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

• URL: http://arxiv.org/abs/2508.10970v1
• Date: Thu, 14 Aug 2025 16:29:34 GMT
• Title: Holistic Bioprocess Development Across Scales Using Multi-Fidelity Batch Bayesian Optimization
• Authors: Adrian Martens, Mathias Neufang, Alessandro Butté, Moritz von Stosch, Antonio del Rio Chanona, Laura Marie Helleckes,
• Abstract summary: We propose a multi-fidelity batch Bayesian optimization framework to accelerate bioprocess development and reduce experimental costs.<n>A custom simulation of a Chinese Hamster Ovary bioprocess, capturing non-linear and coupled scale-up dynamics, is used for benchmarking.<n>Case studies show how the proposed workflow can achieve a reduction in experimental costs and increased yield.
• Score: 37.69303106863453
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• PeroMAS: A Multi-agent System of Perovskite Material Discovery [51.859972927223936]
  Perovskite Solar Cells (PSCs) are renowned for their superior optoelectronic performance and cost potential.<n>Existing AI approaches focus predominantly on discrete models, including material design, process optimization, and property prediction.<n>We propose a multi-agent system for perovskite material discovery, named PeroMAS.
  arXiv  Detail & Related papers  (2026-02-10T09:33:06Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems [2.812898346527047]
  This work proposes a novel approach to optimize the yield of a reaction in a multi-reactor system. It integrates experimental constraints and balances between exploration and exploitation in a sequential batch optimization strategy.
  arXiv  Detail & Related papers  (2024-08-05T15:26:39Z)
• Machine learning in bioprocess development: From promise to practice [58.720142291102135]
  Data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development.
  arXiv  Detail & Related papers  (2022-10-04T13:48:59Z)
• Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design [3.674863913115431]
  This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrated in a simulation case study for a relevant industrial task in process development. The optimized process showed much lower deviation rates regarding viable cell densities.
  arXiv  Detail & Related papers  (2022-05-06T14:33:02Z)
• Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Development and Control [6.580930850408461]
  Key challenges of cell therapy manufacturing include high complexity, high uncertainty, and very limited process data. We propose a framework named "hybridRL" to efficiently guide process development and control. In the empirical study, cell therapy manufacturing examples are used to demonstrate that the proposed hybrid-RL framework can outperform the classical deterministic mechanistic model assisted process optimization.
  arXiv  Detail & Related papers  (2022-01-10T00:01:19Z)
• Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding [48.7576911714538]
  Finding the optimal process parameters for an adhesive bonding process is challenging. Traditional evolutionary approaches (such as genetic algorithms) are then ill-suited to solve the problem. In this research, we successfully applied specific machine learning techniques to emulate the objective and constraint functions.
  arXiv  Detail & Related papers  (2021-12-16T10:14:39Z)
• Multi-objective simulation optimization of the adhesive bonding process of materials [50.591267188664666]
  Finding the optimal process parameters for such adhesive bonding process is challenging. In this research, we successfully applied Bayesian optimization using Gaussian Process Regression and Logistic Regression.
  arXiv  Detail & Related papers  (2021-12-09T09:58:58Z)
• Policy Optimization in Bayesian Network Hybrid Models of Biomanufacturing Processes [3.124775036986647]
  Biomanufacturing processes require close monitoring and control. We develop a novel model-based reinforcement learning framework that can achieve human-level control in low-data environments.
  arXiv  Detail & Related papers  (2021-05-13T20:39:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.