Stochastic simulation of a T-cell signaling pathway

Understanding, quantifying, and controlling the networked T-cell signaling pathways will impact the understanding and potential treatment design for several immunologically-related and malignant diseases. Currently most T-cell signaling pathways have been mapped out in terms of their demonstrated interactions and participating elements; however, the quantification of these pathways and the identification of rate-limiting interactions, and controlling feedback mechanisms remain to be delineated. A mathematical model of the T-cell MAPK signaling pathway has been constructed to assist in this quantification and delineation. This model is a hybrid model comprising a stochastic module capturing T-cell receptor stimulation, and a deterministic module composed of nonlinear differential equations to simulate the subsequently induced MAPK activation. Upon T-cell receptor (TCR) engagement by a MHC-peptide complex, the affinity of the peptide-TCR bond and the surface density of engaged receptors modulates the phosphorylation state of the CD3 ζ-chain which in turn controls the induction of signaling pathways (including the MAPK). Our stochastic module incorporates these probabilistic events and integrates them with the deterministic module capturing the downstream MAPK signaling pathway through an initiation event. Simulations and analysis have helped delineate the signaling pathway.