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1 CUBE, Department of Microbiology and Ecosystem Science 2 SUPA, School of Physics and Astronomy 3 New Zealand Institute for Advanced Study 4 School of Civil Engineering and Geosciences 5 Department of Mathematical Sciences 6 Infrastructure and Environment Research Division, School of Engineering 7 Department of Civil and Environmental Engineering 8 Centre for Immunity, Infection and Evolution, School of Biological Sciences 9 Department of Biology 10 Fred Hutchinson Cancer Research Center 11 Division of Basic Sciences 12 Biomathematics and Statistics Scotland 13 Rowett Institute of Nutrition and Health 14 Biology Department 15 MaIAGE - Mathématiques et Informatique Appliquées du Génome à l-Environnement Jouy-En-Josas 16 School of Biosciences 17 Newe Ya’ar Research Center 18 Department of Bioinformatics 19 LBE - Laboratoire de Biotechnologie de l-Environnement Narbonne 20 Department of Fundamental Microbiology 21 School of Life Sciences 22 Department of Aquatic Microbiology 23 Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Science 24 Department of Biotechnology 25 Warwick Medical School 26 Department of Mathematics 27 Department of Environmental Engineering 28 Department of Systems Biology

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.

Keywords : microbial predictive challenge in microbial ecology challenge





Autor: Stefanie Widder - Rosalind J Allen - Thomas Pfeiffer - Thomas P Curtis - Carsten Wiuf - William T Sloan - Otto X Cordero - Sam P

Fuente: https://hal.archives-ouvertes.fr/



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