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K.U.Leuven Center for Systems Biology

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Presentation on theme: "K.U.Leuven Center for Systems Biology"— Presentation transcript:

1 K.U.Leuven Center for Systems Biology
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 Sequence analysis Genetics Biostatistics
Personalized medicine Nutraceuticals Biostatistics Expression analysis Post-genomic drug development (new targets, toxicogenomics) GMOs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

21 Human genetics identifies key genes in monogenic and multifactorial diseases
5 4 1 Algorithms Technologies Statistical analysis Gene prioritization Module analysis cDNA/ Affy CGH 3 2

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

23 Validation of transcription factor binding to detected motifs
VitD affects bone and calcium homeostasis and has potent anti-proliferative effects O H Cells/tissues treated with 1,25-(OH)2D3 2 1 Bayesian networks Motif analysis Statistical analysis CGH ChIP Proteomics Metabolomics cDNA/ Affy Gene prioritization Algorithms Technologies Validation of transcription factor binding to detected motifs 3 4 TF Identification of signalling cascades and transcription factors important for the effects of 1,25-(OH)2D3 5

24 mRNA expression analysis in pancreatic beta cells: finding mechanisms of diabetes
muscle pituitary non-beta cells <-2.5 >2.5 Discovery of new modules for post-transcriptional gene regulation 1 Mouse models for a common human disease 5 Signal Log Ratio of mRNA in beta -cells versus other tissues 4 Algorithms Technologies Statistical analysis Gene prioritization Motif analysis Generation of antibodies Functional analysis of beta cells Affymetrix Gene System Beta non brain pitui lung kidney fat liver muscl Cells beta cells 2 3 mRNA expression profiles of normal & diabetic beta cells

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

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

27 Toucan 2 Endeavour CGHGate

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

29 growth Turnover since 1998

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

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

32 European bioinformatics landscape

33

34 Integration bioinformatics & stats
Algorithmic methodologiesz

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

36 Known genes related to a disease
(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
(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 Iteration Termination 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 High throughput data Reconstruction of the regulatory network underlying the phenotypic behavior

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

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) Generic -> WP3: Salmonella Software & databases (xM1.4)

45 Workpackages WP2: Regulatory modules WP3: Cellular networks
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)

46 growth Personnel since 1998

47 growth Publications since 1998

48 Bio@SCD 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 Bioi@SCD growth Software portal
Toucan 2 Endeavour

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

51 CAGE

52 2005 Flemish biotech companies Turnhout Antwerp Bruges Ghent Geel
Mechelen Hasselt Kortrijk Leuven Brussels 2005 Flemish biotech companies Leuven 4AZA Bioscience Diatos Neurogenetics PharmaDM reMynd RNA-TEC Thromb-X Tigenix Vivactis 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

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

54

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

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

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

58

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

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
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 Select Model Validate


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