Flow-Based Network Analysis of the Caenorhabditis elegans Connectome

Bacik, Karol A. and Schaub, Michael T. and Beguerisse-Díaz, Mariano and Billeh, Yazan N. and Barahona, Mauricio (2016) Flow-Based Network Analysis of the Caenorhabditis elegans Connectome. PLOS Computational Biology, 12 (8). Art. No. e1005055. ISSN 1553-7358. PMCID PMC4975510. https://resolver.caltech.edu/CaltechAUTHORS:20160815-141402908

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	MS Excel (S1 Data. Supplementary Data as XLS spreadsheet) - Supplemental Material Creative Commons Attribution. 2MB
	PDF (S1 Text. Comparison of MS partitions to other methods) - Supplemental Material Creative Commons Attribution. 159kB
	PDF (S2 Text. Comparison of RBS flow roles with other analyses of roles) - Supplemental Material Creative Commons Attribution. 166kB
	Image (TIFF) (S1 Fig. Full analysis of the C. elegans connectome with Markov Stability (MS)) - Supplemental Material Creative Commons Attribution. 477kB
	Image (TIFF) (S2 Fig. The asymmetry in the normalised conditional entropy of the optimised MS partitions signals a quasi-hierarchical community structure) - Supplemental Material Creative Commons Attribution. 473kB
	Image (TIFF) (S3 Fig. The effect of ablations and other network measures) - Supplemental Material Creative Commons Attribution. 1MB
	Image (TIFF) (S4 Fig. Finding role profiles with RBS) - Supplemental Material Creative Commons Attribution. 3MB
	Image (TIFF) (S5 Fig. Distribution of RBS flow roles across MS communities) - Supplemental Material Creative Commons Attribution. 1MB
	Image (TIFF) (S6 Fig. Comparison of RBS flow roles to roles obtained using Regular Equivalence) - Supplemental Material Creative Commons Attribution. 1MB
	Image (TIFF) (S7 Fig. Summary of the procedure for signal propagation analysis of posterior mechanosensory stimulus scenario (i1)) - Supplemental Material Creative Commons Attribution. 2MB
	Image (TIFF) (S8 Fig. Signal propagation of the anterior mechanosensory stimulus (i2)) - Supplemental Material Creative Commons Attribution. 1MB
	Image (TIFF) (S9 Fig. Signal propagation: posterior chemosensory stimulus (i3)) - Supplemental Material Creative Commons Attribution. 1MB
	Image (TIFF) (S10 Fig. Signal propagation: anterior chemosensory stimulus (i4)) - Supplemental Material Creative Commons Attribution. 1MB
	Image (TIFF) (S11 Fig. Peak times of strong response neurons by RBS roles for each of the four input scenarios (i1)-(i4)) - Supplemental Material Creative Commons Attribution. 558kB
	Image (TIFF) (S12 Fig. Peak overshoots against other network measures) - Supplemental Material Creative Commons Attribution. 748kB

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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.

Item Type:

Article

Related URLs:

URL	URL Type	Description
http://dx.doi.org/10.1371/journal.pcbi.1005055	DOI	Article
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005055	Publisher	Article
http://dx.doi.org/10.1371/journal.pcbi.1005055.s001	DOI	S1 Data
http://dx.doi.org/10.1371/journal.pcbi.1005055.s002	DOI	S1 Text
http://dx.doi.org/10.1371/journal.pcbi.1005055.s003	DOI	S2 Text
http://dx.doi.org/10.1371/journal.pcbi.1005055.s004	DOI	S1 Fig.
http://dx.doi.org/10.1371/journal.pcbi.1005055.s005	DOI	S2 Fig.
http://dx.doi.org/10.1371/journal.pcbi.1005055.s006	DOI	S3 Fig.
http://dx.doi.org/10.1371/journal.pcbi.1005055.s007	DOI	S4 Fig.
http://dx.doi.org/10.1371/journal.pcbi.1005055.s008	DOI	S5 Fig.
http://dx.doi.org/10.1371/journal.pcbi.1005055.s009	DOI	S6 Fig.
http://dx.doi.org/10.1371/journal.pcbi.1005055.s010	DOI	S7 Fig.
http://dx.doi.org/10.1371/journal.pcbi.1005055.s011	DOI	S8 Fig.
http://dx.doi.org/10.1371/journal.pcbi.1005055.s012	DOI	S9 Fig.
http://dx.doi.org/10.1371/journal.pcbi.1005055.s013	DOI	S10 Fig.
http://dx.doi.org/10.1371/journal.pcbi.1005055.s014	DOI	S11 Fig.
http://dx.doi.org/10.1371/journal.pcbi.1005055.s015	DOI	S12 Fig.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4975510/	PubMed Central	Article

ORCID:

Author	ORCID
Schaub, Michael T.	0000-0003-2426-6404
Billeh, Yazan N.	0000-0001-5200-4992
Barahona, Mauricio	0000-0002-1089-5675

Additional Information:

© 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. Received: January 12, 2016; Accepted: July 12, 2016; Published: August 5, 2016. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: KAB acknowledges an Award from the Imperial College Undergraduate Research Opportunities Programme (UROP). MTS acknowledges support from the ARC and the Belgium network DYSCO (Dynamical Systems, Control and Optimisation). YNB acknowledges support from the G. Harold and Leila Y. Mathers Foundation. MBD acknowledges support from the James S. McDonnell Foundation Postdoctoral Program in Complexity Science/Complex Systems Fellowship Award (220020349-CS/PD Fellow). MB acknowledges support from EPSRC grants EP/I017267/1 and EP/N014529/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have declared that no competing interests exist. Author Contributions: Conceived and designed the experiments: KAB MTS MBD MB. Performed the experiments: KB MTS MBD. Analyzed the data: KAB MTS MBD YNB MB. Wrote the paper: KAB MTS MBD YNB MB. Created the figures: KAB MTS MBD MB.

Funders:

Funding Agency	Grant Number
Imperial College	UNSPECIFIED
Australian Research Council	UNSPECIFIED
Belgium Network DYSCO	UNSPECIFIED
G. Harold and Leila Y. Mathers Charitable Foundation	UNSPECIFIED
James S. McDonnell Foundation	220020349-CS
Engineering and Physical Sciences Research Council (EPSRC)	EP/I017267/1
Engineering and Physical Sciences Research Council (EPSRC)	EP/N014529/1

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PubMed Central ID:

PMC4975510

Record Number:

CaltechAUTHORS:20160815-141402908

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https://resolver.caltech.edu/CaltechAUTHORS:20160815-141402908

Official Citation:

Bacik KA, Schaub MT, Beguerisse-Díaz M, Billeh YN, Barahona M (2016) Flow-Based Network Analysis of the Caenorhabditis elegans Connectome. PLoS Comput Biol 12(8): e1005055. doi:10.1371/journal.pcbi.1005055

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ID Code:

69631

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CaltechAUTHORS

Deposited By:

Tony Diaz

Deposited On:

15 Aug 2016 21:48

Last Modified:

09 Mar 2020 13:18

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