Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Network

TL;DR

This paper introduces an adjoint sensitivity analysis method for multi-scale stochastic bioprocess models, enabling efficient parameter learning and deeper understanding of regulatory mechanisms in biomanufacturing systems.

Contribution

It develops a convergent adjoint sensitivity analysis algorithm tailored for enzymatic stochastic reaction networks in bioprocess modeling, integrating diverse data sources.

Findings

Sensitivity analysis reveals key regulatory mechanisms

Method improves sample efficiency in parameter estimation

Enhances interpretability of bioprocess models

Abstract

Motivated by the pressing challenges in the digital twin development for biomanufacturing systems, we introduce an adjoint sensitivity analysis (SA) approach to expedite the learning of mechanistic model parameters. In this paper, we consider enzymatic stochastic reaction networks representing a multi-scale bioprocess mechanistic model that allows us to integrate disparate data from diverse production processes and leverage the information from existing macro-kinetic and genome-scale models. To support forward prediction and backward reasoning, we develop a convergent adjoint SA algorithm studying how the perturbations of model parameters and inputs (e.g., initial state) propagate through enzymatic reaction networks and impact on output trajectory predictions. This SA can provide a sample efficient and interpretable way to assess the sensitivities between inputs and outputs accounting for their causal dependencies. Our empirical study underscores the resilience of these sensitivities and illuminates a deeper comprehension of the regulatory mechanisms behind bioprocess through sensitivities.