Developing a Computational Framework To Advance Bioprocess Scale-Up. Issue 8 (August 2020)

• Record Type: Journal Article
• Authors: Wang, Guan; Haringa, Cees; Noorman, Henk; Chu, Ju; Zhuang, Yingping
• Is Part Of: Trends in biotechnology. Volume 38:Issue 8(2020)
• Page Start: 846
• Page End: 856
• Publication Date: 2020-08
• Subjects: computational fluid dynamics -- industrial -- metabolomics -- metabolic model -- population heterogeneity -- scale-down; Biotechnology -- Periodicals; Biochemical engineering -- Periodicals; Genetic engineering -- Periodicals; Industrial microbiology -- Periodicals; 660.605
• DOI: 10.1016/j.tibtech.2020.01.009
• Languages: English
• ISSNs: 0167-7799

Abstract

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. Highlights: Metabolomics has been used alone or combined with other 'omics' tools to contribute to industrial systems biology and synthetic biology. Mechanisms governing population heterogeneity in industrial bioprocessing need rigorous investigation, and thereafter should be incorporated into more powerful cellular models. A predictive scale-down model should be rationally guided by model fluid studies in large-scale bioprocesses. Coupling cellular kinetics with fluid dynamics accelerates science-based design of both microbial cell factories and industrial-scale bioreactors.