A Guide to Bayesian Optimization in Bioprocess Engineering

Area of Science:

• Bioprocess Engineering
• Experimental Sciences
• Machine Learning

Background:

• Bayesian optimization (BO) is increasingly adopted in experimental sciences for its efficiency with noisy and limited data.
• Biological systems present unique challenges, including high experimental uncertainty, necessitating extensions to standard BO methods.
• Existing literature on BO often assumes advanced statistical knowledge, hindering practical application in bioprocess engineering.

Purpose of the Study:

• To provide an accessible, practical introduction to Bayesian optimization for bioprocess engineering.
• To identify key application areas and algorithmic challenges for future research in machine learning for bioprocesses.

Main Methods:

• This review synthesizes current literature on Bayesian optimization relevant to bioprocess engineering.
• It focuses on practical considerations and extensions needed for biological systems.
• The review highlights opportunities for machine learning advancements in this domain.

Main Results:

• Bayesian optimization offers significant advantages for optimizing complex bioprocesses.
• Specific adaptations are required to handle the inherent uncertainty in biological experimentation.
• Accessible introductions and clear identification of research gaps are crucial for broader adoption.

Conclusions:

• Bayesian optimization is a promising, yet underexplored, tool for advancing bioprocess engineering.
• Future research should focus on developing robust, user-friendly BO algorithms tailored for biological applications.
• Bridging the gap between statistical theory and practical engineering needs will accelerate innovation.