July 19, 2013
Nataša Pržulj
Network Topology as a
Source of Biological
Information
Imperial College London
Department of C...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Networks → biological information
2
Networks can model:
 g...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model:
 gene interactions
 protein structu...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model:
 gene interactions
 protein structu...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model:
 gene interactions
 protein structu...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model:
 gene interactions
 protein structu...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:
• Network topology – new source of biological informa...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:
• Network topology – new source of biological informa...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:
• Network topology – new source of biological informa...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:
• Network topology – new source of biological informa...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:
• Network topology – new source of biological informa...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Exa...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Exa...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Exa...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Exa...
5
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
E...
55
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Exa...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Exa...
GRAAL:
267 nodes and 900 edges
Isorank:
116 nodes and 261 edges
MI-GRAAL:
1,858 nodes and 3,467 edges
6
Nataša Pržulj (htt...
6
GRAAL:
267 nodes and 900 edges
Isorank:
116 nodes and 261 edges
MI-GRAAL:
1,858 nodes and 3,467 edges
6
Nataša Pržulj (h...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
7
Networks → biological information
Some current developmen...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8
Networks → biological information
Some current developmen...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8
Networks → biological information
Some current developmen...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8
Networks → biological information
Some current developmen...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8
Networks → biological information
Some current developmen...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
9
Networks → biological information
Some current developmen...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
12
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
12
Networks → biological information
Network 1 Network 2
Di...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
12
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
12
Networks → biological information
Some current developme...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
1) ...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Sof...
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Final Remarks
 Network topology – contains currently hidde...
Acknowledgements
 Funding: ERC Starting Grant, €1.6M (2012-2017)
NSF CDI: $2M (2010 — 2014)
NSF CAREER: $570K (Jan. 2007 ...
Acknowledgements
 Funding: ERC Starting Grant, €1.6M (2012-2017)
NSF CDI: $2M (2010 — 2014)
NSF CAREER: $570K (Jan. 2007 ...
1. W. Hayes, K. Sun, and N. Przulj, Graphlet-based measures are suitable for biological network comparison, Bioinformatics...
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NetBioSIG2013-KEYNOTE Natasa Przulj

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Keynote presentation for Network Biology SIG 2013 by Natasa Przulj, Assoc. Prof. in Computational Network Biology at Imperial College, London, UK

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NetBioSIG2013-KEYNOTE Natasa Przulj

  1. 1. July 19, 2013 Nataša Pržulj Network Topology as a Source of Biological Information Imperial College London Department of Computing
  2. 2. 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  …
  3. 3. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 2 Networks can model:  gene interactions  protein structure  protein-protein interactions  metabolism  … Networks → biological information
  4. 4. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 2 Networks can model:  gene interactions  protein structure  protein-protein interactions  metabolism  … Networks → biological information
  5. 5. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 2 Networks can model:  gene interactions  protein structure  protein-protein interactions  metabolism  … Networks → biological information
  6. 6. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 2 Networks can model:  gene interactions  protein structure  protein-protein interactions  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 Networks → biological information
  7. 7. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 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 Networks → biological information
  8. 8. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 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 Networks → biological information
  9. 9. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 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 Networks → 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.
  10. 10. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 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 Networks → 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.
  11. 11. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Idea: • Network topology – new source of biological information • Based on results (ERC, NSF): topology ↔ biology  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 Networks → 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.
  12. 12. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information Example Methodology: 4 N. Pržulj, Bioinformatics, 23:e117-e183, 2007. Networks → biological information
  13. 13. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information Example Methodology: 4 N. Pržulj, Bioinformatics, 23:e117-e183, 2007. Networks → biological information Graphlet Degree Vector (GDV) of node u: GDV(u) = (u0, u1, u2, …, u72)
  14. 14. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information Example Methodology: 4 Why?  Edge too simplistic  controversies, e.g.: → network structure / models: scale-free? → hub proteins: lethal? → …  Frustration: network analyses useless? N. Pržulj, Bioinformatics, 23:e117-e183, 2007. Networks → biological information Graphlet Degree Vector (GDV) of node u: GDV(u) = (u0, u1, u2, …, u72)
  15. 15. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information Example Methodology → Results: 5 Why?  Edge too simplistic  controversies, e.g.: → network structure / models: scale-free? → hub proteins: lethal? → …  Frustration: network analyses useless? 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) Networks → biological information SMD1 SMB1RPO26
  16. 16. 5 Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information Example Methodology → Results: Why?  Edge too simplistic  controversies, e.g.: → network structure / models: scale-free? → hub proteins: lethal? → …  Frustration: network analyses useless? 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) Networks → biological information
  17. 17. 55 Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information Example Methodology → Results: Why?  Edge too simplistic  controversies, e.g.: → network structure / models: scale-free? → hub proteins: lethal? → …  Frustration: network analyses useless? 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.) Networks → biological information
  18. 18. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information Example Methodology: Network alignment – approximate subnetwork finding 6 Networks → biological information
  19. 19. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information 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?  … 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) Networks → biological information
  20. 20. GRAAL: 267 nodes and 900 edges Isorank: 116 nodes and 261 edges MI-GRAAL: 1,858 nodes and 3,467 edges 6 Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information Example Methodology GRAAL family:→ Network alignment – approximate subnetwork finding 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) Networks → biological information
  21. 21. 6 GRAAL: 267 nodes and 900 edges Isorank: 116 nodes and 261 edges MI-GRAAL: 1,858 nodes and 3,467 edges 6 Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information Example Methodology GRAAL family:→ Network alignment – approximate subnetwork finding 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) Networks → biological information 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
  22. 22. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 7 Networks → biological information 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
  23. 23. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 8 Networks → biological information Some current development: 1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)  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
  24. 24. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 8 Networks → biological information Some current development: 1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)  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  “spine” of the network  functionally separates the cell  topologically separates the cell Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted
  25. 25. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 8 Networks → biological information Some current development: 1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)  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  “core” of the network  25 disease genes:  mostly brain disorders Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted Vuk, Anida: Poster - O065 Poster - O046
  26. 26. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 8 Networks → biological information Some current development: 1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)  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  11 proteins “similar” to 6 GPCRs  Predicted new GPCRs:  e.g., chromosome 20 open reading frame 39 (TMEM90B) Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted Vuk, Anida: Poster - O065 Poster - O046
  27. 27. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 9 Networks → biological information Some current development: 2. Genetic Interaction Maps (with Prof. Charlie Boone, University of Toronto)
  28. 28. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 10 Networks → biological information Some current development: 3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO) Zitnik, Janjic, Larminie, Zupan and Przulj, “Discovering disease associations by fusing systems-level molecular data,” submitted
  29. 29. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 10 Networks → biological information Some current development: 3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)  New method for integration / fusion of molecular network data Currently primitive “projection” methods  Purely descriptive  Provide no conceptual framework for predictions Zitnik, Janjic, Larminie, Zupan and Przulj, “Discovering disease associations by fusing systems-level molecular data,” submitted
  30. 30. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 10 Networks → biological information Some current development: 3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)  New method for integration / fusion of molecular network data 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 Zitnik, Janjic, Larminie, Zupan and Przulj, “Discovering disease associations by fusing systems-level molecular data,” submitted
  31. 31. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 10 Networks → biological information Some current development: 3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)  New method for integration / fusion of molecular network data PPIs Co-expression Cell signalling Genetic inter. Drug-target Gene annotation Gene-disease Metabolic net DO GO Drug inter.
  32. 32. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 10 Networks → biological information Some current development: 3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO) 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.
  33. 33. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 10 Networks → biological information Some current development: 3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO) 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.
  34. 34. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 10 Networks → biological information Some current development: 3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO) Some Results: PPIs Co-expression Cell signalling Genetic inter. Drug-target Gene annotation Gene-disease Metabolic net DO GO Drug inter.
  35. 35. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 10 Networks → biological information Some current development: 3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO) 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
  36. 36. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 11 Networks → biological information Some current development: 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
  37. 37. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 11 Networks → biological information Some current development: 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
  38. 38. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 11 Networks → biological information Some current development: 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
  39. 39. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 11 Networks → biological information Some current development: 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
  40. 40. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 12 Networks → biological information Some current development: 4. New graphlet-based measures: network dynamics, disease classification,... Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted
  41. 41. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 12 Networks → biological information Network 1 Network 2 Distance = 1.675
  42. 42. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 12 Networks → biological information Some current development: 4. New graphlet-based measures: network dynamics, disease classification,... Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted
  43. 43. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 12 Networks → biological information Some current development: 4. New graphlet-based measures: network dynamics, disease classification Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted
  44. 44. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information 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 13 Summary
  45. 45. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 Network topology – new source of biological information Software  Easy to use for biologists  Open source  Parallel  Benefit biologists:  Methods ready to use  Allow benchmarking  To come: web interface 13 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)
  46. 46. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013 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
  47. 47. 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
  48. 48. 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
  49. 49. 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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