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

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

摘要:

当前，生物制造技术和产业是世界关注的热点。然而，生物过程优化与放大过程中普遍面临以下几个难题，包括：过程检测手段缺乏，难以满足关键指标参数的监控；细胞代谢认知匮乏，无法理性实现过程最优化调控；反应器环境差异大，导致逐级放大效率低下。文中针对以上亟待解决的关键问题，通过案例分析介绍发酵过程实时检测-动态调控-理性放大全链条关键技术创新。在未来，生物过程设计将以集成细胞生理学 (时空多尺度细胞代谢模型) 和流体动力学 (CFD模型) 的全生命周期模型为指导，推进计算机辅助设计与开发，加速生物过程实现大规模智能化生产，开启绿色生物制造新时代。

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.