Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain

BackgroundComplex 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. Methodology/Principal FindingsHere, 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. Conclusions/SignificanceThe 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 networks into its functional (rather than topological) motifs as long as both wiring and functional information is available.

背景：复杂网络通常可拆解为复杂度更低的子网络，这些子网络的结构能够为整体网络的功能组织提供启示。然而，这类结构基序（structural motif）仅能揭示功能层面的部分信息，因为此类分析中所有节点与边均被视作等同。在真实网络中，两个拓扑结构完全一致但节点功能迥异的基序，在网络中所发挥的作用也会截然不同。
方法/主要研究结果：本研究结合了源自秀丽隐杆线虫（Caenorhabditis elegans）神经元网络拓扑结构的信息，以及这些节点的生物学功能信息，从而基于功能对节点进行着色标注。我们发现，特定功能着色的基序在该线虫脑内的丰度显著高于随机预期水平，且这类基序具备特定的计算功能：它们强化了网络中的信息前馈处理结构，同时规避了反馈环路。中间神经元在常见功能着色基序中显著富集，这印证了这类基序负责处理并传递感觉神经元向肌肉传递的信息这一观点。在筛选显著功能着色基序时发现的部分高频基序，在调控该线虫运动的神经元系统中发挥着关键作用。
结论/意义：基于功能着色基序的复杂网络分析方法整合了两个独立数据集，能够获取仅依靠单一数据集无法获得的网络相关认知。该方法具有普适性，只要同时获取网络的连接结构与功能信息，便可将任意复杂网络拆解为功能基序（而非拓扑基序）。