Towards data-driven modelling of SUMOylation following heat shock

Understanding how cell fate is determined when exposed to extreme stresses such as heat shock is critical in biomedical systems. It has long been understood that exposure of cells to high temperature typically protect themselves with a heat shock response (HSR), where accumulation of denatured or unfolded proteins triggers the synthesis of heat shock proteins (HSPs) through the heat shock transcription factor, e.g., heat shock factor 1 (HSF1). Recent experimental work has also shown that protein posttranslational modifications (PTMs) such as SUMOylation play crucial roles in cellular responses to heat shock. As a complementary approach to the current experimental methodologies, in this study we aim to develop a mathematical model of SUMOylation-development synergism of HSR for the purpose of studying the dynamical behaviour of HSR quantitatively. The structure of our dynamical model is derived mostly from mass action kinetics while the model parameters are optimized by using a genetic algorithm (GA) based data-driven approach. The preliminary results show GA based data-driven approach has potentials for our modelling purpose.