
Elucidation of complexity and prediction of interactions in microbial communities
Author(s) -
Zuñiga Cristal,
Zaramela Livia,
Zengler Karsten
Publication year - 2017
Publication title -
microbial biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.287
H-Index - 74
ISSN - 1751-7915
DOI - 10.1111/1751-7915.12855
Subject(s) - metaproteomics , metagenomics , microbiome , computational biology , biology , data science , microbial population biology , computer science , biochemical engineering , bioinformatics , genetics , gene , engineering , bacteria
Summary Microorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ . Interpretation of these multi‐omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.