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ISME J. 2016 Nov;10(11):2557-2568. doi: 10.1038/ismej.2016.45. Epub 2016 Mar 29.

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

Author information

1
CUBE, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.
2
SUPA, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK.
3
New Zealand Institute for Advanced Study, Massey University, Auckland, New Zealand.
4
School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, UK.
5
Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark.
6
Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, UK.
7
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
8
Centre for Immunity, Infection and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
9
Department of Biology, Boston College, Chestnut Hill, MA, USA.
10
Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
11
Biomathematics and Statistics Scotland, Edinburgh, UK.
12
Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, UK.
13
Biology Department, San Diego State University, San Diego, CA, USA.
14
Département de Mathématiques Informatiques Appliquées, INRA, Jouy-en-Josas, France.
15
School of Biosciences, University of Birmingham, Birmingham, UK.
16
Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel.
17
Department of Bioinformatics, Friedrich-Schiller-University Jena, Jena, Germany.
18
INRA, UR0050, Laboratoire de Biotechnologie de l'Environnement, Narbonne, France.
19
Department of Fundamental Microbiology, Université de Lausanne, Lausanne, Switzerland.
20
School of Life Sciences, The University of Warwick, Coventry, UK.
21
Department of Aquatic Microbiology, University of Amsterdam, Amsterdam, The Netherlands.
22
Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Science, University of Edinburgh, Edinburgh, UK.
23
Department of Biotechnology, Delft University of Technology, Delft, The Netherlands.
24
Warwick Medical School, University of Warwick, Coventry, UK.
25
Department of Mathematics, Temple University, Philadelphia, PA, USA.
26
Department of Environmental Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.
27
Department of Systems Biology, Columbia University, New York, NY, USA.

Abstract

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.

PMID:
27022995
PMCID:
PMC5113837
DOI:
10.1038/ismej.2016.45
[Indexed for MEDLINE]
Free PMC Article

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