1. Novel directions for biological network alignment - MAGNA
Tijana Milenković
Assistant Professor
Computer Science & Engineering
University of Notre Dame

3. ISMB posters (O – systems biology and networks): O-05 O-08 O-09 O-22

4. Complex Networks (CoNe) Group www.nd.edu/~cone/
Yuriy
Hulovatyy
Joseph
Crawford
Fazle
Faisal
Vikram
Saraph

5. Networks are everywhere!

6. Complex Networks (CoNe) Group
Develop new algorithms for network “mining”
Use the algorithms to study real-world networks
Focus on biological (molecular) networks

7. Network alignment Across-species transfer of biological knowledge

8. Network alignment
Map “similar” nodes between different networks in a way that conserves edges

9. Network alignment IsoRank family (B. Berger, MIT, 2007-2009)
Our methods (2010):
GRAAL
O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes, N. Przulj, "Topological network alignment uncovers biological function and phylogeny", Journal of the Royal Society Interface, 2010.
H-GRAAL
T. Milenkovic, W.L. Ng, W. Hayes, N. Przulj, “Optimal Network Alignment with Graphlet Degree Vectors”, Cancer Informatics, 2010.
MI-GRAAL (N. Przulj, ICL, 2011)
GHOST (C. Kingsford, CMU, 2012) …
Mix-and-match existing methods to improve them
F.E. Faisal, H. Zhao, and T. Milenković, “Global Network Alignment In The Context Of Aging”, IEEE/ACM TCBB, Also, in ACM-BCB 2013.
MAGNA
V. Saraph and T. Milenković, “MAGNA: Maximizing Accuracy of Global Network Alignment”, Bioinformatics, 2014.

10. Mix-and-match existing methods to improve them
Network alignment – algorithmic components:
Node cost function (NCF)
Alignment strategy (AS)

11. Mix-and-match existing methods to improve them
Network alignment – algorithmic components:
Node cost function (NCF)
Alignment strategy (AS)

12. Mix-and-match existing methods to improve them
Network alignment – algorithmic components:
Node cost function (NCF)
Alignment strategy (AS)

13. Mix-and-match existing methods to improve them
Our goal: mix and match node cost functions and alignment strategies of state-of-the-art methods
MI-GRAAL and IsoRankN
Fair evaluation framework
New superior method? YES!
Follow-up study on MI-GRAAL and GHOST
Same conclusions
J. Crawford, Y. Sun, and T. Milenković, “Fair evaluation of global network aligners”, submitted, 2014.

14. MAGNA: Maximizing Accuracy in Global Network Alignment
Existing methods:
Rapidly identify from all possible alignments the “high-scoring” alignments with respect to total NCF
Evaluate alignments with respect to edge conservation
So, align similar nodes between networks hoping to conserve many edges (after the alignment is constructed!)

15. MAGNA: Maximizing Accuracy in Global Network Alignment
Directly optimizes edge conservation while the alignment is constructed
Can optimize any alignment quality measure
E.g., a measure of both node and edge conservation
Outperforms existing state-of-the-art methods
In terms both node and edge conservation
In terms of both topological and biological quality

16. MAGNA: Maximizing Accuracy in Global Network Alignment
Key idea behind MAGNA:
Cross parent alignments into a superior child alignment
Parent alignments:
Alignments of existing methods
Or completely random alignments
Evolve as long as allowed by computational resources
Software:

17. MAGNA: Maximizing Accuracy in Global Network Alignment
MAGNA on synthetic networks

18. MAGNA: Maximizing Accuracy in Global Network Alignment
MAGNA on real-world (biological) networks

19. MAGNA: Maximizing Accuracy in Global Network Alignment
Running time comparison
MAGNA is run on random alignments

20. Network alignment in aging
Current knowledge about human aging
Human aging - hard to study experimentally
Long lifespan
Ethical constraints
Hence, sequence-based knowledge transfer from model species
I.e., current “ground truth” - computational predictions
But
Not all genes in model species have human orthologs (vice versa)
Importantly, genes’ “connectivities” typically ignored

21. Network alignment in aging
But, genes, i.e., their protein products, carry out biological processes by interacting with each other
And this is exactly what biological networks model!
E.g., protein-protein interaction (PPI) networks
Analogous to genomic sequence research, biological network research is expected to impact our biological understanding, since genes, that is their protein products, carry out most biological processes by interacting with other proteins, and this is exactly what biological networks model. Thus, computational prediction of protein function and the role of proteins in disease from PPI networks have received attention in the post-genomic era.

22. Network alignment in aging
So, predict novel “ground truth” knowledge about human aging via network alignment

23. Network alignment in aging
GenAge: ~250 genes (3!)
We predict novel aging-related candidates:
792 genes in human
311, 522, and 544 genes in yeast, fruitfly, and worm
Examples of validation
Significant overlap with independent “ground truth” data
Significantly enriched diseases:
Brain tumor
Prostate cancer
Cancer
Literature validation: 91% of our top scoring predictions

24. Other projects in my group
E.g., dynamic network analysis
F.E. Faisal and T. Milenković, “Dynamic networks reveal key players in aging”, Bioinformatics, 2014.

25. Other projects in my group
E.g., network clustering
R.W. Solava, R.P. Michaels, and T. Milenkovic, “Graphlet-based edge clustering reveals pathogen-interacting proteins”, Bioinformatics, ECCB 2012 (acceptance rate: 14%).

26. Other projects in my group
E.g., network de-noising via link prediction
Y. Hulovatyy, R.W. Solava, and T. Milenkovic, “Revealing missing parts of the interactome via link prediction”, PLOS ONE, 2014.
B. Yoo, H. Chen, F.E. Faisal, and T. Milenkovic, “Improving identification of key players in aging via network de-noising”, ACM-BCB 2014.

27. Protein synthesis and folding (with Patricia Clark)

28. Protein degradation (with Lan Huang)
R. Kaake, T. Milenkovic, N. Przulj, P. Kaiser, and L. Huang, Journal of Proteome Research, 2010.
C. Guerrero, T. Milenkovic, N. Przulj, J. J. Jones, P. Kaiser, L. Huang, PNAS, 2008.

29. Netsense (with Aaron Striegel)
How do individuals interact in the “always-on” environment?
L. Meng, T. Milenković, and A. Striegel, “Systematic Dynamic and Heterogeneous Analysis of Rich Social Network Data,” Complex Networks V, 2014.
L. Meng, Y. Hulovatyy, A. Striegel, and T. Milenković, “On the Interplay Between Individuals' Evolving Interaction Patterns and Traits in Dynamic Multiplex Social Networks”, submitted, 2014.

30. Physiological networks (with Sidney D’Mello)
Y. Hulovatyy, S. D’Mello, R. Calvo, T. Milenković, “Network Analysis Improves Interpretation of Affective Physiological Data,” Journal of Complex Networks, Also, in IEEE Proceedings of Complex Networks, 2013.

31. Acknowledgements NSF CCF-1319469 ($453K) NSF EAGER CCF-1243295 ($208K)
NIH R01 Supplement 3R01GM S1 ($249K)
Google Faculty Research Award ($33K)

32. 25. B. Yoo, H. Chen, F.E. Faisal, T. Milenković, "Improving identification of key players in aging via network de-noising", ACM-BCB 2014.
24. L. Meng, Y. Hulovatyy, A. Striegel, T. Milenković, "On the Interplay Between Individuals' Evolving Interaction Patterns and Traits in Dynamic Multiplex Social Networks", submitted, 2014.
23. V. Saraph, T. Milenković, "MAGNA: Maximizing Accuracy in Global Network Alignment", Bioinformatics,
DOI: /bioinformatics/btu409, 2014.
22. Y. Hulovatyy, S. D'Mello, R.A. Calvo, T. Milenković, "Network Analysis Improves Interpretation of Affective Physiological Data", Journal of Complex Networks, DOI: /comnet/cnu032, 2014.
21. F.E. Faisal, H. Zhao, T. Milenković, "Global Network Alignment In The Context Of Aging", IEEE/ACM Transactions on Computational Biology and Bioinformatics, DOI: /TCBB , 2014.
20. F.E. Faisal, T. Milenković, "Dynamic networks reveal key players in aging", Bioinformatics, DOI: /bioinformatics/btu089, 2014.
19. L. Meng, T. Milenković, A. Striegel, "Systematic Dynamic and Heterogeneous Analysis of Rich Social Network Data", In Proceedings of Complex Networks V, 2014 (acceptance rate: 25%).
18. A.K. Rider, T. Milenković, G.H. Siwo, R.S. Pinapati, S.J. Emrich, M.T. Ferdig, N.V. Chawla, "Networks’ Characteristics Matter for Systems Biology," Network Science, accepted, to appear, 2014.
17. Y. Hulovatyy, R.W. Solava, T. Milenković, “Revealing missing parts of the interactome via link prediction”, PLOS ONE, 9(3), 2014.
16. Y. Hulovatyy, S. D'Mello, R.A. Calvo, T. Milenković, “Network Analysis Improves Interpretation of Affective Physiological Data”, In Proceedings of Workshop on Complex Networks and their Applications at SITIS 2013, DOI: /SITIS
15. T. Milenković, H. Zhao, and F.E. Faisal (2013), “Global Network Alignment In The Context Of Aging”, In Proceedings of ACM-BCB 2013 (acceptance rate: 28%).
14. R. Solava, R. Michaels, T. Milenković, “Graphlet-based edge clustering reveals pathogen-interacting genes,” In Proceedings of ECCB 2012, Bioinformatics, 28 (18): i480-i486, 2012.
13. T. Milenković, V. Memišević, A. Bonato, N. Pržulj, “Dominating biological networks,” PLOS ONE, 6(8), 2011.
12. Arabidopsis Interactome Mapping Consortium, "Evidence for Network Evolution in an Arabidopsis Interactome Map," Science, 333(6042): , 2011.
11. T. Milenković, W.L. Ng, W. Hayes, N. Pržulj, “Optimal network alignment with graphlet degree vectors,” Cancer Informatics, 9, 2010.
10. R. Kaake, T. Milenković, N. Pržulj, P. Kaiser, L. Huang, “Characterization of cell cycle specific protein interaction networks of the yeast 26S proteasome complex by the QTAX strategy,” Journal of Proteome Research, 9(4): , 2010.
9. H. Ho, T. Milenković, V. Memišević, J. Aruri, N. Pržulj, A.K. Ganesan, “Protein Interaction Network Topology Uncovers Melanogenesis Regulatory Network Components Within Functional Genomics Datasets,” BMC Systems Biology, 4:84, 2010 (Highly Accessed).
8. V. Memišević, T. Milenković, N. Pržulj,“Complementarity of network and sequence structure in homologous proteins,” Journal of Integrative Bioinformatics, 7(3):135, 2010.
7. Memišević, T. Milenković, N. Pržulj, “An integrative approach to modeling biological networks,” Journal of Integrative Bioinformatics, 7(3):135, 2010.
6. O. Kuchaiev, T. Milenković, V. Memišević, W. Hayes, N. Pržulj, “Topological network alignment uncovers biological function and phylogeny,” Journal of the Royal Society Interface, 7: , 2010.
5. T. Milenković, V. Memišević, A.K. Ganesan, N. Pržulj, “Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data,” Journal of the Royal Society Interface, 7(44), , 2010.
4. T. Milenković, I. Filippis, M. Lappe, N. Pržulj, “Optimized Null Model of Protein Structure Networks,” PLOS ONE, 4(6): e5967, 2009.
3. C. Guerrero, T. Milenković , N. Pržulj, P. Kaiser, L. Huang, “Characterization of the proteasome interaction network using a QTAX-based tag-team strategy and protein interaction network analysis,” PNAS, 105(36), , 2008.
2. T. Milenković & N. Pržulj, “Uncovering Biological Network Function via Graphlet Degree Signatures,” Cancer Informatics, 2008: , 2008 (Highly Visible).
1. T. Milenković, J. Lai,N. Pržulj, “GraphCrunch: A Tool for Large Network Analyses,” BMC Bioinformatics, 9:70, 2008 (Highly Accessed).