Network Emergence in Immune System Shape Space

We present a model which enables us to study emergent principles of immune system T-cell repertoire self-organisation, based on a stochastic cellular automata model of a simplified lymphatic compartment. An extension of the immune system shape space formalism is developed such that each activated effector T-cell clonotype and viral epitope are represented as nodes, and edges between nodes models the affinity or clearance pressure applied to the antigen presenting cell bearing the target epitope. When the model is repeatedly exposed to infection by heterologous or mutating viruses, a distinct topology of the network space emerges which parallels recent biological experimental results in the area of cytotoxic T-cell activation, apoptosis, crossreactivity, and memory – especially with respect to repeated reinfection. The model presented here is a stochastic agent-based approach, which allows a broad distribution of results to be studied by tuning crucial T-cell life-cycle probabilities.