Abstract:

Yeasts, particularly Saccharomyces cerevisiae, are widely used eukaryotic organisms with relatively simple cellular structures and metabolic networks, and their cellular processes exhibit a certain degree of conservation among eukaryotes. These organisms play a crucial role in synthetic biology and systems biology research. However, due to the complexity of their metabolic networks and the variability of cellular activities, studying and designing their pathways still present considerable challenges. To address these issues, researchers have developed genome-scale models, which are mathematical frameworks that integrate genomic, biochemical, and physiological data to simulate cellular processes and predict the relationship between genotype and phenotype. These models are used to simulate cellular functions and predict cell behavior under different conditions, providing a systematic approach to understanding and engineering biological systems. This review introduces the methods for building and analyzing genome-scale models, including traditional metabolic models and their derived multi-constraint and multi-process models. It also traces the development of yeast models over time. Furthermore, this paper discusses recent applications of yeast models in areas such as designing yeast as cell factories for the production of valuable compounds, studying microbial physiology, optimizing cultivation conditions, and simulating microbial community interactions. These models also provide insights into identifying potential metabolic engineering targets for optimizing cellular functions. Despite the advantages of genome-scale models, their development and application are still limited in several aspects, such as incomplete data on metabolic pathways, limited focus on secondary metabolism, and high barriers to use, particularly for users without programming backgrounds. This review proposes several strategies to address the challenges. To enhance the development of traditional models, it is crucial to incorporate more comprehensive datasets, with a particular emphasis on secondary metabolism and metabolic dark matter. Additionally, improving the accessibility of models requires the development of user-friendly platforms, the provision of clear and standardized tutorials. These strategies can lower barriers for users, especially non-programmers, and promote wider adoption of genome-scale models.

Key words: genome-scale model, yeast, metabolic engineering, cellular metabolism, flux analysis

摘要：

酵母作为常用的真核模式生物，在合成生物学和系统生物学研究中具有重要地位。然而，由于其代谢系统较为复杂，进行代谢网络研究和设计时存在一定困难。因此，研究人员提出了基因组规模的建模方法，利用基因组序列及注释信息，整合细胞内复杂的代谢反应和细胞过程，模拟细胞系统各部分的相互作用，得到对应的表型、功能和行为，辅助寻找代谢工程改造靶点，为理解复杂细胞系统提供了强有力的工具。本文介绍了基因组规模模型中传统代谢模型及整合多种生理学约束和多种细胞过程的模型的构建和分析方法，回顾了酵母属中多种酵母基因组规模模型的发展历程及主要应用，并基于此分析了当前酵母基因组规模模型研究中面临的主要问题，提出了提升模型准确率以及未来进一步优化模型的方法和趋势。

关键词: 基因组规模模型, 酵母, 代谢工程, 细胞代谢, 通量分析