Analysis of Preferential Network Motif Generation in an Artificial Regulatory Network Model Created by Duplication and Divergence
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Previous studies on network topology of artificial gene regulatory networks created by whole genome duplication and divergence processes show subgraph distributions simi- lar to gene regulatory networks found in nature. In particular, certain network motifs are prominent in both types of networks. In this contribution, we analyze how duplica- tion and divergence processes influence network topology and preferential generation of network motifs. We show that in the artificial model such preference originates from a stronger preservation of protein than regulatory sites by duplication and divergence. If these results can be transferred to regulatory networks in nature, we can infer that after duplication the paralogous transcription factor binding site is less likely to be preserved than the corresponding paralogous protein.