Journal Article

FZJ-2022-05310

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Machine learning in bioprocess development: from promise to practice

Helleckes, L. M. (Corresponding author)FZJ* ; Hemmerich, J. ; Wiechert, W.FZJ* ; von Lieres, E.FZJ* ; Grünberger, A.

2023
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.tibtech.2022.10.010  doi:10.34734/FZJ-2022-05310

Abstract: Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

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Contributing Institute(s):

1. Biotechnologie (IBG-1)

Research Program(s):

1. 2172 - Utilization of renewable carbon and energy sources and engineering of ecosystem functions (POF4-217) (POF4-217)

Appears in the scientific report 2023

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Database coverage:
; ; BIOSIS Previews ; BIOSIS Reviews Reports And Meetings ; Clarivate Analytics Master Journal List ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 15 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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