Theoretical methods have been successfully applied to other areas of cell biology as well, such as gene networks ( 7), cell metabolism ( 8, 9), molecular motors ( 10, 11), cell signaling ( 12), chemotaxis ( 13), cell cycle ( 14), calcium dynamics ( 15), cell motility ( 16), and others. Not only had the Hodgkin-Huxley model explained the dynamics of membrane potential in neurons, it also became a prototype for modeling the dynamics of other excitable cells, such as cardiac myocytes, pancreatic beta-cells, gonadotrophs etc., and more broadly, laid a foundation for a new field: the theory of excitable systems ( 6). It is striking how a careful quantitative analysis of ion currents led to the prediction of gating mechanisms even before ion channels were discovered and characterized ( 5). Huxley ( 4), developed as part of the Nobel Prize-winning study of electric pulses in a giant squid axon, is perhaps one of the most successful examples of an application of modeling in cell-biological research. While cell biology, unlike physics, remains largely qualitative and mathematical modeling may not always be appropriate ( 2) or even possible, certain areas of cell science have benefited from combining experimental studies with physics-based modeling ( 3). An adequate model almost always yields interesting predictions that, in turn, can be tested experimentally, but even a failure of the model to explain experimental observations often leads to a better understanding of the process under study. Formulating assumptions mathematically allows an experimentalist to perform a rigorous logical test of a `theory' that he or she might have in mind. More specifically, given the complexity of cell processes, qualitative reasoning alone may not be sufficient for adequate interpretation of experimental data or for predicting the system behavior. Why computational modeling in cell biology? A short answer is because it can help gain new insights and knowledge and make testable predictions ( 1).