Disentangling Interactions in the Microbiome: A Network Perspective

By M. Layeghifard, D. Hwang, David S. Guttman. 2016

679 citations recorded by the source corpus.

Microbiota are now widely recognized as being central players in the health of all organisms and ecosystems, and subsequently have been the subject of intense study. However, analyzing and converting microbiome data into meaningful biological insights remain very challenging. In this review, we highlight recent advances in network theory and their applicability to microbiome research. We discuss emerging graph theoretical concepts and approaches used in other research disciplines and demonstrate how they are well suited for enhancing our understanding of the higher-order interactions that occur within microbiomes. 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.

Subfields: Experimental and Cognitive Psychology, Molecular Biology, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

In Molecular Biology

In Statistical and Nonlinear Physics

Citations in the corpus

Prioritizing network communities (2018, methodology): “Furthermore, these methods do not apply in domains at the frontier of science where domain-speci ﬁ c knowledge is scarce or non-existent, such as in the case of cell – cell similarity networks 17 , microbiome networks 18,19 , and chemical interaction networks 20 .”
Network analysis methods for studying microbial communities: A mini review (2021, methodology): “Apart from these classical approaches, more complex methods have been proposed based on probabilistic graphical, Gaussian graphical and complex multiple regression models to construct microbial interaction networks [6,21].”
Microbial interactions within the plant holobiont (2018, neutral): “Currently, bioinformatics tools have been also developed to infer microbial co-occurrence networks from community profiling or metagenomic data [207, 208].”
Core microbiomes for sustainable agroecosystems (2018, background): “Diverse functional groups of microorganisms interact with each other within and around plants, forming complex webs of interactions 66 (Fig.”