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Robustness, clustering & evolutionary conservation Stefan Wuchty Center of Network Research...

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

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Robustness, clustering & evolutionary conservation Stefan Wuchty Center of Network Research Department of Physics University of Notre Dame title Complex systems Made of many non-identical elements connected by diverse interactions. NETWORK New York Times protein- gene interactions protein- protein interactions PROTEOME GENOME Citrate Cycle METABOLISM Bio- chemical reactions Bio-Map protein- protein interactions PROTEOME Bio-Map Yeast protein network Nodes: proteins Links: physical interactions (binding) P. Uetz, et al. Nature, 2000; Ito et al., PNAS, 2001; Prot Interaction map Topology of the protein network H. Jeong, S.P. Mason, A.-L. Barabasi & Z.N. Oltvai, Nature, 2001 Prot P(k) Robustness Complex systems maintain their basic functions even under errors and failures (cell mutations; Internet router breakdowns) node failure fcfc 01 Fraction of removed nodes, f 1 S Robustness Robustness of scale-free networks 1 S 01 f fcfc Attacks 3 : f c =1 (R. Cohen et. al., PRL, 2000) Failures Topological error tolerance Robust-SF R. Albert et.al. Nature, 2000 Yeast protein network - lethality and topological position - Highly connected proteins are more essential (lethal)... Prot- robustness H. Jeong et al., Nature, 2001 Modules in biological systems Metabolic networks Protein networks E. Ravasz et al., Science, 2002 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 Metabolism: E. Ravasz et al., Science, 2002 Protein interactions: Rives and Galitski, PNAS, 2003 Spirin and Mirny, PNAS, 2003 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. 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) From the particular to the universal A.-L- Barabasi & Z. Oltvai, Science, 2002 Topology and Evolution S. Wuchty, Z. Oltvai & A.-L. Barabasi, Nature Genetics, 2003 S. Wuchty, Genome Res., 2004 Topology and evolution - General distribution of orthologs: E = N(o)/N(p) - degree-dependent distribution of orthologs e k = N k (o)/N k Orthologous Excess Retention: ER k = e k /E Clustering in protein interaction networks Goldberg and Roth, PNAS, 2003 high clustering = high quality of interaction Does that also hold for evolutionary conservation? Protein-protein interaction data are highly flawed: 90% false positives, 50% false negatives Von Mering et al., Nature, 2002 How stable are these results? Something else? Eisen et al., PNAS, 1998 Open question ? Wuchty et al., submitted, 2004 ? 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) Plasmodium falciparum Inferred protein interaction network in P. falciparum 667 nodes, 3,564 weighted interactions Clustering - Iteratively pruning edges starting with the least weighted link - Quality of clusters is assessed by their modularity until a maximum is reached. All edges shown with C vw > 1. Colorcode red: C vw > 4, yellow: C vw > 3, green: C vw > 2, blue: C vw > 1 What does that mean? Validation of results? Co-expression patterns Bozdech et al. PLoS, 2003 replicationexo/protesomeDNA processing translationRNA processingribososome Wuchty, Barabasi, Ferdig and Adams, in preperation Whats 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. T H X !


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