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  1. titletitle Robustness, clustering &evolutionary conservation Stefan Wuchty Center of Network Research Department of Physics University of Notre Dame

  2. New York Times Complex systems Made of many non-identical elements connected by diverse interactions. NETWORK

  3. Bio-Map GENOME protein-gene interactions PROTEOME protein-proteininteractions METABOLISM Bio-chemical reactions Citrate Cycle

  4. Bio-Map PROTEOME protein-proteininteractions

  5. Prot Interaction map Yeast protein network Nodes: proteins Links: physical interactions (binding) P. Uetz, et al. Nature, 2000; Ito et al., PNAS, 2001; …

  6. Prot P(k) Topology of the protein network H. Jeong, S.P. Mason, A.-L. Barabasi & Z.N. Oltvai, Nature, 2001

  7. Robustness 1 node failure S fc 0 1 Fraction of removed nodes, f Robustness Complex systems maintain their basic functions even under errors and failures (cell  mutations; Internet  router breakdowns)

  8. Robust-SF Failures Topological error tolerance   3 : fc=1 (R. Cohen et. al., PRL, 2000) fc Attacks Robustness of scale-free networks 1 R. Albert et.al. Nature, 2000 S 0 f 1

  9. Prot- robustness Yeast protein network - lethality and topological position - Highly connected proteins are more essential (lethal)... H. Jeong et al., Nature, 2001

  10. Metabolic networks Protein networks Modules in biological systems E. Ravasz et al.,Science, 2002

  11. Can we identify the modules? J(i,j): # of nodes both i and j link to; +1 if there is a direct (i,j) link

  12. Metabolism: E. Ravasz et al.,Science, 2002 Protein interactions: Rives and Galitski, PNAS, 2003 Spirin and Mirny, PNAS, 2003

  13. Open questions Does the application of standart clustering algorithms reflect real modules well? Since e.g. one protein can be part of more than one protein complex overlapping clustering algorithms should give better results.

  14. Motifs Small subnetworks that appear in real world networks significantly more often than in random graphs. (Milo et al., Science, 2002; Conant and Wagner, Nature Gen., 2003, Shen-Orr et al., Nature Gen., 2002, Milo et al, Science, 2004)

  15. From theparticularto theuniversal A.-L- Barabasi & Z. Oltvai, Science, 2002

  16. Topology and Evolution

  17. Topology and Evolution S. Wuchty, Z. Oltvai & A.-L. Barabasi, Nature Genetics, 2003

  18. Topology and evolution - General distribution of orthologs: E = N(o)/N(p) - degree-dependent distribution of orthologs ek = Nk(o)/Nk Orthologous Excess Retention: ERk = ek/E S. Wuchty, Genome Res., 2004

  19. Clustering in protein interaction networks high clustering = high quality of interaction Goldberg and Roth, PNAS, 2003

  20. Does that also hold for evolutionary conservation?

  21. Protein-protein interaction data are highly flawed: 90% false positives, 50% false negatives Von Mering et al., Nature, 2002 How stable are these results?

  22. Something else? Eisen et al., PNAS, 1998

  23. Open question ? ? Wuchty et al., submitted, 2004

  24. Plasmodium falciparum • Eukaryotic organism • Malaria parasite • Genome size 23 MB, 14 chromosomes • 5300 genes (estimated, Hall et al., Nature 2002, Gardner et al., Nature, 2002) • No protein interaction data available • Co-expression data available (Bozdech et al., PloS, 2003, LeRoch et al., Science, 2003) • 868 orthologs with Yeast (InParanoid, Remm et al. J. Mol. Biol., 2001)

  25. Plasmodium falciparum

  26. Plasmodium falciparum

  27. Iteratively pruning edges starting with the least weighted link • Quality of clusters is assessed by their modularity until a maximum is reached. Inferred protein interaction networkin P. falciparum • 667 nodes, 3,564 weighted interactions • Clustering

  28. All edges shown with Cvw > 1. Colorcode red: Cvw > 4, yellow: Cvw > 3, green: Cvw > 2, blue: Cvw > 1

  29. Co-expression patterns Bozdech et al. PLoS, 2003 What does that mean? Validation of results?

  30. ribososome RNA processing translation DNA processing exo/protesome replication Wuchty, Barabasi, Ferdig and Adams, in preperation

  31. What‘s next? • Uncovering evolutionary cores of interactions in other organisms. • Application of a Maximum Set Cover Algorithm to predict protein interactions (Huang, Kaanan, Wuchty, Izaguirre and Cheng, submitted) to unfold the interactome using the evolutionary cores and experimentally derived interactions.

  32. T H X ! http://www.nd.edu/~swuchtyswuchty@nd.edu