Du Yuan-Hang, Wang Min-Yu, Yang Lin-Hui, Tong Ling-Ling, Guo Dong-Sheng, Ji Xiao-Jun
School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China.
State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China.
Bioengineering (Basel). 2022 Sep 14;9(9):473. 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.
在可持续发展时代，利用细胞工厂通过发酵生产各种化合物已引起广泛关注；然而，工业发酵不仅需要高效的生产菌株，还需要合适的细胞外条件、培养基成分以及放大过程。在这方面，生物模型的应用受到了广泛关注，本综述将为生物模型的快速选择提供指导。本文首先介绍两种机理建模方法，即动力学建模和基于约束的建模（CBM），并概括它们在实际中的应用。接下来，我们回顾基于机器学习（ML）的数据驱动建模，并突出不同学习算法的应用范围。进一步讨论了ML和CBM结合用于构建混合模型的情况。最后，我们还讨论了通过结合生物模型和计算流体动力学（CFD）模型预测生物反应器放大和培养行为的最新策略。
