Elucidation of complexity and prediction of interactions in microbial communities
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.
- Authors:
-
- 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)
- OSTI Identifier:
- 1392703
- Alternate Identifier(s):
- OSTI ID: 1392704; OSTI ID: 1485598
- Grant/Contract Number:
- SC0012658
- Resource Type:
- Journal Article: 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. https://doi.org/10.1111/1751-7915.12855
Zuñiga, Cristal, Zaramela, Livia, and Zengler, Karsten. 2017.
"Elucidation of complexity and prediction of interactions in microbial communities". United Kingdom. https://doi.org/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 = {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.},
doi = {10.1111/1751-7915.12855},
url = {https://www.osti.gov/biblio/1392703},
journal = {Microbial Biotechnology (Online)},
issn = {1751-7915},
number = 6,
volume = 10,
place = {United Kingdom},
year = {Tue Sep 19 00:00:00 EDT 2017},
month = {Tue Sep 19 00:00:00 EDT 2017}
}
Web of Science
Figures / Tables:
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