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Wu S, Qu Z, Chen D, Wu H, Caiyin Q, Qiao J. Deciphering and designing microbial communities by genome-scale metabolic modelling. Comput Struct Biotechnol J 2024; 23:1990-2000. [PMID: 38765607 PMCID: PMC11098673 DOI: 10.1016/j.csbj.2024.04.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/21/2024] [Accepted: 04/21/2024] [Indexed: 05/22/2024] Open
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.
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Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Zheping Qu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Danlei Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Hao Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Qinggele Caiyin
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
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