数据驱动的生物制造过程多组学分析与建模

Data-driven multi-omics analyses and modelling for bioprocesses

摘要:

生物制造是新质生产力的重要代表，是通过工程细胞或无细胞体系实现物质高效转化的创新生产方式。其生产过程具有时空异质性、复杂性及动态性，给过程系统认知、优化调控带来了显著挑战。本文总结了生物过程多组学数据获取及分析的关键技术，归纳了基于多组学数据的生物过程建模方法，深入探讨了多组学和建模在过程参数调整、发酵控制、环境应激机制解析、营养供给优化及生产实时监测等关键环节的实际应用，详细阐释了多组学数据深度整合及模型构建在提升生物过程操作精度方面的巨大潜力。此外，本文讨论了目前生物过程优化面临的若干挑战，并探索了可能的解决策略。未来通过克服这些挑战，可以更好地理解和控制复杂的生物过程，推动生物制造领域的快速发展。

Abstract:

Biomanufacturing has emerged as a crucial driving force for efficient material conversion through engineered cells or cell-free systems. However, the intrinsic spatiotemporal heterogeneity, complexity, and dynamic characteristics of these processes pose significant challenges to systematic understanding, optimization, and regulation. This review summarizes essential methodologies for multi-omics data acquisition and analyses for bioprocesses and outlines modelling approaches based on multi-omics data. Furthermore, we explore practical applications of multi-omics and modelling in fine-tuning process parameters, improving fermentation control, elucidating stress response mechanisms, optimizing nutrient supplementation, and enabling real-time monitoring and adaptive adjustment. The substantial potential offered by integrating multi-omics with computational modelling for precision bioprocessing is also discussed. Finally, we identify current challenges in bioprocess optimization and propose the possible solutions, the implementation of which will significantly deepen understanding and enhance control of complex bioprocesses, ultimately driving the rapid advancement of biomanufacturing.