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. Why study human aging?
• Heart attack
• Cancer
• Alzheimer’s disease
• …
tmilenko@nd.edu
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. • 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. So …
Predict novel “ground truth” knowledge
about human aging from network data
tmilenko@nd.edu
6. Key idea 1
Use network alignment to transfer aging-related
knowledge from model species to human
tmilenko@nd.edu
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. 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. 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. • Map “similar” nodes between different networks
in a way that conserves edges
Global network alignment in the context of aging
tmilenko@nd.edu
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. • Pairwise network alignment
Global network alignment in the context of aging
tmilenko@nd.edu
13. • Multiple network alignment
Global network alignment in the context of aging
tmilenko@nd.edu
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. 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. 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. 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. 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. 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. 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. 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!
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.
