The local topology of dynamical network models for biology

The search for motifs-recurrent patterns in network topology-has led to the identification of universal classes of complex systems across diverse fields and has served as a quantitative tool to reveal common properties in both evolved and designed networks. In this study, we investigate the presence and significance of network superfamilies-previously identified through the census of triadic motifs-in the largest data set of dynamic, biological network models. We present triad significance profiles of 71 existing biological network models, all experimentally inspired. The generated data are treated in an unbiased manner and consistently clustered into two classes using several unsupervised techniques. The more prevalent class does exhibit a strong correlation with the superfamily of sensory transmission networks, which are characterized by the feedforward loop motif commonly found in signal-processing systems. Surprisingly, the remaining class shows a better correlation with the superfamily of word-adjacency networks. To better understand this, the results are analysed for varying network size thresholds, and their connection to the effect of model building activity is examined. It is highlighted that the more the model focuses on smaller portions of the regulatory network, as a result of the coarse-graining of the boundary dynamics and the peripheral regions of the network, the more its topology starts resembling that of 'sentences' of word-adjacency networks.