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NetBioSIG2013-KEYNOTE Michael Schroeder
 

NetBioSIG2013-KEYNOTE Michael Schroeder

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Keynote presentation for Network Biology SIG 2013 by Michael Schroeder, Director of Biotechnology Center at Technical University Dresden, Germany

Keynote presentation for Network Biology SIG 2013 by Michael Schroeder, Director of Biotechnology Center at Technical University Dresden, Germany

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  • What is complexity of finding bi cliques?
  • FIX: Motivate the need to look a DNA repair proteins CADUC FIX: Put before and after DONE FIX: What are the 6 subunits? OK FIX: add legend DONE fiX: annimate DONE FIX: tell the story better DONE
  • This is the probability that the cluster has k or more proteins with domain or GO term X, if the cluster's contents were drawn randomly from the set of known proteins.
  • This is the probability that the cluster has k or more proteins with domain or GO term X, if the cluster's contents were drawn randomly from the set of known proteins.
  • Hif1a mouse knock out phenotype? MicroRNA-125b promotes neuronal differentiation in human cells by repressing multiple targets expression of either miR-124a or miR-125b increases the percentage of differentiated SH-SY5Y cells with neurite outgrowth embryonic carcinoma cells with those of differentiated neural stem cells showed that the expression level of 65 miRNAs changed (2-fold) after differentiation. MiR-124a was dramatically upregulated
  • Hif1a mouse knock out phenotype? MicroRNA-125b promotes neuronal differentiation in human cells by repressing multiple targets expression of either miR-124a or miR-125b increases the percentage of differentiated SH-SY5Y cells with neurite outgrowth embryonic carcinoma cells with those of differentiated neural stem cells showed that the expression level of 65 miRNAs changed (2-fold) after differentiation. MiR-124a was dramatically upregulated
  • How do we measure compressibility in networks? Because we have to account for the fact that even random networks are compressible. We measure the compressibility of a network relative to a random baseline. The network is first randomized many times in a way that preserves the topological properties. The network and its randomized variants are all compressed using the power graph algorithm. The compression rate of the network is compared to the average compressibility of the randomized networks. The relative compression rate is defined as the difference between the compression rate of the original network and The average compressibility.
  • FIX: socio-affinity explain FIX: mention databases and litterature derived networks We computed the absolute and relative compression rates for 29 networks 21 of which are derived from Y2H or AP/MS experiments 5 are multi-species databases that provide a view on the ‘ average signal ’ 2 are derived from manual curation of literature. 1 is a network derived from structure.
  • FIX: socio-affinity explain FIX: mention databases and litterature derived networks We computed the absolute and relative compression rates for 29 networks 21 of which are derived from Y2H or AP/MS experiments 5 are multi-species databases that provide a view on the ‘ average signal ’ 2 are derived from manual curation of literature. 1 is a network derived from structure.
  • We chose networks hat are accurately and completely known.
  • What is complexity of finding bi cliques?
  • What is complexity of finding bi cliques?

NetBioSIG2013-KEYNOTE Michael Schroeder NetBioSIG2013-KEYNOTE Michael Schroeder Presentation Transcript

  • PowerGraphs: from network quality to drug repositioning Michael Schroeder TU Dresden
  • Jeong et al. Nature, 20012
  • Comprehension is compressionGregory Chainitin 3
  • How to compress a network? 4
  • Network motifs Hubs in networks (stars) Protein Complexes (cliques) Domain and motif- based interactions (bi-cliques) Royer et al., PLoS Comp. Bio., 20085
  • Power graph algorithm compresses networks Example: SWR1 & INO80 chromatin remodeling complexes Before After Modules in Networks
  • Algorithm • Identify cliques and bi-cliques in networks • Greedy search • Sub-quadratic runtime 7
  • Power nodes are enriched in shared domains 8
  • 9 Power nodes are enriched in shared GO annotation
  • Application: Master regulators in stem cell differentiation 10
  • Network for mesenchymal to neural stem cell conversion Maisel et. al. Experimental Cell Research, 201011
  • Network for mesenchymal to neural stem cell conversion Maisel et. al. Experimental Cell Research, 201012 2010: miR-124 plays a role in neural stem cell conversion
  • 13 ...repressing PTB via miR- 124 is sufficient to induce trans-differentiation of fibroblasts into functional neurons (Cell, 2013)
  • Network compression as quality measure 14
  • Relative compression rate Original Random
  • Validation • Adding noise • Gold standard data sets • Confidence thresholds • Correlation to – co-expression, – co-localisation and – functional annotation
  • Implications? • AP/MS vs. Y2H ? • Experimental set-up ?
  • relativecompressionrate compression rate Edge reduction from 30% to 70% Reduction relative to random up to 50% Royer et. al. 2012, PLoS One
  • relativecompressionrate compression rate Y2H (binary interactions) AP/MS (cooperative effects) Y2H: Two phase pooling AP/MS: His tag + cDNA Royer et. al. 2012, PLoS One
  • Royer et. al. 2012, PLoS One20
  • Complete and accurate networks • Protein interactions are incomplete and noisy • How about complete and accurate networks? 21
  • Complete and accurate networks • Protein interactions are incomplete and noisy • How about complete and accurate networks? – Class hierarchy of Cytoscape, – US Airports, – US corporate ownership, – Characters in Bible, – Power grid, – Internet routers, ... 22
  • Royer et. al. 2012, PLoS One
  • Incomplete bi-cliques • Power Graph are lossless – A-B in G iff A-B in PG • Idea: Accept small violations and – Increase compression by adding new edges – Completing incomplete bi-cliques 24
  • Completing incomplete bi-cliques
  • Algorithm Find all edges e1 and e2 with n2 inside n1 Rank by score: •Ratio total edges after (e3) to edges added (e4) •Weight by ratio e1 to e2 •s = (e3 / e4) x (e1 / e2) e1 e4 e3 e2 n1 n2
  • Drug repositioning 27
  • Drug-Target-Disease Network • 147 promiscuous drugs • 553 targets from PDB • 27 disease • 17 pharmacological actions • Total: – 744 nodes – 1351 edges – avg deg 3.6 28
  • 29
  • 30 Completing bi-cliques Completing bi-cliques increases shared binding sites in power nodes Random addition Disrupting bi-cliques Random rem oval
  • 31
  • 32
  • Daminell, et al. Intr. Bio., 2012 Niacinamide Benzylamine CID1746 Pentamidine Suramin
  • Daminell, et al. Intr. Bio., 2012 Niacinamide Benzylamine CID1746 Pentamidine Suramin ?
  • Daminell, et al. Intr. Bio., 2012 Niacinamide Benzylamine CID1746 Pentamidine Suramin
  • Daminell, et al. Intr. Bio., 2012 Binding sites are similar (SMAP p-value 10-5 – 10-12 )
  • Conclusions • Power graphs find meaningful modules – enriched GO, PFAM, binding sites,... – pinpoint master regulators – can assess network quality • Completing bi-cliques suitable for hypotheses in drug repositioning 37
  • Acknowledgement Jörg Heinrich, Joachim Haupt, Simone Daminelli 38 Former: Matthias Reimann Loic Royer Collaborators: Yixin Zhang, Aliz Emyei, BCUBE Alexander Storch, MedFak Francis Stewart, Biotec Christian Pilarsky, MedFak Robert Grützmann, MedFak Dresden Supercomputer Department Sainitin Donakonda, Zerrin Isik, Janine Roy, Sebastian Salentin, George Tsatsaronis, Maria Kissa, Daniel Eisinger, Jan Mönnich, Alina Petrova
  • Openings: groupleader, postdoc, PhD ms@biotec.tu-dresden.de Michael Schroeder TU Dresden Source pasch-net.de