文献类型：期刊文献

英文题名：Developing a Computational Framework To Advance Bioprocess Scale-Up

作者：Wang, Guan[1];Haringa, Cees[2];Noorman, Henk[2,3];Chu, Ju[1];Zhuang, Yingping[1]

机构：[1]East China Univ Sci & Technol ECUST, State Key Lab Bioreactor Engn, Shanghai, Peoples R China;[2]DSM Biotechnol Ctr, Delft, Netherlands;[3]Delft Univ Technol, Dept Biotechnol, Delft, Netherlands

年份：2020

卷号：38

期号：8

起止页码：846

外文期刊名：TRENDS IN BIOTECHNOLOGY

收录：;EI(收录号：20200908243440);WOS:【SCI-EXPANDED(收录号:WOS:000548668900006)】；

基金：The authors are grateful to Wouter van Winden, Wenjun Tang, and Jianye Xia for critical comments, and Siliang Zhang for providing data sets regarding guanosine fermentations. This work was financially supported by the National Natural Science Foundation of China (Grant no. 31900073), the Science and Technology Commission of Shanghai Municipality (Grant no. 19ZR1413600), the National Key Research and Development Program (Grant no. 2017ZX7402003), and the 111 Project (Grant no. B18022).

语种：英文

外文关键词：Metabolism

摘要：Bioprocess scale-up is a critical step in process development. However, loss of production performance upon scaling-up, including reduced titer, yield, or productivity, has often been observed, hindering the commercialization of biotech innovations. Recent developments in scale-down studies assisted by computational fluid dynamics (CFD) and powerful stimulus-response metabolic models afford better process prediction and evaluation, enabling faster scale-up with minimal losses. In the future, an ideal bioprocess design would be guided by an in silico model that integrates cellular physiology (spatiotemporal multiscale cellular models) and fluid dynamics (CFD models). Nonetheless, there are challenges associated with both establishing predictive metabolic models and CFD coupling. By highlighting these and providing possible solutions here, we aim to advance the development of a computational framework to accelerate bioprocess scale-up.