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Title: Elucidation of complexity and prediction of interactions in microbial communities

Abstract

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. As a result, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.

Authors:
 [1];  [1];  [1]
  1. Department of Pediatrics University of California, San Diego 9500 Gilman Drive La Jolla CA 92093‐0760 USA
Publication Date:
Research Org.:
Johns Hopkins Univ., Baltimore, MD (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1392703
Alternate Identifier(s):
OSTI ID: 1392704; OSTI ID: 1485598
Grant/Contract Number:  
DE‐SC0012586; DE‐SC0012658; SC0012658
Resource Type:
Published Article
Journal Name:
Microbial Biotechnology (Online)
Additional Journal Information:
Journal Name: Microbial Biotechnology (Online) Journal Volume: 10 Journal Issue: 6; Journal ID: ISSN 1751-7915
Publisher:
Wiley-Blackwell
Country of Publication:
United Kingdom
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Zuñiga, Cristal, Zaramela, Livia, and Zengler, Karsten. Elucidation of complexity and prediction of interactions in microbial communities. United Kingdom: N. p., 2017. Web. doi:10.1111/1751-7915.12855.
Zuñiga, Cristal, Zaramela, Livia, & Zengler, Karsten. Elucidation of complexity and prediction of interactions in microbial communities. United Kingdom. doi:10.1111/1751-7915.12855.
Zuñiga, Cristal, Zaramela, Livia, and Zengler, Karsten. Tue . "Elucidation of complexity and prediction of interactions in microbial communities". United Kingdom. doi:10.1111/1751-7915.12855.
@article{osti_1392703,
title = {Elucidation of complexity and prediction of interactions in microbial communities},
author = {Zuñiga, Cristal and Zaramela, Livia and Zengler, Karsten},
abstractNote = {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. As a result, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.},
doi = {10.1111/1751-7915.12855},
journal = {Microbial Biotechnology (Online)},
number = 6,
volume = 10,
place = {United Kingdom},
year = {2017},
month = {8}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1111/1751-7915.12855

Citation Metrics:
Cited by: 7 works
Citation information provided by
Web of Science

Figures / Tables:

Fig. 1 Fig. 1: Milestones in microbiology and computational modelling for the study of microbial communities.

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    Works referencing / citing this record:

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      Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.