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Computational analysis of biological systems: Past, present and future Sven Bergmann UNIL tenure track commission 5 January 2010
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Research Overview Large (genomic) systems many uncharacterized elements relationships unknown computational analysis should: improve annotation reveal relations reduce complexity Small systems elements well-known many relationships established aim at quantitative modeling of systems properties like: Dynamics Robustness Logics
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PAST Large-scale data analyses
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Search for transcription modules: Set of genes co-regulated under a certain set of conditions context specific allow for overlaps How to extract information from very large-scale expression data? J Ihmels, G Friedlander, SB, O Sarig, Y Ziv & N Barkai Nature Genetics (2002)
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Identification of transcription modules using many random “seeds” random “seeds” Transcription modules Independent identification: Modules may overlap! SB, J Ihmels & N Barkai Physical Review E (2003)
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New Tools: Module Visualization http://maya.unil.ch:7575/ExpressionView
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Data Integration: Example NCI60 60 cancer cell lines (9 tissue types) ~23,000 gene probes Gene Expression Data ~5,000 drugs Drug Response Data
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How to identify Co-modules? Iteratively refine genes, cell-lines and drugs to get co-modules Z Kutalik, J Beckmann & SB, Nature Biotechnology (2008)
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6’189 individuals Phenotypes 159 measurement 144 questions Genotypes 500.000 SNPs CoLaus = Cohort Lausanne Collaboration with: Vincent Mooser (GSK), Peter Vollenweider & Gerard Waeber (CHUV)
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PCA of POPRES cohort
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Impact: Web of Science 2005-2009
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Impact: Who cites our work?
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PRESENT Large-scale data analysis
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Current insights from GWAS: Well-powered (meta-)studies with (ten-)thousands of samples have identified a few (dozen) candidate loci with highly significant associations Many of these associations have been replicated in independent studies
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Current insights from GWAS: Each locus explains but a tiny (<1%) fraction of the phenotypic variance All significant loci together explain only a small (<10%) of the variance David Goldstein: “~93,000 SNPs would be required to explain 80% of the population variation in height.” Common Genetic Variation and Human Traits, NEJM 360;17
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1.Other variants like Copy Number Variations or epigenetics may play an important role 2.Interactions between genetic variants (GxG) or with the environment (GxE) 3.Many causal variants may be rare and/or poorly tagged by the measured SNPs 4.Many causal variants may have very small effect sizes So what do we miss?
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Status: - Dec: submitted to PLoS Computational Biology (IF=6.2) (after positive reply to pre-submission inquiry)
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Status: accepted for publication in Nature (IF=31.4 )
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Status: - Dec: submitted to PLoS Genetics (IF=8.7), currently under review
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Status: - submitted to Biostatistics (IF=3.4, 2 nd best of 92 journals for Statistics & Probability) - Revision accounting for reviewers ’ comments to be submitted soon
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Status: accepted for publication GASTROENTEROLOGY (IF=12.6).
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Status: submitted as application note to Bioinformatics (IF=4.32, 2 nd best of 28 journals for Mathematical & Computational Biology)
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Status: manuscript ready for submission to PLoS Comp Biology
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Research Overview Large (genomic) systems many uncharacterized elements relationships unknown computational analysis should: improve annotation reveal relations reduce complexity Small systems elements well-known many relationships established aim at quantitative modeling of systems properties like: Dynamics Robustness Logics
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PAST Modeling
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Drosophila as model for Development
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Quantitative Experimental Study using Automated Image Processing a: mark anterior and posterior pole, first and last eve-stripe b: extract region around dorsal midline c: semi-automatic determination of stripes/boundaries
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Experimental Results: Positions Bergmann S, Sandler S, Sberro H, Shnider S, Shilo B-Z, Schejter E and Barkai N Pre-Steady-State Decoding of the Bicoid Morphogen Gradient, PLoS Biology 5(2) (2007) e46. Bergmann S, Tamari Z, Shilo B-Z, Schejter E and Barkai N Stability of the Bicoid Gradient? Cell 132 (2008) 15.
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A bit of Theory… The morphogen density M(x,t) can be modeled by a differential equation (reaction diffusion equation): Change in concentration of the morphogen at position x, time t Diffusion D: diffusion const. SourceDegradation α: decay rate The Canonical Model
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Model including nuclear trapping knkn k -n s0s0 D M n (x,t) nuclear emission nuclear absorbtion nuclear morphogen diffusion production free morphogen M(x,t) Nuclei density B(x,t) N N N
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PRESENT Modeling
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Precision is highest at mid-embryo Similar trend in direct measurements of Bcd noise by Gregor et al. (Cell 2007) 1xbcd 2xbcd 4xbcd Δ: Gt Δ: Kr □: Hb o: Eve
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Scaling is position-dependent! “hyper-scaling” at anterior pole
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Status: - May: submitted to Molecular Systems Biology (IF=12.2) - Aug: first resubmission after mostly positive reviews - Dec: second submission (informally) accepted subject to proper response with respect to minor issues
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Partner in SystemsX.ch project WingX - PhD student: Aitana Morton Delachapelle - PostDoc: Sascha Dalessi Image processing to obtain spatio- temporal measures of proteins Modeling Dpp gradient formation with focus on scaling Modeling the Drosophila wing disk
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Modeling the plant growth Partner in SystemsX.ch project PlantX - PostDoc: Micha Hersch - PostDoc: Tim Hohm Image processing to obtain spatio- temporal measures of seedlings Modeling shade avoidance behavior
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Future directions
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Organisms Data types –Genotypic (SNPs/CNVs) –Epigenetic data –Gene/protein expression –Protein interactions –Organismal data ? Biological Insight The challenge of many datasets: How to integrate all the information?
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Modular Approach for Integrative Analysis of Genotypes and Phenotypes Individuals Genotypes Phenotypes Measurements SNPs/Haplotypes Modular links
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Association of (average) module expression is often stronger than for any of its constituent genes
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Towards interactions: Network Approaches for Integrative Association Analysis Using knowledge on physical gene-interactions or pathways to prioritize the search for functional interactions
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Modeling: Cross-talk between Drosophila and Arabidopsis modeling Both systems are growing multi-cellular tissues: Modelers (in my group and within the two RTDs) may learn from each other and exchange tools
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Acknowledgements to my group http://serverdgm.unil.ch/bergmann Funding: SystemsX.ch, SNSF, SIB, Cavaglieri, Leenaards, European FP People: Zoltán Kutalik Micha Hersch Aitana Morton Diana Marek Barbara Piasecka Bastian Peter Karen Kapur Alain Sewer* Toby Johnson* Armand Valsessia Gabor Csardi Sascha Dalessi Tim Hohm *alumni
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Acknowledgements to my collaborators DGM: Jacqui Beckmann Roman Chrast Carlo Rivolta CIG: Christian Fankhauser Sophie Martin Alexandre Reymond Mehdi Tafti Bernard Thorens UNIL/CHUV: Murielle Bochud Pierre-Yves Bochud Fabienne Maurer Marc Robinson-Rechavi Amalio Telenti Peter Vollenweider Gerard Weber Uni Geneva: Stylianos Antonarakis Manolis Dermitzakis Jacques Schrenzel Uni Bern: Cris Kuhlemeier Andri Rauch Richard Smith ETH & Uni Zurich: Konrad Basler Ernst Hafen Matthias Heinemann Christian v. Mehring Markus Noll Eckart Zitzler EPFL: Dario Floreano Felix Naef Uni Basel: Markus Affolter Mihaela Zavolan Weizmann: Naama Barkai Benny Shilo Orly Reiner MRC Cambridge: Ruth Loos Nick Wareham Uni Minnesota: Judith Berman GSK: Vincent Moser Dawn Waterworth UCSD: Trey Ideker UCLA: John Novembre
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Teaching: Past and Present http://www2.unil.ch/cbg/index.php?title=Teaching
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Teaching: Future 1.How can we equip Biology students at UNIL with basic knowledge in Computational Biology? more “ hands on ” training! group projects new Master 2.How can we educate proficient Computational Biologists? New Master program jointly with SIB, UniGE? Develop ties with EPFL?
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Integration: Past & Present
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Integration: Future How can UNIL/FBM strengthen its position in Computational Biology? 1. Networking! 2. Create new senior positions!
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Integration: Future How can UNIL/FBM strengthen its ties with the industry? Vincent Moser Andreas Schupert Manuel Peitsch Ulrich Genick Pierre Farmer David Heard Pietro Scalfaro CBG
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