A Time-Based Approach to Stochastic Modeling of Intracellular Signaling Events

This paper presents a modeling framework for an intracellular signaling network based on formalisms derived from the fundamental concepts in probability theory. Cellular behavior is mediated by a network of intracellular protein activations that originate at the membrane in response to stimulation of cell surface receptors. Multiple protein signal transductions occur concurrently through diverse pathways triggered by different extracellular cues. Through crosstalk, these pathways intersect at various node proteins. The state of a particular node protein is dependent on the binding order of molecules from various pathways. The probability of a particular binding order is evaluated using state dependent transduction time probabilities associated with each pathway. In this way, the probability of the cell to be in a given internal state is tracked and used to gain insight into the cell’s phenotypic behavior. A simulation example illustrates the approach. Future work will incorporate the proposed method into the development of a feedback control strategy for the development of an in silico control design of endothelial cell migration during angiogenesis.