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

Stefanie Widder 1 Rosalind J Allen 2 Thomas Pfeiffer 3 Thomas P Curtis 4 Carsten Wiuf 5 William T Sloan 6 Otto X Cordero 7 Sam P Brown 8 Babak Momeni 9, 10 Wenying Shou 11 Helen Kettle 12 Harry J Flint 13 Andreas F Haas 14 Béatrice Laroche 15 Jan-Ulrich Kreft 16 Paul B Rainey 3 Shiri Freilich 17 Stefan Schuster 18 Kim Milferstedt 19 Jan R van der Meer 20 Tobias Groszkopf 21 Jef Huisman 22 Andrew Free 23 Cristian Picioreanu 24 Christopher Quince 25 Isaac Klapper 26 Simon Labarthe 15 Barth F Smets 27 Harris Wang 28 Jérôme Hamelin 19 Jérôme Harmand 19 Jean-Philippe Steyer 19 Eric Trably 19 Isaac Newton Institute Fellows 29 Orkun S Soyer 21
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.The ISME Journal advance online publication, 29 March 2016; doi:10.1038/ismej.2016.45.
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Stefanie Widder, Rosalind J Allen, Thomas Pfeiffer, Thomas P Curtis, Carsten Wiuf, et al.. Challenges in microbial ecology: building predictive understanding of community function and dynamics.. ISME Journal, Nature Publishing Group, 2016, 10, pp.1-12. ⟨10.1038/ismej.2016.45⟩. ⟨hal-01512087⟩

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