A potential surrogate model for efficient inference of stochastic GLUT4 translocation

Stochastic models can be highly computationally expensive. This limits the range of parameters and scenarios that can be realistically explored. Previously, a queuing network model was developed for the insulin-stimulated intracellular translocation of the glucose transporter GLUT4. Whilst one hypothesis of insulin action was tested, alternative hypotheses were too computationally expensive for parameter inference. In this study, a deterministic surrogate model is developed for the queuing network. The surrogate model uses feedback terms in a system of differential equations to approximate the blocking mechanisms seen in the queuing network. A sensitivity analysis of the surrogate model was performed and its correspondence to the queuing network assessed. This surrogate model may be useful in a parameter inference recalibration process, allowing posteriors for the queuing network to be acquired with lower computational cost.