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Mapping the ecological networks of microbial communities.

Yandong XiaoMarco Tulio AnguloJonathan FriedmanMatthew K WaldorScott T WeissYang-Yu Liu
Published in: Nature communications (2017)
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.
Keyphrases
  • electronic health record
  • big data
  • high resolution
  • climate change
  • human health
  • antibiotic resistance genes
  • microbial community
  • single cell
  • deep learning