Mathematical Modeling of Microbial Community Dynamics: A Methodological Review
By Hyun‐Seob Song, William R. Cannon, Alexander S. Beliaev, Allan E. Konopka. 2014
166 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/befa8a631b96e741c05311c9e384d9a911bd156e
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In Molecular Biology
Citations in the corpus
- The Role of Soil Microorganisms in Plant Mineral Nutrition—Current Knowledge and Future Directions (2017, background): “For a wide overview we point the interested reader to specific reviews (Song et al., 2015; Zomorrodi and Segre, 2016), while here we will focus on metabolic models, pointing out advantages and limitations.”