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Network control principles predict neuron function in the Caenorhabditis elegans connectome

Journal Article


Abstract


  • Recent studies on the controllability of complex systems offer

    a powerful mathematical framework to systematically explore

    the structure–function relationship in biological, social, and

    technological networks1–3. Despite theoretical advances, we lack

    direct experimental proof of the validity of these widely used control

    principles. Here we fill this gap by applying a control framework

    to the connectome of the nematode Caenorhabditis elegans4–6,

    allowing us to predict the involvement of each C. elegans neuron

    in locomotor behaviours. We predict that control of the muscles or

    motor neurons requires 12 neuronal classes, which include neuronal

    groups previously implicated in locomotion by laser ablation7–13, as

    well as one previously uncharacterized neuron, PDB. We validate

    this prediction experimentally, finding that the ablation of PDB

    leads to a significant loss of dorsoventral polarity in large body

    bends. Importantly, control principles also allow us to investigate

    the involvement of individual neurons within each neuronal class.

    For example, we predict that, within the class of DD motor neurons,

    only three (DD04, DD05, or DD06) should affect locomotion when

    ablated individually. This prediction is also confirmed; single

    cell ablations of DD04 or DD05 specifically affect posterior body

    movements, whereas ablations of DD02 or DD03 do not. Our

    predictions are robust to deletions of weak connections, missing

    connections, and rewired connections in the current connectome,

    indicating the potential applicability of this analytical framework

    to larger and less well-characterized connectomes.

Authors


  •   Yan, Gang (external author)
  •   Vertes, Petra E. (external author)
  •   Towlson, Emma K. (external author)
  •   Yee Lian Chew
  •   Walker, Denise S. (external author)
  •   Schafer, William R. (external author)
  •   Barabasi, Albert-Laszlo (external author)

Publication Date


  • 2017

Published In


Citation


  • Yan, G., Vertes, P. E., Towlson, E. K., Chew, Y. Lian., Walker, D. S., Schafer, W. R. & Barabasi, A. (2017). Network control principles predict neuron function in the Caenorhabditis elegans connectome. Nature, 550 (7677), 519-523.

Scopus Eid


  • 2-s2.0-85031788946

Number Of Pages


  • 4

Start Page


  • 519

End Page


  • 523

Volume


  • 550

Issue


  • 7677

Place Of Publication


  • United Kingdom

Abstract


  • Recent studies on the controllability of complex systems offer

    a powerful mathematical framework to systematically explore

    the structure–function relationship in biological, social, and

    technological networks1–3. Despite theoretical advances, we lack

    direct experimental proof of the validity of these widely used control

    principles. Here we fill this gap by applying a control framework

    to the connectome of the nematode Caenorhabditis elegans4–6,

    allowing us to predict the involvement of each C. elegans neuron

    in locomotor behaviours. We predict that control of the muscles or

    motor neurons requires 12 neuronal classes, which include neuronal

    groups previously implicated in locomotion by laser ablation7–13, as

    well as one previously uncharacterized neuron, PDB. We validate

    this prediction experimentally, finding that the ablation of PDB

    leads to a significant loss of dorsoventral polarity in large body

    bends. Importantly, control principles also allow us to investigate

    the involvement of individual neurons within each neuronal class.

    For example, we predict that, within the class of DD motor neurons,

    only three (DD04, DD05, or DD06) should affect locomotion when

    ablated individually. This prediction is also confirmed; single

    cell ablations of DD04 or DD05 specifically affect posterior body

    movements, whereas ablations of DD02 or DD03 do not. Our

    predictions are robust to deletions of weak connections, missing

    connections, and rewired connections in the current connectome,

    indicating the potential applicability of this analytical framework

    to larger and less well-characterized connectomes.

Authors


  •   Yan, Gang (external author)
  •   Vertes, Petra E. (external author)
  •   Towlson, Emma K. (external author)
  •   Yee Lian Chew
  •   Walker, Denise S. (external author)
  •   Schafer, William R. (external author)
  •   Barabasi, Albert-Laszlo (external author)

Publication Date


  • 2017

Published In


Citation


  • Yan, G., Vertes, P. E., Towlson, E. K., Chew, Y. Lian., Walker, D. S., Schafer, W. R. & Barabasi, A. (2017). Network control principles predict neuron function in the Caenorhabditis elegans connectome. Nature, 550 (7677), 519-523.

Scopus Eid


  • 2-s2.0-85031788946

Number Of Pages


  • 4

Start Page


  • 519

End Page


  • 523

Volume


  • 550

Issue


  • 7677

Place Of Publication


  • United Kingdom