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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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Computer Science and EngineeringUniversity of Notre Dame
What can biological networks tell us about aging?
Fazle E. Faisal, Han Zhao, and Tijana Milenković
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
• 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
Key idea 1
Use network alignment to transfer aging-related knowledge from model species to human
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
Key idea 2
Data integration: form dynamic age-specific networks
Then, measure change of proteins’ network positions with age•Many centrality measures
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.
• Map “similar” nodes between different networks in a way that conserves edges
Global network alignment in the context of aging
• Pairwise (two networks at a time)• Multiple (more than two networks at a time)
Global network alignment in the context of aging
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
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
Global network alignment in the context of aging
• MI-GRAAL (pairwise aligner)• IsoRankN (multiple aligner)• Total of 8 mix-and-match aligners
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
Global network alignment in the context of aging
• Different cost functions under same align. strategy– MI-GRAAL’s cost function is always superior
Global network alignment in the context of aging
• Different align. strategies under same cost function– N/A: the two strategies are very different
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!
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!
Dynamic networks reveal key players in aging
• Relationships between different network centralities
Complex Networks (CoNe) Lab
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)[email protected]
