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.
Sherlock et al. (Wed,) studied this question.