Mapping the ecological networks of microbial communities

Yandong Xiao, Marco Tulio Angulo, Jonathan Friedman, Matthew K. Waldor, Scott T. Weiss and Yang-Yu Liu ()
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Yandong Xiao: Brigham and Women’s Hospital and Harvard Medical School
Marco Tulio Angulo: Universidad Nacional Autónoma de México
Jonathan Friedman: Massachusetts Institute of Technology
Matthew K. Waldor: Brigham and Women’s Hospital and Harvard Medical School
Scott T. Weiss: Brigham and Women’s Hospital and Harvard Medical School
Yang-Yu Liu: Brigham and Women’s Hospital and Harvard Medical School

Nature Communications, 2017, vol. 8, issue 1, 1-12

Abstract: Abstract Mapping 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.

Date: 2017
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DOI: 10.1038/s41467-017-02090-2

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