NetBioSIG2013-KEYNOTE Natasa Przulj

July 19, 2013

Nataša Pržulj

Network Topology as a Source of Biological

Information

Imperial College LondonDepartment of Computing

Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013

Networks → biological information

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Networks can model: gene interactions protein structure protein-protein interactions metabolism …


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Turning point in biology and bioinformatics

Advances in experimental biology data Interesting & important problems to CS Computational advances contribute:

Biological understanding (disease, pathogens, aging)

Therapeutics healthcare benefits (e.g., GSK)

Booming research area



Idea:• Network topology – new source of biological information• Based on results (ERC, NSF): topology ↔ biology

Genetic Sequence:● Revolutionized our understanding of:

Biology Diseases Evolution

Networks:● Similar ground-breaking impact

3Networks → biological information













100% sequence identity 65% network wiring similarity

Degrees 54 and 9

V. Memisevic, T. Milenkovic and N. Przulj, J. Integrative Bioinformatics, 7(3):135,2010.








Degrees 54 and 9




Need tools to mine networks

Why?● Analysing sequences is “computationally easy” (polynomial

time)● Analysing networks (i.e., graphs) is “computationally hard”

E.g., Is X sub-network of Y? ̶ Computationally intractable Cannot exactly compare / align biological networks

heuristics (approximate solutions)



Degrees 54 and 9



Network topology – new source of biological information

Example Methodology:

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N. Pržulj, Bioinformatics, 23:e117-e183, 2007.





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Graphlet Degree Vector (GDV) of node u:

GDV(u) = (u0, u1, u2, …, u72)




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Why? Edge too simplistic controversies, e.g.:

→ network structure / models: scale-free?→ hub proteins: lethal?→ …

Frustration: network analyses useless?



Graphlet Degree Vector (GDV) of node u:

GDV(u) = (u0, u1, u2, …, u72)



Example Methodology → Results:

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Why? Edge too simplistic controversies, e.g.:



90% similar topology ↔ significantly enriched:

→ Biological function→ Protein complexes→ Sub-cellular localization→ Tissue expression→ Disease

1. T. Milenković & N. Pržulj, Cancer Informatics, 4:257-273, 2008. (Highly visible)


SMD1

SMB1RPO26

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Example Methodology → Results:Why? Edge too simplistic controversies, e.g.:



Cancer research:

→ Find new members of melanin production pathways: phenotypically validated (siRNA)

→ Same cancer type - more similar topology in PPI net

→ Could not have been identified by existing approaches

2. T. Milenković, V. Memisević, A. K. Ganesan, and N. Pržulj, J. Roy. Soc. Interface, 7(44):423-437, 2010. 3. H. Ho, T. Milenković, V. Memisević, J. Aruri, N. Pržulj, and A. K. Ganesan, BMC Systems Biology, 4:84, 2010. (Highly accessed)


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Example Methodology → Results:Why? Edge too simplistic controversies, e.g.:



Find new members of yeast proteosome PPI network

4. C. Guerrero, T. Milenkovic, N. Przulj, P. Keiser, L. Huang, “Characterization of the proteasome interaction network using a QTAX-based tag-team strategy and protein interaction network analysis,” PNAS, 105 (36), pg. 13333-13338 2008.)





Network alignment – approximate subnetwork finding

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Example Methodology → GRAAL family:

Network alignment – approximate subnetwork finding Why?

Analogous to sequence alignment Predict function, disease − by knowledge transfer Evolution − global similarity between networks of different species

Problems: Noise in the data → all methods must be robust to noise Computational intractability → computational problems:

Node similarity function? Alignment search algorithm? How to measure “goodness” of an inexact fit between networks?

…

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V. Memisevic & N. Przulj, Integrative Biology, doi:10.1039/c2ib00140c, 2012O. Kuchaiev & N. Pržulj, Bioinformatics, 27(10): 1390-6, 2011O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes, & N. Pržulj, J. Royal Society Interface, 7:1341-

1354, 2010T. Milenkovic, W.L. Wong, W. Hayes, & N. Pržulj, Cancer Informatics, 9:121-37, June 30, 2010 (Highly

visible)


GRAAL: 267 nodes and 900 edges

Isorank: 116 nodes and 261 edges

MI-GRAAL: 1,858 nodes and 3,467 edges

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visible)


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GRAAL: 267 nodes and 900 edges

Isorank: 116 nodes and 261 edges

MI-GRAAL: 1,858 nodes and 3,467 edges

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visible)


R. Patro and C. Kingsford. Global network alignment using multiscale spectral signatures. Bioinformatics 28(23):3105-3114 (2012).

Dr. Noel Malod-Dognin, GrAlign + Poster - L103



Some current development:

1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)

2. Genetic Interaction Maps (with Prof. Charlie Boone, University of Toronto)

3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)

4. New graphlet-based measures: disease associations and network dynamics





Robert Lefkowitz and Brian Kobilka – 2012 Nobel Prize in Chemistry

Stagljar lab: new network of 50 human GPCRs and their interactors Analysis of it in the context of the entire human PPI network Analysis of this new network Predictions of new GPCRs

Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted







“spine” of the network functionally separates the cell topologically separates the

cell








“core” of the network 25 disease genes: mostly brain disorders


Vuk, Anida: Poster - O065Poster - O046







11 proteins “similar” to 6 GPCRs Predicted new GPCRs: e.g., chromosome 20 open

reading frame 39 (TMEM90B)


Vuk, Anida: Poster - O065Poster - O046




2. Genetic Interaction Maps (with Prof. Charlie Boone, University of Toronto)





Zitnik, Janjic, Larminie, Zupan and Przulj, “Discovering disease associations by fusing systems-level molecular data,” submitted





New method for integration / fusion of molecular network data

Currently primitive “projection” methods Purely descriptive Provide no conceptual framework for predictions






New method for integration / fusion of molecular network dataBased on: matrix representation of the data their fusion by:

simultaneous matrix factorization and mining of the resulting decomposition

PPIs

Co-expression

Cell signalling

Genetic inter.

Drug-targetGene annotation

Gene-disease

Metabolic netDO

GODrug inter.

4 Objects: Genes, GO terms, DO terms, Drugs

Constraints: Ѳi

Relation matrices: Rij






New method for integration / fusion of molecular network dataPPIs

Co-expression

Cell signalling

Genetic inter.


Gene-disease

Metabolic netDO

GODrug inter.





Alg. 1: Data fusion by matrix factorization:PPIs

Co-expression

Cell signalling

Genetic inter.


Gene-disease

Metabolic netDO

GODrug inter.





Alg. 2: Disease class and association prediction:PPIs

Co-expression

Cell signalling

Genetic inter.


Gene-disease

Metabolic netDO

GODrug inter.





Some Results:PPIs

Co-expression

Cell signalling

Genetic inter.


Gene-disease

Metabolic netDO

GODrug inter.





Some Results: DO ∩ disease class − DO (pathological analysis and clinical symptoms)

from only molecular data

PPIs

Co-expression

Cell signalling

Genetic inter.


Gene-disease

Metabolic netDO

GODrug inter.

X




4. New graphlet-based measures: suitable for biological network analysis

Hayes, Sun, and Przulj, “Graphlet-based measures are suitable for biological network comparison,” Bioinformatics, 29:4, pp 483-91, 2013

Poster - O025






Poster - O025






Poster - O025






Poster - O025




4. New graphlet-based measures: network dynamics, disease classification,...

Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted



Network 1 Network 2

Distance = 1.675




4. New graphlet-based measures: network dynamics, disease classification,...





4. New graphlet-based measures: network dynamics, disease classification




1) New network analysis methods to mine complex network data Network alignment Cell’s functional organization Network integration/fusion of various network types Graphlet-based network encoding for dynamics ...

2) High-performance software package

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Summary



Software

Easy to use for biologists Open source Parallel

Benefit biologists: Methods ready to use

Allow benchmarking To come: web interface

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GraphCrunch 2: second most accessed in BMC3400 downloads since Feb’11

Software

O. Kuchaiev, A. Stefanovic, W. Hayes, and N. Przulj, GraphCrunch 2: Software tool for network modeling, alignment and clustering, BMC Bioinformatics, 12(24):1-13, 2011 (highly accessed)T. Milenkovic, J. Lai, and N. Przulj, GraphCrunch: A Tool for Large Network Analyses, BMC Bioinformatics, 9:70, January 30, 2008 (highly accessed)


Final Remarks

Network topology – contains currently hidden biological information

Need new computational tools to mine network data biology

In close collaboration with biologists “Network biology:”

• In its infancy & rich in open research problems• Many unforeseen problems will emerge• Good area to be in

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Acknowledgements

Funding: ERC Starting Grant, €1.6M (2012-2017)NSF CDI: $2M (2010 — 2014)NSF CAREER: $570K (Jan. 2007 — Dec.

2011) GlaxoSmithKline: £80K (2010-2014)

Alumni:1. Tijana Milenković, Ph.D.

Assistant Prof., U. of Notre Dame

2. Oleksii Kuchaiev, Ph.D.Microsoft, Redmond

3. Vesna Memišević, Ph.D.US Army, Bioinformatics Res.

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Acknowledgements

Funding: ERC Starting Grant, €1.6M (2012-2017)NSF CDI: $2M (2010 — 2014)NSF CAREER: $570K (Jan. 2007 — Dec.

2011) GlaxoSmithKline: £80K (2010-2014)

Post-docs:Noel Malod-Dognin

PhD students: Omer Yaveroglu, Kai Sun, Vuk Janjic, Anida Sarajlic

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1. W. Hayes, K. Sun, and N. Przulj, Graphlet-based measures are suitable for biological network comparison, Bioinformatics, 2013

2. V. Janic and N. Przulj, The Core Diseasome, Molecular BioSystems, 8:2614-2625, July 4, 20123. V. Janic and N. Przulj, Biological function through network topology: a survey of the human diseasome, Briefings in

Functional Genomics, September 8, 20124. Arabidopsis Interactome Mapping Consortium, Evidence for Network Evolution in an Arabidopsis Interactome Map,

Science, 333:601-607, July 29, 20115. T. Milenkovic, V. Memisevic and N. Przulj, Dominating Biological Networks, PLoS ONE, 6(8):e23016, 20116. N. Pržulj, “Protein-protein interactions: making sense of networks via graph-theoretic modeling,” Bioessays, 33(2), 2011.7. O. Kuchaiev and N. Przulj, “Integrative Network Alignment Reveals Large Regions of Global Network Similarity in Yeast and Human”,

Bioinformatics, 27(10): 1390-1396 , 2011.8. O. Kuchaiev, A. Stevanovic, W. Hayes and N. Przulj, “GraphCrunch 2: software tool for network modeling, alignment and clustering”,

BMC Bioinformatics, 12(24):1-13, 2011. Highly accessed.9. T. Milenkovic, W. L. Ng, W. Hayes and N. Przulj, “Optimal Network Alignment Using Graphlet Degree Vectors”, Cancer Informatics,

9:121-137, 2010. Highly visible.10. O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes and N. Przulj, “Topological Network Alignment Uncovers Biological Function and

Phylogeny”, J. Roy Soc. Interface, 7:1341–1354, 2010.11. N. Przulj, O. Kuchaiev, A. Stevanovic, and W. Hayes “Geometric Evolutionary Dynamics of Protein Interaction Network”, Pacific

Symposium on Biocomputing (PSB’10), Hawaii, USA, 2010.12. T. Milenkovic, V. Memisevic, A. K. Ganesan, and N. Przulj, “Systems-level Cancer Gene Identification from Protein Interaction Network

Topology Applied to Melanogenesis-related Interaction Networks”, J. Roy. Soc. Interface, 2009.13. O. Kuchaiev, M. Rasajski, D. Higham, and N. Przulj, “Geometric De-noising of Protein-Protein Interaction Networks”, PLoS

Computational Biology 5(8), e1000454, 2009.14. C. Guerrero, T. Milenkovic, N. Przulj, P. Keiser, L. Huang, “Characterization of the proteasome interaction network using a QTAX-

based tag-team strategy and protein interaction network analysis,” PNAS, 105 (36), pg. 13333-13338 2008.15. T. Milenkovic and N. Przulj, “Uncovering Biological Network Function via Graphlet Degree Signatures,” Cancer Informatics, vol. 4, pg.

257-273, 2008. Highly visible.16. T. Milenkovic, J. Lai, N. Przulj, “GraphCrunch: A Tool for Large Network Analyses,” BMC Bioinform., 9:70, 2008. Highly accessed.17. F. Hormozdiari, P. Berenbrink, N. Przulj, C. Sahinalp, “Not all Scale Free Networks are Born Equal: the Role of the Seed Graph in PPI

Network Emulation,” PLoS Computational Biology, 3(7), 2007.18. N. Przulj, “Geometric Local Structure in Biological Networks,” IEEE ITW’07 Invited Paper, 2007.19. N. Przulj, “Biological Network Comparison Using Graphlet Degree Distribution,” Bioinformatics proc. of ECCB’06,23:e177-e183, 2007.20. N. Przulj and D. Higham, “Modelling Protein-Protein Interaction Networks via a Stickiness Index,” J Roy Soc Interf, 3(10):711-6,2006. 21. N. Przulj, D. G. Corneil, and I. Jurisica, “Efficient Estimation of Graphlet Frequency Distributions in Protein-Protein Interaction

Networks,” Bioinformatics, vol. 22, num. 8, pg 974-980, 2006.22. M. Barrios-Rodiles, K. R. Brown, B. Ozdamar, Z. Liu, R. S. Donovan, F. Shinjo, Y. Liu, R. Bose, J. Dembowy, I. W. Taylor, V. Luga, N.

Przulj, M. Robinson, H. Suzuki, Y. Hayashizaki, I. Jurisica, and J. L. Wrana, “High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells,” Science, vol. 307, num. 5715, pg. 1621-1625, 2005.

23. N. Przulj, D. G. Corneil, and I. Jurisica, “Modeling Interactome: Scale-Free or Geometric?,” Bioinformatics, 20(18):3508-3515, 2004.24. N. Przulj, D. Wigle, and I. Jurisica, “Functional Topology in a Network of Protein Interactions,” Bioinformatics, 20(3):340-348, 2004.

Health & Medicine

NetBioSIG2013-KEYNOTE Natasa Przulj