The Distance Between: Stochastic Models of Cellular Protein Transport

Translocation of proteins is essential for cell metabolism. Whilst mean-field models of the molecular movements within cells have identified dominant processes at the macroscopic scale, stochastic models may provide further insight into mechanisms at the molecular scale. The aim of this study was to develop a distance metric between stochastic data sets which evolve over time. This would enable the quantitative comparison of the outputs of a candidate stochastic model and the different experimental measurements of the system. A candidate stochastic model is developed for the translocation in mammalian cells of the insulin-dependent glucose transporter protein, GLUT4. The model is a closed queueing network. Various outputs of the system are compared to different experimental data sets, and synthetic data produced. Using empirical probability distributions to compare the time courses of stochastic measurements with the stochastic outputs of the model, we test different quantitative comparisons between the model output and the synthetic data, with the ultimate aim of driving parameter inference and model selection.