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Disentangling Interactions in the Microbiome: A Network Perspective.

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TLDR
Network-based analytical approaches have the potential to help disentangle complex polymicrobial and microbe–host interactions, and thereby further the applicability of microbiome research to personalized medicine, public health, environmental and industrial applications, and agriculture.
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This article is published in Trends in Microbiology.The article was published on 2017-03-01 and is currently open access. It has received 502 citations till now. The article focuses on the topics: Microbiome.

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Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data.

TL;DR: This protocol details MicrobiomeAnalyst, a user-friendly, web-based platform for comprehensive statistical, functional, and meta-analysis of microbiome data, a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces.
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Microbial interactions within the plant holobiont.

TL;DR: The fundamental role of microbe-microbe interactions (prokaryotes and micro-eukaryotes) for microbial community structure and plant health is discussed and a conceptual framework illustrating that interactions among microbiota members are critical for the establishment and the maintenance of host-microbial homeostasis is provided.
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Biodiversity and species competition regulate the resilience of microbial biofilm community.

TL;DR: This study investigated the resilience of biofilm communities with a gradient of diversity, and explored the relationship between biodiversity and stability in response to a pH shock, showing that all bioreactors could recover to stable performance after pH disturbance.
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Environmental stress destabilizes microbial networks.

TL;DR: In this paper, the authors present the first evidence that naturally-occurring microbiomes display network properties characteristic of unstable communities when under persistent stress, showing that prokaryotic and fungal diversity decline in high stress, and negative:positive cohesion have a clear negative relationship with environmental stress.
References
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Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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Emergence of Scaling in Random Networks

TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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Centrality in social networks conceptual clarification

TL;DR: In this article, three distinct intuitive notions of centrality are uncovered and existing measures are refined to embody these conceptions, and the implications of these measures for the experimental study of small groups are examined.
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The anatomy of a large-scale hypertextual Web search engine

TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
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Community structure in social and biological networks

TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
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