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 La compétition de tous contre tous
A system’s biology approach to understand stochasticity in gene expression
Olivier Gandrillon. Mars 2009 1. Why investigate stochasticity in gene expression?
2. A system’s biology approach
3. The models
4. The experiments
5. Toward a virtuous circle
1. Why investigate stochasticity in gene expression?
2. A system’s biology approach
3. The models
4. The experiments
5. Toward a virtuous circle
Gene expression is a stochastic phenomenon
• Elowitz et al., Science 2002
« Given the exploding experimental progress there is little doubt that stochastic gene
expression is establishing itself as one of the most central and exciting problems in molecular biology. » Johan Paulsson, 2005
Stochasticity in gene expression could play a role in various phenomena, The fingerprints of identical
twins are readily distinguished on close examination (Jain AK, et al. Pattern Recognition 2002;35:2653.)
including differentiation…
Stochastic generation of diversity…
Kupiec JJ. (1997). Mol Gen Genet 255, 201­208.
Inter­stabilisation
… upon which a selective constraint is applied
Kupiec JJ. (1997). Mol Gen Genet 255, 201­208.
Quantitative variations can unravel qualitative differences:
Chang et al (2008). Nature 453, 544­548
1. Why investigate stochasticity in gene expression?
2. A system’s biology approach
3. The models
4. The experiments
5. Toward a virtuous circle
In order to analyze the biological function of stochasticity, one has to be able to manipulate it. For this, it is mandatory to understand its molecular basis.
Data
Models
In contrast with most system’s biology approaches which are data­
driven, we decided in the present project to opt for a question­
driven approach. 1. Why investigate stochasticity in gene expression?
2. A system’s biology approach
3. The models
4. The experiments
5. Toward a virtuous circle
Gene expression is a multistep process. Stochasticity can arise at all steps
Transcription factors
Chromatin
Gene
mRNA
Transcription
Nucleus
Cytoplasm
mRNA
Protein
Maturation
Fluorescent Protein
Translation
Previous models tended to underestimate the promoter contribution:
Our model focuses on the promoter level
2nd model: taking space into account. Transcription factors
TF binding site
DNA
[Soula et al. 2005 , BMC Bioinformatics; Coulon et al. 2008, Methods in Molecular Biology]
1. Why investigate stochasticity in gene expression?
2. A system’s biology approach
3. The models
4. The experiments
5. Toward a virtuous circle
T2EC: primary avian erythrocytic progenitor cells
Self renewal
TGFα
TGFβ1
Dexamethasone
Differentiation ­> erythrocytes
Anaemic chicken serum
Insulin
4.1: Involvement of the chromatin structure
T2ECs
Transfection
Tol2
CMV
Cherry
Tol2
mKO
Tol2
or
Tol2
CMV
Mass culture
Mass culture
FACS
Clone 1
Clone 2
Clone 3
Clone 1
Extracting DNA
Acquiring fluorescence
Cloning and sequencing integration points (work in progress)
FACS or single cells in real time
4.1: measuring endogenous genes stochasticity
Quantum Dot + Molecular Beacon
Preliminary conclusions:
•
The construction of the theoretical model has revealed many previously unsuspected counter­intuitive sources of stochasticity;
•
We have designed an experimental system through which we will be able to correlate the position of a transgene within the genome to the amount of stochasticity it displays; •
We have set up an experimental system thank’s to which one can acquire gene expression levels through fluorescence at a very high frequency (up to 0.08 Hz; every 2 minutes) on normal eukaryotic cells.
1. Why investigate stochasticity in gene expression?
2. A system’s biology approach
3. The models
4. The experiments
5. Toward a virtuous circle
Raise the temperature
Compare
Change the promoter
Compare
Antoine COULON
Mathieu GINESTE
Gaël KANEKO
Camila MEJIA­PEREZ
José VINUELAS
Labo Biopuce (CEA Grenoble)
François CHATELAIN
Alexandra FUCHS
Manuel THERY
ENS Paris
Jean Jacques KUPIEC
Antoine COULON
Guillaume BESLON
Gaël KANEKO