Introduction to transcriptional networks Regulation of the expression of the Lac operon Finding Biclusters in Bipar

1. Systems Biology Today’s lecture will cover the following three topics Introduction to transcriptional networks Regulation of the expression of the Lac operon Finding Biclusters in Bipartite Graphs

2. transcriptional networks By the term transcriptional networks we generally mean gene regulatory networks Unlike protein-protein interaction networks the transcriptional networks are directed networks

3. transcriptional networks: Basic mechanism of gene regulation

4. transcriptional networks

5. transcriptional networks Most genes are regulated at transcription level and it is assumed that 5-10% of protein coding genes encode regulatory proteins. Some regulatory proteins play targeted role i.e. they take part in regulation of a few genes. Some regulatory proteins play more general role in initiating transcription (for example the eukaryotic transcription factors of type II or the RNA polymerase itself that is essential for the transcription of all genes). It is considered that dedicated regulatory proteins are those that affect up to 5% genes of a genome. However the boundary between the generalist and dedicated regulatory proteins is blurred.

6. transcriptional networks • Experiments and methods used to determine regulatory relations • Complementary DNA microarrays • Oligonucleotide chips • Reverse transcription polymerase chain reaction • Serial analysis of gene expression • Chromatin Immunoprecipitation • Bioinformatics—e.g. by way of identifying binding sites

7. Transcriptional Networks: Case study 1 • An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs • Hong-Wu Ma, Bharani Kumar, Uta Ditges2, Florian Gunzer2, Jan Buer1,2 and An-Ping Zeng* • Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649 • This work combined data sets from 3 different sources: • RegulonDB (version 4.0, http://www.cifn.unam.mx/Computational_Genomics/regulondb/) • Ecocyc (version 8.0, www.ecocyc.org) • Shen-Orr,S.S., Milo,R., Mangan,S. and Alon,U. (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genet., 31, 64–68.

8. Transcriptional Network: Case study 1 Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649 Comparison of the TRN of E.coli from three different data sources (A) Based on number of genes (B) Based on number regulatory interactions

9. Transcriptional Network: Case study 1 Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649 A combined network that includes all the 2624 interactions from the three data sets has been produced. In addition, this work extended this network by adding 23 additional genes and around 100 regulatory relationships through literature survey. The final TRN altogether includes 1278 genes and 2724 interactions.

10. Transcriptional Network: Case study 1 Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649 • This work discovered a hierarchical structure in the TRN. • The hierachical structure was identified according to the following way: • genes which do not code for transcription factors (TFs) or code for a TF which only regulates its own expression (auto-regulatory loop) were assigned to layer 1 (the lowest layer); • then we removed all the genes in layer 1 and from the remaining network identified TFs which do not regulate other genes and assigned the corresponding genes in layer 2; • we repeated step 2 to remove nodes which have been assigned to a layer and identified a new layer until all the genes were assigned to different layers. As a result, a nine layer hierarchical structure was uncovered. From BMC Bioinformatics 2004, 5:199 of the related authors

11. Transcriptional Network: Case study 1 Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649

12. Transcriptional Network: Case study 1 Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649 The hierarchical structure implies absence of cycles in the network i.e. feedback loops (though auto regulatory and inter-regulatory loops exist) As the network is not complete, we cannot say that feedback loop could not be found in future however it seems they would not be too many. A possible biological explanation for the existence of this hierarchical structure is that the interactions in this particular TRN are between proteins and genes without involving metabolites. Only after a regulating gene has been transcribed, translated and eventually further modified by cofactors or other proteins, it can regulate the target gene. A feedback from the regulated gene at transcriptional level may delay the process for the target gene to access a desired expression level in a new environment.

13. Transcriptional Network: Case study 1 Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649 Feedback control may be mainly through other interactions (e.g. metabolite and protein interaction) at post-transcriptional level rather than through transcriptional interactions between proteins and genes. For example, a gene at the bottom layer may code for a metabolic enzyme, the product of which can bind to a regulator which in turn regulates its expression. In this case, the feedback is through metabolite–protein interaction to change the activity of the transcription factor and then to affect the expression of the regulated gene. Therefore, to fully understand the gene expression regulation, an integrated network that includes different interactions is needed.

14. Transcriptional Network: Case study 1 Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649 To calculate network motifs in the E.coli TRN, this work removed all the loops in the network (including the autoregulatory loops and the two-gene regulatory loops). Then they used the program Mfinder developed by Kashtan et al. to generate the motif profiles. The first four types are the so-called coherent FFLs in which the direct effect of the up regulator is consistent with its indirect effect through the mid regulator. In contrast, the last four types of FFLs are incoherent because the direct effect of the up regulator is contradictive with its indirect effect

15. Transcriptional Network: Case study 1 Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649 (A) Gene gadA is regulated by six FFLs (B)Gene lpd is regulated by five FFLs (C) Gene slp is regulated by 17 regulators

16. Transcriptional Network: Case study 1 Nucleic Acids Research, 2004, Vol. 32, No. 22 6643–6649

17. Transcriptional Network: Case study 2 Topological and causal structure of the yeast transcriptional regulatory network Nabil Guelzim1,2, Samuele Bottani3, Paul Bourgine2 & François Képès1 nature genetics • volume 31 • may 2002 In this work the yeast transcriptional network was constructed by manual inspection of the websites of MIPS, SwissProt, Yeast Protein Database, S. cerevisiae Promoter Database and the Saccharomyces Genome Database The network consists of 491 genes and 909 regulatory relations

18. Transcriptional Network: Case study 2 nature genetics • volume 31 • may 2002 The network consists of 491 genes and 909 regulatory relations Bold type indicates self-activation, bold italics indicates self-inhibition and borders indicate essential genes. Thick lines represent activation, thin lines represent inhibition and the dashed gray line represents dual regulation.

19. Transcriptional Network: Case study 2 nature genetics • volume 31 • may 2002 Indegree distribution of this yeast transcriptional network is exponential Typical exponential distribution on normal scale

20. Transcriptional Network: Case study 2 nature genetics • volume 31 • may 2002 Indegree distribution of this yeast transcriptional network is exponential open squares, full line --for all 402 regulated genes (367 nonregulatory and 35 interregulatory genes), 909 connections, p(k)=157e–0.45k; R=0.99) filled circles, broken line ---for the subset of 35 interregulatory genes, 72 connections; p(k)=15e–0.43k; R=0.94 Indegree distribution of the transcriptional network on semi-log scale

21. Transcriptional Network: Case study 2 nature genetics • volume 31 • may 2002 Outdegree distribution of this yeast transcriptional network follows power law Typical power law distribution on normal scale

22. Transcriptional Network: Case study 2 nature genetics • volume 31 • may 2002 Outdegree distribution of this yeast transcriptional network follows power law Open squares, full line --for all 124 regulating proteins (909 connections; P(k)=23k−0.87; R=0.95) filled circles, broken line – for 37 regulating proteins that control regulatory genes (72 connections; P(k)=19k−1.14; R=0.99) Outdegree distribution of the transcriptional network on log-log scale

23. The operon an operon is a functioning unit of genomic material containing a cluster of genes under the control of a single regulatory signal or promoter. The genes are transcribed together into an mRNA strand and either translated together in the cytoplasm, or undergo trans-splicing to create monocistronic mRNAs that are translated separately. The result of this is that the genes contained in the operon are either expressed together or not at all. Originally operons were thought to exist solely in prokaryotes but since the discovery of the first operons in eukaryotes in the early 1990s, more evidence has arisen to suggest they are more common than previously assumed.

24. The Lac operon The lacoperon of e.coli consists of three genes LacZ, LacY and LacA They are the codes of enzymes needed for processing lactose LacI is an adjacent gene which is a regulator ( transcriptional repressor) of the Lac operon Besides the promoter operator region there is a region where a complex called CAP binds which affect the transcription positively LacZ codes for the enzyme B-galactosidase and LacY codes for lactose permease, an enzyme that facilitates the flux of lactose through the cell membrane LacA is not directly involved in processing Lactose Source: Models of cellular regulation by Baltazar D. Aguda and Avner Friedman

25. The Lac operon The LacI tetramer binds at the promoter region and stops the transcription The CAP complex binds the cap region and enhance the binding of RNA polymerase Static model of the regulation of the expression of the Lac operon Source: Models of cellular regulation by Baltazar D. Aguda and Avner Friedman

26. cAMP binds and LacI is suppressed by Allolactose cAMP cannot bind and repressor protein LacI binds cAMP binds and repressor protein LacI binds cAMP cannot bind and LacI is suppressed by Allolactose Summary in Table

27. Introduction to transcriptional networks Regulation of the expression of the Lac operon Finding Biclusters in Bipartite Graphs The technique of finding biclusters can be used to determine co-expressed gene groups

28. Definition of a bicluster Given a nxp data matrix X, where n is the number of objects (e.g. genes) and p is the number of conditions (e.g. array), a bicluster is defined as a submatrix XIJ of X within which a subset of objects I express similar behavior across the subset of conditions J. A nxp data matrix X can be easily converted to a bipartite graph by considering a threshold or so. Finding bicluster (densely connected regions) in a bipartite graph is a similar problem.

29. V1 V2 A Graph G=(V,E) is bipartite if its vertex set V can be partitioned into two subsets V1, V2 such that each edge of E has one end vertex in V1 and another in V2.

30. Biclusters are densely connected regions in a bipartite graph

31. Gene expression data can be represented as bipartite graphs Before transforming, the data can be normalized Biclusters in gene expression data represents transcription modules/co-expressed gene groups By transforming highest 5% values to 1

32. Tanay,A. et al. (2002) Discovering statistically significant biclusters in gene expression data. Bioinformatics, 18 (Suppl. 1), S136–S144. • Ihmels,J. et al. (2002) Revealing modular organization in the yeast transcriptional network. Nat. Genet., 31, 370–377. • Ben-Dor,A., Chor,B., Karp,R. and Yakhini,Z. (2002) Discovering local structure in gene expression data: the order-preserving sub-matrix problem. In Proceedings of the 6th Annual International Conference on Computational Biology, ACM Press, New York, NY, USA, pp. 49–57. • Cheng,Y. and Church,G. (2000) Biclustering of expression data. Proc. Int. Conf. Intell. Syst. Mol. Biol. pp. 93–103. • Murali,T.M. and Kasif,S. (2003) Extracting conserved gene expression motifs from gene expression data. Pac. Symp. Biocomput., 8, 77–88.

33. We propose a biclustering method incorporating DPClus An example bipartite graph and its corresponding matrix (for ik)

34. BiClus:Biclustering method incorporating DPClus Concerning each row i (i=0 to |G|-1) of MCN, we calculate thresholdi=avgi+(maxi- avgi)  Gmargin and set (MSG)ik =(MSG)ki=1if (MCN)ikthresholdi and thresholdi is not an indeterminate number (for k=0 to |G|-1). Here, avgi = SUMi/niwhere ni is the number of non-zero entries in row i of MCN and maxiis the maximum value of the entries in row i of MCN Gmargin is a user defined value 1. Common neighbor matrix of the bipartite graph

35. BiClus:Biclustering method incorporating DPClus This matrix represents a simple graph

36. BiClus:Biclustering method incorporating DPClus Simple graph derived from the common neighbor matrix. We can use DPClus to find clusters in the simple graph.

37. BiClus:Biclustering method incorporating DPClus Clustering by DPClus

38. BiClus:Biclustering method incorporating DPClus Clustering by DPClus

39. Finally determined biclusters BiClus:Biclustering method incorporating DPClus

40. Evaluation of BiClus -Using Synthetic data -Using real data

41. Evaluation of BiClus Synthetic data Artificially embedded biclusters with noise

42. Evaluation of BiClus Synthetic data Artificially embedded biclusters with overlap

43. Evaluation of BiClus Let M1, M2 be two sets of biclusters. The gene match score of M1 with respect to M2 is given by the function A systematic comparison and evaluation of biclustering methods for gene expression data Amela Prelic´, Stefan Bleuler, Philip Zimmermann, Anja Wille, Peter Bu¨ hlmann, Wilhelm Gruissem, Lars Hennig, Lothar Thiele and Eckart Zitzle BIOINFORMATICS, Vol. 22 no. 9 2006, pages 1122–1129

44. Evaluation of BiClus Synthetic data Artificially embedded biclusters with noise

45. Evaluation of BiClus Synthetic data Artificially embedded biclusters with overlap

46. Gasch,A.P. et al. (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell, 11, 4241–4257. Gene expression data collected from the above work

47. Gene expression data can be represented as bipartite graphs Before transforming, the data can be normalized Biclusters in gene expression data represents transcription modules By transforming highest 5% values to 1

48. 0.001 0.002 0.003 0.01 Evaluation of BiClus Real gene expression data of yeast P-values represents statistical significance of functional richness of the modules P-Values calculated using FuncAssociate: The Gene Set Functionator from http://llama.med.harvard.edu/cgi/func/funcassociate