IMMSIM, a flexible model for in machina experiments on immune system responses
Future Generation Computer Systems - Cellular automata CA 2000 and ACRI 2000
Immunological self-tolerance: lessons from mathematical modeling
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Regulation function of the environment in agent-based simulation
E4MAS'06 Proceedings of the 3rd international conference on Environments for multi-agent systems III
E4MAS'05 Proceedings of the 2nd international conference on Environments for Multi-Agent Systems
Bone remodelling: a complex automata-based model running in BIOSHAPE
ACRI'10 Proceedings of the 9th international conference on Cellular automata for research and industry
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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 article, 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 meaning 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.