A Guide to Bayesian Optimization in Bioprocess Engineering

TL;DR

This paper explains how Bayesian optimization can be used in bioprocess engineering, making it easier for practitioners to understand and apply.

Contribution

The paper introduces Bayesian optimization in bioprocess engineering in an accessible way and highlights future research opportunities.

Findings

Bayesian optimization is well-suited for bioprocess engineering due to its ability to handle noisy data and small datasets.

The paper identifies specific extensions needed for Bayesian optimization to address uncertainties in biological systems.

It outlines open challenges and application areas for future machine learning research in this domain.

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.

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