Many activities of cells are controlled by cell surface receptors, which respond to ligands by triggering intracellular signaling reactions. Improved understanding of receptor signaling has a number of potential practical applications, such as the rational design of drugs and vaccines. The process of signaling through receptors involves highly connected networks of interacting components. Understanding the behavior of these networks requires the development of mathematical and computational modeling. Here, an agent-based simulator is developed in C to study diverse molecular interactions in complex signaling pathways with spatial resolution and single molecule detail. It permits stochastic modeling of protein clustering, protein diffusion and biochemical reactions within an idealized cellular geometry. Components diffuse and react with nearby particles in accord with chemical rate reactions. The modularized designconfers flexibility.

The model was applied to investigate mechanisms of EGFR homodimerization and activation as functions of time and receptor conformation, density and spatial distribution. Results indicate that receptor density is a principal factor in the ability to form a measurable amount of active homodimers in the absence of ligands. Our results also predict that receptor clustering exacerbates the density-dependent homodimerization of unoccupied receptors. We next considered how the previously observed topographic distributions of three ErbB family members might influence homo and heterodimerization rates. Our spatial model predicts significantly different receptor dimerization patterns from those derived in well-mixed models. This study demonstrates that agent-based, spatially realistic models offer new insight into the roles of membrane architecture on receptor behavior. We are testing the model for interactions of downstream signaling molecules, based in part on spatial constraints of
docking partners and on observed patterns of signaling proteins in membranes, which will be a powerful tool for studying growth factor signaling in the context of breast and other cancers.