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When bioprocess engineering meets machine learning: A survey from the perspective of automated bioprocess development

Duong-Trung, Nghia; Born, Stefan; Kim, Jong Woo; Schermeyer, Marie-Therese; Paulick, Katharina; Borisyak, Maxim; Cruz-Bournazou, Mariano Nicolas; Werner, Thorben; Scholz, Randolf; Schmidt-Thieme, Lars; Neubauer, Peter; Martinez, Ernesto

Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation has been accelerated by increasing levels of lab automation, experimental planning and data modeling are still largerly depend on human intervention. ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks. First, probabilistic programming is used for the autonomous building of predictive models. Second, machine learning automatically assesses alternative decisions by planning experiments to test hypotheses and conducting investigations to gather informative data that focus on model selection based on the uncertainty of model predictions. This review provides a comprehensive overview of ML-based automation in bioprocess development. On the one hand, the biotech and bioengineering community should be aware of the potential and, most importantly, the limitation of existing ML solutions for their application in biotechnology and biopharma. On the other hand, it is essential to identify the missing links to enable the easy implementation of ML and Artificial Intelligence (AI) tools in valuable solutions for the bio-community. There is no one-fits-all procedure; however, this review should help identify the potential for automating model building by combining first-principles biotechnology knowledge and ML methods to address the reproducibility crisis in bioprocess development.
Published in: Biochemical Engineering Journal, 10.1016/j.bej.2022.108764, Elsevier