Developing a Computational Framework To Advance Bioprocess Scale-Up

Review (2020)
Authors

Guan Wang (East China University of Science and Technology)

Cees Haringa (TU Delft - BT/Bioprocess Engineering, DSM)

H.J. Noorman (DSM)

Ju Chu (East China University of Science and Technology)

Yingping Zhuang (East China University of Science and Technology)

Research Group
BT/Bioprocess Engineering
To reference this document use:
https://doi.org/10.1016/j.tibtech.2020.01.009
More Info
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Publication Year
2020
Language
English
Research Group
BT/Bioprocess Engineering
Issue number
8
Volume number
38
Pages (from-to)
846-856
DOI:
https://doi.org/10.1016/j.tibtech.2020.01.009

Abstract

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.

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