Stochastic Molecular Reaction Queueing Network Modeling for In Vitro Transcription Process

TL;DR

This paper introduces a stochastic queueing network model for in vitro transcription, aiming to improve prediction and analysis of enzymatic reactions in mRNA vaccine production, supporting industry 4.0 advancements.

Contribution

It presents a novel stochastic molecular reaction queueing network with a regulatory kinetic model for IVT, enhancing process understanding and prediction accuracy.

Findings

Model shows promising performance across various production conditions.

Potential to improve mRNA quality and yield.

Supports rapid response to pandemic threats.

Abstract

To facilitate a rapid response to pandemic threats, this paper focuses on developing a mechanistic simulation model for in vitro transcription (IVT) process, a crucial step in mRNA vaccine manufacturing. To enhance production and support industry 4.0, this model is proposed to improve the prediction and analysis of IVT enzymatic reaction network. It incorporates a novel stochastic molecular reaction queueing network with a regulatory kinetic model characterizing the effect of bioprocess state variables on reaction rates. The empirical study demonstrates that the proposed model has a promising performance under different production conditions and it could offer potential improvements in mRNA product quality and yield.