Pairwise local alignment of protein interaction networks guided by models of evolution

Lecture Notes in Bioinformatics (Subseries of Lecture Notes in Computer Science)

Volumn 3500, Issue , 2005, Pages 48-65

  Koyutürk, Mehmet ^a   Grama, Ananth ^a   Szpankowski, Wojciech ^a  

^a Purdue University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COSTS; DATABASE SYSTEMS; MOLECULAR BIOLOGY; PROBLEM SOLVING; BIOLOGY; EVOLUTIONARY ALGORITHMS; GRAPH THEORY;

COMPUTATIONAL MOLECULAR BIOLOGY; PROTEIN-PROTEIN INTERACTION (PPI); SUBNET QUERY; PROTEIN COMPLEXES;

PROTEINS;

EID: 26444593028     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11415770_4     Document Type: Conference Paper

References (34)

1. Altschul, S.F., Madden, T.L., Schffer, A.A., J. Zhang, Z.Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nuc. Acids Res. 25 (1997) 3389-3402
2. Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTAL-W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nuc. Acids Res. 22 (1994) 4673-4680
3. Hartwell, L.H., Hopfield, J.J., Leibler, S., Murray, A.W.: From molecular to modular cell biology. Nature 402 (1999) C47-C51
4. Titz, B., Schlesner, M., Uetz, P.: What do we learn from high-throughput protein interaction data? Exp. Rev. Prot. 1 (2004) 111-121
5. Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., Sakaki, Y.: A comprehensive two-hybrid analysis to explore the yeast protein interactome. PNAS 98 (2001) 4569-4574
6. Ho, Y. et al.: Systematic identification of protein complexes in Saccharomyces cerevisae by mass spectrometry. Nature 415 (2002) 180-183
7. Gavin, A.C. et al.: Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415 (2002) 141-147
8. Wuchty, S., Oltvai, Z.N., Barabási, A.L.: Evolutionary conservation of motif constituents in the yeast protein interaction network. Nature Gen. 35 (2003) 176-179
9. Tohsato, Y., Matsuda, H., Hashimoto, A.: A multiple alignment algorithm for metabolic pathway analysis using enzyme hierarchy. In: 8th Intl. Conf. Intel. Sys. Mol. Bio. (ISMB'00). (2000) 376-383
10. Koyutürk, M., Grama, A., Szpankowski, W.: An efficient algorithm for detecting frequent subgraphs in biological networks. In: Bioinformatics Suppl. 12th Intl. Conf. Intel. Sys. Mol. Bio. (ISMB'04). (2004) 1200-1207
11. Kelley, B.P., Yuan, B., Lewitter, F., Sharan, R., Stockwell, B.R., Ideker, T.: Path-BLAST: a tool for aligment of protein interaction networks. Nuc. Acids Res. 32 (2004) W83-W88
12. Vázquez, A., Flammini, A., Maritan, A., Vespignani, A.: Modeling of protein interaction netwokrs. ComPlexUs 1 (2003) 38-44
13. Dandekar, T., Schuster, S., Snel, B., Huynen, M., Bork, P.: Pathway alignment: application to the comparative analysis of glycolytic enzymes. Biochem. J 343 (1999) 115-124
14. Lotem, E.Y., Sattath, S., Kashtan, N., Itzkovitz, S., Milo, R., Pinter, R.Y., Alon, U., Margalit, H.: Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction. PNAS 101 (2004) 5934-5939
15. 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. In: 8th Intl. Conf. Res. Comp. Mol. Bio. (RECOMB'04). (2004) 282-289
16. Barabási, A., Albert, R.: Emergence of scaling in random networks. Science 286 (1999) 509-512
17. Eisenberg, E., Levanon, Y.: Preferential attachment in the protein network evolution. Phys. Rev. Let. 91 (2003) 138701
18. Qin, H., Lu, H.H.S., Wu, W.B., Li, W.: Evolution of the yeast protein interaction network. PNAS 100 (2003) 12820-12824
19. Pastor-Satorras, R., Smith, E., Solé, R.V.: Evolving protein interaction networks through gene duplication. J Theo. Bio. 222 (2003) 199-210
20. Wagner, A.: The yeast protein interaction network evolves rapidly and contains few redundant duplicate genes. Mol. Bio. Evol. 18 (2001) 1283-1292
21. Wagner, A.: How the global structure of protein interaction networks evolves. Proc. R. Soc. Lond. Biol. Sci. 270 (2003) 457-466
22. Chung, F., Lu, L., Dewey, T.G., Galas, D.J.: Duplication models for biological networks. J Comp. Bio. 10 (2003) 677-687
23. Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J Mol. Bio. 147 (1981) 195-197
24. Feige, U., Peleg, D., Kortsarz, G.: The dense k-subgraph problem. Algorithmica 29 (2001) 410-421
25. Hassin, R., Rubinstein, S., Tamir, A.: Approximation algorithms for maximum dispersion. Oper. Res. Let. 21 (1997) 133-137
26. Tornow, S., Mewes, H.W.: Functional modules by relating protein interaction networks and gene expression. Nuc. Acids Res. 31 (2003) 6283-6289
27. Bader, J.S.: Greedily building protein networks with confidence. Bioinformatics 19 (2003) 1869-1874
28. Remm, M., Storm, C.E.V., Sonnhammer, E.L.L.: Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. J Mol. Bio. 314 (2001) 1041-1052
29. Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N.J., Chung, S., Emili, A., Snyder, M., Greenblatt, J.F., Gerstein, M.: A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302 (2003) 449-453
30. Ashtana, S., King, O.D., Gibbons, F.D., Roth, F.P.: Predicting protein complex membership using probabilistic network reliability. Genome Research 14 (2004) 1170-1175
31. Gilchrist, M.A., Salter, L.A., Wagner, A.: A statistical framework for combining and interpreting proteomic datasets. Bioinformatics 20 (2003) 689-700
32. Kelley, B.P., Sharan, R., Karp, R.M., Sittler, T., Root, D.E., Stockwell, B.R., Ideker, T.: Conserved pathways withing bacteria and yeast as revealed by global protein network alignment. PNAS 100 (2003) 11394-11399
33. Xenarios, I., Salwinski, L., Duan, X.J., Higney, P., Kim, S., Eisenberg, D.: DIP: The Database of Interacting Proteins. A research tool for studying cellular networks of protein interactions. Nuc. Acids Res. 30 (2002) 303-305
34. Boonyaratanakornkit, V. et al.: High-mobility group chromatin proteins 1 and 2 functionally interact with steroid hormone receptors to enhance their DNA binding in vitro and transcriptional activity in mammalian cells. Mol. Cell. Bio. 18 (1998) 4471-4488