人类肠道微生物群的代谢模型:进展与挑战。

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Daniel Rios Garza, Didier Gonze, Haris Zafeiropoulos, Bin Liu, Karoline Faust
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引用次数: 2

摘要

人类肠道是一个复杂的生态系统,由数百种微生物相互作用,并与人类宿主。肠道微生物组的数学模型整合了我们对这一系统的了解,并有助于制定假说来解释观察结果。广义Lotka-Volterra模型已被广泛用于此目的,但它没有描述相互作用机制,因此不能解释代谢灵活性。最近,明确描述肠道微生物代谢物产生和消耗的模型变得流行起来。这些模型已被用于研究塑造肠道微生物组成的因素,并将特定肠道微生物与疾病中发现的代谢物浓度变化联系起来。在这里,我们回顾了这些模型是如何建立的,以及到目前为止我们从它们在人类肠道微生物组数据中的应用中学到了什么。此外,我们还讨论了这些模型当前面临的挑战,以及如何在未来解决这些挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Metabolic models of human gut microbiota: Advances and challenges.

The human gut is a complex ecosystem consisting of hundreds of microbial species interacting with each other and with the human host. Mathematical models of the gut microbiome integrate our knowledge of this system and help to formulate hypotheses to explain observations. The generalized Lotka-Volterra model has been widely used for this purpose, but it does not describe interaction mechanisms and thus does not account for metabolic flexibility. Recently, models that explicitly describe gut microbial metabolite production and consumption have become popular. These models have been used to investigate the factors that shape gut microbial composition and to link specific gut microorganisms to changes in metabolite concentrations found in diseases. Here, we review how such models are built and what we have learned so far from their application to human gut microbiome data. In addition, we discuss current challenges of these models and how these can be addressed in the future.

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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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