Introduction to Biological Network Analysis IV: Network Alignment and Querying

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" summary

Argues that conserved subnetworks across species expose functional modules and improves specificity of interaction predictions, demonstrating tools like PATHBLAST and probabilistic NetworkBLAST plus evolutionary scoring (MAWISH). Shows an implicit alignment graph algorithm that avoids n^k blowup and runs roughly M·2^k, and reports experimental validation rates around 40–50% validation for cross-species predictions. For querying, implements dynamic programming with color-coding and ILP to find connected or topology-resilient matches, trading sensitivity for reliable, testable predictions.

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" chapters (26)

1. 0:00Intro
2. 0:18Multiple Species PPI Data
3. 1:30Being Comparative
4. 2:29Main challenges
5. 3:55Problem definition
6. 6:29PathBLAST (Kelley et al.'03)
7. 12:17A Word on PPI Evolution
8. 13:36Scoring (MaWish)
9. 16:23Score improvement (cont.)
10. 17:403-way comparison?
11. 17:59Generalizing Network Alignment
12. 22:32Interaction Prediction
13. 24:27Experimental Validation
14. 24:57The Scalability Problem
15. 26:24Scaling Up Network Alignment
16. 29:44Isomorphic Alignment
17. 30:22Homeomorphic Alignment
18. 30:48Score of Alignment
19. 32:26Complexity
20. 33:49DP-Based Approach
21. 34:28Cross-Species Comparison of Signaling Pathways
22. 35:31Ideas can be generalized to tree queries and beyond (QNet)
23. 38:03TORQUE: Topology-free querying
24. 38:23Algorithmic idea
25. 39:43Comparison with QNet
26. 41:21Summary & the road ahead...

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