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

Stefanie Widder; Rosalind J Allen; Thomas Pfeiffer; Thomas P Curtis; Carsten Henrik 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

doi:10.1038/ismej.2016.45

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

Stefanie Widder, Rosalind J Allen, Thomas Pfeiffer, Thomas P Curtis, Carsten Henrik 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 Groβkopf, Jef Huisman, Andrew Free, Cristian Picioreanu, Christopher Quince, Isaac Klapper, Simon Labarthe, Barth F Smets, Harris Wang, Orkun S Soyer

Department of Mathematical Sciences

286 Citations (Scopus)

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.

Original language	English
Journal	I S M E Journal
Volume	10
Issue number	11
Pages (from-to)	2557-2568
Number of pages	12
ISSN	1751-7362
DOIs	https://doi.org/10.1038/ismej.2016.45
Publication status	Published - 1 Nov 2016

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Widder, S., Allen, R. J., Pfeiffer, T., Curtis, T. P., Wiuf, C. H., Sloan, W. T., Cordero, O. X., Brown, S. P., Momeni, B., Shou, W., Kettle, H., Flint, H. J., Haas, A. F., Laroche, B., Kreft, J-U., Rainey, P. B., Freilich, S., Schuster, S., Milferstedt, K., ... Soyer, O. S. (2016). Challenges in microbial ecology: building predictive understanding of community function and dynamics. I S M E Journal, 10(11), 2557-2568. https://doi.org/10.1038/ismej.2016.45

Widder, S, Allen, RJ, Pfeiffer, T, Curtis, TP, Wiuf, CH, Sloan, WT, Cordero, OX, Brown, SP, Momeni, B, Shou, W, Kettle, H, Flint, HJ, Haas, AF, Laroche, B, Kreft, J-U, Rainey, PB, Freilich, S, Schuster, S, Milferstedt, K, van der Meer, JR, Groβkopf, T, Huisman, J, Free, A, Picioreanu, C, Quince, C, Klapper, I, Labarthe, S, Smets, BF, Wang, H & Soyer, OS 2016, 'Challenges in microbial ecology: building predictive understanding of community function and dynamics', I S M E Journal, vol. 10, no. 11, pp. 2557-2568. https://doi.org/10.1038/ismej.2016.45

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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.",

author = "Stefanie Widder and Allen, {Rosalind J} and Thomas Pfeiffer and Curtis, {Thomas P} and Wiuf, {Carsten Henrik} and Sloan, {William T} and Cordero, {Otto X} and Brown, {Sam P} and Babak Momeni and Wenying Shou and Helen Kettle and Flint, {Harry J} and Haas, {Andreas F} and B{\'e}atrice Laroche and Jan-Ulrich Kreft and Rainey, {Paul B} and Shiri Freilich and Stefan Schuster and Kim Milferstedt and {van der Meer}, {Jan R} and Tobias Groβkopf and Jef Huisman and Andrew Free and Cristian Picioreanu and Christopher Quince and Isaac Klapper and Simon Labarthe and Smets, {Barth F} and Harris Wang and Soyer, {Orkun S}",

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AU - Widder, Stefanie

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AU - Wiuf, Carsten Henrik

AU - Sloan, William T

AU - Cordero, Otto X

AU - Brown, Sam P

AU - Momeni, Babak

AU - Shou, Wenying

AU - Kettle, Helen

AU - Flint, Harry J

AU - Haas, Andreas F

AU - Laroche, Béatrice

AU - Kreft, Jan-Ulrich

AU - Rainey, Paul B

AU - Freilich, Shiri

AU - Schuster, Stefan

AU - Milferstedt, Kim

AU - van der Meer, Jan R

AU - Groβkopf, Tobias

AU - Huisman, Jef

AU - Free, Andrew

AU - Picioreanu, Cristian

AU - Quince, Christopher

AU - Klapper, Isaac

AU - Labarthe, Simon

AU - Smets, Barth F

AU - Wang, Harris

AU - Soyer, Orkun S

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N2 - 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.

AB - 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.

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

Abstract

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