基因组尺度代谢模型(GEM)是代谢工程的有效工具,已被广泛用于指导细胞代谢调节。然而,GEM中的单一基因-蛋白质反应数据类型限制了对生物学复杂性的理解.因此,已经开发了基于GEM添加约束或整合组学数据的多尺度模型,以更准确地预测基因型的表型。这篇综述总结了多尺度GEM发展的最新进展,包括多约束,多元性,和全细胞模型,并概述了机器学习在GEM建设中的应用。这次审查的重点是框架,工具包,以及构建多尺度GEM的算法。还讨论了多尺度GEM发展的挑战和前景。
Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene-protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed.