Holistic Bioprocess Development Across Scales Using Multi-Fidelity Batch Bayesian Optimization
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915446922412032 |
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| author | Martens, Adrian Neufang, Mathias Butté, Alessandro von Stosch, Moritz Chanona, Antonio del Rio Helleckes, Laura Marie |
| author_facet | Martens, Adrian Neufang, Mathias Butté, Alessandro von Stosch, Moritz Chanona, Antonio del Rio Helleckes, Laura Marie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10970 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Holistic Bioprocess Development Across Scales Using Multi-Fidelity Batch Bayesian Optimization Martens, Adrian Neufang, Mathias Butté, Alessandro von Stosch, Moritz Chanona, Antonio del Rio Helleckes, Laura Marie Quantitative Methods Machine Learning 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. |
| title | Holistic Bioprocess Development Across Scales Using Multi-Fidelity Batch Bayesian Optimization |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2508.10970 |