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Analysis Of Preferential Network Motif Generation In An Artificial Regulatory Network Model Created By Duplication And Divergence

Author

Listed:
  • ANDRÉ LEIER

    (Advanced Computational Modelling Centre, University of Queensland, Brisbane, QLD 4072, Australia)

  • P. DWIGHT KUO

    (Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA)

  • WOLFGANG BANZHAF

    (Department of Computer Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada)

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.

Suggested Citation

  • André Leier & P. Dwight Kuo & Wolfgang Banzhaf, 2007. "Analysis Of Preferential Network Motif Generation In An Artificial Regulatory Network Model Created By Duplication And Divergence," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 155-172.
    • Handle: RePEc:wsi:acsxxx:v:10:y:2007:i:02:n:s0219525907000994
    DOI: 10.1142/S0219525907000994
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