Download BiS732 Bio-Network Draft for Term

BiS732 Bio-Network Draft for Term-project
20063390 Sunjae LEE
Abstract
To construct genetic regulatory network, many researchers used microarray data with
the various condition. But, due to the lack of information which has lower information
than its problem space, it is hardly to find exact genetic regulatory network. And also,
in eukaryotes, transcription factor (TF) regulate gene expression by not alone, but
combinatorially. So, in this project, we focused on the combinatorial behavior of TFs,
and used TF knock out microarray data for finding its combinatorial behavior. And also
TF knock out data provides the significant and higher information about regulation
relationship because the basic role of TF is reguation. After modelling the combinatorial
regulatory network, we will construct combinatorial regulatory network using bZIP TF
especially.
Introduction
There have been many research for inferring genetic regulatory network. Using
microarray data, we can catch the snapshot of internal state of the cell, the expression
level of
transcript. With the various condition, we can infer the dependence of genes,
moreover,
infer the genetic regulatory network. But these research are based on the
assumption that genetic regulatory network can be decomposed of simple relationship,
“A regulates B”. In eukaryotic transcription, this assumption does not support the
rational of inferring network. In comparision with prokaryotes, higher eukaryotic
organism regulate their gene expression by using TFs combinatorially rather than
belonging many TFs.
In this project, we use the TF knock out data for finding combinatorial regulatory
network. If the profile of TF1 knock out is similar to the profile of TF2 knock out, then
it is probable that TF1, TF2 co-regulate the certain target gene. So, TF knock out data
can be the way of finding regulatory module. And also examining dependence of TF
profile, has more abundant information content. Because comparing relation among a
few experiments (Knock out experiment) has more information than comparing relation
among the thousands of genes.
Before constructing the network, we have to develop the model of combinatorial
regulatory network using TF knock out data. To decompose the network into the simple
unit, two types of elements are included in the unit. One is the “regulator”, that is, the
TF. The other is the “expressioner”, that is, the target gene. The sort of TFs and their
target gene will identify the certain regulatory module.
Regulators
T
F
T
T F
F
Expressioner
[Regulatory module]
Fig.1 the unit of combinatorial regulatory network
After identifying the unit of regulatory network, the interaction among the unit also has
to be defined. This will be called “module interaction”. If some units share the same
element, interactions are occurred. These interactions are “regulator-regulator”,
“regulator-expressioner”,
“expressioner-experssioner”.
Especillay,
“regulator-
expressioner” relation explains the regulating relationship between regulatory module.
And another relationship explains the divere of regulation mechanism.
These work will construct the combinatorial regulatory network, and these framework
will give the fundamentals of understanding combinatorial network. Finishing the exact
combinatorial regulatory network, more complicate and well-organized network can be
constructed from this framework.
Fig.2 whole combinatorial regulatory network
Method
1. Unit Identification
Two types of element are included in the unit. As metioned before, these are TFs,
target genes. To identify the regulating TFs to a certain target gene, we make the two
-step strategy. First, find the TF groups of dependent profile. And then, decompose
the group according to the target gene. Similarly regulating TFs supposed to have
ability to be included in the same regulatory module. After classifying the gene groups,
we exactly separate module by its target gene. To find out its target gene, we regarded
the differetially expressed gene (DEG) as the target gene.
2. Unit Interaction
Type of interaction can be classified as three in the majority. First case is when same
element occurred at regulator of one unit, and regulator of another. Second case is
when same element occurred at regulator of one unit, and expressioner of another. Last
case is when occurred at expressioner of one unit, and expressioner of anthor unit. If
same element occurred at “regulator-regulator” relation, it can be regarded as the
independent. Many TFs can regulate many other genes, so this type of interaction can
be happened diversely. And another interaction type is the “regulator-expressioner”
interaction.
especially this relationship
regulates some module.
“expressioner-
expressioner” relationship can be regarded as the different mechanism of regulation.
Because two regulatory module has same target gene, but they did not has same
regulator TFs. To identify these interactions, find out the co-occurrenced element in
the network. And based on the type of elements, we can draw the relationships between
modules.
Result
Analysis
Discussion
1. sort of TF
knock out data is hardly obtained, so profile information of few sort of TFs can be
obtained. So, many regulatory modules are not able to be gathered.
2. Difficuly of verification
there are few data about regulatory network, and also the eukaryotic transcription is
more difficult environment to gather data. so, verification method or experiment will be
needed to confirm this network.
Reference
Friedman N, Linial M, Nachman I, Pe'er D. Using Bayesian networks to analyze
expression data. J Comput Biol. 2000;7(3-4):601-20