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

Department of Biomedical Engineering, University of Virginia, Health System, Charlottesville, VA 22908, USA.
Trends in Immunology (Impact Factor: 10.4). 01/2009; 29(12):589-99. DOI: 10.1016/
Source: PubMed


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.


Available from: Jason A Papin
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    • "In the study of the immune system and related pathologies, one method for constructing multiscale models that has been used by various authors resorts to agents to represent the mesoscopic level of cells of the immune system (i.e., the multicellular rule-based modeling in [76]) while employing ordinary differential equations to describe the intracellular events as intracellular signalling and partial differential equations to describe cytokines diffusion at the extracellular or tissue scale. Level coupling is then performed in a quite straightforward way using concentrations as input variables to the cellular agents. "
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    BioMed Research International 07/2014; 2014(902545). DOI:10.1155/2014/902545 · 1.58 Impact Factor
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    • "The evolution of homogeneous mathematical model of cell populations (17) toward “spatialized,” discrete, and heterogeneous software models (18) has allowed the reproduction and observation of more detailed and thus complex behaviors. For example, this made possible to model lymphocyte dynamics from thymic selection (19, 20) up to quantitative modeling of immune responses, as extensively reviewed (21) with development of agent-based and automata models (22). However, both population-based mathematical model (a top-down approach) and discrete cell-based model (a bottom-up approach) and the various platforms developed have limitations (23). "
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    Frontiers in Immunology 10/2013; 4:300. DOI:10.3389/fimmu.2013.00300
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