Abstract:Bioprocesses are central to modern biotechnology, enabling sustainable production of pharmaceuticals, specialty chemicals, cosmetics, and food. However, developing high-performing processes remains 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 present 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. At each iteration, the algorithm proposes not only the next experimental conditions but also the appropriate scale and choice of biocatalyst (i.e., cell clones). To benchmark performance, we developed a custom simulation of a Chinese hamster ovary bioprocess that captures the non-linear, coupled dynamics of scale-up across different clones. Multiple case studies demonstrate that the proposed workflow achieves a reduction in experimental costs while improving yield compared to industrial DoE baselines.
This work provides a data-efficient strategy for bioprocess optimization and highlights opportunities for incorporating transfer learning and uncertainty-aware design for sustainable biotechnology.
From: Laura Helleckes [view email]
[v1]
Thu, 14 Aug 2025 16:29:34 UTC (298 KB)
[v2]
Wed, 9 Sep 2026 17:49:35 UTC (285 KB)
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