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Computational analysis of biological systems: Past, present and future Sven Bergmann. UNIL tenure track commission 5 January 2010. Large (genomic) systems many uncharacterized elements relationships unknown - PowerPoint PPT Presentation
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Computational analysis of biological Computational analysis of biological systems: Past, present and futuresystems: Past, present and future
Sven BergmannSven Bergmann
UNIL tenure track commission 5 January 2010
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
PASTPAST
Large-scale data analysesLarge-scale data analyses
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)
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)
New Tools: Module Visualization
http://maya.unil.ch:7575/ExpressionView
Data Integration: Example NCI60
60 cancer cell lines (9 tissue types)
~23,000 gene probes
GeneExpression
Data
~5,000 drugs
Drug Response
Data
How to identify Co-modules?
Iteratively refine genes, cell-lines
and drugs to get co-modules
Z Kutalik, J Beckmann & SB, Nature Biotechnology (2008)
6’18
9 in
divi
dual
s
Phenotypes
159 measurement
144 questions
Genotypes
500.000 SNPs
CoLaus = Cohort Lausanne
Collaboration with:Vincent Mooser (GSK), Peter Vollenweider & Gerard Waeber (CHUV)
PCA of POPRES cohort
Impact: Web of Science 2005-2009
Impact: Who cites our work?
PRESENTPRESENT
Large-scale data analysisLarge-scale data analysis
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
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
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?
Status: - Dec: submitted to PLoS Computational Biology (IF=6.2) (after positive reply to pre-submission inquiry)
Status: accepted forpublication in Nature (IF=31.4 )
Status: - Dec: submitted to PLoS Genetics (IF=8.7), currently under review
Status: - submitted to Biostatistics (IF=3.4, 2nd best of 92 journals for Statistics & Probability)- Revision accounting for reviewers’ comments to be submitted soon
Status: accepted for publication GASTROENTEROLOGY (IF=12.6).
Status: submitted as application note to Bioinformatics (IF=4.32, 2nd best of 28 journals for Mathematical & Computational Biology)
Status: manuscript ready for submission to PLoS Comp Biology
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
PASTPAST
Modeling Modeling
Drosophila as model for Development
Quantitative Experimental Study using Automated Image
Processing
a: mark anterior and posterior pole, first and last eve-stripeb: extract region around dorsal midlinec: semi-automatic determination of stripes/boundaries
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.
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 morphogenat position x, time t
DiffusionD: diffusion const.
SourceDegradationα: decay rate
The Canonical Model
Model including nuclear trapping
2
02( )n n n
nn n n
M MD k MB k M s x
t xM
k MB k Mt
kn k-n
s0
D
Mn(x,t) nuclear emissionnuclear absorbtion
nuclear morphogen
diffusion
production
free morphogenM(x,t)
Nuclei density
B(x,t)
N
N
N
PRESENTPRESENT
Modeling Modeling
Precision is highest at mid-embryo
Similar trend in direct measurementsof Bcd noise byGregor et al. (Cell 2007)
1xbcd2xbcd4xbcd
Δ: GtΔ: Kr□: Hb o: Eve
Scaling is position-dependent!
“hyper-scaling” at anterior pole
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
• 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
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
Future directionsFuture directions
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?
Modular Approach for Integrative Analysis of Genotypes and Phenotypes
Individuals
Genotypes
Phenotypes
Me
as
ure
me
nts
SN
Ps/H
ap
lotyp
es
Modular links
Association of (average) module expression is often stronger than for any
of its constituent genes
Towards interactions: Network Approaches
for Integrative Association Analysis
Using knowledge on physical gene-interactions or pathways to prioritize the search for functional interactions
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
Acknowledgements to my group
http://serverdgm.unil.ch/bergmann
Funding: SystemsX.ch, SNSF, SIB, Cavaglieri, Leenaards, European FP
People: Zoltán KutalikMicha HerschAitana MortonDiana MarekBarbara PiaseckaBastian PeterKaren KapurAlain Sewer*Toby Johnson*Armand ValsessiaGabor CsardiSascha DalessiTim Hohm*alumni
Acknowledgements to my collaborators
DGM:Jacqui BeckmannRoman ChrastCarlo Rivolta
CIG:Christian FankhauserSophie MartinAlexandre ReymondMehdi TaftiBernard Thorens
UNIL/CHUV:Murielle BochudPierre-Yves BochudFabienne MaurerMarc Robinson-RechaviAmalio TelentiPeter VollenweiderGerard Weber
Uni Geneva:Stylianos AntonarakisManolis DermitzakisJacques Schrenzel
Uni Bern:Cris Kuhlemeier Andri RauchRichard Smith
ETH & Uni Zurich:Konrad BaslerErnst HafenMatthias HeinemannChristian v. MehringMarkus NollEckart ZitzlerEPFL:
Dario FloreanoFelix Naef
Uni Basel:Markus AffolterMihaela Zavolan
Weizmann:Naama BarkaiBenny ShiloOrly Reiner
MRC Cambridge:Ruth LoosNick Wareham
Uni Minnesota:Judith Berman
GSK:Vincent MoserDawn Waterworth
UCSD:Trey Ideker
UCLA:John Novembre
Teaching: Past and PresentTeaching: Past and Presenthttp://www2.unil.ch/cbg/index.php?title=Teaching
Teaching: FutureTeaching: 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?
Integration: Past & PresentIntegration: Past & Present
Integration: FutureIntegration: Future
How can UNIL/FBM strengthen its position in Computational Biology?
1. Networking!
2. Create new senior positions!
Integration: FutureIntegration: 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