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

Stefanie Widder, Rosalind J Allen, Thomas Pfeiffer, Thomas P Curtis, Carsten Wiuf, William T Sloan, Otto X Cordero, Sam P Brown, Babak Momeni, Wenying Shou, Helen Kettle, Harry J Flint, Andreas F Haas, Béatrice Laroche, Jan-Ulrich Kreft, Paul B Rainey, Shiri Freilich, Stefan Schuster, Kim Milferstedt, Jan R van der MeerTobias Groszkopf, Jef Huisman, Andrew Free, Cristian Picioreanu, Christopher Quince, Isaac Klapper, Simon Labarthe, Barth F Smets, Harris Wang, Orkun S Soyer, Isaac Newton Institute Fellows

Research output: Contribution to journalArticlepeer-review

439 Citations (Scopus)
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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.The ISME Journal advance online publication, 29 March 2016; doi:10.1038/ismej.2016.45.

Original languageEnglish
Pages (from-to)2557-2568
Number of pages12
JournalThe ISME Journal
Issue number11
Early online date29 Mar 2016
Publication statusPublished - Nov 2016

Bibliographical note

We thank the Isaac Newton Institute of Mathematical Sciences for hosting the programme ‘Understanding microbial communities: structure, function dynamics’, which made this paper possible. This programme was partially funded by the Biotechnology and Biological Sciences Research Council and the US Army Research Office under grant number W911NF-14-1-0445. We thank all participants of the programme for inspiring discussions, and Aglika Gungova and Stela Ilieva for assistance with the figures.


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