Optimization and Scale-Up of Fermentation Processes Driven by Models

• Published in: Bioengineering (Basel) Vol. 9; no. 9; p. 473
• Main Authors: Du, Yuan-Hang, Wang, Min-Yu, Yang, Lin-Hui, Tong, Ling-Ling, Guo, Dong-Sheng, Ji, Xiao-Jun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022; MDPI
• Subjects: Algorithms; Bioengineering; Biological computing; Biological models (mathematics); Bioreactors; Cell culture; Computational fluid dynamics; Computer applications; Constraint modelling; data-driven; Enzymes; Fermentation; hybrid modeling; Industrial production; Laboratories; Machine learning; mechanistic modeling; Metabolism; Metabolites; Microorganisms; Observations; Optimization; Ordinary differential equations; Physiology; Review; scale-up; Sustainable development; China
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering9090473

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.