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

2025-08-14

AI Summary

Addressing the challenges of jointly optimizing reaction conditions and biocatalysts, as well as the high cost of multiscale process scale-up in bioprocess development, this paper proposes a novel framework integrating multi-fidelity modeling with batch Bayesian optimization. The method innovatively combines Gaussian processes with a mixed-variable, multi-fidelity optimization strategy, enabling joint experimental design and catalyst screening across scales while significantly improving data efficiency. It natively handles both continuous and discrete variables and respects realistic process constraints. Evaluated on a Chinese Hamster Ovary (CHO) cell culture simulation platform, the framework reduces experimental cost by 30–50% and increases product yield by 12–28% compared to conventional Design of Experiments (DoE) approaches across multiple case studies. The framework demonstrates high performance, strong generalizability, and excellent scalability.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Machine Learning: Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Optimize bioprocess development across multiple scales
• Reduce experimental costs in bioprocess optimization
• Address joint optimization of conditions and biocatalysts

Innovation

• Multi-fidelity batch Bayesian optimization framework
• Gaussian Processes for multi-fidelity modeling
• Mixed-variable optimization across scales