Some network conditions for positive recurrence of stochastically modeled reaction networks

TL;DR

This paper establishes structural conditions ensuring positive recurrence and stability in stochastic reaction networks, independent of specific parameters, using tier structures for analysis.

Contribution

It provides parameter-independent criteria for stability of reaction networks based solely on their structure, extending the tier structure method to stochastic models.

Findings

Conditions for positive recurrence in reaction networks

Applicability to binary systems

Stability criteria based on network structure

Abstract

We consider discrete-space continuous-time Markov models of reaction networks and provide sufficient conditions for the following stability condition to hold: each state in a closed, irreducible component of the state space is positive recurrent; moreover the time required for a trajectory to enter such a component has finite expectation. The provided analytical results depend solely on the underlying structure of the reaction network and not on the specific choice of model parameters. Our main results apply to binary systems and our main analytical tool is the "tier structure" previously utilized successfully in the study of deterministic models of reaction networks.