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SymBioSys K.U.Leuven Center for Systems Biology. Topics to be addressed International trend Project concept Project structure 3 problems and 3 cases Computational.

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Presentation on theme: "SymBioSys K.U.Leuven Center for Systems Biology. Topics to be addressed International trend Project concept Project structure 3 problems and 3 cases Computational."— Presentation transcript:

1 SymBioSys K.U.Leuven Center for Systems Biology

2 Topics to be addressed International trend Project concept Project structure 3 problems and 3 cases Computational methodology leads to user-friendly tools and real biological impact Strategic importance internationally Strategic importance K.U.Leuven Coherence of the consortium

3 Systems biology BiostatisticsGenetics Sequence analysis Expression analysis Personalize d medicine Nutraceutical s Post-genomic drug development (new targets, toxicogenomics) GMO s

4 Systems biology Biological question & model High-throughput technology Computers & databases Mathematical models

5 The Human Genome Project has catalyzed striking paradigm changes in biology - biology is an information science. [...] Systems biology will play a central role in the 21st century; there is a need for global (high throughput) tools of genomics, proteomics, and cell biology to decipher biological information; and computer science and applied math will play a commanding role in converting biological information into knowledge. Leroy Hood, Institute for Systems Biology, Seattle, WA, 2002

6 Center of Excellence Become a world-leading bioinformatics center for systems biology Bioinformatics & microarrays Three topics of excellence Gene prioritization by integrative genomics Graphical models of regulatory motifs and modules Inference of regulatory networks We will achieve this goal through Further build-up of existing expertise Symbiosis between computational and biological partners Concrete cases for real biological relevance Diverse cases for generic applicability in biology

7 Systems biology Genes Modules Networks

8 Probabilistic models Integrative genomics Regulatory modules Cellular networks Case Project concept Case

9 Probabilistic models Integrative genomics Regulatory modules Cellular networks Genetical genomics Endocrinology Salmonella genomics Biological problem Research concept & consortium

10 Probabilistic models Integrative genomics Regulatory modules Cellular networks Genetical genomics Endocrinology Salmonella genomics Biological problem Experiment design Research concept & consortium

11 Probabilistic models Integrative genomics Regulatory modules Cellular networks Genetical genomics Endocrinology Salmonella systems biology Biological problem Experiment design Biological data Research concept & consortium

12 Probabilistic models Integrative genomics Regulatory modules Cellular networks Genetical genomics Endocrinology Salmonella genomics Biological problem Experiment design Biological data Data analysis Research concept & consortium

13 Probabilistic models Integrative genomics Regulatory modules Cellular networks Genetical genomics Endocrinology Salmonella genomics Biological problem Experiment design Biological data Data analysis Biological validation Research concept & consortium

14 Probabilistic models Integrative genomics Regulatory modules Cellular networks Genetical genomics Endocrinology Salmonella genomics Biological problem Experiment design Biological data Data analysis Biological validation Improved method Research concept & consortium

15 Integrative genomics Regulatory modules Cellular networks Genetical genomics Endocrinology Salmonella genomics Biological problem Experiment design Biological data Data analysis Biological validation Improved method New biology Probabilistic models Research concept & consortium

16 Integrative genomics Regulatory modules Cellular networks Genetical genomics Endocrinology Salmonella genomics Biological problem Experiment design Biological data Data analysis Biological validation Improved method New biology Probabilistic models Research concept & consortium

17 Integrative genomics Regulatory modules Cellular networks Genetical genomics Endocrinology Salmonella genomics DME-VIB Prometa KUL & DME-VIB World Probabilistic models Peripheral groups & visibility Yeast (CMPG & Bio)

18 Project structure WP1. Candidate genes WP2. Regulatory modules WP3. Cellular networks Human genetics Glucose regulation VitD modes of action Salmonella systems biology

19 Network inference Motif analysis Primary analysis CGH ChIP chip Proteomics Metabol omics Candidate genes Regulatory modules Cellular networks cDNA/ Affy Gene prioritization Data analysis Data generation Project structure (SysBio -> 3 partners) Genetical genomics Endocrinology Salmonella genomics

20 WP1. Candidate gene prioritization High-throughput genomics Statistics & data mining Candidate genes ?

21 Human genetics identifies key genes in monogenic and multifactorial diseases Module analysis Statistical analysis CGH cDNA/ Affy Gene prioritization Algorithms Technologies

22 WP2. Module discovery ACT C MYLA MYL1 MYOG MYF6 CHRM2 MEF2 MYOD SRF

23 Bayesian networks Motif analysis Statistical analysis CGH ChIP Proteomics Metabolomics cDNA/ Affy Gene prioritization Algorithms Technologies OH OHH O H Cells/tissues treated with 1,25- (OH) 2 D 3 Identification of signalling cascades and transcription factors important for the effects of 1,25-(OH) 2 D 3 TF Validation of transcription factor binding to detected motifs VitD affects bone and calcium homeostasis and has potent anti-proliferative effects

24 mRNA expression analysis in pancreatic beta cells: finding mechanisms of diabetes Motif analysis Statistical analysis Generation of antibodies Functional analysis of beta cells Affymetrix Gene System Gene prioritization Algorithms Technologies Discovery of new modules for post-transcriptional gene regulation Beta non brain pitui lung kidney fat liver muscl Cells beta cells muscle pituitary non-beta cells <-2.5 >2.5 Signal Log Ratio of mRNA in beta -cells versus other tissues mRNA expression profiles of normal & diabetic beta cells 2 Mouse models for a common human disease

25 Microarray-data ChIP-chip-data Sequence data Network inference REMODISCOVERY Combinatorial algorithm WP3. Network inference

26 Salmonella is a powerful model for systems biology (illustration size) Network inference Module analysis Statistical analysis CGH ChIP Proteomics Metabolomics cDNA/ Affy Gene prioritization Algorithms Technologies Preprocessing Heterogeneous data Motif compendium Inferred network

27 Toucan 2 CGHGate Endeavour

28 Real biological impact Screenshots of titles of papers demonstrating a real biological impact of bioinformatics methods?

29 growth Turnover since 1998

30 CMPG J. Vanderleyden J. Michiels B. Cammue Dept. of Mol. Microbiology J. Thevelein CME-MG B. Hassan P. Marynen B. De Strooper W. Van de Ven Lab of Clin. & Evolut. Virology A. Vandamme Dept. of Transgene Tech. & Gene Therapy P. Carmeliet CME-UZ JJ. Cassiman (CME-KUL) J. Vermeesch Intensive Care G. Van Den Berghe Obstetrics & Gynaecology I. Vergote T. D‘Hooghe D. Timmerman Paper Lab of Functional Biology J. Winderickx LEGENDO C. Mathieu

31 CMPG J. Vanderleyden J. Michiels B. Cammue Lab of Clin. & Evolut. Virology A. Vandamme QuantPsy I. Van Mechelen Lab of Functional Biology J. Winderickx LEGENDO C. Mathieu Mol.Cell Biology BioChemistry F. Schuit BioStat G. Verbeke Dept. of Mol. Microbiology J. Thevelein Dept. of Transgene Tech. & Gene Therapy P. Carmeliet CME-MG B. Hassan P. Marynen B. De Strooper W. Van de Ven CME-UZ JJ. Cassiman J. Vermeersch Intensive Care G. Van Den Berghe Obstetrics & Gynaecology I. Vergote T. D‘Hooghe D. Timmerman CoE

32 European bioinformatics landscape

33

34 Integration bioinformatics & stats Algorithmic methodologiesz

35 Three topics of excellence Bioinformatics & microarrays 1. Gene prioritization by integrative genomics 2. Graphical models of regulatory motifs and modules 3. Bayesian networks for prokaryotic systems biology

36 (1) Genomic data fusion After an experiment, many sources of information are available to select the best candidates for modeling and validation Probabilistic methods can optimize the prioritization Known genes related to a disease or pathway Candidate genes  Locus  Screening Multiple data sources  Sequence  Expression  Function

37 Endeavour [Methodological impact]

38 (2) Regulatory modules [what is a module? What is transcript. regulation?] © Davidson EH et al. Science Mar 1;295(5560):

39 Gibbs motif finding Initialization Sequences Random motif matrix Iteration Sequence scoring Alignment update Motif instances Motif matrix Termination Convergence of the alignment and of the motif matrix

40 MotifSampler & TOUCAN

41 (3) Network inference Reconstruction of the regulatory network underlying the phenotypic behavior High throughput data

42 Benchmarking network inference methods Realistic network structures Realistic network dynamics Simulated networks Inferred networks Graphical models System identification Network simulation Network Inference

43

44 Workpackages WP1: Candidate genes Preliminary data analysis Microarrays (xM1.1) Generic CGH microarrays (gWP1) Genetical genomics Dealing with noise (xM2.1) Knowledge mining (gWP2) & Combined modeling of different data sets (xM2.3) Genetical genomics Generic -> WP3: Salmonella Software & databases (xM1.4)

45 Workpackages WP2: Regulatory modules Motif and module discovery (xM1.2) Expression profiling in vitD and analogs pathways (xM3.1, xM3.2) Beta cell regulation Transcriptional regulation Post-transcriptional regulation Genetic modules Multiple genome scans and gene modifiers? Software & databases (xM1.4) WP3: Cellular networks Network inference (xM1.3) Salmonella high-throughput technologies (xM4.1) Salmonella high-throughput data and analysis (xM4.2) VitD pathway modeling? Glucose sensing? Detection of dependence relations (xM2.2) Software & databases (xM1.4)

46 growth Personnel since 1998

47 growth Publications since 1998

48 growth 5 successful PhDs Gert Thijs (juni 2003) : Probabilistic methods to search for regulatory elements in sets of coregulated genes Frank De Smet (mei 2004) : Microarrays : algorithms for knowledge discovery in oncology and molecular biology Stein Aerts (mei 2004): Computational discovery of cis- regulatory modules in animal genomes Geert Fannes (juni 2004): Bayesian learning with expert knowledge : Transforming informative priors between Bayesian networks and multilayer perceptrons Patrick Glenisson (juni 2004) : Integrating scientific literature with large scale gene expression analysis

49 growth Software portal Toucan 2 Endeavour

50 CMPG J. Vanderleyden J. Michiels B. Cammue Dept. of Mol. Microbiology J. Thevelein CME-MG B. Hassan P. Marynen B. De Strooper W. Van de Ven Intensive Care G. Van Den Berghe Obstetrics & Gynaecology I. Vergote T. D‘Hooghe D. Timmerman IDO, BOF PostDoc GBOU, PhD Project, PhD, PostDoc

51 CAGE

52 Bruges Kortrijk Ghent Antwerp Brussels Leuven Turnhout 2005 Geel Hasselt Mechelen Bruges Genencor International Ghent Ablynx AlgoNomics Applied Maths Bayer BioScience Bioin4matrix BioMARIC CropDesign deVGen Innogenetics Maize Technologies Int’l Methexis Genomics Xcellentis Yakult Peakadilly Antwerp DCI-labs Flen Pharma Histogenex Memo Bead Technologies Turnhout DiaMed EuroGen Janssen Pharmaceutica Geel Barrier Therapeutics Genzyme Flanders Maia Scientific Mechelen Bio-Art CryoSave Galapagos Genomics Tibotec Virco Brussels Beta-cell Dentech EggCentris R.E.D. Laboratories Leuven 4AZA Bioscience Diatos Neurogenetics PharmaDM reMynd RNA-TEC Thromb-X Tigenix Vivactis Flemish biotech companies

53 Bayesian networks Motif analysis Statistical analysis CGH ChIP chip Proteomics Metabol omics Candidate genes PI: Regulatory modules PI: Cellular networks PI: cDNA/ Affy Gene prioritization Algorithmic research Data generation Project structure – budget (750 KEuro?) Genetical genomics Endocrinology Salmonella genomics Postdoc 2 Phd 2 Techn 1 Postdoc 3 Phd 3 Postdoc 1 Phd 1 Techn 2 Techn 3 Phd 4

54

55 allerlei Eerste citaties met “bioinformatics” Trends Biotechnol 1993 Ann N Y Acad Sci 1993

56 Network reconstruction based on heterogeneous data Microarray-data ChIP-chip-data Sequence data Preprocessing Network inference

57 Network structures based on real biological networks Realistic network dynamics Simulated networks Benchmarking network inference methodologies

58

59 Realistic network structures Realistic network dynamics Simulated networks Benchmarking network inference methodologies Inferred networks Graphical models System identification

60 Now: the molecular pipeline Powerful high-throughput technologies enable genomewide screening Sequencing, microarrays, etc. Some genes selected (arbitrarily) for validation After a long validation the best-known genes are integrated into a biological model (maken van predictieve modellen op beperkte genen is niet het onderwerp van het project) Screen Validate Model

61 Future: the systems genomics pipeline Validate Select By integrating computation tightly with biological experiments, promising genes are selected and integrated to computational models to retain only the best candidates for validation There is a continuous interchange between the different levels of analysis Screen Model


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