Not All Scale Free Networks Are Born Equal: The Role of the Seed Graph in PPI Network Emulation

  • Fereydoun Hormozdiari
  • Petra Berenbrink
  • Nataša Pržulj
  • Cenk Sahinalp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4532)

Abstract

The (asymptotic) degree distributions of the best known “scale free” network models are all similar and are independent of the seed graph used. Hence it has been tempting to assume that networks generated by these models are similar in general. In this paper we observe that several key topological features of such networks depend heavily on the specific model and the seed graph used. Furthermore, we show that starting with the “right” seed graph, the duplication model captures many topological features of publicly available PPI networks very well.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alfarano, C., et al.: The biomolecular interaction network database and related tools. Nucl Acids Res. 33(Database Issue), 418–424 (2005)CrossRefGoogle Scholar
  2. 2.
    Aiello, W., Chung, F., Lu, L.: Random graph model for power law graphs. In: Proc ACM STOC, pp. 171–180 (2000)Google Scholar
  3. 3.
    Barabási, A.-L., Albert, R.A.: Emergence of scaling in random networks. Science 286, 509–512 (1999)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Bebek, G., Berenbrink, P., Cooper, C., Friedetzky, T., Nadeau, J., Sahinalp, S.C.: The degree distribution of the general duplication models, Theor Comp Sci. (to appear)Google Scholar
  5. 5.
    Bebek, G., Berenbrink, P., Cooper, C., Friedetzky, T., Nadeau, J., Sahinalp, S.C.: Topological Properties of proteome networks. In: Proc RECOMB Sat. Mtg. on Sys. Bio. (LNBI) (2005)Google Scholar
  6. 6.
    Berger, N., Bollobás, B., Borgs, C., Chayes, J., Riordan, O.: Degree distribution of the FKP network model. In: Baeten, J.C.M., Lenstra, J.K., Parrow, J., Woeginger, G.J. (eds.) ICALP 2003. LNCS, vol. 2719, pp. 725–738. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Bhan, A., Galas, D.J., Dewey, T.G.: A duplication growth model of gene expression networks. Bioinformatics 18, 1486–1493 (2002)CrossRefGoogle Scholar
  8. 8.
    Bollobás, B., Riordan, O., Spencer, J., Tusanády, G.: The degree sequence of a scale-free random graph process. Random Str. & Alg. 18, 279–290 (2001)MATHCrossRefGoogle Scholar
  9. 9.
    Chung, F., Lu, L., Dewey, G.T., Galas, J.D.: Duplication models for biological networks. J. Comp Bio. 10, 677–687 (2003)CrossRefGoogle Scholar
  10. 10.
    Cooper, C., Frieze, A.: A general model of webgraphs. Random Str. & Alg. 22(3), 311–335 (2003)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Deane, C.M., Salwinski, L., Xenarios, I., Eisenberg, D.: Protein interactions: Two methods for assessment of the reliability of high-troughput observations. Mol. Cell Port 1, 349–356 (2002)CrossRefGoogle Scholar
  12. 12.
    De Silva, E., Stumpf, M.P.H.: Complex networks and simple models in biology. J. of the Royal Society Interface 2, 419–430 (2005)CrossRefGoogle Scholar
  13. 13.
    Erdös, P., Rényi, A.: On random graphs I. Publicationes Mathematicae Debrecen 6, 290–297 (1959)MATHMathSciNetGoogle Scholar
  14. 14.
    Jeong, H., Mason, S., Barabasi, A.-L., Oltvai, Z.N.: Lethality and centrality in protein networks. Nature 411, 41 (2001)CrossRefGoogle Scholar
  15. 15.
    Hermjakob, H., et al.: IntAct - an open source molecular interaction database. Nucl Acids Res. 32, 452–455 (2004)CrossRefGoogle Scholar
  16. 16.
    Przulj, N., Corneil, D.G., Jurisica, I.: Modeling Interactome: Scale-Free or Geometric? Bioinformatics 150(1-3), 216–231 (2005)MATHGoogle Scholar
  17. 17.
    Ohno, S.: Evolution by gene duplication. Springer, Heidelberg (1970)Google Scholar
  18. 18.
    Pastor-Satorras, R., Smith, E., Sole, R.V.: Evolving protein interaction networks through gene duplication. J. Theor Biol. 222, 199–210 (2003)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Przytycka, T., Yu, Y.K.: Scale-free networks versus evolutionary drift. Comp Bio. & Chem. 28, 257–264 (2004)MATHCrossRefGoogle Scholar
  20. 20.
    Salwinski, L., et al.: The Database of interacting Proteins:2004 update. Nucl Acids Res. 32(Database issue:D), 449–451 (2004)CrossRefGoogle Scholar
  21. 21.
    Tanaka, R., et al.: Some protein interaction data do not exhibit power law statistics. FEBS Letters 579, 5140–5144 (2005)CrossRefGoogle Scholar
  22. 22.
    Vázquez, A., Flammini, A., Maritan, A., Vespignani, A.: Modelling of protein interaction networks. Complexus 1, 38–44 (2003)CrossRefGoogle Scholar
  23. 23.
    Han, J., Dupuy, D., Bertin, N., Cusick, M., Vidal, M.: Effect of sampling on topology predictions of protein-protein interaction networks. Nature Biotech. 23, 839–844 (2005)CrossRefGoogle Scholar
  24. 24.
    Wagner, A.: The Yeast protein interaction network evolves rapidly and contains few redundant duplicate genes. Mol. Biol. Evol. 18, 1283–1292 (2001)Google Scholar
  25. 25.
    Watts, D.J.: Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton Univ Press, Princeton (1999)Google Scholar
  26. 26.
    Wasserman, S., Faust, K.: Social network analysis: methods and applications. Cambridge Univ. Press, New York (1994)Google Scholar
  27. 27.
    Xenarios, I., et al.: DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucl. Acids Res. 30, 303–305 (2002)CrossRefGoogle Scholar
  28. 28.
    Zanzoni, A. et al.: MINT: a Molecular INteraction database. FEBS Letters 513(1), 135–140 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fereydoun Hormozdiari
    • 1
  • Petra Berenbrink
    • 1
  • Nataša Pržulj
    • 2
  • Cenk Sahinalp
    • 1
  1. 1.School of Computing Science, Simon Fraser UniversityCanada
  2. 2.Department of Computer Science, University of California, IrvineUSA

Personalised recommendations