HYBRID SIMULATION ALGORITHMS FOR AN AGENT-BASED MODEL OF THE IMMUNE RESPONSE

Authored by Johannes Textor, Bjorn Hansen

Date Published: 2009

DOI: 10.1080/01969720902922384

Sponsors: No sponsors listed

Platforms: C++

Model Documentation: Other Narrative Pseudocode Mathematical description

Model Code URLs: Model code not found

Abstract

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.

Tags

Celada-Seiden model Cellular automata Computational biology Immune system