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Flow-Based Network Analysis of the Caenorhabditis elegans Connectome

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Title: Flow-Based Network Analysis of the Caenorhabditis elegans Connectome
Authors: Bacik, KA
Schaub, MT
Beguerisse-Diaz, M
Billeh, YN
Barahona, M
Item 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.
Issue Date: 5-Aug-2016
Date of Acceptance: 12-Jul-2016
URI: http://hdl.handle.net/10044/1/38786
DOI: http://dx.doi.org/10.1371/journal.pcbi.1005055
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.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
James S. McDonnell Foundation
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/I017267/1
220020349
EP/N014529/1
Keywords: q-bio.NC
physics.soc-ph
Bioinformatics
06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
Open Access location: http://dx.doi.org/10.1371/journal.pcbi.1005055
Article Number: e1005055
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences



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