Tier structure of strongly endotactic reaction networks

TL;DR

This paper characterizes strongly endotactic reaction networks using tier structures, connecting geometric and analytical perspectives, and introduces a new proof technique applicable to both deterministic and stochastic models.

Contribution

It provides an analytical characterization of strongly endotactic networks via tier structures, linking previous geometric approaches with new analytical methods.

Findings

Previous results on strongly endotactic networks are recoverable using the new characterization.

A subclass of strongly endotactic networks is shown to be positive recurrent in stochastic models.

An example demonstrates that some strongly endotactic networks can be transient or explosive.

Abstract

Reaction networks are mainly used to model the time-evolution of molecules of interacting chemical species. Stochastic models are typically used when the counts of the molecules are low, whereas deterministic models are used when the counts are in high abundance. In 2011, the notion of `tiers' was introduced to study the long time behavior of deterministically modeled reaction networks that are weakly reversible and have a single linkage class. This `tier' based argument was analytical in nature. Later, in 2014, the notion of a strongly endotactic network was introduced in order to generalize the previous results from weakly reversible networks with a single linkage class to this wider family of networks. The point of view of this later work was more geometric and algebraic in nature. The notion of strongly endotactic networks was later used in 2018 to prove a large deviation principle for a class of stochastically modeled reaction networks. We provide an analytical characterization of strongly endotactic networks in terms of tier structures. By doing so, we shed light on the connection between the two points of view, and also make available a new proof technique for the study of strongly endotactic networks. We show the power of this new technique in two distinct ways. First, we demonstrate how the main previous results related to strongly endotactic networks, both for the deterministic and stochastic modeling choices, can be quickly obtained from our characterization. Second, we demonstrate how new results can be obtained by proving that a sub-class of strongly endotactic networks, when modeled stochastically, is positive recurrent. Finally, and similarly to recent independent work by Agazzi and Mattingly, we provide an example which closes a conjecture in the negative by showing that stochastically modeled strongly endotactic networks can be transient (and even explosive).