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

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dc.contributor.authorDuong-Trung, Nghia
dc.contributor.authorBorn, Stefan
dc.contributor.authorKim, Jong Woo
dc.contributor.authorSchermeyer, Marie-Therese
dc.contributor.authorPaulick, Katharina
dc.contributor.authorBorisyak, Maxim
dc.contributor.authorCruz-Bournazou, Mariano Nicolas
dc.contributor.authorWerner, Thorben
dc.contributor.authorScholz, Randolf
dc.contributor.authorSchmidt-Thieme, Lars
dc.contributor.authorNeubauer, Peter
dc.contributor.authorMartinez, Ernesto
dc.date.accessioned2023-03-03T11:27:24Z
dc.date.available2023-03-03T11:27:24Z
dc.date.issued2022-12-12
dc.description.abstractMachine 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.en
dc.description.sponsorshipBMBF, 01DD20002A, Verbundprojekt: Internationales Zukunftslabor für KI-gestützte Bioprozessentwicklung "KIWI-biolab"; Teilvorhaben: Koordination und Aufbau eines KI-Exzellenzzentrums
dc.identifier.eissn1873-295X
dc.identifier.issn1369-703X
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/18273
dc.identifier.urihttps://doi.org/10.14279/depositonce-17066
dc.language.isoen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc600 Technik, Medizin, angewandte Wissenschaften::660 Chemische Verfahrenstechnik::660 Chemische Verfahrenstechnik
dc.subject.otheractive learningen
dc.subject.otherautomationen
dc.subject.otherbioprocess developmenten
dc.subject.otherreinforcement learningen
dc.subject.otherreproducibility crisisen
dc.titleWhen bioprocess engineering meets machine learning: A survey from the perspective of automated bioprocess development
dc.typeArticle
dc.type.versionacceptedVersion
dcterms.bibliographicCitation.articlenumber108764
dcterms.bibliographicCitation.doi10.1016/j.bej.2022.108764
dcterms.bibliographicCitation.journaltitleBiochemical Engineering Journal
dcterms.bibliographicCitation.originalpublishernameElsevier
dcterms.bibliographicCitation.originalpublisherplaceAmsterdam
dcterms.bibliographicCitation.volume190
dcterms.rightsHolder.reference§ 38 (4) UrhG
tub.accessrights.dnbdomain*
tub.affiliationFak. 3 Prozesswissenschaften::Inst. Biotechnologie::FG Bioverfahrenstechnik
tub.publisher.universityorinstitutionTechnische Universität Berlin

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