When Bioprocess Engineering Meets Machine Learning: A Survey from the
Perspective of Automated Bioprocess Development
- URL: http://arxiv.org/abs/2209.01083v1
- Date: Fri, 2 Sep 2022 14:30:49 GMT
- Title: When Bioprocess Engineering Meets Machine Learning: A Survey from the
Perspective of Automated Bioprocess Development
- Authors: Nghia Duong-Trung, Stefan Born, Jong Woo Kim, Marie-Therese
Schermeyer, Katharina Paulick, Maxim Borisyak, Ernesto Martinez, Mariano
Nicolas Cruz-Bournazou, Thorben Werner, Randolf Scholz, Lars Schmidt-Thieme,
Peter Neubauer
- Abstract summary: Machine learning (ML) has significantly contributed to the development of bioprocess engineering, but its application is still limited.
This review provides a comprehensive overview of ML-based automation in bioprocess development.
- Score: 3.687740185234604
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Machine learning (ML) has significantly contributed to the development of
bioprocess engineering, but its application is still limited, hampering the
enormous potential for bioprocess automation. ML for model building automation
can be seen as a way of introducing another level of abstraction to focus
expert humans in the most cognitive tasks of bioprocess development. 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) solutions in valuable solutions for the bio-community. We
summarize recent ML implementation across several important subfields of
bioprocess systems and raise two crucial challenges remaining the bottleneck of
bioprocess automation and reducing uncertainty in biotechnology development.
There is no one-fits-all procedure; however, this review should help identify
the potential automation combining biotechnology and ML domains.
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