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Techniques for grounding agent-based simulations in the real domain: a case study in experimental autoimmune encephalomyelitis

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  • Mark Read
  • Paul S. Andrews
  • Jon Timmis
  • Vipin Kumar

Abstract

For computational agent-based simulation, to become a serious tool for investigating biological systems requires the implications of simulation-derived results to be appreciated in terms of the original system. However, epistemic uncertainty regarding the exact nature of biological systems can complicate the calibration of models and simulations that attempt to capture their structure and behaviour, and can obscure the interpretation of simulation-derived experimental results with respect to the real domain. We present an approach to the calibration of an agent-based model of experimental autoimmune encephalomyelitis (EAE), a mouse proxy for multiple sclerosis (MS), which harnesses interaction between a modeller and domain expert in mitigating uncertainty in the data derived from the real domain. A novel uncertainty analysis technique is presented that, in conjunction with a latin hypercube-based global sensitivity analysis, can indicate the implications of epistemic uncertainty in the real domain. These analyses may be considered in the context of domain-specific knowledge to qualify the certainty placed on the results of in silico experimentation.

Suggested Citation

  • Mark Read & Paul S. Andrews & Jon Timmis & Vipin Kumar, 2011. "Techniques for grounding agent-based simulations in the real domain: a case study in experimental autoimmune encephalomyelitis," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 18(1), pages 67-86, May.
  • Handle: RePEc:taf:nmcmxx:v:18:y:2011:i:1:p:67-86
    DOI: 10.1080/13873954.2011.601419
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    1. Hiroaki Kitano, 2002. "Computational systems biology," Nature, Nature, vol. 420(6912), pages 206-210, November.
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