Biological SystemsNo Access

ANALYSIS OF PREFERENTIAL NETWORK MOTIF GENERATION IN AN ARTIFICIAL REGULATORY NETWORK MODEL CREATED BY DUPLICATION AND DIVERGENCE

ANDRÉ LEIER

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

Search for more papers by this author

P. DWIGHT KUO

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

Search for more papers by this author

, and

WOLFGANG BANZHAF

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

Search for more papers by this author

https://doi.org/10.1142/S0219525907000994Cited by:4

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.

Keywords:

References

M. Babuet al., Curr. Opin. Struc. Biol. 14, 283 (2004), DOI: 10.1016/j.sbi.2004.05.004. Crossref, ISI, Google Scholar
M. Babu and S. A. Teichmann, Nucleic Acids Res. 31(4), 1234 (2003), DOI: 10.1093/nar/gkg210. Crossref, ISI, Google Scholar
W. Banzhaf, Genetic Programming Theory and Practice, eds. R. L. Riolo and B. Worzel (Kluwer, 2003) pp. 43–62. Crossref, Google Scholar
W. Banzhaf, On the dynamics of an artificial regulatory network, Advances in Artificial Life — Proc. 7th European Conf. Artificial Life (ECAL)2801, Lecture Notes in Artificial Intelligence, eds. W. Banzhafet al. (Springer, 2003) pp. 217–227. Google Scholar
W. Banzhaf and P. D. Kuo, J. Biol. Phys. Chem. 4(2), 85 (2004), DOI: 10.4024/2040405.jbpc.04.02. Crossref, Google Scholar
S. B. Carroll , J. K. Grenier and S. D. Weatherbee , From DNA to Diversity , 2nd edn. ( Blackwell Publishing , 2005 ) . Google Scholar
G. Conant and A. Wagner, Nature Genet. 34, 264 (2003), DOI: 10.1038/ng1181. Crossref, ISI, Google Scholar
B. Dujonet al., Nature 430(6995), 35 (2004), DOI: 10.1038/nature02579. Crossref, ISI, Google Scholar
R. Friedman and A. L. Hughes, Genome Res. 11(3), 373 (2001), DOI: 10.1101/gr.155801. Crossref, ISI, Google Scholar
T. J. Gibson and J. Spring, Biochem. Soc. Trans. 128, 259 (2000). Google Scholar
P. H. W. Holland, Semin. Cell. Dev. Biol. 10, 541 (1999). Crossref, ISI, Google Scholar
A. L. Hughes, Proc. Nat. Acad. Sci. (USA) 102(25), 8791 (2005), DOI: 10.1073/pnas.0503922102. Crossref, ISI, Google Scholar
J. Ihmelset al., Science 309(5736), 938 (2005), DOI: 10.1126/science.1113833. Crossref, ISI, Google Scholar
N. Kashtanet al., Phys. Rev. E 70, 031909 (2004), DOI: 10.1103/PhysRevE.70.031909. Crossref, Google Scholar
M. Kellis, B. Birren and E. Lander, Nature 428, 617 (2004), DOI: 10.1038/nature02424. Crossref, ISI, Google Scholar
K. Klemm and S. Bornholdt, Proc. Nat. Acad. Sci. (USA) 102, 18414 (2005), DOI: 10.1073/pnas.0509132102. Crossref, ISI, Google Scholar
P. Koehl, Curr. Opin. Struct. Biol. 11(3), 348 (2001), DOI: 10.1016/S0959-440X(00)00214-1. Crossref, ISI, Google Scholar
P. D. Kuo and W. Banzhaf, Scale-free and small world network topologies in an artificial regulatory network model, 9th Int. Conf. Simulation and Synthesis of Living Systems (ALIFE), ed. J. Pollack (MIT Press, 2004) pp. 404–409. Google Scholar
P. D. Kuo, A. Leier and W. Banzhaf, Evolving dynamics in an artificial regulatory network model, Proc. 8th Conf. Parallel Problem Solving from Nature (PPSN)3242, Lecture Notes in Computer Science, eds. X. Yaoet al. (Springer, 2004) pp. 571–580. Google Scholar
P. D. Kuo, W. Banzhaf and A. Leier, Biosystems (2006). Google Scholar
A. Mazurie, S. Bottani and M. Vergassola, Genome Biol. 6(4), R35 (2005), DOI: 10.1186/gb-2005-6-4-r35. Crossref, Google Scholar
R. Miloet al., Nature 303, 1538 (2004). Google Scholar
R. Miloet al., Science 298, 824 (2002), DOI: 10.1126/science.298.5594.824. Crossref, ISI, Google Scholar
J. Nadeau and D. Sankoff, Genetics 147, 1259 (1997). Crossref, ISI, Google Scholar
S. Ohno , Evolution by Gene Duplication ( Springer , 1970 ) . Crossref, Google Scholar
F. Rodriguez-Trelles, R. Tarrio and F. J. Ayala, Int. J. Dev. Biol. 47, 665 (2003). ISI, Google Scholar
D. Sankoff, Curr. Opin. Genet. Dev. 11, 681 (2001), DOI: 10.1016/S0959-437X(00)00253-7. Crossref, ISI, Google Scholar
S. Shen-Oret al., Nature Genet. 31, 64 (2002), DOI: 10.1038/ng881. Crossref, ISI, Google Scholar
A. Sidow, Curr. Opin. Genet. Dev. 6, 715 (1996), DOI: 10.1016/S0959-437X(96)80026-8. Crossref, ISI, Google Scholar
E. J. Stellwag, Integr. Comp. Biol. 44, 358 (2004), DOI: 10.1093/icb/44.5.358. Crossref, ISI, Google Scholar
J. R. Stone and G. A. Wray, Mol. Biol. Evol. 18, 1764 (2001). Crossref, ISI, Google Scholar
S. A. Teichmann and M. Babu, Nature Genet. 36(5), 492 (2004), DOI: 10.1038/ng1340. Crossref, ISI, Google Scholar
K. Wolfe and D. Shields, Nature 387(6634), 708 (1997), DOI: 10.1038/42711. Crossref, ISI, Google Scholar
S. Wuchty, Z. Oltvai and A.-L. Barabási, Nature Genet. 35(2), 176 (2003), DOI: 10.1038/ng1242. Crossref, ISI, Google Scholar

Remember to check out the Most Cited Articles!
Check out our titles in Complex Systems today!

Vol. 10, No. 02

Metrics

History

Received 29 March 2006

Revised 24 July 2006

Keywords

PDF download

System Upgrade on Tue, Oct 25th, 2022 at 2am (EDT)

ANALYSIS OF PREFERENTIAL NETWORK MOTIF GENERATION IN AN ARTIFICIAL REGULATORY NETWORK MODEL CREATED BY DUPLICATION AND DIVERGENCE

Abstract

References

Recommended