Continuous Dynamic Bayesian
Network For Gene Regulatory
Network Modeling
Presented by:
Norhaini
Binti
Baba
Supervisor: Dr.
Mohd
Saberi
Bin
Mohamad
Outline
Introduction
Literature
review
Research
Methodology
Materials &
Methods
Conclusion
Introduction
Literature
review
Conclusion
Materials and
methods
Research
methodology
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Literature
review
Research
Methodology
Materials &
Methods
Conclusion
Outline
Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
INTRODUCTION
Gene regulatory network
•
Collection of DNA segment
•
Plays role in governing the rates at which gene are transcribed
into mRNA to protein.
Introduction
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review
Research
Methodology
Materials &
Methods
Conclusion
Outline
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Continuous Dynamic Bayesian Network
•
Directed graphical models of stochastic process
•
Contain nodes
gene, edges
interaction between genes
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Methodology
Materials &
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Conclusion
Outline
Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Problem background
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Parametric model provide
limited information on
GRNs
(
Tarmo
Aijo
et al
., 2009)
Difficult of handling cyclic
network of gene regulation
(
Sunyong
Kim
et al
., 2004)
Discretisation might cause
information loss
(
Sunyong
Kim et al
., 2003)
Objectives
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Outline
Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
To study and understand the framework of the continuous Dynamic
Bayesian Networks.
To construct gene regulatory networks and evaluate the
performance based on implementation of continuous Dynamic
Bayesian Networks.
To model the continuous Dynamic Bayesian Network for the
construction of gene regulatory networks using the
Bayes
Net
Toolbox.
Scope
Literature
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Methodology
Materials &
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Justification
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Can optimize the network structure, which can give
best representation of the gene interactions
Can analyze the data as a continuous data
Avoiding the ambiguity about edge directions common
to static Bayesian network
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
LITERATURE REVIEW
Gene regulation
•
Process of cell or viruses regulate gene into gene product
•
Occur at 4 level in eukaryotes.
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Materials &
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Gene Regulatory Network
•
Genes
nodes.
•
The interaction between any pair of genes
edges.
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Methodology
Materials &
Methods
Conclusion
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Microarray technology
•
To monitor and quantify changes in gene expression.
•
Lead to revolution in speed and scope of genetic analysis of
regulatory networks.
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Gene Expression Data
•
Structure of how data is organized after hybridization of DNA
microarray.
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Comparison of Previous Methods
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Conclusion
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
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Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
RESEARCH METHODOLOGY
Research framework
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Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Phase
1
•
The studying and understanding the framework of
dynamic Bayesian network.
Phase
2
•
The modeling of dynamic Bayesian network using
BNT.
Phase
3
•
The construction of gene regulatory network and
evaluation of performance.
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review
Research
Methodology
Materials &
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Conclusion
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Data and
resource
collection
Imputation
missing value
Model cDBN using
BNT
Visualize DAG (
graphviz
)
Compare with KEGG database
Find optimal network give best
performance
Data sources
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Materials &
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Saccharomyces
cerevisiae
Dataset
Escherichia coli
Dataset
Spellman et al.,
1988
DNA Microarray
Datasets website
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Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
MATERIALS & METHODS
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Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Imputation missing value
•
Prepare a complete dataset for constructing dynamic Bayesian
networks.
•
Missing values must be imputed.
•
Using
knnimpute
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Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
>> A = data;
>>
ecoli
=
knnimpute
(A)
Algorithm to model cDBN in BNT
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Learning
Parameter learning
Structure learning

Parameter estimation for continuous
variables.

Maximum Likelihood estimation.

Data
䍐Cs

Constrained

based : test independencies

Search

and

score: define selection of
criterion that measures goodness of a
model based on scoring function.

Select the highest scoring model.
Construct gene regulatory network
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Implement
the cDBN model in
BNT using
Saccharomyces
cerevisiae
and
Escherichia coli
datasets.
Gene regulatory constructed is
displayed in DAG using
GraphViz
.
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Methodology
Materials &
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Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
CONCLUSION
Contributions
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Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
cDBN

to construct the
gene regulatory network
Overcome the problem
of
discretisation
Identify the best
performance of gene
regulatory network
Future works
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Methodology
Materials &
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Conclusion
Outline
Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Model the continuous
Dynamic Bayesian Network
using the
Bayes
Net Toolbox
Construct gene regulatory
network and visualize the
DAG using
GraphViz
Compare gene network with
KEGG database to evaluate
the performance
Conclusion
•
Continuous dynamic
bayesian
network constructed
systematically and criteria for learning network structures for
continuous data.
•
Advantage of using continuous dynamic
B
ayesian network is
discretisation
is not required.
•
Helps to study interaction of gene regulation.
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Research
Methodology
Materials &
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Conclusion
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Introduction
Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
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Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
Q & A
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Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling
THANK YOU
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