Colored motifs reveal computational building blocks in the C. elegans brain

Details Colored motifs reveal computational building blocks in the C. elegans brain Colored motifs reveal computational building blocks in the C. elegans brain

2010-12-16

arxiv.org/abs/1012.3641v1

PLoS ONE 6 (2011) e17013

Biology

Quantitative Biology

Molecular Networks

16 pages, 6 figures, two supplementary figures, 1 table. Extended Supplementary Figure S2 available upon request. To appear in

Scientific paper

Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm's locomotion. The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex network into its functional (rather than topological) motifs as long as both wiring and functional information is available.

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