Inferring structure and parameters of stochastic reaction networks with logistic regression.

ABSTRACT: Identifying network structure and estimating reaction parameters remain central challenges in modeling chemical reaction networks. In this work, we develop likelihood-based methods that use multinomial logistic regression to infer both stoichiometries and network connectivity from full time-series trajectories of stochastic reaction systems. When molecular counts for all species are observed, stoichiometric coefficients can be recovered provided that each reaction occurs at least once during the sampling window and has a unique stoichiometric vector. We illustrate the proposed regression approach by recovering the network structure in three stochastic models involving catalytic interactions in open networks-namely, the Togashi-Kaneko model, a heat-shock protein network model, and a Suscept

SUBMITTER: Choi B 

PROVIDER: S-EPMC12900436 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature