Holistic Bioprocess Development Across Scales Using Multi-Fidelity Batch Bayesian Optimization

TL;DR

This paper introduces a multi-fidelity Bayesian optimization framework that accelerates bioprocess development across scales, reducing costs and improving yields through efficient experiment guidance and modeling.

Contribution

It presents a novel multi-fidelity Bayesian optimization method tailored for bioprocess scale-up, integrating Gaussian Processes for mixed-variable optimization and demonstrating its effectiveness with simulated case studies.

Findings

Achieves significant reduction in experimental costs

Increases bioprocess yield compared to traditional methods

Effective across multiple bioprocess scales

Abstract

Bioprocesses are central to modern biotechnology, enabling sustainable production in pharmaceuticals, specialty chemicals, cosmetics, and food. However, developing high-performing processes is costly and complex, requiring iterative, multi-scale experimentation from microtiter plates to pilot reactors. Conventional Design of Experiments (DoE) approaches often struggle to address process scale-up and the joint optimization of reaction conditions and biocatalyst selection. We propose a multi-fidelity batch Bayesian optimization framework to accelerate bioprocess development and reduce experimental costs. The method integrates Gaussian Processes tailored for multi-fidelity modeling and mixed-variable optimization, guiding experiment selection across scales and biocatalysts. A custom simulation of a Chinese Hamster Ovary bioprocess, capturing non-linear and coupled scale-up dynamics, is used for benchmarking against multiple simulated industrial DoE baselines. Multiple case studies show how the proposed workflow can achieve a reduction in experimental costs and increased yield. This work provides a data-efficient strategy for bioprocess optimization and highlights future opportunities in transfer learning and uncertainty-aware design for sustainable biotechnology.