https://doi.org/10.1140/epjb/s10051-024-00848-4
Regular Article - Statistical and Nonlinear Physics
Discovering motifs to fingerprint multi-layer networks: a case study on the connectome of C. Elegans
1
Department of Computer Science, Christian-Albrechts Universität zu Kiel, Christian-Albrechts-Platz 4, 24118, Kiel, Germany
2
Theoretical Physics and Center for Biophysics, Saarland University, Campus E2 6, 66123, Saarbrücken, Germany
a
philipp.hoevel@uni-saarland.de
Received:
2
September
2024
Accepted:
12
December
2024
Published online:
20
January
2025
Motif discovery is a powerful and insightful method to quantify network structures and explore their function. As a case study, we present a comprehensive analysis of regulatory motifs in the connectome of the model organism Caenorhabditis elegans (C. elegans). Leveraging the Efficient Subgraph Counting Algorithmic PackagE (ESCAPE) algorithm, we identify network motifs in the multi-layer nervous system of C. elegans and link them to functional circuits. We further investigate motif enrichment within signal pathways and benchmark our findings with random networks of similar size and link density. Our findings provide valuable insights into the organization of the nerve net of this well-documented organism and can be easily transferred to other species and disciplines alike.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjb/s10051-024-00848-4.
© The Author(s) 2025
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