{"ID":2882439,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.10970","arxiv_id":"2508.10970","title":"Holistic Bioprocess Development Across Scales Using Multi-Fidelity Batch Bayesian Optimization","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.","short_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 Des...","url_abs":"https://arxiv.org/abs/2508.10970","url_pdf":"https://arxiv.org/pdf/2508.10970v1","authors":"[\"Adrian Martens\",\"Mathias Neufang\",\"Alessandro Butté\",\"Moritz von Stosch\",\"Antonio del Rio Chanona\",\"Laura Marie Helleckes\"]","published":"2025-08-14T16:29:34Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"stat.ML\"]","methods":"[]","has_code":false}