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פותח על ידי קלירמאש פתרונות בע"מ -
Challenges in microbial ecology: Building predictive understanding of community function and dynamics
Year:
2016
Source of publication :
ISME Journal
Authors :
פרייליך, שירי
;
.
Volume :
10
Co-Authors:
Widder, S., CUBE, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria
Allen, R.J., SUPA, School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh, United Kingdom
Pfeiffer, T., New Zealand Institute for Advanced Study, Massey University, Auckland, New Zealand
Curtis, T.P., School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, United Kingdom
Wiuf, C., Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
Sloan, W.T., Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, United Kingdom
Cordero, O.X., Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
Brown, S.P., Centre for Immunity, Infection and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
Momeni, B., Department of Biology, Boston College, Chestnut Hill, MA, United States, Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
Shou, W., Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
Kettle, H., Biomathematics and Statistics Scotland, Edinburgh, United Kingdom
Flint, H.J., Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, United Kingdom
Haas, A.F., Biology Department, San Diego State University, San Diego, CA, United States
Laroche, B., Département de Mathématiques Informatiques Appliquées, INRA, Jouy-en-Josas, France
Kreft, J.-U., School of Biosciences, University of Birmingham, Birmingham, United Kingdom
Rainey, P.B., New Zealand Institute for Advanced Study, Massey University, Auckland, New Zealand
Freilich, S., Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel
Schuster, S., Department of Bioinformatics, Friedrich-Schiller-University Jena, Jena, Germany
Milferstedt, K., INRA, UR0050, Laboratoire de Biotechnologie de L'Environnement, Narbonne, France
Van Der Meer, J.R., Department of Fundamental Microbiology, Université de Lausanne, Lausanne, Switzerland
Grobkopf, T., School of Life Sciences, University of Warwick, Coventry, United Kingdom
Huisman, J., Department of Aquatic Microbiology, University of Amsterdam, Amsterdam, Netherlands
Free, A., Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Science, University of Edinburgh, Edinburgh, United Kingdom
Picioreanu, C., Department of Biotechnology, Delft University of Technology, Delft, Netherlands
Quince, C., Warwick Medical School, University of Warwick, Coventry, United Kingdom
Klapper, I., Department of Mathematics, Temple University, Philadelphia, PA, United States
Labarthe, S., Département de Mathématiques Informatiques Appliquées, INRA, Jouy-en-Josas, France
Smets, B.F., Department of Environmental Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
Wang, H., Department of Systems Biology, Columbia University, New York, NY, United States
Soyer, O.S., School of Life Sciences, University of Warwick, Coventry, United Kingdom
Facilitators :
From page:
2557
To page:
2568
(
Total pages:
12
)
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. © 2016 International Society for Microbial Ecology All rights reserved.
Note:
Related Files :
Animalia
biogeochemical cycle
community dynamics
Ecosystem function
experimental study
microbial ecology
numerical model
עוד תגיות
תוכן קשור
More details
DOI :
10.1038/ismej.2016.45
Article number:
Affiliations:
Database:
סקופוס
Publication Type:
סקירה
;
.
Language:
אנגלית
Editors' remarks:
ID:
23773
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:02
Scientific Publication
Challenges in microbial ecology: Building predictive understanding of community function and dynamics
10
Widder, S., CUBE, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria
Allen, R.J., SUPA, School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh, United Kingdom
Pfeiffer, T., New Zealand Institute for Advanced Study, Massey University, Auckland, New Zealand
Curtis, T.P., School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, United Kingdom
Wiuf, C., Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
Sloan, W.T., Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, United Kingdom
Cordero, O.X., Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
Brown, S.P., Centre for Immunity, Infection and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
Momeni, B., Department of Biology, Boston College, Chestnut Hill, MA, United States, Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
Shou, W., Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
Kettle, H., Biomathematics and Statistics Scotland, Edinburgh, United Kingdom
Flint, H.J., Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, United Kingdom
Haas, A.F., Biology Department, San Diego State University, San Diego, CA, United States
Laroche, B., Département de Mathématiques Informatiques Appliquées, INRA, Jouy-en-Josas, France
Kreft, J.-U., School of Biosciences, University of Birmingham, Birmingham, United Kingdom
Rainey, P.B., New Zealand Institute for Advanced Study, Massey University, Auckland, New Zealand
Freilich, S., Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel
Schuster, S., Department of Bioinformatics, Friedrich-Schiller-University Jena, Jena, Germany
Milferstedt, K., INRA, UR0050, Laboratoire de Biotechnologie de L'Environnement, Narbonne, France
Van Der Meer, J.R., Department of Fundamental Microbiology, Université de Lausanne, Lausanne, Switzerland
Grobkopf, T., School of Life Sciences, University of Warwick, Coventry, United Kingdom
Huisman, J., Department of Aquatic Microbiology, University of Amsterdam, Amsterdam, Netherlands
Free, A., Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Science, University of Edinburgh, Edinburgh, United Kingdom
Picioreanu, C., Department of Biotechnology, Delft University of Technology, Delft, Netherlands
Quince, C., Warwick Medical School, University of Warwick, Coventry, United Kingdom
Klapper, I., Department of Mathematics, Temple University, Philadelphia, PA, United States
Labarthe, S., Département de Mathématiques Informatiques Appliquées, INRA, Jouy-en-Josas, France
Smets, B.F., Department of Environmental Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
Wang, H., Department of Systems Biology, Columbia University, New York, NY, United States
Soyer, O.S., School of Life Sciences, University of Warwick, Coventry, United Kingdom
Challenges in microbial ecology: Building predictive understanding of community function and dynamics
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. © 2016 International Society for Microbial Ecology All rights reserved.
Scientific Publication
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