Guan Wang
1 State Key Laboratory of Bioreactor Engineering, Shanghai 200237, China;2 School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China;3 National Center of Bio-Engineering & Technology (Shanghai), Shanghai 200237, ChinaXiwei Tian
1 State Key Laboratory of Bioreactor Engineering, Shanghai 200237, China;2 School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China;3 National Center of Bio-Engineering & Technology (Shanghai), Shanghai 200237, ChinaJianye Xia
1 State Key Laboratory of Bioreactor Engineering, Shanghai 200237, China;2 School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China;3 National Center of Bio-Engineering & Technology (Shanghai), Shanghai 200237, ChinaJu Chu
1 State Key Laboratory of Bioreactor Engineering, Shanghai 200237, China;2 School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China;3 National Center of Bio-Engineering & Technology (Shanghai), Shanghai 200237, ChinaSiliang Zhang
1 State Key Laboratory of Bioreactor Engineering, Shanghai 200237, China;2 School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China;3 National Center of Bio-Engineering & Technology (Shanghai), Shanghai 200237, ChinaYingping Zhuang
1 State Key Laboratory of Bioreactor Engineering, Shanghai 200237, China;2 School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China;3 National Center of Bio-Engineering & Technology (Shanghai), Shanghai 200237, ChinaNational Natural Science Foundation of China (Nos. 31900073, 21978085), Natural Science Foundation of Shanghai, China (No. 19ZR1413600).
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.
王冠,田锡炜,夏建业,储炬,张嗣良,庄英萍. 大数据-模型混合驱动下生物过程优化与放大的新机遇与挑战[J]. Chinese Journal of Biotechnology, 2021, 37(3): 1004-1016
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