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Agent-Based Modeling in Translational Systems Biology

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Complex Systems and Computational Biology Approaches to Acute Inflammation

Abstract

Agent-based modeling is an object-oriented, discrete event, population-focused method for the computational representation of dynamic systems. Agent-based models (ABMs) treat systems as aggregates of populations of interacting components governed by rules. This means of system representation allows ABMs to map well to how biological knowledge is represented and communicated. As a result, agent-based modeling is an intuitive means by which biomedical researchers can represent their knowledge in dynamic computational form, and in so doing lower the threshold for the general biological researcher to engage in computational modeling. ABMs are particularly suited for representing the behavior of populations of cells (i.e., “cell-as-agents”), but have also been used to model lower level processes, such as molecular interactions when spatial and structural properties are involved, as well as higher level systems, such as in human populations in epidemiological studies. For purposes of its use in Translational Systems Biology, we will focus on the use of cell/tissue-as-agent ABMs and demonstrate how agent-based modeling can serve as an integrating framework for dynamic knowledge representation of biological systems.

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Authors and Affiliations

  1. Department of Surgery, University of Vermont, Burlington, VT, USA

    Gary An

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  1. Gary An
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  1. Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA

    Yoram Vodovotz

  2. Department of Surgery, University of Vermont, Burlington, VT, USA

    Gary An

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An, G. (2021). Agent-Based Modeling in Translational Systems Biology. In: Vodovotz, Y., An, G. (eds) Complex Systems and Computational Biology Approaches to Acute Inflammation. Springer, Cham. https://doi.org/10.1007/978-3-030-56510-7_3

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