Mapping the ecological networks of microbial communities
Angulo, Marco Tulio
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CitationXiao, Yandong, Marco Tulio Angulo, Jonathan Friedman, Matthew K. Waldor, Scott T. Weiss, and Yang-Yu Liu. 2017. “Mapping the ecological networks of microbial communities.” Nature Communications 8 (1): 2042. doi:10.1038/s41467-017-02090-2. http://dx.doi.org/10.1038/s41467-017-02090-2.
AbstractMapping the ecological networks of microbial communities is a necessary step toward understanding their assembly rules and predicting their temporal behavior. However, existing methods require assuming a particular population dynamics model, which is not known a priori. Moreover, those methods require fitting longitudinal abundance data, which are often not informative enough for reliable inference. To overcome these limitations, here we develop a new method based on steady-state abundance data. Our method can infer the network topology and inter-taxa interaction types without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka–Volterra model, our method can infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental data sets. Our method represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:34651958