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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-11

AUTHORS

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 Meer, Tobias Groβkopf, Jef Huisman, Andrew Free, Cristian Picioreanu, Christopher Quince, Isaac Klapper, Simon Labarthe, Barth F Smets, Harris Wang, , Orkun S Soyer

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. More... »

PAGES

2557-2568

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ismej.2016.45

    DOI

    http://dx.doi.org/10.1038/ismej.2016.45

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1043038872

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27022995


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