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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