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      Microbial model communities: To understand complexity, harness the power of simplicity

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          Abstract

          Natural microbial communities are complex ecosystems with myriads of interactions. To deal with this complexity, we can apply lessons learned from the study of model organisms and try to find simpler systems that can shed light on the same questions. Here, microbial model communities are essential, as they can allow us to learn about the metabolic interactions, genetic mechanisms and ecological principles governing and structuring communities. A variety of microbial model communities of varying complexity have already been developed, representing different purposes, environments and phenomena. However, choosing a suitable model community for one’s research question is no easy task. This review aims to be a guide in the selection process, which can help the researcher to select a sufficiently well-studied model community that also fulfills other relevant criteria. For example, a good model community should consist of species that are easy to grow, have been evaluated for community behaviors, provide simple readouts and – in some cases – be of relevance for natural ecosystems. Finally, there is a need to standardize growth conditions for microbial model communities and agree on definitions of community-specific phenomena and frameworks for community interactions. Such developments would be the key to harnessing the power of simplicity to start disentangling complex community interactions.

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          The human microbiome: at the interface of health and disease.

          Interest in the role of the microbiome in human health has burgeoned over the past decade with the advent of new technologies for interrogating complex microbial communities. The large-scale dynamics of the microbiome can be described by many of the tools and observations used in the study of population ecology. Deciphering the metagenome and its aggregate genetic information can also be used to understand the functional properties of the microbial community. Both the microbiome and metagenome probably have important functions in health and disease; their exploration is a frontier in human genetics.
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            The ecology of the microbiome: Networks, competition, and stability.

            The human gut harbors a large and complex community of beneficial microbes that remain stable over long periods. This stability is considered critical for good health but is poorly understood. Here we develop a body of ecological theory to help us understand microbiome stability. Although cooperating networks of microbes can be efficient, we find that they are often unstable. Counterintuitively, this finding indicates that hosts can benefit from microbial competition when this competition dampens cooperative networks and increases stability. More generally, stability is promoted by limiting positive feedbacks and weakening ecological interactions. We have analyzed host mechanisms for maintaining stability-including immune suppression, spatial structuring, and feeding of community members-and support our key predictions with recent data.
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              Bacterial competition: surviving and thriving in the microbial jungle.

              Most natural environments harbour a stunningly diverse collection of microbial species. In these communities, bacteria compete with their neighbours for space and resources. Laboratory experiments with pure and mixed cultures have revealed many active mechanisms by which bacteria can impair or kill other microorganisms. In addition, a growing body of theoretical and experimental population studies indicates that the interactions within and between bacterial species can have a profound impact on the outcome of competition in nature. The next challenge is to integrate the findings of these laboratory and theoretical studies and to evaluate the predictions that they generate in more natural settings.
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                Author and article information

                Contributors
                Journal
                Comput Struct Biotechnol J
                Comput Struct Biotechnol J
                Computational and Structural Biotechnology Journal
                Research Network of Computational and Structural Biotechnology
                2001-0370
                02 December 2020
                2020
                02 December 2020
                : 18
                : 3987-4001
                Affiliations
                [a ]Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10, SE-413 46 Gothenburg, Sweden
                [b ]Centre for Antibiotic Resistance Research (CARe) at University of Gothenburg, Gothenburg, Sweden
                Author notes
                [* ]Address at: University of Gothenburg, Department of Infectious Diseases, Guldhedsgatan 10A, SE-413 46 Gothenburg, Sweden. johan.bengtsson-palme@ 123456microbiology.se
                Article
                S2001-0370(20)30510-9
                10.1016/j.csbj.2020.11.043
                7744646
                33363696
                d046a7bc-17c7-4648-90be-5f8304d0209a
                © 2020 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 31 August 2020
                : 23 November 2020
                : 23 November 2020
                Categories
                Review

                community-intrinsic properties,interactions,microbial communities,model systems,standardization

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