# Coclique level structure for stochastic chemical reaction networks

## TL;DR

This paper introduces a new method to analyze stochastic chemical reaction networks by using coclique level structures to simplify the calculation of mean first passage times.

## Contribution

The paper introduces the concept of coclique level structure and provides algorithms and theorems to analyze stochastic chemical reaction networks.

## Key findings

- Coclique level structures allow for closed-form bounds on mean first passage times in SCRNs.
- The method works for SCRNs with finite state spaces and non-mass-action kinetics.
- Applications in epigenetics, neurobiology, and ecology demonstrate the method's versatility.

## Abstract

Continuous time Markov chains are commonly used as models for the stochastic behavior of chemical reaction networks. More precisely, these Stochastic Chemical Reaction Networks (SCRNs) are frequently used to gain a mechanistic understanding of how chemical reaction rate parameters impact the stochastic behavior of these systems. One property of interest is mean first passage times (MFPTs) between states. However, deriving explicit formulas for MFPTs can be highly complex. In order to address this problem, we first introduce the concept of \documentclass[12pt]{minimal}
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\begin{document}$$coclique\, level\, structure$$\end{document} and develop theorems to determine whether certain SCRNs have this feature by studying associated graphs. Additionally, we develop an algorithm to identify, under specific assumptions, all possible coclique level structures associated with a given SCRN. Finally, we demonstrate how the presence of such a structure in a SCRN allows us to derive closed form formulas for both upper and lower bounds for the MFPTs. Our methods can be applied to SCRNs taking values in a generic finite state space and can also be applied to models with non-mass-action kinetics. We illustrate our results with examples from the biological areas of epigenetics, neurobiology and ecology.

The online version contains supplementary material available at 10.1007/s00285-025-02261-6.