Name
Title | ||
|---|---|---|
Analysis of Preferential Network Motif Generation in an Artificial Regulatory Network Model Created by Duplication and Divergence |
Abstract | ||
|---|---|---|
Previous studies on network topology of artificial gene regulatory networks created by whole genome duplication and divergence processes show subgraph distributions similar 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 duplication 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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1142/S0219525907000994 | ADVANCES IN COMPLEX SYSTEMS |
Keywords | Field | DocType |
gene duplication,network motif,gene regulatory networks,artificial regulatory networks | Genome,Network motif,Biology,Artificial intelligence,Computational biology,Functional divergence,Gene duplication,DNA binding site,Biological network,Network topology,Gene regulatory network,Genetics,Machine learning | Journal |
Volume | Issue | ISSN |
10 | 2 | 0219-5259 |
Citations | PageRank | References |
2 | 0.43 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| André Leier | 1 | 197 | 19.87 |
| P. Dwight Kuo | 2 | 42 | 3.92 |
| Wolfgang Banzhaf | 3 | 2627 | 367.13 |