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Processes 2014, 2(4), 711-752; doi:10.3390/pr2040711

Mathematical Modeling of Microbial Community Dynamics: A Methodological Review

Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
Author to whom correspondence should be addressed.
Received: 11 August 2014 / Revised: 17 September 2014 / Accepted: 29 September 2014 / Published: 17 October 2014
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Microorganisms in nature form diverse communities that dynamically change in structure and function in response to environmental variations. As a complex adaptive system, microbial communities show higher-order properties that are not present in individual microbes, but arise from their interactions. Predictive mathematical models not only help to understand the underlying principles of the dynamics and emergent properties of natural and synthetic microbial communities, but also provide key knowledge required for engineering them. In this article, we provide an overview of mathematical tools that include not only current mainstream approaches, but also less traditional approaches that, in our opinion, can be potentially useful. We discuss a broad range of methods ranging from low-resolution supra-organismal to high-resolution individual-based modeling. Particularly, we highlight the integrative approaches that synergistically combine disparate methods. In conclusion, we provide our outlook for the key aspects that should be further developed to move microbial community modeling towards greater predictive power. View Full-Text
Keywords: microbial communities; mathematical models; dynamics; integrative modeling approaches microbial communities; mathematical models; dynamics; integrative modeling approaches

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Song, H.-S.; Cannon, W.R.; Beliaev, A.S.; Konopka, A. Mathematical Modeling of Microbial Community Dynamics: A Methodological Review. Processes 2014, 2, 711-752.

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