1. Reconstructing gene networks Analysing the properties of gene networks Gene Networks Using gene expression data to reconstruct gene networks

2. 1)Parts list – genes, transcription factors, promoters, binding sites, … 2)Architecture – a graph depicting the connections of the parts 3)Logics – how combinations of regulatory signals interact (e.g., promoter logics) 4)Dynamics – how does it all work in real time Gene Networks four different levels

3. 1)Parts list – genes, transcription factors, promoters, binding sites, … 2)Architecture – a graph depicting the connections of the parts 3)Logics – how combinations of regulatory signals interact (e.g., promoter logics) 4)Dynamics – how does it all work in real time Gene Networks four different levels

4. AD w Gene AGene Drelation Arc/EdgeNode Networks and Graphs describing gene networks using graphs

5. G1G2 The product of gene G1 is a transcription factor, which binds to the promoter of gene G2 – physical networks G1G2 G1G2 Gene G2 is mentioned in a paper about gene G1 – literature networks The disruption of gene G1 changes the expression level of gene G2 – expression networks Networks and Graphs Different interpretations of arcs

6. The dataset used is coming from Hughes et al.: “Functional discovery via a compendium of expression profiles”, Cell 102, 109-126 (2000) Yeast data, 6316 gene expression profiles over 300 experiments 276 deletion mutants (274 single, 2 double) 11 tet-promoter mutants 13 compound treatments selected a subset of 248 experiments: Single deletion mutants All chromosomes present Gene Disruption Networks the dataset

7. The normalized expression log(ratios) are discretized using the threshold  : Discretization of the data Hughes et al. X <    d(X) =  1    X    d(X) = 0 X >   d(X) = 1

8. AA BB CC gene B gene C gene D gene A AD BC Network construction disruption network

9. outdegree = 4indegree = 3 Indegree and Outdegree degree of a node

10. Indegree Most genes have only a few incoming / outgoing edges, but some have many (>500) Outdegree Indegree and Outdegree degree distributions

11. Indegree and Outdegree powerlaw distribution

12. Cellular role table showing the top 5 groups with the highest median degrees for the networks with a minimum group size of 3 for outdegree and 40 for the indegree (  : significance threshold, m: the median degree, n: the group size) Median Out-/Indegree

13. Is there one “big” dominant connected component and possibly a number of small components, or several components of comparable sizes? Can the network be broken down in several components of comparable size by removing nodes of high degree (i.e., nodes with many incoming or outgoing edges)? network modularity

14. Number of connected components in the networks

15. network modularity Number of connected components in the networks

16.  componentfull network 1% removed 5% removed 10% removed 2.0largest second total 5383 1 4707 1 3682 2 2614 5 2 3.0largest second total 3556 2 2461 2 1385 4 9 764 6 17 4.0largest second total 2354 3 4 1205 3 7 542 6 22 45 28 51 Number of connected components in the networks network modularity

17. Wagner, Genome Research 2002 – there exist many independent modules Featherstone and Broadie, Bioessays 2002 - there is only one giant module All depends on the definition of the ‘module’ Modularity other opinions

18. a closer look

19. This subnetwork is the result of filtering the full network at  =2.0 for the core set marked in red and their next neighbours (red arcs: down- regulation, green arcs: upregulation). Mating subnetwork

20. This subnetwork is the result of filtering the full network at  =4.0 for the core set marked in red and their next neighbours (red arcs: downregulation, green arcs: upregulation). Mating subnetwork

21. more information than randomised networks no optimal  powerlaw distribution of arcs no obvious modules local networks make sense Conclusion

22. ... and now to something completely different

23. ChIP „theoretic“ ChIP= bindingsite network Gene disruption Comparison of gene neighbourhoods in graphs First, take three types of networks...

24. source gene target gene T1 T2 T3 Target Sets

25. T1 T2 T3

26. All genes – target sets All genes – source sets s1s1 T1T1 s2s2 T2T2 T1T1 T’ 1 Transcription factors Disrupted genes Genes with binding sites for s 1 Genes affected by disruption s 2 s1  s2s1  s2 s 1 = s 2

27. known relationships target set overlap small target set overlap large predicted relationship

28. protein-protein interaction (Y2H, cellzome, etc.) MIPS (C. v. Mering „reference set“) Co-citation network (PubMed) Comparison of gene neighbourhoods in graphs... and three more networks...

29. rank for p-value: 1) s1 - s2 - p-value - tp 2) s1 - s3 - p-value - fp 3) s1 - s4 - p-value - tp 4) s2 - s3 - p-value - tn 5) s2 - s4 - p-value - fn 6) s3 - s4 - p-value - tn Comparison of gene neighbourhoods in graphs... and then: