Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Network

TL;DR

This paper introduces a Bayesian inference method using linear noise approximation for partially observed stochastic reaction networks, enabling efficient online learning and digital twin development in biomanufacturing.

Contribution

It develops an interpretable LNA-based metamodel for likelihood approximation and a gradient-enhanced MCMC sampling method for mechanistic stochastic models.

Findings

Demonstrates promising empirical performance

Enables efficient Bayesian inference for complex SRNs

Supports online learning and digital twin applications

Abstract

To support mechanism online learning and facilitate digital twin development for biomanufacturing processes, this paper develops an efficient Bayesian inference approach for partially observed enzymatic stochastic reaction network (SRN), a fundamental building block of multi-scale bioprocess mechanistic model. To tackle the critical challenges brought by the nonlinear stochastic differential equations (SDEs)-based mechanistic model with partially observed state and having measurement errors, an interpretable Bayesian updating linear noise approximation (LNA) metamodel, incorporating the structure information of the mechanistic model, is proposed to approximate the likelihood of observations. Then, an efficient posterior sampling approach is developed by utilizing the gradients of the derived likelihood to speed up the convergence of Markov Chain Monte Carlo (MCMC). The empirical study demonstrates that the proposed approach has a promising performance.