Challenges in microbial ecology
Building predictive understanding of community function and dynamics
Stefanie Widder (University of Vienna)
Rosalind J. Allen (The University of Edinburgh)
Thomas Pfeiffer (Massey University)
Thomas P. Curtis (Newcastle University)
Carsten Wiuf (University of Copenhagen)
William T. Sloan (University of Glasgow)
Otto X. Cordero (Massachusetts Institute of Technology)
Sam P. Brown (The University of Edinburgh)
Cristian Picioreanu (TU Delft - Applied Sciences)
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Babak Momeni (Boston College, Fred Hutchinson Cancer Research Center)
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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.