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[混合数据与模型驱动的生物过程优化及放大的新机遇与挑战]

[New opportunities and challenges for hybrid data and model driven bioprocess optimization and scale-up].

作者信息

Wang Guan, Tian Xiwei, Xia Jianye, Chu Ju, Zhang Siliang, Zhuang Yingping

机构信息

State Key Laboratory of Bioreactor Engineering, Shanghai 200237, China.

School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China.

出版信息

Sheng Wu Gong Cheng Xue Bao. 2021 Mar 25;37(3):1004-1016. doi: 10.13345/j.cjb.200634.

Abstract

Currently, biomanufacturing technology and industry are receiving worldwide attention. However, there are still great challenges on bioprocess optimization and scale-up, including: lacing the process detection methods, which makes it difficult to meet the requirement of monitoring of key indicators and parameters; poor understanding of cell metabolism, which arouses problems to rationally achieve process optimization and regulation; the reactor environment is very different across the scales, resulting in low efficiency of stepwise scale-up. Considering the above key issues that need to be resolved, here we summarize the key technological innovations of the whole chain of fermentation process, i.e., real-time detection-dynamic regulation-rational scale-up, through case analysis. In the future, bioprocess design will be guided by a full lifecycle in-silico model integrating cellular physiology (spatiotemporal multiscale metabolic models) and fluid dynamics (CFD models). This will promote computer-aided design and development, accelerate the realization of large-scale intelligent production and serve to open a new era of green biomanufacturing.

摘要

目前,生物制造技术和产业正受到全球关注。然而,生物过程优化和放大仍面临巨大挑战,包括:缺乏过程检测方法,难以满足关键指标和参数监测的要求;对细胞代谢了解不足,难以合理实现过程优化和调控;不同规模下反应器环境差异很大,导致逐步放大效率低下。考虑到上述需要解决的关键问题,在此我们通过案例分析总结发酵过程全链条的关键技术创新,即实时检测-动态调控-合理放大。未来,生物过程设计将由整合细胞生理学(时空多尺度代谢模型)和流体动力学(计算流体力学模型)的全生命周期计算机模拟模型指导。这将推动计算机辅助设计与开发,加速大规模智能生产的实现,并开启绿色生物制造的新时代。

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