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Reconstruction of regulatory modules based on heterogeneous data sources Karen Lemmens PhD Defence September 29th 2008.

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Presentation on theme: "Reconstruction of regulatory modules based on heterogeneous data sources Karen Lemmens PhD Defence September 29th 2008."— Presentation transcript:

1 Reconstruction of regulatory modules based on heterogeneous data sources Karen Lemmens PhD Defence September 29th 2008

2 PhD defence 29 September 2008 Karen Lemmens Outline 1.Introduction & objectives 2.Strategy –Data integration –Association rule mining algorithms 3.Main achievements –ReMoDiscovery: Unraveling the yeast transcriptional network –DISTILLER: Condition-dependent combinatorial regulation in E. coli 4.Conclusions and perspectives 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

3 PhD defence 29 September 2008 Karen Lemmens DNA 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

4 PhD defence 29 September 2008 Karen Lemmens DNA & genes TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG DNA mRNA protein GENE 1 GENE 2 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

5 PhD defence 29 September 2008 Karen Lemmens DNA & genes TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG DNA mRNA protein GENE 1 GENE 2 GENE 1GENE 2 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

6 PhD defence 29 September 2008 Karen Lemmens DNA & genes TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG DNA mRNA protein GENE 1 GENE 2 GENE 1GENE 2 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

7 PhD defence 29 September 2008 Karen Lemmens DNA & genes TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG DNA mRNA protein GENE 1 GENE 2 GENE 1GENE 2 1. Introduction 2. Strategy 3. Achievements 4. Conclusions TRANSCRIPTION

8 PhD defence 29 September 2008 Karen Lemmens DNA & genes TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG DNA mRNA protein GENE 1 GENE 2 GENE 1GENE 2 1. Introduction 2. Strategy 3. Achievements 4. Conclusions TRANSCRIPTION TRANSLATION

9 PhD defence 29 September 2008 Karen Lemmens Condition-dependent transcription DNA mRNA protein GENE 1 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

10 PhD defence 29 September 2008 Karen Lemmens Condition-dependent transcription DNA mRNA protein GENE 1 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

11 PhD defence 29 September 2008 Karen Lemmens Condition-dependent transcription DNA mRNA protein GENE 1 TRANSCRIPTION TRANSLATION 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

12 PhD defence 29 September 2008 Karen Lemmens Condition-dependent transcription DNA mRNA protein GENE 1 TRANSCRIPTION TRANSLATION 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

13 PhD defence 29 September 2008 Karen Lemmens Condition-dependent transcription DNA mRNA protein GENE 1 TRANSCRIPTION TRANSLATION 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

14 PhD defence 29 September 2008 Karen Lemmens Condition-dependent transcription DNA mRNA protein GENE 1 TRANSCRIPTION TRANSLATION 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

15 PhD defence 29 September 2008 Karen Lemmens Transcriptional regulation GENE 1 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

16 PhD defence 29 September 2008 Karen Lemmens Transcriptional regulation Regulatory motifs GENE 1 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

17 PhD defence 29 September 2008 Karen Lemmens Transcriptional regulation Regulatory motifs GENE 1 Regulators 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

18 PhD defence 29 September 2008 Karen Lemmens Transcriptional regulation Regulatory motifs GENE 1 Regulators 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

19 PhD defence 29 September 2008 Karen Lemmens Transcriptional regulation Regulatory motifs GENE 1 Regulators 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

20 PhD defence 29 September 2008 Karen Lemmens Transcriptional regulation Regulatory motifs GENE 1 Regulators 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

21 PhD defence 29 September 2008 Karen Lemmens Transcriptional regulation Regulatory motifs GENE 1 Regulators 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

22 PhD defence 29 September 2008 Karen Lemmens Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

23 PhD defence 29 September 2008 Karen Lemmens Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

24 PhD defence 29 September 2008 Karen Lemmens Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

25 PhD defence 29 September 2008 Karen Lemmens Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

26 PhD defence 29 September 2008 Karen Lemmens Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

27 PhD defence 29 September 2008 Karen Lemmens Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

28 PhD defence 29 September 2008 Karen Lemmens Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

29 PhD defence 29 September 2008 Karen Lemmens Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

30 PhD defence 29 September 2008 Karen Lemmens Transcriptional modules 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

31 PhD defence 29 September 2008 Karen Lemmens Outline 1.Introduction & objectives 2.Strategy –Data integration –Association rule mining algorithms 3.Main achievements –ReMoDiscovery: Unraveling the yeast transcriptional network –DISTILLER: Condition-dependent combinatorial regulation in E. coli 4.Conclusions and perspectives 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

32 PhD defence 29 September 2008 Karen Lemmens Data integration GENE 1 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

33 PhD defence 29 September 2008 Karen Lemmens Data integration GENE 1 ChIP-chip data 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

34 PhD defence 29 September 2008 Karen Lemmens Data integration GENE 1 Regulatory motifs ChIP-chip data 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

35 PhD defence 29 September 2008 Karen Lemmens Data integration GENE 1 Regulatory motifs ChIP-chip data Microarray data 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

36 PhD defence 29 September 2008 Karen Lemmens Network reconstruction Several methods for reconstruction of the transcriptional network exist Not all aspects of transcription taken into account by these methods ** Van den Bulcke T., Lemmens K., Van de Peer Y., Marchal K. (2006) Inferring Transcriptional Networks by Mining Omics Data. Current Bioinformatics, vol. 1, no. 3, pp. 301-313. ** Dhollander T., Sheng Q., Lemmens K., De Moor B., Marchal K., Moreau Y. (2007) Query-driven module discovery in microarray data. Bioinformatics, vol. 23, no. 19, pp. 2573-2580. Boolean ODE Bayesian Association (CLR, ARACNE) Clustering Biclustering Query-driven biclustering Method of Segal et al. LeMoNe Bayesian SEREND GRAM MA-Networker SAMBA Inferelator COGRIM Expression dataData integration Individual interactions Transcriptional modules 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

37 PhD defence 29 September 2008 Karen Lemmens Association rule mining Association rule mining algorithms –Advantages: Enable exhaustive search Elegant and concurrent data integration No co-expression assumption between regulator and target Overlapping modules –Problems Binary or discretized data Filtering method necessary 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

38 PhD defence 29 September 2008 Karen Lemmens Outline 1.Introduction & objectives 2.Strategy –Data integration –Association rule mining algorithms 3.Main achievements –ReMoDiscovery: Unraveling the yeast transcriptional network –DISTILLER: Condition-dependent combinatorial regulation in E. coli 4.Conclusions and perspectives 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

39 PhD defence 29 September 2008 Karen Lemmens ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

40 PhD defence 29 September 2008 Karen Lemmens ReMoDiscovery: Unraveling the yeast transcriptional network Represent data in a mathematical way 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

41 PhD defence 29 September 2008 Karen Lemmens ReMoDiscovery: Unraveling the yeast transcriptional network Transcriptional module –Genes are regulated by a minimum number of regulators –Genes share minimum number of common regulatory motifs –Genes are co-expressed 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

42 PhD defence 29 September 2008 Karen Lemmens ReMoDiscovery: Unraveling the yeast transcriptional network Transcriptional module –Genes are regulated by a minimum number of regulators –Genes share minimum number of common regulatory motifs –Genes are co-expressed 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

43 PhD defence 29 September 2008 Karen Lemmens ReMoDiscovery: Unraveling the yeast transcriptional network Transcriptional module –Genes are regulated by a minimum number of regulators –Genes share minimum number of common regulatory motifs –Genes are co-expressed 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

44 PhD defence 29 September 2008 Karen Lemmens ReMoDiscovery: Unraveling the yeast transcriptional network Transcriptional module –Genes are regulated by a minimum number of regulators –Genes share minimum number of common regulatory motifs –Genes are co-expressed 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

45 PhD defence 29 September 2008 Karen Lemmens ReMoDiscovery: Unraveling the yeast transcriptional network Regulatory program: Regulators:Motifs: MBP1 SWI4 SWI6 STB1 Co-expressed genes: YDL003W YER001W YGR109C YGR189C YGR221C YHR149C YER070W YPL256C YNL300W YPL163C YPL267W YPR120C YMR199W YMR199W YMR179W YML027W YKL113C 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

46 PhD defence 29 September 2008 Karen Lemmens ReMoDiscovery: Unraveling the yeast transcriptional network ReMoDiscovery outperforms related methods for module detection –GRAM –SAMBA Conclusions –Meaningful biological results –Better performance than related methods association rule mining algorithms are well suited for identification of regulatory modules through data integration Lemmens K., Dhollander T., De Bie T., Monsieurs P., Engelen K., Smets B., Winderickx J., De Moor B., Marchal K. (2006) Inferring transcriptional module networks from ChIP-chip-, motif- and microarray data. Genome Biology, vol. 7, no. 5, pp. R37. 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

47 PhD defence 29 September 2008 Karen Lemmens ReMoDiscovery: Unraveling the yeast transcriptional network Many aspects of transcription into account: –Regulatory motifs –Regulators –Co-expression of genes Condition dependency of the interactions is missing 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

48 PhD defence 29 September 2008 Karen Lemmens ReMoDiscovery: Unraveling the yeast transcriptional network Many aspects of transcription into account: –Regulatory motifs –Regulators –Co-expression of genes Condition dependency of the interactions is missing 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

49 PhD defence 29 September 2008 Karen Lemmens ReMoDiscovery: Unraveling the yeast transcriptional network Many aspects of transcription into account: –Regulatory motifs –Regulators –Co-expression of genes Condition dependency of the interactions is missing 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

50 PhD defence 29 September 2008 Karen Lemmens Outline 1.Introduction & objectives 2.Strategy –Data integration –Association rule mining algorithms 3.Main achievements –ReMoDiscovery: Unraveling the yeast transcriptional network –DISTILLER: Condition-dependent combinatorial regulation in E. coli 4.Conclusions and perspectives 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

51 PhD defence 29 September 2008 Karen Lemmens DISTILLER: Condition-dependent combinatorial regulation in E. coli ReMoDiscovery: –Co-expression in all conditions by correlation –Apriori algorithm –No filtering procedure DISTILLER: –Condition dependency: bandwidth concept –CHARM algorithm –Filtering procedure to identify the most interesting modules 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

52 PhD defence 29 September 2008 Karen Lemmens DISTILLER: Condition-dependent combinatorial regulation in E. coli Pastor D., Cortes-Calabuig A., Lemmens K., De Moor B., Marchal K., Denecker M. (2007) GeneReg: Integration of Experimental Data on the DNA Transcription Process. Proceedings of BNAIC 2007. 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

53 PhD defence 29 September 2008 Karen Lemmens DISTILLER: Condition-dependent combinatorial regulation in E. coli Example: FNR module 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

54 PhD defence 29 September 2008 Karen Lemmens DISTILLER: Condition-dependent combinatorial regulation in E. coli Example: FNR module 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

55 PhD defence 29 September 2008 Karen Lemmens DISTILLER: Condition-dependent combinatorial regulation in E. coli Example: FNR module 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

56 PhD defence 29 September 2008 Karen Lemmens DISTILLER: Condition-dependent combinatorial regulation in E. coli FNR = one of the most extensively studied regulators Experimental validation of novel FNR targets –High confidence: ydhY (b1674) Partridge et al, 2008 yfgG (b2504) hscC (b0650) treF (b3519) –Medium confidence: yjhB (b4279) ydjX (b1750) yjtD (b4403) ydaT (b1358) yehD (b2111) yhjA (b3518) Partridge et al, 2007 ftnB (b1902) 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

57 PhD defence 29 September 2008 Karen Lemmens DISTILLER: Condition-dependent combinatorial regulation in E. coli Condition dependency –Arrays were grouped into conditional categories –Colors show to what extent the conditions of the modules of a particular regulator are enriched for a specific category 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

58 PhD defence 29 September 2008 Karen Lemmens DISTILLER: Condition-dependent combinatorial regulation in E. coli Combinatorial regulation –Static –Highly combinatorial: 42 regulons one regulator 66 complex regulons two regulators 70 complex regulons three or more regulators (maximum of 8) 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

59 PhD defence 29 September 2008 Karen Lemmens DISTILLER: Condition-dependent combinatorial regulation in E. coli Combinatorial regulation at the module level Lower combinatorial complexity 25/150 modules at least two regulators (maximum of 3) 24 modules involve at least one global regulator such as CRP, FNR or ArcA 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

60 PhD defence 29 September 2008 Karen Lemmens DISTILLER: Condition-dependent combinatorial regulation in E. coli Combinatorial regulation at connector gene level One regulator may be sufficient to alter the expression of a connector gene upon a specific environmental cue 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

61 PhD defence 29 September 2008 Karen Lemmens DISTILLER: Condition-dependent combinatorial regulation in E. coli Conclusions –Reliable predictions –Dynamic view on the network –Combinatorial regulation ** Lemmens K., De Bie T., Dhollander T., Monsieurs P., De Moor B., Collado-Vides J., Engelen K., Marchal K. (2008) The condition- dependent transcriptional network in Escherichia coli. Accepted for publication in Annals of NYAS, DREAM2. ** Lemmens K., De Bie T., Dhollander T., De Keersmaecker S., Thijs I., Schoofs G., De Weerdt A., De Moor B., Vanderleyden J., Collado- Vides J., Engelen K., Marchal K. (2008) DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli. Submitted to Genome Biology. 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

62 PhD defence 29 September 2008 Karen Lemmens Outline 1.Introduction & objectives 2.Strategy –Data integration –Association rule mining algorithms 3.Main achievements –ReMoDiscovery: Unraveling the yeast transcriptional network –DISTILLER: Condition-dependent combinatorial regulation in E. coli 4.Conclusions and perspectives 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

63 PhD defence 29 September 2008 Karen Lemmens Conclusions Main contributions of this thesis: –Automated collection of data –ReMoDiscovery –DISTILLER Goals obtained via: –Data integration –Association rule mining algorithms well suited for data integration and reconstruction of transcriptional network Several algorithmic problems were solved Novel biological findings 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

64 PhD defence 29 September 2008 Karen Lemmens Perspectives Conceptual extensions: –Inclusion of other data sources Additional motifs from de novo motif detection Small RNAs –Comparison of networks Implementation-related and algorithmic improvements: –User-friendly interface –Microarray compendium –Filtering step –Motif detection algorithms 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

65 PhD defence 29 September 2008 Karen Lemmens Acknowledgements CMPG - BioI –Prof. Dr. K. Marchal –BioI group ESAT/SCD – BioI –Prof. Dr. B. De Moor –Prof. Dr. Y. Moreau –BioI group –Prof. Dr. T. De Bie CMPG –Prof. Dr. J. Vanderleyden –Dr. S. De Keersmaecker Computer Sciences –Prof. Dr. M. Denecker –A. Cortés Calabuig –Prof. Dr. J. Collado-Vides 1. Introduction 2. Strategy 3. Achievements 4. Conclusions

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