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Figure 1 | Genome Biology

Figure 1

From: Sustained-input switches for transcription factors and microRNAs are central building blocks of eukaryotic gene circuits

Figure 1

An illustrative overview of network motif discovery, and two possible problems that may be encountered using the classic edge-switching method for network randomization. (A) How network motif discovery works, illustrated using a subgraph of interest M. The method determines whether M is observed a significantly large number of times in an original network N, compared with randomized networks RN. If the P-value falls below a small pre-determined value, the subgraph count of M that is observed in original network N is considered to be significant, and therefore M is called a network motif for N. (B) Edge switching is a method for generating randomized networks RN in (A) from an original network N. It operates by repeatedly switching the endpoints of two edges. (Left) The two edges in red are selected for switching (top) and their endpoints are exchanged, resulting in a valid graph (bottom). (Right) An example of a failed edge switch: the two edges in red are selected for switching (top), but the exchange of endpoints (bottom) results in an invalid graph, in this case a graph with a double edge. (C) An example of a low-variance count distribution for a particular subgraph, which indicates that the subgraph is highly significant and therefore a motif. Low-variance count distributions (that is, those with small standard deviations) are one symptom of insufficiently randomized networks RN (A), and can result from edge switching in large multi-layer networks.

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