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Challenges in microbial ecology: building predictive understanding of community function and dynamics

The ISME Journal, 2016-11, Vol.10 (11), p.2557-2568 [Peer Reviewed Journal]

Copyright Nature Publishing Group Nov 2016 ;Distributed under a Creative Commons Attribution 4.0 International License ;Copyright © 2016 International Society for Microbial Ecology 2016 International Society for Microbial Ecology ;ISSN: 1751-7362 ;EISSN: 1751-7370 ;DOI: 10.1038/ismej.2016.45 ;PMID: 27022995

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Title:
Challenges in microbial ecology: building predictive understanding of community function and dynamics
Author: Widder, Stefanie ; Allen, Rosalind J ; Pfeiffer, Thomas ; Curtis, Thomas P ; Wiuf, Carsten ; Sloan, William T ; Cordero, Otto X ; Brown, Sam P ; Momeni, Babak ; Shou, Wenying ; Kettle, Helen ; Flint, Harry J ; Haas, Andreas F ; Laroche, Béatrice ; Kreft, Jan-Ulrich ; Rainey, Paul B ; Freilich, Shiri ; Schuster, Stefan ; Milferstedt, Kim ; van der Meer, Jan R ; Groβkopf, Tobias ; Huisman, Jef ; Free, Andrew ; Picioreanu, Cristian ; Quince, Christopher ; Klapper, Isaac ; Labarthe, Simon ; Smets, Barth F ; Wang, Harris ; Soyer, Orkun S
Subjects: Air Microbiology ; Animals ; Computer Science ; Ecosystem ; Humans ; Life Sciences ; Microbiology and Parasitology ; Mini Review ; Modeling and Simulation ; Models, Theoretical ; Seawater - microbiology ; Soil Microbiology
Is Part Of: The ISME Journal, 2016-11, Vol.10 (11), p.2557-2568
Description: The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 1751-7362
EISSN: 1751-7370
DOI: 10.1038/ismej.2016.45
PMID: 27022995
Source: Hyper Article en Ligne (HAL) (Open Access)
MEDLINE
PubMed Central
ProQuest Central

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Challenges in microbial ecology: building predictive understanding of community function and dynamics

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