Computational analysis of biological systems: Past, present and future Sven Bergmann

1. Computational analysis of biological systems: Past, present and futureSven Bergmann UNIL tenure track commission 5 January 2010

2. Large (genomic) systems • many uncharacterizedelements • 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 Research Overview

3. PASTLarge-scale data analyses

4. Search for transcription modules: • Set of genes co-regulated undera 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)

5. Transcription modules random“seeds” Independent identification:Modules may overlap! Identification of transcription modules using many random “seeds” SB, J Ihmels & N Barkai Physical Review E (2003)

6. New Tools: Module Visualization http://maya.unil.ch:7575/ExpressionView

7. Drug Response Data Gene Expression Data ~5,000 drugs ~23,000 gene probes Data Integration: Example NCI60 60 cancer cell lines (9 tissue types)

8. How to identify Co-modules? Iteratively refine genes, cell-lines and drugs to get co-modules Z Kutalik, J Beckmann & SB, Nature Biotechnology(2008)

9. Phenotypes Genotypes 159 measurement 144 questions 500.000 SNPs CoLaus = Cohort Lausanne 6’189 individuals Collaboration with:Vincent Mooser (GSK), Peter Vollenweider & Gerard Waeber (CHUV)

10. PCA of POPRES cohort

15. Impact: Web of Science 2005-2009

16. Impact: Who cites our work?

17. PRESENTLarge-scale data analysis

18. 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

19. 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

20. So what do we miss? • Other variants like Copy Number Variations or epigenetics may play an important role • Interactions between genetic variants (GxG) or with the environment (GxE) • Many causal variants may be rare and/or poorly tagged by the measured SNPs • Many causal variants may have very small effect sizes

21. Status: • Dec: submitted to PLoS Computational Biology (IF=6.2) (after positive reply to pre-submission inquiry)

22. Status: accepted forpublication in Nature (IF=31.4)

23. Status: • Dec: submitted to PLoS Genetics (IF=8.7), currently under review

24. Status: • submitted to Biostatistics (IF=3.4, 2nd best of 92 journals for Statistics & Probability) • Revision accounting for reviewers’ comments to be submitted soon

25. Status: accepted for publication GASTROENTEROLOGY (IF=12.6).

26. Status: submitted as application note to Bioinformatics (IF=4.32, 2nd best of 28 journals for Mathematical & Computational Biology)

27. Status: manuscript ready for submission to PLoS Comp Biology

28. Large (genomic) systems • many uncharacterizedelements • 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 Research Overview

29. PAST Modeling

30. Drosophila as model for Development

31. Quantitative Experimental Study using Automated Image Processing a: mark anterior and posterior pole, first and last eve-stripe b: extract region around dorsal midlinec: semi-automatic determination of stripes/boundaries

32. Experimental Results: Positions • Bergmann S, Sandler S, Sberro H, Shnider S, Shilo B-Z, Schejter E and Barkai NPre-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 NStability of the Bicoid Gradient? Cell 132 (2008) 15.

33. Change in concentration of the morphogen at position x, time t Degradationα: decay rate Source Diffusion D: diffusion const. The Canonical Model A bit of Theory… The morphogen density M(x,t) can be modeled by a differential equation (reaction diffusion equation):

34. kn k-n s0 D Model including nuclear trapping N N nuclear morphogen Mn(x,t) nuclear absorbtion nuclear emission free morphogen M(x,t) production diffusion Nuclei density N B(x,t)

35. PRESENT Modeling

36. 1xbcd2xbcd4xbcdΔ:Gt Δ:Kr □: Hb o:Eve Similar trend in direct measurementsof Bcd noise byGregor et al. (Cell 2007) Precision is highest at mid-embryo

37. Scaling is position-dependent! “hyper-scaling” at anterior pole

38. 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

39. Modeling the Drosophila wing disk • 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

40. 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

41. Future directions

42. Organisms Biological Insight Data types ? The challenge of many datasets: How to integrate all the information? • Genotypic (SNPs/CNVs) • Epigenetic data • Gene/protein expression • Protein interactions • Organismal data

43. Modular Approach for Integrative Analysis of Genotypes and Phenotypes Individuals Modular links Phenotypes Measurements SNPs/Haplotypes Genotypes

44. Association of (average) module expression is often stronger than for any of its constituent genes

45. Towards interactions: Network Approaches for Integrative Association Analysis Using knowledge on physical gene-interactions or pathways to prioritize the search for functional interactions

46. 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

47. 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 Acknowledgements to my group Funding:SystemsX.ch, SNSF, SIB, Cavaglieri, Leenaards, European FP http://serverdgm.unil.ch/bergmann

48. Uni Geneva: Stylianos Antonarakis Manolis Dermitzakis Jacques Schrenzel Weizmann: Naama Barkai Benny Shilo Orly Reiner DGM: Jacqui Beckmann Roman Chrast Carlo Rivolta Acknowledgements to my collaborators Uni Bern: Cris Kuhlemeier Andri Rauch Richard Smith CIG: Christian Fankhauser Sophie Martin Alexandre Reymond Mehdi Tafti Bernard Thorens MRC Cambridge: Ruth Loos Nick Wareham Uni Minnesota: Judith Berman Uni Basel: Markus Affolter Mihaela Zavolan UNIL/CHUV: Murielle Bochud Pierre-Yves Bochud Fabienne Maurer Marc Robinson-Rechavi Amalio Telenti Peter Vollenweider Gerard Weber GSK: Vincent Moser Dawn Waterworth ETH & Uni Zurich: Konrad Basler Ernst Hafen Matthias Heinemann Christian v. Mehring Markus Noll Eckart Zitzler UCSD: Trey Ideker EPFL: Dario Floreano Felix Naef UCLA: John Novembre

49. Teaching: Past and Present http://www2.unil.ch/cbg/index.php?title=Teaching