Optimization and Scale-Up of Fermentation Processes Driven by Models
In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/9/473 |
| _version_ | 1797491056489529344 |
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| author | Yuan-Hang Du Min-Yu Wang Lin-Hui Yang Ling-Ling Tong Dong-Sheng Guo Xiao-Jun Ji |
| author_facet | Yuan-Hang Du Min-Yu Wang Lin-Hui Yang Ling-Ling Tong Dong-Sheng Guo Xiao-Jun Ji |
| author_sort | Yuan-Hang Du |
| collection | DOAJ |
| description | In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models. |
| first_indexed | 2024-03-10T00:41:54Z |
| format | Article |
| id | doaj.art-2dcafd1eb6f3491c84252923e93d785e |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T00:41:54Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-2dcafd1eb6f3491c84252923e93d785e2023-11-23T15:06:11ZengMDPI AGBioengineering2306-53542022-09-019947310.3390/bioengineering9090473Optimization and Scale-Up of Fermentation Processes Driven by ModelsYuan-Hang Du0Min-Yu Wang1Lin-Hui Yang2Ling-Ling Tong3Dong-Sheng Guo4Xiao-Jun Ji5School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, ChinaState Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, ChinaSchool of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, ChinaSchool of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, ChinaSchool of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, ChinaState Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, ChinaIn the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.https://www.mdpi.com/2306-5354/9/9/473mechanistic modelingdata-drivenhybrid modelingscale-upcomputational fluid dynamics |
| spellingShingle | Yuan-Hang Du Min-Yu Wang Lin-Hui Yang Ling-Ling Tong Dong-Sheng Guo Xiao-Jun Ji Optimization and Scale-Up of Fermentation Processes Driven by Models Bioengineering mechanistic modeling data-driven hybrid modeling scale-up computational fluid dynamics |
| title | Optimization and Scale-Up of Fermentation Processes Driven by Models |
| title_full | Optimization and Scale-Up of Fermentation Processes Driven by Models |
| title_fullStr | Optimization and Scale-Up of Fermentation Processes Driven by Models |
| title_full_unstemmed | Optimization and Scale-Up of Fermentation Processes Driven by Models |
| title_short | Optimization and Scale-Up of Fermentation Processes Driven by Models |
| title_sort | optimization and scale up of fermentation processes driven by models |
| topic | mechanistic modeling data-driven hybrid modeling scale-up computational fluid dynamics |
| url | https://www.mdpi.com/2306-5354/9/9/473 |
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