A Computational Approach to Extinction Events in Chemical Reaction Networks with Discrete State Spaces

TL;DR

This paper presents a Python-based computational method to identify extinction events in chemical reaction networks with discrete states, validated on biological models and a large database of models.

Contribution

It provides a practical implementation of theoretical conditions for extinction events, enabling analysis of complex biochemical networks.

Findings

Successfully applied to biological models of metabolism and pathways.

Analyzed 458 models from BioModels Database.

Identified extinction events in multiple biochemical networks.

Abstract

Recent work of M.D. Johnston et al. has produced sufficient conditions on the structure of a chemical reaction network which guarantee that the corresponding discrete state space system exhibits an extinction event. The conditions consist of a series of systems of equalities and inequalities on the edges of a modified reaction network called a domination-expanded reaction network. In this paper, we present a computational implementation of these conditions written in Python and apply the program on examples drawn from the biochemical literature, including a model of polyamine metabolism in mammals and a model of the pentose phosphate pathway in Trypanosoma brucei. We also run the program on 458 models from the European Bioinformatics Institute's BioModels Database and report our results.