NetBioSIG2013-KEYNOTE Michael Schroeder

PowerGraphs: from network quality to drug repositioning

Michael Schroeder TU Dresden

Jeong et al. Nature, 20012

Comprehension is compression

Gregory Chainitin

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How to compress a network?

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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

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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 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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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

Daminell, et al. Intr. Bio., 2012

Niacinamide Benzylamine CID1746 Pentamidine Suramin


Niacinamide Benzylamine CID1746 Pentamidine Suramin

?


Niacinamide

Benzylamine

CID1746

Pentamidine

Suramin


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

Openings: groupleader, postdoc, [email protected]

Michael Schroeder TU DresdenSource pasch-net.de

NetBioSIG2013-KEYNOTE Michael Schroeder

Health & Medicine

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