netbiosig2013-talk tijana milenkovic

Computer Science and EngineeringUniversity of Notre Dame

What can biological networks tell us about aging?

Fazle E. Faisal, Han Zhao, and Tijana Milenković

Why study human aging?

• Heart attack• Cancer• Alzheimer’s disease• …

tmilenko@nd.edu

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

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• 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

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So …

Predict novel “ground truth” knowledge about human aging from network data

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Key idea 1

Use network alignment to transfer aging-related knowledge from model species to human

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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

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Key idea 2

Data integration: form dynamic age-specific networks

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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.

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

Global network alignment in the context of aging

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• Pairwise (two networks at a time)• Multiple (more than two networks at a time)

Global network alignment in the context of aging

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• Pairwise network alignment

Global network alignment in the context of aging

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• Multiple network alignment

Global network alignment in the context of aging

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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

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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

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Global network alignment in the context of aging

• MI-GRAAL (pairwise aligner)• IsoRankN (multiple aligner)• Total of 8 mix-and-match aligners

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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

Global network alignment in the context of aging

• Different cost functions under same align. strategy– MI-GRAAL’s cost function is always superior

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Global network alignment in the context of aging

• Different align. strategies under same cost function– N/A: the two strategies are very different

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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!

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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

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Dynamic networks reveal key players in aging

• Validation of predictions

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Dynamic networks reveal key players in aging

• Relationships between different network centralities

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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)tmilenko@nd.edu