Reverse engineering of regulatory networks

1. Reverse engineering of regulatory networks Dirk Husmeier & Adriano Werhli

2. Systems biology Learning signalling pathways and regulatory networks from postgenomic data

3. Reverse Engineering of Regulatory Networks • Can we learn the network structure from postgenomic data themselves? • Statistical methods to distinguish between • Direct correlations • Indirect correlations • Challenge: Distinguish between • Correlations • Causal interactions • Breaking symmetries with active interventions: • Gene knockouts (VIGs, RNAi)

8. Shrinkage estimation and the lemma of Ledoit-Wolf

9. Shrinkage estimation and the lemma of Ledoit-Wolf

10. Shrinkage estimation and the lemma of Ledoit-Wolf

11. Shrinkage estimation and the lemma of Ledoit-Wolf

12. Shrinkage estimation and the lemma of Ledoit-Wolf

13. Bayesian networks versus Graphical Gaussian models Directed versus undirected graphs Score based versus constrained based inference

14. Evaluation • On real experimental data, using the gold standard network from the literature • On synthetic data simulated from the gold-standard network

16. Evaluation: Raf signalling pathway • Cellular signalling network of 11 phosphorylated proteins and phospholipids in human immune systems cell • Deregulation  carcinogenesis • Extensively studied in the literature  gold standard network

18. Data • Laboratory data from cytometry experiments • Down-sampled to 100 measurements • Sample size indicative of microarray experiments

20. Two types of experiments

22. Evaluation • On real experimental data, using the gold standard network from the literature • On synthetic data simulated from the gold-standard network

23. Comparison with simulated data 1

24. Raf pathway

25. Comparison with simulated data 2

26. Comparison with simulated data 2 Steady-state approximation

30. Evaluation 1: AUC scores

31. Evaluation 2: TP scores We set the threshold such that we obtained 5 spurious edges (5 FPs) and counted the corresponding number of true edges (TP count).

35. AUC scores

36. TP scores

37. Raf pathway

38. Conclusions 1 • BNs and GGMs outperform RNs, most notably on Gaussian data. • No significant difference between BNs and GGMs on observational data. • For interventional data, BNs clearly outperform GGMs and RNs, especially when taking the edge direction (DGE score) rather than just the skeleton (UGE score) into account.

39. Conclusions 2 Performance on synthetic data better than on real data: • Real data: more complex • Real interventions are not ideal • Errors in the gold-standard network

41. Reconstructing gene regulatory networks with Bayesian networks by combining microarray data with biological prior knowledge

42. MOTIVATION

43. Use TF binding motifs in promoter sequences

44. Use prior knowledge from KEGG

45. Prior knowledge

47. Biological prior knowledge matrix Indicates some knowledge about the relationship between genes i and j Biological Prior Knowledge

48. Biological prior knowledge matrix Indicates some knowledge about the relationship between genes i and j Biological Prior Knowledge Define the energy of a Graph G

49. Energy of a network Prior distribution over networks

50. Energy of a network Rewriting the energy