Modeling the inflammatory response in the hypothalamus ensuing heat stroke: iterative cycle of model calibration, identifiability analysis, experimental design and data collection

Abstract

Heat Stroke (HS) is a life-threatening illness caused by prolonged exposure to heat that causes severe hyperthermia and nervous system abnormalities. The long term consequences of HS are poorly understood and deeper insight is required to find possible treatment strategies. Elevated pro- and anti-inflammatory cytokines during HS recovery suggest to play a major role in the immune response. In this study, we developed a mathematical model to understand the interactions and dynamics of cytokines in the hypothalamus, the main thermoregulatory center in the brain. Uncertainty and identifiability analysis of the calibrated model parameters revealed non-identifiable parameters due to the limited amount of data. To overcome the lack of identifiability of the parameters, an iterative cycle of optimal experimental design, data collection, re-calibration and model reduction was applied and further informative experiments were suggested. Additionally, a new method of approximating the prior distribution of the parameters for Bayesian optimal experimental design based on the profile likelihood is presented.

Keywords: Bayesian; Heat stroke; Hypothalamus; Mathematical modeling; Optimal experimental design.