Model selection in stochastic chemical reaction networks using flow cytometry data

The model selection problem, that is picking the model that best explains an experimental data set from a list of candidates, arises frequently when studying unknown biological processes. Here, we propose a new method for model selection in stochastic chemical reaction networks using measurements from flow cytometry. A distinctive feature of our approach is its ability to perform statistically significant selection using a very small number of Monte Carlo simulations of the candidate stochastic models. After a comprehensive review of the theory associated with our procedure, we describe the model selection algorithm and we demonstrate it on an example drawn from molecular biology.