| Autor: |
Wang, Lvjing, Wang, Xiaoyu, Wu, Hao, Wang, Haixia, Wang, Yihan, Lu, Zhenmei |
| Předmět: |
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| Zdroj: |
Critical Reviews in Environmental Science & Technology; 2023, Vol. 53 Issue 24, p2092-2111, 20p |
| Abstrakt: |
The genome-scale metabolic model (GEM), a mathematical representation of whole-cell metabolism, enables in silico high-throughput simulations of metabolic flux distribution under different conditions. As a mechanistic and quantitative framework at the system level, GEMs have been applied to investigate microbial interactions and provide guidelines for the rational design of synthetic microbial communities that can perform the desired functions and provide novel strategies for bioremediation. Here, we review different types of microbial interactions and their research progress in environmental remediation. The detailed process of GEM reconstruction and the current main automatic reconstruction programs as well as knowledge databases are summarized. Exciting recent application studies combining metabolic models, omics, and machine learning approaches are also presented. The combination of these methods improves the predictive ability and broadens the applications of GEMs. However, extensive work is still needed to deeply understand and arbitrarily use GEMs to describe the metabolic interactions of microbial communities and apply them in pollutant biodegradation. Finally, an in-depth discussion of the current challenges and limitations of metabolic models provides us with an outlook for their future development in environmental science, especially synthetic microbiology. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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