The community-function landscape of microbial consortia.

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Cell Systems Pub Date : 2023-02-15 DOI:10.1016/j.cels.2022.12.011
Alvaro Sanchez, Djordje Bajic, Juan Diaz-Colunga, Abigail Skwara, Jean C C Vila, Seppe Kuehn
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引用次数: 19

Abstract

Quantitatively linking the composition and function of microbial communities is a major aspiration of microbial ecology. Microbial community functions emerge from a complex web of molecular interactions between cells, which give rise to population-level interactions among strains and species. Incorporating this complexity into predictive models is highly challenging. Inspired by a similar problem in genetics of predicting quantitative phenotypes from genotypes, an ecological community-function (or structure-function) landscape could be defined that maps community composition and function. In this piece, we present an overview of our current understanding of these community landscapes, their uses, limitations, and open questions. We argue that exploiting the parallels between both landscapes could bring powerful predictive methodologies from evolution and genetics into ecology, providing a boost to our ability to engineer and optimize microbial consortia.

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微生物群落功能景观。
定量联系微生物群落的组成和功能是微生物生态学的主要愿望。微生物群落的功能产生于细胞间分子相互作用的复杂网络,这导致了菌株和物种之间种群水平的相互作用。将这种复杂性整合到预测模型中是极具挑战性的。受遗传学中从基因型预测定量表型的类似问题的启发,可以定义一个生态群落功能(或结构功能)景观,以映射群落组成和功能。在这篇文章中,我们概述了我们目前对这些社区景观的理解,它们的用途、限制和开放的问题。我们认为,利用这两种景观之间的相似之处,可以将进化和遗传学的强大预测方法引入生态学,从而提高我们设计和优化微生物群落的能力。
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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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