Inference for stochastic reaction networks via logistic regression

TL;DR

This paper introduces likelihood-based logistic regression methods to infer network structure and parameters of stochastic chemical reaction networks from time-series data, demonstrating effectiveness on synthetic models and real-world COVID-19 data.

Contribution

It develops a novel framework using multinomial and Bayesian logistic regression to recover network connectivity, stoichiometry, and parameters, even with partial observability.

Findings

Logistic regression can recover full network structure from complete trajectories.

Bayesian logistic regression estimates parameters effectively in epidemic models.

Method handles partial data, such as only infectious counts in epidemic settings.

Abstract

Identifying network structure and inferring parameters are central challenges in modeling chemical reaction networks. In this study, we propose likelihood-based methods grounded in multinomial logistic regression to infer both stoichiometries and network connectivity structure from full time-series trajectories of stochastic chemical reaction networks. When complete molecular count trajectories are observed for all species, stoichiometric coefficients are identifiable, provided each reaction occurs at least once during the observation window. However, identifying catalytic species remains difficult, as their molecular counts remain unchanged before and after each reaction event. Through three illustrative stochastic models involving catalytic interactions in open networks, we demonstrate that the logistic regression framework, when applied properly, can recover the full network structure, including stoichiometric relationships. We further apply Bayesian logistic regression to estimate model parameters in real-world epidemic settings, using the COVID-19 outbreak in the Greater Seoul area of South Korea as a case study. Our analysis focuses on a Susceptible--Infected--Recovered (SIR) network model that incorporates demographic effects. To address the challenge of partial observability, particularly the availability of data only for the infectious subset of the population, we develop a method that integrates Bayesian logistic regression with differential equation models. This approach enables robust inference of key SIR parameters from observed COVID-19 case trajectories. Overall, our findings demonstrate that simple, likelihood-based techniques such as logistic regression can recover meaningful mechanistic insights from both synthetic and empirical time-series data.