Flow-Based Network Analysis of the Caenorhabditis elegans Connectome
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Published version
Author(s)
Bacik, KA
Schaub, MT
Beguerisse-Diaz, M
Billeh, YN
Barahona, M
Type
Journal Article
Abstract
We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios.
Date Issued
2016-08-05
Date Acceptance
2016-07-12
Citation
PLOS Computational Biology, 2016, 12 (8)
ISSN
1553-734X
Publisher
Public Library of Science
Journal / Book Title
PLOS Computational Biology
Volume
12
Issue
8
Copyright Statement
© 2016 Bacik et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
James S. McDonnell Foundation
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/I017267/1
220020349
EP/N014529/1
Subjects
q-bio.NC
physics.soc-ph
Bioinformatics
06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
Article Number
e1005055