Developing a Computational Framework To Advance Bioprocess Scale-Up

Review (2020)

Authors

Guan Wang East China University of Science and Technology
C. Haringa BT/Bioprocess Engineering - AS , DSM
Ju Chu East China University of Science and Technology
Yingping Zhuang East China University of Science and Technology
Research Group
BT/Bioprocess Engineering (AS) (TU Delft)
Computational fluid dynamics Metabolomics Population heterogeneity Metabolic model Industrial Scale-down
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Published Date

2020

Language

English

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Faculty

Applied Sciences

Department

BT/Biotechnologie

Research Group

BT/Bioprocess Engineering

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.