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PowerGraphs: from network quality to drug repositioning
Michael Schroeder TU Dresden
Jeong et al. Nature, 20012
Comprehension is compression
Gregory Chainitin
3
How to compress a network?
4
Network motifsHubs in networks
(stars)
Protein Complexes(cliques)
Domain and motif- based interactions
(bi-cliques)
Royer et al., PLoS Comp. Bio., 20085
Power graph algorithm compresses networksExample: SWR1 & INO80 chromatin remodeling complexesBefore After
Modules in Networks
Algorithm
• Identify cliquesand bi-cliques innetworks
• Greedy search
• Sub-quadratic runtime
7
Power nodes are enriched in shared domains
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Power nodes are enriched in shared GO annotation
Application:
Master regulators in stem cell differentiation
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Network for mesenchymal to neural stem cell conversion
Maisel et. al. Experimental Cell Research, 2010 11
Network for mesenchymal to neural stem cell conversion
Maisel et. al. Experimental Cell Research, 2010 12
2010: miR-124 plays a role in neural stem cell conversion
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...repressing PTB via miR-124 is sufficient to induce trans-differentiation of fibroblasts into functional neurons (Cell, 2013)
Network compressionas quality measure
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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 ?
rela
tive c
om
pre
ssio
n
rate
compression rate
Edge reduction from 30% to 70%Reduction relative to random up to 50%
Royer et. al. 2012, PLoS One
rela
tive c
om
pre
ssio
n r
ate
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?
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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, ...
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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
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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
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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
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Completing bi-cliques
Completing bi-cliques increases shared binding sites in power nodes
Random addition
Disrupting bi-cliques
Random removal
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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
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AcknowledgementJörg Heinrich,Joachim Haupt, Simone Daminelli
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Former: Matthias ReimannLoic Royer
Collaborators:Yixin Zhang, Aliz Emyei, BCUBEAlexander Storch, MedFakFrancis Stewart, BiotecChristian Pilarsky, MedFakRobert Grützmann, MedFakDresden Supercomputer Department
Sainitin Donakonda,Zerrin Isik,Janine Roy,Sebastian Salentin,George Tsatsaronis,Maria Kissa,Daniel Eisinger,Jan Mönnich,Alina Petrova