Multiple Gaussian process models based global sensitivity analysis and efficient optimization of in vitro mRNA transcription process

TL;DR

This paper employs multiple Gaussian Process models to perform global sensitivity analysis and optimize the in vitro mRNA transcription process, enhancing RNA yield efficiency through data-driven modeling and validation.

Contribution

It introduces a novel approach combining multiple GP models with GSA and multi-start optimization for IVT process improvement.

Findings

Identified key reaction factors NTP and Mg concentrations.

Optimized operational conditions increased RNA yield.

Validated optimized conditions with experimental data.

Abstract

The in vitro transcription (IVT) process is a critical step in RNA production. To ensure the efficiency of RNA manufacturing, it is essential to optimize and identify its key influencing factors. In this study, multiple Gaussian Process (GP) models are used to perform efficient optimization and global sensitivity analysis (GSA). Firstly, multiple GP models were constructed using the data from multiple experimental replicates, accurately capturing the complexities of the IVT process. Then GSA was conducted to determine the dominant reaction factors, specifically the concentrations of reactants NTP and Mg across all data-driven models. Concurrently, a multi-start optimization algorithm was applied to these GP models to identify optimal operational conditions that maximize RNA yields across all surrogate models. These optimized conditions are subsequently validated through additional experimental data.