1. Large Scale Metabolic Network Alignments by Compression
Michael Dang, Ferhat Ay, Tamer Kahveci
ACM-BCB 2011
Bioinformatics Lab.
University of Florida

2. Network Alignment
Bayati et al. ICDM 2009
Ferhat Ay
11/16/2018

3. Metabolic Network Alignment
Alignment with
Heterogeneous Entities
Network Alignment
Querying Network
databases
Subnetwork
Mappings
Functional Similarity
of Reactions
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4. Existing Work
Heymans et al. (2003) – Undirected, Hierarchical Enzyme Similarity
Pinter et al. (2005) – Directed, Only Multi-Source Trees
Singh et al. (2007) – PPI Networks, Sequence Similarity
Dost et al. (2007) – QNET, Color Coding, Tree queries of size at most 9
Kuchaiev et al. (2010) – GRAAL, Solely Based on Network Topology
Ay et al. (2011) – SubMAP, Considers Subnetwork Mappings
Shih et al. – Next Talk!
Clustering of input networks is necessary for aligning large networks
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5. Alignment Phase - SubMAP
Ay et. al., RECOMB 2010, JCB 2011
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6. Performance Bottleneck
2 Gigabytes
30 minutes
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7. Alignment with Compression
Refine
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8. Alignment with/without compression
With Compression
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9. Outline of the method Compression Phase
Minimum Degree Selection (MDS) method
Optimality analysis
Alignment Phase
Refinement Phase
Overall Complexity
How Much Should We Compress?
Experimental Results
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10. Compression Phase Original Network Compressed Network Encapsulated
View
What Alignment
Algorithm Sees
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11. MDS – Minimum Degree Selection
After
After
Before
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12. Overall Compression Step 1 Step 2 Step i Level 1 ………… Level 2 …………
Level c
…….…
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13. Optimality Condition for MDS
Minimum Degree
Node
Optimal?
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14. Optimality Condition for MDS
- Can be optimal
- At most one node away
Minimum Degree
Node
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15. How Far Away We Are from Optimal Compression?
Number of compression steps for the optimal compression and MDS
Sizes of the compressed networks for the optimal compression and MDS
By the inequality
“How far is our compression
method from the optimal compression?”
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16. Alignment Phase Alignment Network 1 Network 2 Compressed Network 1
Network Alignment
Algorithm
Alignment
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17. Refinement Phase Refine Alignment Algorithm Refined Alignment
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18. Complexity Analysis Compression Phase: Alignment Phase:
Refinement Phase:
Overall Complexity
(with compression):
Complexity of SubMAP
(without compression):
k = largest subnetwork size
c = compression level
n, m = sizes of networks
Ferhat Ay
11/16/2018

19. How much should we compress?
Examples
n=20, m=20, k=2  c ~ 1.37
n=20, m=80, k=1  c ~ 2.11
n=80, m=80, k=2  c ~ 2.15
n=200, m=400, k=1  c ~ 3.11
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20. Experimental Results
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21. To compress or not to compress?
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22. Compression Rates in Practice
KEGG Metabolic Networks with sizes ranging from 10 to 279
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23. What Do We Gain by Compression?
Subnetwork size k=2
Subnetwork size k=1
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24. What Do We Lose by Compression?
k/c 1 2 3
0.89
0.56
0.53
0.85
0.58
0.50
0.84
0.57
0.49
Correlation of mappings scores found by compressed
alignment with the ones found by SubMAP
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25. Conclusions
We developed a scalable compression technique with optimality bounds.
Our method respects network topology while aligning the networks unlike clustering-based methods.
It provides significant improvement on resource utilization of existing network alignment algorithms.
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26. Future Directions
Improving the scale of alignment to genome-wide metabolic networks (without initial clustering).
Evaluating the performance of our compression technique on PPI networks.
Improving the accuracy of compressed alignment w.r.t original alignment for larger levels of compression.
Integrating our compression framework with other existing network alignment methods.
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11/16/2018

27. Acknowledements Michael Dang NSF IIS-0845439 NSF CCF-0829867
Tamer Kahveci
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28. A bit of advertisement Computing Innovation Fellow
University of Washington
Department of Genome Sciences

29. THANK YOU.
QUESTIONS?
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30. APPENDIX
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31. Ferhat Ay
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32. Ferhat Ay
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