This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.34734/FZJ-2022-05310 in citations.
Please use the identifier: http://dx.doi.org/10.1016/j.tibtech.2022.10.010 in citations.
Machine learning in bioprocess development: from promise to practice
Machine learning in bioprocess development: from promise to practice
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 t...
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Personal Name(s): | Helleckes, Laura M. (Corresponding author) |
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Hemmerich, Johannes / Wiechert, Wolfgang / von Lieres, Eric / Grünberger, Alexander | |
Contributing Institute: |
Biotechnologie; IBG-1 |
Published in: | Trends in biotechnology, 41 (2023) 6, S. S0167779922002815 |
Imprint: |
Amsterdam [u.a.]
Elsevier Science
2023
|
DOI: |
10.34734/FZJ-2022-05310 |
DOI: |
10.1016/j.tibtech.2022.10.010 |
Document Type: |
Journal Article |
Research Program: |
Utilization of renewable carbon and energy sources and engineering of ecosystem functions |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1016/j.tibtech.2022.10.010 in citations.
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. |