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Mapping the ecological networks of microbial communities

Author

Listed:
  • Yandong Xiao

    (Brigham and Women’s Hospital and Harvard Medical School
    National University of Defense Technology)

  • Marco Tulio Angulo

    (Universidad Nacional Autónoma de México
    National Council for Science and Technology (CONACyT))

  • Jonathan Friedman

    (Massachusetts Institute of Technology)

  • Matthew K. Waldor

    (Brigham and Women’s Hospital and Harvard Medical School
    Howard Hughes Medical Institute)

  • Scott T. Weiss

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Yang-Yu Liu

    (Brigham and Women’s Hospital and Harvard Medical School
    Dana-Farber Cancer Institute)

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.

Suggested Citation

  • Yandong Xiao & Marco Tulio Angulo & Jonathan Friedman & Matthew K. Waldor & Scott T. Weiss & Yang-Yu Liu, 2017. "Mapping the ecological networks of microbial communities," Nature Communications, Nature, vol. 8(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-02090-2
    DOI: 10.1038/s41467-017-02090-2
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    Cited by:

    1. Linying Xiang & Guanrong Chen, 2019. "Minimal Edge Controllability Of Directed Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-23, December.
    2. Dong Luo & Arya Ebadi & Kristen Emery & Yilun He & William Stafford Noble & Uri Keich, 2023. "Competition‐based control of the false discovery proportion," Biometrics, The International Biometric Society, vol. 79(4), pages 3472-3484, December.
    3. Aming Li & Yang-Yu Liu, 2020. "Controlling Network Dynamics," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-19, February.
    4. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    5. Ren, Baoan & Zhang, Yu & Chen, Jing & Shen, Lincheng, 2019. "Efficient network disruption under imperfect information: The sharpening effect of network reconstruction with no prior knowledge," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 196-207.
    6. Mendler, Isabella-Hilda & Drossel, Barbara & Hütt, Marc-Thorsten, 2024. "Microbiome abundance patterns as attractors and the implications for the inference of microbial interaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).

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