Discovering Motifs to Fingerprint Multi-Layer Networks: a Case Study on the Connectome of C. Elegans

TL;DR

This paper uses motif discovery to analyze the multi-layer connectome of C. elegans, revealing insights into its neural organization and functional circuits, with potential applications across species.

Contribution

It introduces a comprehensive motif analysis method applied to a multi-layer neural network using ESCAPE, linking motifs to functional circuits in C. elegans.

Findings

Identified key network motifs in C. elegans connectome

Linked motifs to specific neural functions

Benchmarking against random networks validated motif significance

Abstract

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.