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Network control principles predict neuron function in the Caenorhabditis elegans connectome

Author

Listed:
  • Gang Yan

    (Northeastern University
    School of Physics Science and Engineering, Tongji University)

  • Petra E. Vértes

    (Behavioural and Clinical Neuroscience Institute, University of Cambridge)

  • Emma K. Towlson

    (Northeastern University)

  • Yee Lian Chew

    (MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus)

  • Denise S. Walker

    (MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus)

  • William R. Schafer

    (MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus)

  • Albert-László Barabási

    (Northeastern University
    Center for Cancer Systems Biology, Dana Farber Cancer Institute
    Brigham and Women’s Hospital, Harvard Medical School
    Center for Network Science, Central European University)

Abstract

Application of network control theory to the neuronal connectome of Caenorhabditis elegans, allowing prediction of the involvement of individual neurons in locomotion.

Suggested Citation

  • Gang Yan & Petra E. Vértes & Emma K. Towlson & Yee Lian Chew & Denise S. Walker & William R. Schafer & Albert-László Barabási, 2017. "Network control principles predict neuron function in the Caenorhabditis elegans connectome," Nature, Nature, vol. 550(7677), pages 519-523, October.
    • Handle: RePEc:nat:nature:v:550:y:2017:i:7677:d:10.1038_nature24056
    DOI: 10.1038/nature24056
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    Cited by:

    1. Liu, Suling & Xu, Qiong & Chen, Aimin & Wang, Pei, 2020. "Structural controllability of dynamic transcriptional regulatory networks for Saccharomyces cerevisiae," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    2. Zhou, Ming-Yang & Xiong, Wen-Man & Wu, Xiang-Yang & Zhang, Yu-Xia & Liao, Hao, 2018. "Overlapping influence inspires the selection of multiple spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 76-83.
    3. Li, Sheng & Liu, Wenwen & Wu, Ruizi & Li, Junli, 2023. "An adaptive attack model to network controllability," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Duan, Dongli & Wu, Xixi & Bai, Xue & Yan, Qi & Lv, Changchun & Bian, Genqing, 2022. "Dimensionality reduction method of dynamic networks for evolutionary mechanism of neuronal systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    5. Richard F Betzel & Katherine C Wood & Christopher Angeloni & Maria Neimark Geffen & Danielle S Bassett, 2019. "Stability of spontaneous, correlated activity in mouse auditory cortex," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-25, December.
    6. 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).
    7. van Elteren, Casper & Quax, Rick & Sloot, Peter, 2022. "Dynamic importance of network nodes is poorly predicted by static structural features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    8. Zhongyu Chen & Yuguo Yu & Xiangyang Xue, 2023. "A Connectome-Based Digital Twin Caenorhabditis elegans Capable of Intelligent Sensorimotor Behavior," Mathematics, MDPI, vol. 11(11), pages 1-23, May.
    9. Robert Peach & Alexis Arnaudon & Mauricio Barahona, 2022. "Relative, local and global dimension in complex networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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