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Incremental Network Querying in Biological Networks

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Presentation on theme: "Incremental Network Querying in Biological Networks"โ€” Presentation transcript:

1 Incremental Network Querying in Biological Networks
Md Mahmudul Hasan and Tamer Kahveci University of Florida

2 Md Mahmudul Hasan and Tamer Kahveci
Network alignment R8 R2 R1 R3 R4 R5 R6 R7 Network-1 Alignment S7 S2 S1 S3 S4 S5 S6 Network-2 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

3 Md Mahmudul Hasan and Tamer Kahveci
Network query Target Query Insert: unmatched target node in alignment e.g. Delete: unmatched query node in alignment AKA indels Local alignment is NP-Complete 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

4 Existing works in a nutshell
Heuristic alignment IsoRANK (Singh et al., 2007) GRAAL (Kuchaiev et al., 2010) ... Approximate alignment QNet (Dost et al., 2008) TOPAC (Gulsoy et al., 2012) ColT (Hasan et al., 2013)... Let n and m be the number of nodes in target and query, respectively. In the worst case, O(nm). Color-coding elegantly reduces it to O(2m nm), but requires many iterations. 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

5 Why Incremental alignment?
QA QB Target, T Existing algorithms: (QB, T) QA QC QB Dynamically evolving query sequence 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

6 Incremental Network Query(INQ)
Target, T ii. Incremental alignment i. Initial alignment Q2 Q1 Target, T 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

7 Md Mahmudul Hasan and Tamer Kahveci
Dynamic programming Match Delete Insert 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

8 Md Mahmudul Hasan and Tamer Kahveci
Experiments Datasets Protein-protein interaction(PPI) network (fly) 7, 481 proteins, 26, 201 interactions Synthetic networks Erdล‘s-Rรฉnyi model Barabรกsi-Albert model Watts-Strogatz model (small world) Gene regulatory networks 297 networks, 46 organisms, 21 signaling pathways ๐œบ = 99% confidence Query generated by random walk 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

9 PPI network MAPK (human) Dynamically evolving query sequence sos1 ras
gap1m raf1 egfr grb2 sos1 ras mek1 gap1m raf1 egfr grb2 sos1 erk ras mek1 gap1m raf1 egfr grb2 Dynamically evolving query sequence 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

10 PPI network MAPK (human) Alignment subnetwork (fly) INQ ColT sos1 erk
ras mek1 gap1m raf1 egfr grb2 C3G Rolled Ras85D Dsor1 gap1m ph1 Tec src Tec src Rgl Ras85D gap1m ph1 Dsor1 Rolled 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

11 B-A (Barabรกsi-Albert)
Synthetic networks B-A (Barabรกsi-Albert) E-R (Erdล‘s-Rรฉnyi) W-S (Watts-Strogatz) Directed Undirected INQ consistently outperforms ColT. For directed query networks of nine nodes, INQ(1) is ~40X - ~50X faster. 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

12 B-A (Barabรกsi-Albert)
Synthetic networks B-A (Barabรกsi-Albert) E-R (Erdล‘s-Rรฉnyi) W-S (Watts-Strogatz) Quality of Score (QoS) = ๐’‚๐’๐’Š๐’ˆ๐’๐’Ž๐’†๐’๐’• ๐’”๐’„๐’๐’“๐’† ๐’ƒ๐’š ๐‘ฐ๐‘ต๐‘ธ ๐’‚๐’๐’Š๐’ˆ๐’๐’Ž๐’†๐’๐’• ๐’”๐’„๐’๐’“๐’† ๐’ƒ๐’š ๐‘ช๐’๐’๐‘ป Highly accurate alignment obtained by INQ for all models. For undirected B-A models, QoS is at least 98% even after 10 edit operations. 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

13 Gene regulatory networks
As query size grows to nine nodes, INQ(1) is ~30 times faster than ColT. QoS remains well above 90% up to nine nodes. 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

14 Md Mahmudul Hasan and Tamer Kahveci
Conclusions INQ is the first study of incremental network alignment. Significant improvement in running time without losing much of the quality of the result. Finds functionally similar networks in different species faster. 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

15 Md Mahmudul Hasan and Tamer Kahveci
Acknowledgements NSF DBI Tamer Kahveci 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

16 Md Mahmudul Hasan and Tamer Kahveci
Thank You ๏Š 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

17 Md Mahmudul Hasan and Tamer Kahveci
12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

18 Md Mahmudul Hasan and Tamer Kahveci
Color Coding T Q m = 4 Find a subnetwork of T matching Q Find a โ€œcolorfulโ€ subnetwork of T matching Q Only O(2m) instead of O(nm) 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

19 Md Mahmudul Hasan and Tamer Kahveci
Color coding details Reduces the complexity significantly. Probability of finding a colorful subnetwork per iteration: (๐’‘= ๐’Ž! ๐’Ž ๐’Ž ) #iterations to reach a confidence ๐œบ log 1โˆ’๐œ€ log 1โˆ’๐‘ Indels do not consume color. For ๐œบ = 99% confidence, m = 7, we need 752 iterations. 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

20 Semi-synthetic networks
B-A (Barabรกsi-Albert), E-R (Erdล‘s-Rรฉnyi), and W-S (Watts-Strogatz) models 12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

21 Gene regulatory networks
12/3/2018 Md Mahmudul Hasan and Tamer Kahveci

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