Discrete-state Stochastic Modeling of T-cell Activation

Abstract

One of the most important functions of immune T-cells is to recognize the presence of the pathogen-derived ligands and quickly respond to them while at the same time not to respond to its own ligands. This is known as absolute discrimination, and it is one of the most challenging phenomena to explain in the immune system response. The effectiveness of pathogen detection by T cell receptor is limited by the molecular similarity of foreign and self-peptides since the healthy body produces very similar proteins. So, it is not clear what is the discrimination criteria. We developed a discrete-state stochastic model for T cell signaling, that adopts a single molecule view of the antigen discrimination process. Our theory, which relies on modeling T cell receptor (TCR) phosphorylation events as discrete-state stochastic processes, suggests that the decision to activate or not to activate the immune response is controlled by the time to reach the stationary concentration of the TCR-ligand activated complex. Our result is consistent with the experimental observation that sometimes the T cell activation occurs even for very large ligand association rates. Mover, experimental evidence suggests that three characteristics are crucial to the function of T cells. These characteristics, which are speed, sensitivity and specificity, are described by our stochastic model.