Reverse Engineering Gene Networks Using Approximate Bayesian

111 Pages·2011·3.58 MB·English

by  Andrea Rau

Introduction ABC-MCMCforNetworks Simulations DataAnalysis Discussion
Reverse Engineering Gene Networks Using Approximate
Bayesian Computation (ABC)
Rencontre de statistique autour des mod`eles hi´erarchiques:
Universit´e de Strasbourg
Andrea Rau
January 14, 2011
1/34
Introduction ABC-MCMCforNetworks Simulations DataAnalysis Discussion
Outline
1. Introduction
• Gene regulatory networks
2. Approximate Bayesian Computation
• Background and motivation
• Monte Carlo approaches
3. ABC-MCMC for Networks
• Simulation studies
• Real data analysis: SOS DNA repair system in E. coli
4. Discussion
2/34
Introduction ABC-MCMCforNetworks Simulations DataAnalysis Discussion
Gene Expression
• Genes: Functional regions of DNA that encode proteins and RNA
molecules
3/34
Introduction ABC-MCMCforNetworks Simulations DataAnalysis Discussion
Gene Expression
• Genes: Functional regions of DNA that encode proteins and RNA
molecules
• Expression levels of thousands of genes can be measured using
“high-throughput” technologies (e.g., microarrays, serial analysis of
gene expression, next-generation sequencing)
AAAAA
AAAAA
AAAAA
AAAAA
AAAAA
AAAAA
AAAAA

A B C D E F G
3/34
Introduction ABC-MCMCforNetworks Simulations DataAnalysis Discussion
Time-Course Gene Expression
• Time-course gene expression data can elucidate information about
patterns of relationships of gene expression in a cell
• Large number of genes, few biological replicates or time points...
N (cid:28) P paradigm
4/34
Introduction ABC-MCMCforNetworks Simulations DataAnalysis Discussion
Gene Regulatory Networks
• Gene regulatory networks: set of genes that interact indirectly with
one another through proteins called transcription factors (TF)

Gene 1 Gene 2 Gene 3 Gene 4
DNA
Coding DNA (gene)
Transcrip!on factor
TF binding site in promoter region
5/34
Introduction ABC-MCMCforNetworks Simulations DataAnalysis Discussion
Gene Regulatory Networks
• Gene regulatory networks: set of genes that interact indirectly with
one another through proteins called transcription factors (TF)

Gene 1 Gene 2 Gene 3 Gene 4
DNA
Coding DNA (gene)
Transcrip!on factor
TF binding site in promoter region
• Abundance of TF is difficult to measure ⇒ expression levels of
corresponding genes usually used as proxy
5/34
Introduction ABC-MCMCforNetworks Simulations DataAnalysis Discussion
Gene Regulatory Networks
• Gene regulatory networks: set of genes that interact indirectly with
one another through proteins called transcription factors (TF)

Gene 1 Gene 2 Gene 3 Gene 4
DNA
Coding DNA (gene)
Transcrip!on factor
TF binding site in promoter region
• Abundance of TF is difficult to measure ⇒ expression levels of
corresponding genes usually used as proxy
5/34
Introduction ABC-MCMCforNetworks Simulations DataAnalysis Discussion
Gene Regulatory Networks
• Gene regulatory networks: set of genes that interact indirectly with
one another through proteins called transcription factors (TF)

Gene 1 Gene 2 Gene 3 Gene 4
DNA
2
Coding DNA (gene) 1 3
Transcrip!on factor
TF binding site in promoter region 4
• Abundance of TF is difficult to measure ⇒ expression levels of
corresponding genes usually used as proxy
5/34
Introduction ABC-MCMCforNetworks Simulations DataAnalysis Discussion
Gene Regulatory Networks
• Gene regulatory networks: set of genes that interact indirectly with
one another through proteins called transcription factors (TF)

Gene 1 Gene 2 Gene 3 Gene 4
DNA
2
Coding DNA (gene) 1 3
Transcrip!on factor
TF binding site in promoter region 4
• Abundance of TF is difficult to measure ⇒ expression levels of
corresponding genes usually used as proxy
5/34