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Song, H.-S., et al. Mathematical Modeling of Microbial Community Dynamics: A Methodological Review. Processes 2014, 2, 711–752
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Correction published on 14 September 2015, see Processes 2015, 3(3), 699-700.
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Mathematical Modeling of Microbial Community Dynamics: A Methodological Review

Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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Processes 2014, 2(4), 711-752; https://doi.org/10.3390/pr2040711
Received: 11 August 2014 / Revised: 17 September 2014 / Accepted: 29 September 2014 / Published: 17 October 2014
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
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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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