A Guide to Bayesian Optimization in Bioprocess Engineering

Biotechnology and Bioengineering, vol. 123, pp. 805–830

Abstract

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.

Authors 6

1. Maximilian Siska Aachen Computational Systems Biotechnology

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Computational Systems Biotechnology RWTH Aachen University Aachen Germany

   IBG‐1: Biotechnology Forschungszentrum Jülich Jülich Germany

2. Emma Pajak

   Imperial College London

   Affiliation as printed

   The Sargent Centre for Process Systems Engineering, Department of Chemical Engineering Imperial College London London UK

3. Katrin Rosenthal

   Constructor University · Bielefeld University

   Affiliation as printed

   Bioprocess Engineering Faculty of Technology Bielefeld University Bielefeld Germany

   School of Science Constructor University Bremen Germany

4. Ehecatl Antonio del Rio‐Chanona

   Imperial College London

   Affiliation as printed

   The Sargent Centre for Process Systems Engineering, Department of Chemical Engineering Imperial College London London UK

5. Eric von Lieres Aachen Computational Systems Biotechnology

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Computational Systems Biotechnology RWTH Aachen University Aachen Germany

   IBG‐1: Biotechnology Forschungszentrum Jülich Jülich Germany

6. Laura M. Helleckes corresponding

   Imperial College London

   Affiliation as printed

   I‐X Centre for AI in Science Imperial College London London UK

   The Sargent Centre for Process Systems Engineering, Department of Chemical Engineering Imperial College London London UK