Hybrid Simulation Algorithms for an Agent-Based Model of the Immune Response

The immune system is of central interest for the life sciences, but its high complexity makes it a challenging system to study. Computational models of the immune system can help to improve our understanding of its fundamental principles. In this paper, we analyze and extend the Celada-Seiden model, a simple and elegant agent-based model of the entire immune response, which, however, lacks biophysically sound simulation methodology. We extend the stochastic model to a stochastic-deterministic hybrid, and link the deterministic version to continuous physical and chemical laws. This gives precise meanings to all simulation processes, and helps to increase performance. To demonstrate an application for the model, we implement and study two different hypotheses about T cell-mediated immune memory.