Coclique level structure for stochastic chemical reaction networks

TL;DR

This paper introduces the coclique level structure concept for stochastic chemical reaction networks, enabling derivation of bounds for mean first passage times and applicable to various biological models.

Contribution

It develops a novel graph-based framework and algorithms to identify coclique level structures, facilitating explicit bounds for MFPTs in SCRNs.

Findings

Coclique level structures help derive bounds for MFPTs.

Algorithms can identify all possible coclique structures in SCRNs.

Applicable to models with non-mass-action kinetics.

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 coclique level structure 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.