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

James Paul Jonathan Chong; 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; Beatrice Laroche; Jan-Ulrich Kreft; Paul B. Rainey; Shiri Freilich; Stefan Schuster; Kim Milferstedt; Jack R. van der Meer; Tobias Grosskopf; 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

James Paul Jonathan Chong, 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, Beatrice Laroche, Jan-Ulrich Kreft, Paul B. Rainey, Shiri Freilich, Stefan Schuster, Kim MilferstedtJack R. van der Meer, Tobias Grosskopf, Jef Huisman, Andrew Free, Cristian Picioreanu, Christopher Quince, Isaac Klapper, Simon Labarthe, Barth F Smets, Harris Wang, Orkun S. Soyer

Biology

Research output: Contribution to journal › Literature review › peer-review

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
Number of pages	12
Journal	The ISME Journal
DOIs	https://doi.org/10.1038/ismej.2016.45
Publication status	Published - 29 Mar 2016

Bibliographical note

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10.1038/ismej.2016.45

ismej201645a
© 2016 International Society for Microbial Ecology
Final published version, 2.81 MBLicence: CC BY

Understanding Microbial Communities
James Chong (Keynote/plenary speaker)
11 Aug 2014 → 19 Sept 2014
Activity: Talk or presentation › Invited talk

Cite this

Chong, J. P. J., Widder, S., Allen, R. J., Pfeiffer, T., Curtis, T. P., Wiuf, C., 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., ... Soyer, O. S. (2016). Challenges in microbial ecology: building predictive understanding of community function and dynamics. The ISME Journal. https://doi.org/10.1038/ismej.2016.45

@article{4dd04113422e44269304840dbfca1284,

title = "Challenges in microbial ecology: building predictive understanding of community function and dynamics",

abstract = "The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth{\textquoteright}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 = "Chong, {James Paul Jonathan} and Stefanie Widder and Allen, {Rosalind J.} and Thomas Pfeiffer and Curtis, {Thomas P.} and Carsten Wiuf 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 Beatrice Laroche and Jan-Ulrich Kreft and Rainey, {Paul B.} and Shiri Freilich and Stefan Schuster and Kim Milferstedt and {van der Meer}, {Jack R.} and Tobias Grosskopf 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.}",

note = "{\textcopyright} 2016 International Society for Microbial Ecology",

year = "2016",

month = mar,

day = "29",

doi = "10.1038/ismej.2016.45",

language = "English",

journal = "The ISME Journal",

issn = "1751-7362",

publisher = "Nature Publishing Group",

}

Chong, JPJ, Widder, S, Allen, RJ, Pfeiffer, T, Curtis, TP, Wiuf, C, 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, Grosskopf, 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', The ISME Journal. https://doi.org/10.1038/ismej.2016.45

TY - JOUR

T1 - Challenges in microbial ecology: building predictive understanding of community function and dynamics

AU - Chong, James Paul Jonathan

AU - Widder, Stefanie

AU - Allen, Rosalind J.

AU - Pfeiffer, Thomas

AU - Curtis, Thomas P.

AU - Wiuf, Carsten

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, Beatrice

AU - Kreft, Jan-Ulrich

AU - Rainey, Paul B.

AU - Freilich, Shiri

AU - Schuster, Stefan

AU - Milferstedt, Kim

AU - van der Meer, Jack R.

AU - Grosskopf, 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.

PY - 2016/3/29

Y1 - 2016/3/29

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.

U2 - 10.1038/ismej.2016.45

DO - 10.1038/ismej.2016.45

M3 - Literature review

SN - 1751-7362

JO - The ISME Journal

JF - The ISME Journal

ER -

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

Abstract

Bibliographical note

Access to Document

Activities

Understanding Microbial Communities

Cite this