Agent-Based Modeling in Translational Systems Biology



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 a dynamic computational form and in so doing can 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 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.


Cellular Automaton Multiagent System Cellular Automaton Agent Class Computational Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of SurgeryUniversity of ChicagoChicagoUSA

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