Developing a Computational Framework To Advance Bioprocess Scale-Up

Guan Wang, Cees Haringa, Henk Noorman, Ju Chu, Yingping Zhuang

Research output: Contribution to journalReview articleScientificpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)846-856
Number of pages11
JournalTrends in Biotechnology
Volume38
Issue number8
DOIs
Publication statusPublished - 2020

Keywords

  • computational fluid dynamics
  • industrial
  • metabolic model
  • metabolomics
  • population heterogeneity
  • scale-down

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