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

Authored by Mark Read, Paul S. Andrews, Jon Timmis, Vipin Kumar

Date Published: 2012

DOI: 10.1080/13873954.2011.601419

Sponsors: United Kingdom Engineering and Physical Sciences Research Council (EPSRC) United States National Institutes of Health (NIH)

Platforms: MASON

Model Documentation: Other Narrative

Model Code URLs: Model code not found

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.

Tags

agent-based simulation calibration Sensitivity Analysis computational immunology Uncertainty analysis In silico experimentation experimental autoimmune encophalomyelitis interpretation of simulation results stochasticity