NetBioSIG2013-Talk Tijana Milenkovic

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Presentation for Network Biology SIG 2013 by Tijana Milenkovic, University of Notre Dame, USA. “What Can Biological Networks Tell Us About Aging?”

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  • 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.
  • NetBioSIG2013-Talk Tijana Milenkovic

    1. 1. Computer Science and Engineering University of Notre Dame What can biological networks tell us about aging? Fazle E. Faisal, Han Zhao, and Tijana Milenković
    2. 2. Why study human aging? • Heart attack • Cancer • Alzheimer’s disease • … tmilenko@nd.edu
    3. 3. Current knowledge about human aging • Human aging - hard to study experimentally – Long lifespan – Ethical constraints • Hence – Sequence-based knowledge transfer from model species – Differential gene expression • I.e., current “ground truth” - computational predictions • But – Not all genes in model species have human orthologs – Plus, genes’ “connectivities” typically ignored tmilenko@nd.edu
    4. 4. • 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 Biological networks tmilenko@nd.edu
    5. 5. So … Predict novel “ground truth” knowledge about human aging from network data tmilenko@nd.edu
    6. 6. Key idea 1 Use network alignment to transfer aging-related knowledge from model species to human tmilenko@nd.edu
    7. 7. Current network-based research of aging • Relies on static network representations • Plus, it relies on primitive measures of topology • Plus, different biological data types capture different functional slices of the cell tmilenko@nd.edu
    8. 8. Key idea 2 Data integration: form dynamic age-specific networks tmilenko@nd.edu Then, measure change of proteins’ network positions with age •Many centrality measures
    9. 9. This work appears in … • T. Milenković, Han Zhao, and F.E. Faisal, “Global Network Alignment In The Context Of Aging”, in Proceedings of ACM-BCB 2013, to appear. • F.E. Faisal and T. Milenković, “Dynamic networks reveal key players in aging”, arXiv:1307.3388 [cs.CE], 2013. Also, under revision. tmilenko@nd.edu
    10. 10. • Map “similar” nodes between different networks in a way that conserves edges Global network alignment in the context of aging tmilenko@nd.edu
    11. 11. • Pairwise (two networks at a time) • Multiple (more than two networks at a time) Global network alignment in the context of aging tmilenko@nd.edu
    12. 12. • Pairwise network alignment Global network alignment in the context of aging tmilenko@nd.edu
    13. 13. • Multiple network alignment Global network alignment in the context of aging tmilenko@nd.edu
    14. 14. Global network alignment in the context of aging • Network alignment: 2-step approach – Node cost function – Alignment strategy • Different methods use both different cost functions and alignment strategies • Hence, unfair method evaluation tmilenko@nd.edu
    15. 15. Global network alignment in the context of aging • Our goal: mix and match node cost functions and alignment strategies of state-of-the-art methods • Fair evaluation • New superior method? • Application to aging tmilenko@nd.edu
    16. 16. Global network alignment in the context of aging • MI-GRAAL (pairwise aligner) • IsoRankN (multiple aligner) • Total of 8 mix-and-match aligners tmilenko@nd.edu
    17. 17. Global network alignment in the context of aging • Align PPI networks of: – Yeast – Fruitfly – Worm – Human • Evaluate: – All 8 aligners according to all measures from MI-GRAAL and IsoRankN papers, and many others • Compare: – Different cost functions under same alignment strategy – Different alignment strategies under same cost function? tmilenko@nd.edu
    18. 18. Global network alignment in the context of aging • Different cost functions under same align. strategy – MI-GRAAL’s cost function is always superior tmilenko@nd.edu
    19. 19. Global network alignment in the context of aging • Different align. strategies under same cost function – N/A: the two strategies are very different tmilenko@nd.edu
    20. 20. Global network alignment in the context of aging • Compare aligners in the context of aging – Predict aging-related genes from “statistically significant” alignments – Measure prediction accuracy • MI-GRAAL’s cost function is again superior • Now, we can compare different alignment strategies under same cost function – Yet, caution! tmilenko@nd.edu
    21. 21. Global network alignment in the context of aging • Different align. strategies under same cost function – MI-GRAAL: higher recall – IsoRankN: higher precision Hence, new superior multiple network aligner!
    22. 22. Dynamic networks reveal key players in aging tmilenko@nd.edu
    23. 23. Dynamic networks reveal key players in aging • Validation of predictions tmilenko@nd.edu
    24. 24. Dynamic networks reveal key players in aging • Relationships between different network centralities tmilenko@nd.edu
    25. 25. Complex Networks (CoNe) Lab
    26. 26. Acknowledgements • NSF CCF-1319469 ($445K) • NSF EAGER CCF-1243295 ($207K) • NIH R01 Supplement 3R01GM074807-07S1 ($248K) • Google Award ($33K) • ISMB/ECCB Posters – O040 – O052 – O072 (O: Systems Biology & Networks) tmilenko@nd.edu

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