State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai, People's Republic of China.
DSM Biotechnology Center, Delft, The Netherlands.
Trends Biotechnol. 2020 Aug;38(8):846-856. doi: 10.1016/j.tibtech.2020.01.009. Epub 2020 Feb 25.
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.
生物工艺放大是工艺开发的关键步骤。然而,在放大过程中经常会观察到生产性能的损失,包括滴度、产率或生产力的降低,这阻碍了生物技术创新的商业化。借助计算流体动力学 (CFD) 和强大的刺激-反应代谢模型进行的规模缩小研究的最新进展提供了更好的过程预测和评估,使能够以最小的损失更快地放大。在未来,基于整合细胞生理学(时空多尺度细胞模型)和流体动力学(CFD 模型)的计算模型将指导理想的生物工艺设计。尽管如此,建立预测代谢模型和 CFD 耦合都存在挑战。通过在这里强调这些挑战并提供可能的解决方案,我们旨在推进计算框架的开发,以加速生物工艺放大。