Mapping the ecological networks of microbial communities

Yandong Xiao, Marco Tulio Angulo, Jonathan Friedman, Matthew K. Waldor, Scott T. Weiss, Yang Yu Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

95 Scopus citations

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.

Original languageAmerican English
Article number2042
JournalNature Communications
Volume8
Issue number1
DOIs
StatePublished - 1 Dec 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017, The Author(s).

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