Bayesian Inference of Spectral Expansions for Predictability Assessment in Stochastic Reaction Networks


Stochastic reaction networks modeled as jump Markov processes serve as the main mathematical representation of biochemical phenomena in cells, particularly when the relevant molecule count is low, causing deterministic macroscale chemical reaction models to fail. Further, as there is mainly empirical knowledge about the rate parameters, parametric uncertainty analysis becomes very important. The conventional predictability tools for deterministic systems do not readily generalize to the stochastic setting. We use spectral polynomial chaos expansions to represent stochastic processes. Bayesian inference techniques with Markov chain Monte Carlo are used to find the best spectral representation of the system state, taking into account not only intrinsic stochastic noise but also parametric uncertainties. A likelihood-based adaptive domain decomposition is introduced and applied, in particular, for the cases when the parameter range includes deterministic bifurcations. We show that the adaptive multidomain polynomial chaos representation captures the correct system behavior for a benchmark bistable Schlögl model for a wide range of parameter variations.

J. Comput. Theor. Nanosci.