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  4. Phenotype analysis of cultivation processes via unsupervised machine learning: demonstration for Clostridium pasteurianum
 
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Phenotype analysis of cultivation processes via unsupervised machine learning: demonstration for Clostridium pasteurianum

Citation Link: https://doi.org/10.15480/882.4155
Publikationstyp
Journal Article
Publikationsdatum
2022-02
Sprache
English
Author
Hong, Yaeseong 
Nguyen, Tom 
Arbter, Philipp 
Utesch, Tyll 
Zeng, An-Ping orcid-logo
Institut
Bioprozess- und Biosystemtechnik V-1 
DOI
10.15480/882.4155
TORE-URI
http://hdl.handle.net/11420/11356
Lizenz
https://creativecommons.org/licenses/by/4.0/
Enthalten in
Engineering in life sciences 
Volume
22
Issue
2
Start Page
85
End Page
99
Citation
Engineering in Life Sciences 22 (2): 85-99 (2022-02)
Publisher DOI
10.1002/elsc.202100114
Scopus ID
2-s2.0-85120852480
Publisher
Wiley-Blackwell
A novel approach of phenotype analysis of fermentation-based bioprocesses based on unsupervised learning (clustering) is presented. As a prior identification of phenotypes and conditional interrelations is desired to control fermentation performance, an automated learning method to output reference phenotypes (defined as vector of biomass-specific rates) was developed and the necessary computing process and parameters were assessed. For its demonstration, time series data of 90 Clostridium pasteurianum cultivations were used which feature a broad spectrum of solventogenic and acidogenic phenotypes, while 14 clusters of phenotypic manifestations were identified. The analysis of reference phenotypes showed distinct differences, where potential conditionalities were exemplary isolated. Further, cluster-based balancing of carbon and ATP or the use of reference phenotypes as indicator for bioprocess monitoring were demonstrated to highlight the perks of this approach. Overall, such analysis depends strongly on the quality of the data and experimental validations will be required before conclusions. However, the automated, streamlined and abstracted approach diminishes the need of individual evaluation of all noisy dataset and showed promising results, which could be transferred to strains with comparably wide-ranging phenotypic manifestations or as indicators for repeated bioprocesses with clearly defined target.
Schlagworte
automated fermentation analysis
Clostridium pasteurianum
phenotype analysis
process monitoring
unsupervised learning
DDC Class
600: Technik
Projekt(e)
Publikationsfonds 2021 
More Funding Information
Open access funding enabled and organized by Projekt DEAL.
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