基因组尺度代谢网络模型的开发与应用。

2区 生物学 Q1 Immunology and Microbiology
Advances in applied microbiology Pub Date : 2024-01-01 Epub Date: 2024-03-01 DOI:10.1016/bs.aambs.2024.02.002
Jinyi Qian, Chao Ye
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引用次数: 0

摘要

基因组尺度代谢网络模型是表征生物体整个代谢途径中基因-蛋白-反应关系的有效工具。通过结合多种算法,基因组尺度代谢网络模型可以有效模拟特定环境对细胞生理状态的影响,优化菌株的培养条件,预测基因修饰的靶标,实现对菌株的靶向改造。在这篇综述中,我们总结了模型构建的全过程,梳理了模型构建过程中可能涉及的各种工具,并解释了各种算法在模型分析中的作用。此外,我们还总结了 GSMM 在网络特征、细胞表型、代谢工程等方面的应用。最后,我们讨论了 GSMM 当前面临的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and applications of genome-scale metabolic network models.

The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.

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来源期刊
Advances in applied microbiology
Advances in applied microbiology 生物-生物工程与应用微生物
CiteScore
8.20
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Advances in Applied Microbiology offers intensive reviews of the latest techniques and discoveries in this rapidly moving field. The editors are recognized experts and the format is comprehensive and instructive. Published since 1959, Advances in Applied Microbiology continues to be one of the most widely read and authoritative review sources in microbiology. Recent areas covered include bacterial diversity in the human gut, protozoan grazing of freshwater biofilms, metals in yeast fermentation processes and the interpretation of host-pathogen dialogue through microarrays.
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