Treffer: A Guide to Bayesian Optimization in Bioprocess Engineering.
Title:
A Guide to Bayesian Optimization in Bioprocess Engineering.
Authors:
Siska M; IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany.; Computational Systems Biotechnology, RWTH Aachen University, Aachen, Germany., Pajak E; The Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, UK., Rosenthal K; School of Science, Constructor University, Bremen, Germany.; Bioprocess Engineering, Faculty of Technology, Bielefeld University, Bielefeld, Germany., Del Rio Chanona A; The Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, UK., von Lieres E; IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany.; Computational Systems Biotechnology, RWTH Aachen University, Aachen, Germany., Helleckes LM; The Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, UK.; I-X Centre for AI in Science, Imperial College London, London, UK.
Source:
Biotechnology and bioengineering [Biotechnol Bioeng] 2026 Jan 08. Date of Electronic Publication: 2026 Jan 08.
Publication Model:
Ahead of Print
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: United States NLM ID: 7502021 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0290 (Electronic) Linking ISSN: 00063592 NLM ISO Abbreviation: Biotechnol Bioeng Subsets: MEDLINE
Imprint Name(s):
Publication: <2005->: Hoboken, NJ : Wiley
Original Publication: New York, Wiley.
Original Publication: New York, Wiley.
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Contributed Indexing:
Keywords: Bayesian optimization; Design of Experiments; bioprocess engineering; guidelines
Entry Date(s):
Date Created: 20260108 Latest Revision: 20260108
Update Code:
20260109
DOI:
10.1002/bit.70129
PMID:
41504386
Database:
MEDLINE
Weitere Informationen
Bayesian optimization has become widely popular across various experimental sciences due to its favorable attributes: it can handle noisy data, perform well with relatively small data sets, and provide adaptive suggestions for sequential experimentation. While still in its infancy, Bayesian optimization has recently gained traction in bioprocess engineering. However, experimentation with biological systems is highly complex and the resulting experimental uncertainty requires specific extensions to classical Bayesian optimization. Moreover, current literature often targets readers with a strong statistical background, limiting its accessibility for practitioners. In light of these developments, this review has two aims: first, to provide an intuitive and practical introduction to Bayesian optimization; and second, to outline promising application areas and open algorithmic challenges, thereby highlighting opportunities for future research in machine learning.
(© 2026 The Author(s). Biotechnology and Bioengineering published by Wiley Periodicals LLC.)