Network Topology as a Source of Biological Information

July 19, 2013
Nataša Pržulj
Network Topology as a
Source of Biological
Information
Imperial College London
Department of Computing

Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Networks → biological information
2
Networks can model:
 gene interactions
 protein structure
 protein-protein interactions
 metabolism
 …

2
Networks can model:
 metabolism
 …

2
Networks can model:
 metabolism
 …
 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
3

Idea:
 Genetic Sequence:
● Revolutionized our understanding of:
 Biology
 Diseases
 Evolution
 Networks:
● Similar ground-breaking impact
3
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.

Idea:
 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)
3
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.

Network topology – new source of biological information
Example Methodology:
4
N. Pržulj, Bioinformatics, 23:e117-e183, 2007.

4
Graphlet Degree Vector (GDV) of node u:
GDV(u) = (u0, u1, u2, …, u72)

4
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:
5
Why?
→ …
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

5
Example Methodology → Results: Why?
→ …
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)

55
Example Methodology → Results: Why?
→ …
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
6

Example Methodology GRAAL family:→
 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?
 …
6
V. Memisevic & N. Przulj, Integrative Biology, doi:10.1039/c2ib00140c, 2012
O. Kuchaiev & N. Pržulj, Bioinformatics, 27(10): 1390-6, 2011
O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes, & N. Pržulj, J. Royal Society Interface, 7:1341-1354, 2010
T. 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:
MI-GRAAL:
1,858 nodes and 3,467 edges
6

6
GRAAL:
Isorank:
MI-GRAAL:
1,858 nodes and 3,467 edges
6
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

7
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

8
 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

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

8
 “core” of the network
 25 disease genes:
 mostly brain disorders
Vuk, Anida:
Poster - O065
Poster - O046

8
 11 proteins “similar” to 6 GPCRs
 Predicted new GPCRs:
 e.g., chromosome 20 open reading
frame 39 (TMEM90B)
Vuk, Anida:
Poster - O065
Poster - O046

9
2. Genetic Interaction Maps (with Prof. Charlie Boone, University of Toronto)

10
Zitnik, Janjic, Larminie, Zupan and Przulj, “Discovering disease associations by fusing systems-level molecular data,” submitted

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 New method for integration / fusion of molecular network data
Currently primitive “projection” methods
 Purely descriptive
 Provide no conceptual framework for predictions

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Based 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-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.
4 Objects: Genes, GO terms, DO terms, Drugs
Constraints: Ѳi
Relation matrices: Rij

10
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.

10
Alg. 1: Data fusion by matrix factorization:
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.

10
Alg. 2: Disease class and association prediction:
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.

10
Some Results:
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.

10
Some Results: DO disease class∩ − DO (pathological analysis and clinical symptoms)
from only molecular data
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.
X

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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

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4. New graphlet-based measures: network dynamics, disease classification,...
Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted

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Network 1 Network 2
Distance = 1.675

12
4. New graphlet-based measures: network dynamics, disease classification
Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted

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
 ...
1) 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 BMC
3400 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
14

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.
15

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
15

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, 2012
3. V. Janic and N. Przulj, Biological function through network topology: a survey of the human diseasome, Briefings in Functional Genomics,
September 8, 2012
4. Arabidopsis Interactome Mapping Consortium, Evidence for Network Evolution in an Arabidopsis Interactome Map, Science, 333:601-607, July
29, 2011
5. T. Milenkovic, V. Memisevic and N. Przulj, Dominating Biological Networks, PLoS ONE, 6(8):e23016, 2011
6. 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.

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