Stochastic ordering tools for continuous-time Markov chains and applications to reaction network models

TL;DR

This paper develops tools based on stochastic ordering to compare continuous-time Markov chain models, particularly reaction networks, enabling better understanding of how parameter changes affect species abundances.

Contribution

It provides direct, computable conditions for establishing ordered couplings between reaction networks and an algorithm for practical application.

Findings

Conditions for ordered coupling are derived and validated.

Algorithm implementation demonstrates practical utility.

Applications show how parameter changes influence species counts.

Abstract

Stochastic reaction networks are mathematical models with a wide range of applications in biochemistry, ecology, and epidemiology, and are often complex to analyze. Except for some special cases, it is generally difficult to predict how the abundances of all considered species evolve over time. A possible approach to address this issue is to develop tools to compare the model under study with a similar one whose behavior is better understood. The main contribution of our work is to provide direct and computable conditions that can be used to ensure the existence of an ordered coupling between two stochastic reaction networks and to identify which parameter changes in a given model lead to an increase or decrease in the count of certain species. We also make available an algorithm that implements our theory, and we illustrate it with several applications.