biostudies-literatureUnknown119(51)Nandori PThis paper is about a class of stochastic reaction networks. Of interest are the dynamics of interconversion among a finite number of substances through reactions that consume some of the substances and produce others. The models we consider are continuous-time Markov jump processes, intended as idealizations of a broad class of biological networks. Reaction rates depend linearly on "enzymes," which are among the substances produced, and a reaction can occur only in the presence of sufficient upstream material. We present rigorous results for this class of stochastic dynamical systems, the mean-field behaviors of which are described by ordinary differential equations (ODEs). Under the assumption of exponential network growth, we identify certain ODE solutions as being potentially traceableProceedings of the National Academy of Sciences of the United States of Americae2214282119https://www.ebi.ac.uk/biostudies/studies/S-EPMC9907130biostudies-literatureGrowth and depletion in linear stochastic reaction networks.PMC9907130Nandori PYoung LSfalseGrowth and depletion in linear stochastic reaction networks.This paper is about a class of stochastic reaction networks. Of interest are the dynamics of interconversion among a finite number of substances through reactions that consume some of the substances and produce others. The models we consider are continuous-time Markov jump processes, intended as idealizations of a broad class of biological networks. Reaction rates depend linearly on "enzymes," which are among the substances produced, and a reaction can occur only in the presence of sufficient upstream material. We present rigorous results for this class of stochastic dynamical systems, the mean-field behaviors of which are described by ordinary differential equations (ODEs). Under the assumption of exponential network growth, we identify certain ODE solutions as being potentially traceable2022-01-01T00:00:00Z2022 Dec2025-04-22T18:53:14.125Z2025-04-06T02:33:51.47ZS-EPMC99071303652553510.1073/pnas.2214282119