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A theoretical framework for controlling complex microbial communities

Author

Listed:
  • Marco Tulio Angulo

    (Universidad Nacional Autónoma de México)

  • Claude H. Moog

    (Laboratoire des Sciences du Numérique de Nantes)

  • Yang-Yu Liu

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

Abstract

Microbes form complex communities that perform critical roles for the integrity of their environment or the well-being of their hosts. Controlling these microbial communities can help us restore natural ecosystems and maintain healthy human microbiota. However, the lack of an efficient and systematic control framework has limited our ability to manipulate these microbial communities. Here we fill this gap by developing a control framework based on the new notion of structural accessibility. Our framework uses the ecological network of the community to identify minimum sets of its driver species, manipulation of which allows controlling the whole community. We numerically validate our control framework on large communities, and then we demonstrate its application for controlling the gut microbiota of gnotobiotic mice infected with Clostridium difficile and the core microbiota of the sea sponge Ircinia oros. Our results provide a systematic pipeline to efficiently drive complex microbial communities towards desired states.

Suggested Citation

  • Marco Tulio Angulo & Claude H. Moog & Yang-Yu Liu, 2019. "A theoretical framework for controlling complex microbial communities," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-08890-y
    DOI: 10.1038/s41467-019-08890-y
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    Cited by:

    1. Joaquín Gutiérrez Mena & Sant Kumar & Mustafa Khammash, 2022. "Dynamic cybergenetic control of bacterial co-culture composition via optogenetic feedback," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    2. Leyuan Li & Tong Wang & Zhibin Ning & Xu Zhang & James Butcher & Joeselle M. Serrana & Caitlin M. A. Simopoulos & Janice Mayne & Alain Stintzi & David R. Mack & Yang-Yu Liu & Daniel Figeys, 2023. "Revealing proteome-level functional redundancy in the human gut microbiome using ultra-deep metaproteomics," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Xu, Libai & Kong, Dehan & Wang, Lidan & Gu, Hong & Kenney, Toby & Xu, Ximing, 2023. "Proportional stochastic generalized Lotka–Volterra model with an application to learning microbial community structures," Applied Mathematics and Computation, Elsevier, vol. 448(C).
    4. Carolyne Luciane de Almeida Godoy & Lucas Marques Costa & Carlos Alberto Guerra & Vanessa Sales de Oliveira & Breno Pereira de Paula & Wilson José Fernandes Lemos Junior & Vinícius da Silva Duarte & R, 2022. "Potentially Postbiotic-Containing Preservative to Extend the Use-By Date of Raw Chicken Sausages and Semifinished Chicken Products," Sustainability, MDPI, vol. 14(5), pages 1-17, February.
    5. Yu, Xiaoyao & Liang, Yongqing & Wang, Xiaomeng & Jia, Tao, 2021. "The network asymmetry caused by the degree correlation and its effect on the bimodality in control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    6. Yasa Baig & Helena R. Ma & Helen Xu & Lingchong You, 2023. "Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

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