Dividing Protein Interaction Networks by Growing Orthologous Articulations

  • Pavol Jancura
  • Jaap Heringa
  • Elena Marchiori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5265)

Abstract

The increasing growth of data on protein-protein interaction (PPI) networks has boosted research on their comparative analysis. In particular, recent studies proposed models and algorithms for performing network alignment, the comparison of networks across species for discovering conserved modules. Common approaches for this task construct a merged representation of the considered networks, called alignment graph, and search the alignment graph for conserved networks of interest using greedy techniques. In this paper we propose a modular approach to this task. First, each network to be compared is divided into small subnets which are likely to contain conserved modules. To this aim, we develop an algorithm for dividing PPI networks that combines a graph theoretical property(articulation) with a biological one (orthology). Next, network alignment is performed on pairs of resulting subnets from different species. We tackle this task by means of a state-of-the-art alignment graph model for constructing alignment graphs, and an exact algorithm for searching in the alignment graph. Results of experiments show the ability of this approach to discover accurate conserved modules, and substantiate the importance of the notions of orthology and articulation for performing comparative network analysis in a modular fashion.

Keywords

Protein network dividing modular network alignment 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bader, G.D., Donaldson, I., Wolting, C., Ouellette, B.F.F., Pawson, T., Hogue, C.W.V.: Bind–the biomolecular interaction network database. Nucleic Acids Res. 29(1), 242–245 (2001)CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Xenarios, I., Salwínski, Ł., Duan, X.J., Higney, P., Kim, S.M., Eisenberg, D.: Dip, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Research 30(1), 303–305 (2002)CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Kelley, B.P., Sharan, R., Karp, R.M., Sittler, T., Root, D.E., Stockwell, B.R., Ideker, T.: Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proceedings of the National Academy of Science 100, 11394–11399 (2003)CrossRefGoogle Scholar
  4. 4.
    Sharan, R., Ideker, T.: Modeling cellular machinery through biological network comparison. Nature Biotechnology 24(4), 427–433 (2006)CrossRefPubMedGoogle Scholar
  5. 5.
    Srinivasan, B.S., Shah, N.H., Flannick, J., Abeliuk, E., Novak, A., Batzoglou, S.: Current Progress in Network Research: toward Reference Networks for kKey Model Organisms. Brief. in Bioinformatics (Advance access, 2007)Google Scholar
  6. 6.
    Koyutürk, M., Grama, A., Szpankowski, W.: Pairwise local alignment of protein interaction networks guided by models of evolution. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds.) RECOMB 2005. LNCS (LNBI), vol. 3500, pp. 48–65. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Sharan, R., Ideker, T., Kelley, B.P., Shamir, R., Karp, R.M.: Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. Journal of Computional Biology 12(6), 835–846 (2005)CrossRefGoogle Scholar
  8. 8.
    Hirsh, E., Sharan, R.: Identification of conserved protein complexes based on a model of protein network evolution. Bioinformatics 23(2), 170–176 (2007)CrossRefGoogle Scholar
  9. 9.
    Sharan, R., Suthram, S., Kelley, R.M., Kuhn, T., McCuine, S., Uetz, P., Sittler, T., Karp, R.M., Ideker, T.: From the Cover: Conserved patterns of protein interaction in multiple species. Proceedings of the National Academy of Sciences 102(6), 1974–1979 (2005)CrossRefGoogle Scholar
  10. 10.
    Flannick, J., Novak, A., Srinivasan, B.S., McAdams, H.H., Batzoglou, S.: Graemlin: General and robust alignment of multiple large interaction networks. Genome Res. 16(9), 1169–1181 (2006)CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Koyutürk, M., Kim, Y., Topkara, U., Subramaniam, S., Grama, A., Szpankowski, W.: Pairwise alignment of protein interaction networks. Journal of Computional Biology 13(2), 182–199 (2006)CrossRefGoogle Scholar
  12. 12.
    Pržulj, N.: Knowledge Discovery in Proteomics: Graph Theory Analysis of Protein-Protein Interactions. CRC Press, Boca Raton (2005)Google Scholar
  13. 13.
    Singh, R., Xu, J., Berger, B.: Pairwise global alignment of protein interaction networks by matching neighborhood topology, pp. 16–31 (2007)Google Scholar
  14. 14.
    Pržulj, N., Wigle, D., Jurisica, I.: Functional topology in a network of protein interactions. Bioinformatics 20(3), 340–384 (2004)CrossRefPubMedGoogle Scholar
  15. 15.
    Rathod, A.J., Fukami, C.: Mathematical properties of networks of protein interactions. CS374 Fall 2005 Lecture 9, Computer Science Department, Stanford University (2005)Google Scholar
  16. 16.
    Jeong, H., Mason, S.P., Barabasi, A.L., Oltvai, Z.N.: Lethality and centrality in protein networks. NATURE v 411, 41 (2001)CrossRefGoogle Scholar
  17. 17.
    Ekman, D., Light, S., Björklund, A.K., Elofsson, A.: What properties characterize the hub proteins of the protein-protein interaction network of saccharomyces cerevisiae? Genome Biology 7(6), R45 (2006)CrossRefGoogle Scholar
  18. 18.
    Ucar, D., Asur, S., Catalyurek, U., Parthasarathy, S.: Improving functional modularity in protein-protein interactions graphs using hub-induced subgraphs. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 371–382. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Bader, G.D., Lssig, M., Wagner, A.: Structure and evolution of protein interaction networks: a statistical model for link dynamics and gene duplications. BMC Evolutionary Biology 4(51) (2004)Google Scholar
  20. 20.
    Li, X.L., Tan, S.H., Foo, C.S., Ng, S.K.: Interaction graph mining for protein complexes using local clique merging. Genome Informatics 16(2), 260–269 (2005)PubMedGoogle Scholar
  21. 21.
    Yook, S.H., Oltvai, Z.N., Barabsi, A.L.: Functional and topological characterization of protein interaction networks. PROTEOMICS 4, 928–942 (2004)CrossRefPubMedGoogle Scholar
  22. 22.
    Abou-Rjeili, A., Karypis, G.: Multilevel algorithms for partitioning power-law graphs. In: 20th International Parallel and Distributed Processing Symposium (IPDPS) (2006)Google Scholar
  23. 23.
    Tarjan, R.: Depth-first search and linear graph algorithms. SIAM Journal on Computing 1(2), 146–160 (1972)CrossRefGoogle Scholar
  24. 24.
    Hopcroft, J., Tarjan, R.: Algorithm 447: efficient algorithms for graph manipulation. Commun. ACM 16(6), 372–378 (1973)CrossRefGoogle Scholar
  25. 25.
    Maslov, S., Sneppen, K.: Specificity and stability in topology of protein networks. Science 296, 910–913 (2002)CrossRefPubMedGoogle Scholar
  26. 26.
    Wolsey, L.A.: Integer Programming, 1st edn. Wiley, Chichester (1998)Google Scholar
  27. 27.
    Jancura, P., Heringa, J., Marchiori, E.: Divide, align and full-search for discovering conserved protein complexes. In: Marchiori, E., Moore, J.H. (eds.) EvoBIO 2008. LNCS, vol. 4973, pp. 71–82. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Pavol Jancura
    • 1
  • Jaap Heringa
    • 2
  • Elena Marchiori
    • 1
  1. 1.Intelligent Systems, ICISRadboud Universiteit NijmegenThe Netherlands
  2. 2.IBIVUVrije Universiteit AmsterdamThe Netherlands

Personalised recommendations