Matthew D. Johnston
Published 2017-01-08Version 1
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.
A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks
Model Reduction in Chemical Reaction Networks: A Data-Driven Sparse-Learning Approach
Entropic and displacement interpolation: a computational approach using the Hilbert metric
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