Network control principles predict neuron function in the Caenorhabditis elegans connectome
By Gang Yan, Petra E. Vértes, Emma K. Towlson, Yee Lian Chew, Denise S Walker, W. Schafer, Albert-László Barabási. Nature, 2017
341 citations recorded by the source corpus.
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 networks. 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 elegans, 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 ablation, 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.
Subfields: Aging, Endocrine and Autonomic Systems, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/85890a5f1b7f5299f275de8660bab47b9b2d5254
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- What are higher-order networks? (2021, background): “For instance, by comparing to a random null model, we can assess how likely it is that certain features of the connectome have arisen by chance [132, 291].”