Systematic Reduction of a Stochastic Signalling Cascade Model

Biochemical systems involve chemical reactions occurring in low-number regimes, wherein fluctuations are not negligible and thus stochastic models are required to capture the system behaviour. The resulting models are often quite large and complex, involving many reactions and species. For clarity and computational tractability, it is important to be able to simplify these systems to equivalent ones involving fewer elements. While many model simplification approaches have been developed for deterministic systems, there has been limited work on applying these approaches to stochastic modelling. Here, we propose a method that reduces the complexity of stochastic biochemical network models, and apply this method to the reduction of a mammalian signalling cascade. Our results indicate that the simplified model gives an accurate representation for not only the average number of all species, but also for the associated fluctuations and statistical parameters.