Abstract
Biochemical networks are a widely used framework to quantitatively model gene expression, metabolic pathways, allosteric enzyme regulation, and other processes. These networks represent biochemical phenomena as a system of nodes (metabolites) and their connecting edges (the chemical reactions between them), with each edge weighted by a real number (rate) related to the reaction's propensity. Inferring rates from supplied data, usually in the form of reaction product counts across time, requires solving the chemical master equation (CME), which in turn requires calculating a matrix exponential — known to scale poorly with the number of states.
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