Publisher's Synopsis
This paper describes how to conduct Baysesian inference for fitting a dynamic stochastic process model using a stochastic simulator. In particular, we consider a complex nonlinear continuous time latent stochastic process of the celluar response to DNA damage. it is compared to time course data on the levels of two key proteins involved in this response, captured at the level of individual cells in a human cancer cell line. The primary goal is to "calibrate"the model by finding parameters of the model (kinetic rate constants) that are most consistent with the experimental data. Significant amounts of prior information are the model parameters. We use sophisticated Markov chain Monte Carlo methods to overcome the formidable computational challenges. Fuller details of the building and assessment of this model can be found in.
