Comparison Theorems for Stochastic Chemical Reaction Networks

TL;DR

This paper develops comparison theorems for stochastic chemical reaction networks, providing tools to analyze how stochastic behaviors depend on parameters, with applications to biological models like enzymatic kinetics and epigenetics.

Contribution

The paper introduces novel comparison theorems that establish stochastic ordering and parameter dependence in SCRNs, extending analysis beyond general Markov chains and applicable to various kinetics.

Findings

Provides sufficient conditions for monotonic dependence on parameters.

Derives theorems for comparing stationary distributions and mean first passage times.

Offers explicit coupling methods for bounded propensity functions.

Abstract

Continuous-time Markov chains are frequently used as stochastic models for chemical reaction networks, especially in the growing field of systems biology. A fundamental problem for these Stochastic Chemical Reaction Networks (SCRNs) is to understand the dependence of the stochastic behavior of these systems on the chemical reaction rate parameters. Towards solving this problem, in this paper we develop theoretical tools called comparison theorems that provide stochastic ordering results for SCRNs. These theorems give sufficient conditions for monotonic dependence on parameters in these network models, which allow us to obtain, under suitable conditions, information about transient and steady state behavior. These theorems exploit structural properties of SCRNs, beyond those of general continuous-time Markov chains. Furthermore, we derive two theorems to compare stationary distributions and mean first passage times for SCRNs with different parameter values, or with the same parameters and different initial conditions. These tools are developed for SCRNs taking values in a generic (finite or countably infinite) state space and can also be applied for non-mass-action kinetics models. When propensity functions are bounded, our method of proof gives an explicit method for coupling two comparable SCRNs, which can be used to simultaneously simulate their sample paths in a comparable manner. We illustrate our results with applications to models of enzymatic kinetics and epigenetic regulation by chromatin modifications.