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通过基因组规模代谢建模解析和设计微生物群落

Deciphering and designing microbial communities by genome-scale metabolic modelling.

作者信息

Wu Shengbo, Qu Zheping, Chen Danlei, Wu Hao, Caiyin Qinggele, Qiao Jianjun

机构信息

School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.

Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China.

出版信息

Comput Struct Biotechnol J. 2024 Apr 22;23:1990-2000. doi: 10.1016/j.csbj.2024.04.055. eCollection 2024 Dec.

Abstract

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

摘要

微生物群落是由生物与环境之间的复杂相互作用所塑造的。基因组尺度代谢模型(GEMs)能够更深入地洞察各种微生物群落的复杂性和生态特性,揭示它们之间错综复杂的相互作用。许多研究人员已根据特定需求对微生物群落的GEMs进行了修改。因此,需要对GEMs进行全面总结,以便更好地理解其发展趋势。在本综述中,我们总结了使用不同GEMs来解读和设计微生物群落的关键进展。GEMs中选定亮点的时间线表明,该领域正从单菌株水平向微生物群落水平发展。然后,我们概述了构建微生物群落GEMs的框架。我们还总结了静态和动态群落水平GEMs的模型和资源。我们重点关注外部环境和细胞内资源在塑造微生物群落组装过程中的作用。最后,我们讨论了GEMs的关键挑战和未来方向,重点是GEMs与群体感应机制、微生物生态相互作用、机器学习算法以及自动建模的整合,所有这些都有助于在不同领域基于联合体的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/996b/11098673/c9c7f1e20c8a/ga1.jpg

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