Elucidation of complexity and prediction of interactions in microbial communities. Issue 6 (19th September 2017)

• Record Type: Journal Article
• Authors: Zuñiga, Cristal; Zaramela, Livia; Zengler, Karsten
• Other Names: Averous Luc guestEditor.; Blank Lars M. guestEditor.; O'Connor Kevin guestEditor.; Diaz Eduardo guestEditor.; Prieto Auxi guestEditor.; Wierckx Nick guestEditor.; Zimmermann Wolfgang guestEditor.
• Is Part Of: Microbial biotechnology. Volume 10:Issue 6(2017:Nov.)
• Page Start: 1500
• Page End: 1522
• Publication Date: 2017-09-19
• Subjects: Microbial biotechnology -- Periodicals; Biotechnology; Microbiology; 660.62
• DOI: 10.1111/1751-7915.12855
• Languages: English
• ISSNs: 1751-7915

Abstract

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. Abstract : Omic‐data and modeling to understand communities Latest experimental and mathematical approaches to unravel communities.