Analysis of Structure and Dynamics in Three-Neuron Motifs

Recurrent neural networks can produce ongoing state-to-state transitions without any driving inputs, and the dynamical properties of these transitions are determined by the neuronal connection strengths. Due to non-linearity, it is not clear how strongly the system dynamics is affected by discrete local changes in the connection structure, such as the removal, addition, or sign-switching of individual connections. Moreover, there are no suitable metrics to quantify structural and dynamical differences between two given networks with arbitrarily indexed neurons. In this work, we present such permutation-invariant metrics and apply them to motifs of three binary neurons with discrete ternary connection strengths, an important class of building blocks in biological networks. Using multidimensional scaling, we then study the similarity relations between all 3,411 topologically distinct motifs with regard to structure and dynamics, revealing a strong clustering and various symmetries. As expected, the structural and dynamical distance between pairs of motifs show a significant positive correlation. Strikingly, however, the key parameter controlling motif dynamics turns out to be the ratio of excitatory to inhibitory connections.

Network motif-based method for identifying coronary artery disease

The present study aimed to develop a more efficient method for identifying coronary artery disease (CAD) than the conventional method using individual differentially expressed genes (DEGs). GSE42148 gene microarray data were downloaded, preprocessed and screened for DEGs. Additionally, based on transcriptional regulation data obtained from ENCODE database and protein-protein interaction data from the HPRD, the common genes were downloaded and compared with genes annotated from gene microarrays to screen additional common genes in order to construct an integrated regulation network. FANMOD was then used to detect significant three-gene network motifs. Subsequently, GlobalAncova was used to screen differential three-gene network motifs between the CAD group and the normal control data from GSE42148. Genes involved in the differential network motifs were then subjected to functional annotation and pathway enrichment analysis. Finally, clustering analysis of the CAD and control samples was performed based on individual DEGs and the top 20 network motifs identified. In total, 9,008 significant three-node network motifs were detected from the integrated regulation network; these were categorized into 22 interaction modes, each containing a minimum of one transcription factor. Subsequently, 1,132 differential network motifs involving 697 genes were screened between the CAD and control group. The 697 genes were enriched in 154 gene ontology terms, including 119 biological processes, and 14 KEGG pathways. Identifying patients with CAD based on the top 20 network motifs provided increased accuracy compared with the conventional method based on individual DEGs. The results of the present study indicate that the network motif-based method is more efficient and accurate for identifying CAD patients than the conventional method based on individual DEGs.