TY  - JOUR
AU  - Helleckes, Laura M.
AU  - Hemmerich, Johannes
AU  - Wiechert, Wolfgang
AU  - von Lieres, Eric
AU  - Grünberger, Alexander
TI  - Machine learning in bioprocess development: from promise to practice
JO  - Trends in biotechnology
VL  - 41
IS  - 6
SN  - 0167-7799
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - FZJ-2022-05310
SP  - S0167779922002815
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 36456404
UR  - <Go to ISI:>//WOS:001196982900001
DO  - DOI:10.1016/j.tibtech.2022.10.010
UR  - https://juser.fz-juelich.de/record/912083
ER  -