Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA; Department of Microbial Biotechnology, CNB-CSIC, Campus de Cantoblanco, Madrid, Spain.
Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA.
Cell Syst. 2023 Feb 15;14(2):122-134. doi: 10.1016/j.cels.2022.12.011.
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.
定量关联微生物群落的组成和功能是微生物生态学的主要目标。微生物群落功能源自细胞间复杂的分子相互作用网络,从而导致菌株和物种之间的种群水平相互作用。将这种复杂性纳入预测模型极具挑战性。受遗传学中从基因型预测定量表型这一类似问题的启发,可以定义一个生态群落功能(或结构-功能)景观,该景观可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群落景观的理解,包括它们的用途、局限性和未解决的问题。我们认为,利用这两个景观之间的相似性,可以将进化和遗传学中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。