TY - JOUR
T1 - Techniques for Grounding Agent-Based Simulations in the Real Domain
T2 - a case study in Experimental Autoimmune Encephalomyelisis
AU - Timmis, Jon
AU - Read, Mark Norman
AU - Andrews, PAUL Simon
AU - Kumar, V.
PY - 2011/12/1
Y1 - 2011/12/1
N2 - 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.
AB - 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.
U2 - 10.1080/13873954.2011.601419
DO - 10.1080/13873954.2011.601419
M3 - Article
SN - 1387-3954
VL - 18
SP - 296
EP - 302
JO - Journal of Mathematical and Computational Modelling of Dynamical Systems
JF - Journal of Mathematical and Computational Modelling of Dynamical Systems
IS - 1
ER -