Uncoupled Analysis of Stochastic Reaction Networks in Fluctuating Environments

Abstract

The dynamics of stochastic reaction networks within cells are inevitably modulated by factors considered extrinsic to the network such as for instance the fluctuations in ribsome copy numbers for a gene regulatory network. While several recent studies demonstrate the importance of accounting for such extrinsic components, the resulting models are typically hard to analyze. In this work we develop a general mathematical framework that allows to uncouple the network from its dynamic environment by incorporating only the environment's effect onto the network into a new model. More technically, we show how such fluctuating extrinsic components (e.g., chemical species) can be marginalized in order to obtain this decoupled model. We derive its corresponding process- and master equations and show how stochastic simulations can be performed. Using several case studies, we demonstrate the significance of the approach. For instance, we exemplarily formulate and solve a marginal master equation describing the protein translation and degradation in a fluctuating environment.

arXiv comment7 pages, 4 figures, Appendix attached as SI.pdf, under submission

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• Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach
• Synchronization of stochastic hybrid oscillators driven by a common switching environment
• Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks
• A Molecular Implementation of the Least Mean Squares Estimator
• Exact derivation and practical application of a hybrid stochastic simulation algorithm for large gene regulatory networks