Generalized Method of Moments for Estimating Parameters of Stochastic Reaction Networks

TL;DR

This paper introduces a generalized method of moments for estimating parameters in stochastic biochemical reaction networks, improving accuracy and efficiency by leveraging higher-order moments and large sample sizes.

Contribution

It develops a novel GMM-based parameter inference method for stochastic reaction networks, utilizing advanced moment-matching techniques for improved estimation accuracy.

Findings

Accurate parameter estimation with the proposed GMM method.

Higher-order moments enhance estimation precision.

More samples reduce estimator variance.

Abstract

Discrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years severalmethods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks. We propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies. The generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included.