Biophys J 105:1533-43|