Characterizing emergent properties of immunological systems with multi-cellular rule-based computational modeling

The immune system is comprised of numerous components that interact with one another to give rise to phenotypic behaviors that are sometimes unexpected. Agent-based modeling (ABM) and cellular automata (CA) belong to a class of discrete mathematical approaches in which autonomous entities detect loc...

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Bibliographic Details
Published in:Trends in immunology 2008-12, Vol.29 (12), p.589-599
Main Authors: Chavali, Arvind K, Gianchandani, Erwin P, Tung, Kenneth S, Lawrence, Michael B, Peirce, Shayn M, Papin, Jason A
Format: Article
Language:English
Subjects:
Allergy and Immunology
Animals
Behavior
Cell Polarity - immunology
Computer Simulation
Cytokines
Genomes
Growth rate
Humans
Immune system
Immune System - immunology
Infections
Lymphocytes
Models, Immunological
Peptides
Receptors, Antigen, T-Cell - immunology
Systems analysis
T cell receptors
T-Lymphocytes - immunology
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Summary:The immune system is comprised of numerous components that interact with one another to give rise to phenotypic behaviors that are sometimes unexpected. Agent-based modeling (ABM) and cellular automata (CA) belong to a class of discrete mathematical approaches in which autonomous entities detect local information and act over time according to logical rules. The power of this approach lies in the emergence of behavior that arises from interactions between agents, which would otherwise be impossible to know a priori . Recent work exploring the immune system with ABM and CA has revealed novel insights into immunological processes. Here, we summarize these applications to immunology and, particularly, how ABM can help formulate hypotheses that might drive further experimental investigations of disease mechanisms.
ISSN:1471-4906
1471-4981
DOI:10.1016/j.it.2008.08.006