CIS 4930/6930 – Recent Advances in Bioinformatics Spring 2014

1. CIS 4930/6930 – Recent Advances in BioinformaticsSpring 2014 Network construction from RNAi data Tamer Kahveci

2. Signaling Networks MAPK network

3. Signal reachability Luciferase Reporter Receptor

4. Signaling and RNA Interference Luciferase X Not critical X Reporter Receptor Critical

5. Signaling Network Reconstruction from RNAi data Reporter Receptor Not critical Critical

6. RNAi data and Reference Network Reference network Insert Reporter Receptor Not consistent ! Consistent ! Delete Not critical Critical

7. Overview Goal: Minimize the number of edit operations to make the reference consistent. NP-Complete ! Reference network GR = (VR, ER) Target network Find GT = (VT, ET) Given Constraints 1 0 0 1 0 1 0 SiNeC (Signal Network Constructor) S-SiNeC (Scalable Signal Network Constructor)

8. SiNeC algorithm Three steps • Order the critical genes left to right based on the topology of GR. [Sloan, 1986] • v1, v2, …, vc • Edge deletion phase • Edge insertion phase

9. Step 1: Order critical genes 2 3 Receptor Reporter 1 Prioritize based on distance to the reporter + degree

10. Step 2: Edge deletion Purpose: Eliminate detours around critical genes Bypassed !!! Receptor Reporter vk vi vj • Find all (undesirable) paths between non-consecutive critical genes. • i.e., Paths which go through only noncritical genes • Edges are weighted with the number of such paths they belong to. • Remove greedily starting from the largest weight until al paths are disrupted.

11. Step 3: Edge insertion Purpose: Make sure that critical are connected + noncritical genes are consistent Receptor Reporter vi+1 vi-1 vi • Insert an edge from vi-1 to vi if • There is no path from vi-1 to vi. • There is a noncritical gene on all paths from vi-1 to vi.

12. Overview Reference network GR = (VR, ER) Target network Find GT = (VT, ET) Given Constraints Finding all the paths can be too time consuming for large networks 1 0 0 1 0 1 0 SiNeC (Signal Network Constructor) S-SiNeC (Scalable Signal Network Constructor)

13. S-SiNeCalgorithm Reference network Right reachable Left reachable vs vt Critical vi Edge insertion Edge deletion

14. S-SiNeC: Edge insertion (A1) Purpose: Make sure that noncriticalgenes are consistent Reference network vs vt vi L R

15. S-SiNeC: Edge insertion (A2) Purpose: Make sure that criticalgenes are left reachable Reference network vs vt vi R L

16. S-SiNeC: Edge insertion (A3) Purpose: Make sure that criticalgenes are right reachable Reference network vs vt vi R L

17. S-SiNeC: Edge insertion

18. S-SiNeC: Edge deletion (A4) Purpose: Make sure that no detours exist around critical genes Solve minimum cut between L & R Reference network vs vt vi R L

19. Dataset • Reference networks are obtained by random edge shuffling at 5% to 40% mutation rates. • 200 references per target network & per mutation rate.

20. Average distance to the true network

21. Accuracy based on edge class Hot vs vt Cold

22. Running time results SiNeC > 1 hour per reference network.

23. Success rate on constraints

24. Accuracy

25. Functional Enrichment of the Pathway

26. Last Remarks • Constructing very large signaling networks from RNAi data is possible in practical running time. • Both SiNeC and S-SiNeC are robust to errors in reference network. • We recommend • S-SiNeC for very large OR dense networks. • SiNeC otherwise.

27. Acknowledgements CCF - 0829867 IIS - 0845439 260429