NetBioSIG2014-Talk by Tijana Milenkovic

Tijana Milenković

Assistant Professor

Computer Science & EngineeringUniversity of Notre Dame

Novel directions for biological network alignment - MAGNA

ISMB posters (O – systems biology and networks):

• O-05• O-08• O-09• O-22

tmilenko@nd.edu

Complex Networks (CoNe) Groupwww.nd.edu/~cone/

Joseph Crawford

YuriyHulovatyy

Fazle Faisal

VikramSaraph

tmilenko@nd.edu

Networks are everywhere!

Complex Networks (CoNe) Group

• Develop new algorithms for network “mining”

• Use the algorithms to study real-world networks– Focus on biological (molecular) networks

tmilenko@nd.edu

Network alignment

Across-species transfer of biological knowledge

tmilenko@nd.edu

• Map “similar” nodes between different networks in a way that conserves edges

Network alignment

tmilenko@nd.edu

• 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-GRAALT. 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, 2014. Also, in ACM-BCB 2013.

• MAGNA– V. Saraph and T. Milenković, “MAGNA: Maximizing Accuracy of Global

Network Alignment”, Bioinformatics, 2014.

Network alignment

tmilenko@nd.edu

Mix-and-match existing methods to improve them

• Network alignment – algorithmic components:1. Node cost function (NCF)2. Alignment strategy (AS)

tmilenko@nd.edu

Mix-and-match existing methods to improve them

• Network alignment – algorithmic components:1. Node cost function (NCF)2. Alignment strategy (AS)

tmilenko@nd.edu

Mix-and-match existing methods to improve them

• Network alignment – algorithmic components:1. Node cost function (NCF)2. Alignment strategy (AS)

tmilenko@nd.edu

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 conclusionsJ. Crawford, Y. Sun, and T. Milenković, “Fair evaluation of global network aligners”,

submitted, 2014.

tmilenko@nd.edu

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!)

tmilenko@nd.edu

• MAGNA:

– 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

MAGNA: Maximizing Accuracy in Global Network Alignment

tmilenko@nd.edu

• 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: http://nd.edu/~cone/MAGNA

MAGNA: Maximizing Accuracy in Global Network Alignment

tmilenko@nd.edu

• MAGNA on synthetic networks

MAGNA: Maximizing Accuracy in Global Network Alignment

tmilenko@nd.edu

MAGNA: Maximizing Accuracy in Global Network Alignment

tmilenko@nd.edu

• MAGNA on real-world (biological) networks

MAGNA: Maximizing Accuracy in Global Network Alignment

tmilenko@nd.edu

• Running time comparison – MAGNA is run on random alignments

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

tmilenko@nd.edu

• 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

Network alignment in aging

tmilenko@nd.edu

Network alignment in aging

So, predict novel “ground truth” knowledge about human aging via network alignment

tmilenko@nd.edu

• 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

Network alignment in aging

tmilenko@nd.edu

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.

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%).

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.

Protein synthesis and folding (with Patricia Clark)

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.

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.

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, 2014. Also, in IEEE Proceedings of Complex Networks, 2013.

Acknowledgements

• NSF CCF-1319469 ($453K)• NSF EAGER CCF-1243295 ($208K)• NIH R01 Supplement 3R01GM074807-07S1

($249K)• Google Faculty Research Award ($33K)

tmilenko@nd.edu

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: 10.1093/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: 10.1093/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: 10.1109/TCBB.2014.2326862, 2014.20. F.E. Faisal, T. Milenković, "Dynamic networks reveal key players in aging", Bioinformatics, DOI: 10.1093/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: 10.1109/SITIS.2013.82. 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):601-607, 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): 2016-2029, 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, 7:1341-1354, 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), 423-437, 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), 13333-13338, 2008.2. T. Milenković & N. Pržulj, “Uncovering Biological Network Function via Graphlet Degree Signatures,” Cancer Informatics, 2008:6 257-

273, 2008 (Highly Visible).1. T. Milenković, J. Lai,N. Pržulj, “GraphCrunch: A Tool for Large Network Analyses,” BMC Bioinformatics, 9:70, 2008 (Highly Accessed).