• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.237-240
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• 2007

본 논문에서는 유전자 발현데이터로부터 유전자 조절네트워크를 추론하는 유전자 알고리즘을 제안한다. 근래에 유전자 알고리즘을 이용하여 유전자 조절네트워크를 추론하려는 시도가 있었으나 그리 성공적이지 못하였다. 우리는 본 논문에서 유전자 조절네트워크를 보다 효율적으로 추론할 수 있게 하기 위하여 새로운 유전자 인코딩 기법을 개발하여 적용하였다. 선형 유전자 조절네트워크로 모델링 된 인공 유전자 조절네트워크를 사용하여 실험한 결과 대부분의 경우에 있어서 주어진 인공 유전자 조절네트워크와 유사한 네트워크를 추론하였으며 완전히 동일한 유전자네트워크를 추론하기도 하였다. 향후 실제 유전자 발현 데이터를 이용하여 추론해 보는 것이 필요하다.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.339-343
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• 2005

Boolean networks(BN) construction is one of the commonly used methods for building gene networks from time series microarray data. However, BN has two major drawbacks. First, it requires heavy computing times. Second, the binary transformation of the microarray data may cause a loss of information. This paper propose two methods using liner regression to construct gene regulatory networks. The first proposed method uses regression based BN variable selection method, which reduces the computing time significantly in the BN construction. The second method is the regression based network method that can flexibly incorporate the interaction of the genes using continuous gene expression data. We construct the network structure from the simulated data to compare the computing times between Boolean networks and the proposed method. The regression based network method is evaluated using a microarray data of cell cycle in Caulobacter crescentus.

• Bioinformatics and Biosystems
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• v.2 no.1
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• pp.12-16
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• 2007

DNA repair means a collection of processes that a cell identifies and corrects damage to genome sequence. The DNA repair processes are important because a genome would not be able to maintain its essential cellular functions without the processes. In this research, we make some gene regulatory networks of DNA repair in S. cerevisiae to know how each gene interacts with others. Two approaches are adapted to make the networks; Bayesian Network and ARACNE. After construction of gene regulatory networks based on the two approaches, the two networks are compared to each other to predict which genes have important roles in the DNA repair processes by finding conserved interactions and looking for hubs. In addition, each interaction between genes in the networks is validated with interaction information in S. cerevisiae genome database to support the meaning of predicted interactions in the networks.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.202-205
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• 2005

Cell phenotypes are determined by the concerted activity of thousands of genes and their products. This activity is coordinated by a complex network that regulates the expression of genes. Understanding this organization is crucial to elucidate cellular activities, and many researches have tried to construct gene regulatory networks from mRNA expression data which are nowadays the most available and have a lot of information for cellular processes. Several computational tools, such as Boolean network, Qualitative network, Bayesian network, and so on, have been applied to infer these networks. Among them, Bayesian networks that we chose as the inference tool have been often used in this field recently due to their well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. To alleviate this problem, we had developed the algorithm of MONET(MOdularized NETwork learning), which is a new method for inferring modularized gene networks by utilizing two complementary sources of information: biological annotations and gene expression. Afterward, we have packaged and improved MONET by combining dispersed functional blocks, extending species which can be inputted in this system, reducing the time complexities by improving algorithms, and simplifying input/output formats and parameters so that it can be utilized in actual fields. In this paper, we present the architecture of MONET system that we have improved.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.164-169
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• 2003

In this paper, we propose the integrated Bayesian network framework to reconstruct genetic regulatory networks from genome expression data. The proposed model overcomes the dimensionality problem of multivariate analysis by building coherent sub-networks from confined gene clusters and combining these networks via intermediary points. Gene Shaving algorithm is used to cluster genes that share a common function or co-regulation. Retrieved clusters incorporate prior biological knowledge such as Gene Ontology, pathway, and protein protein interaction information for extracting other related genes. With these extended gene list, system builds genetic sub-networks using Bayesian network with MDL score and Sparse Candidate algorithm. Identifying functional modules of genes is done by not only microarray data itself but also well-proved biological knowledge. This integrated approach can improve there liability of a network in that false relations due to the lack of data can be reduced. Another advantage is the decreased computational complexity by constrained gene sets. To evaluate the proposed system, S. Cerevisiae cell cycle data [1] is applied. The result analysis presents new hypotheses about novel genetic interactions as well as typical relationships known by previous researches [2].

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.457-463
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• 2015

The pathogenesis of mantle cell lymphoma, a special subtype of lymphoma that is invasive and indolent and has a median survival of 3 to 4 years, is still partially unexplained. Much research about genes and miRNAs has been conducted in recent years, but interactions and regulatory relations of genetic elements which may play a vital role in genesis of MCL have attracted only limited attention. The present study concentrated on regulatory relations about genes and miRNAs contributing to MCL pathogenesis. Numerous experimentally validated raw data were organized into three topology networks, comprising differentially expressed, associated and global examples. Comparison of similarities and dissimilarities of the three regulating networks, paired with the analysis of the interactions between pairs of elements in every network, revealed that the differentially expressed network illuminated the carcinogenicity mechanism of MCL and the related network further described the regulatory relations involved, including prevention, diagnosis, development and therapy. Three kinds of regulatory relations for host genes including miRNAs, miRNAs targeting genes and genes regulating miRNAs were concluded macroscopically. Regulation of the differentially expressed miRNAs was also analyzed, in terms of abnormal gene expression affecting the MCL pathogenesis. Special regulatory relations were uncovered. For example, auto-regulatory loops were found in the three topology networks, key pathways of the nodes being highlighted. The present study focused on a novel point of view revealing important influencing factors for MCL pathogenesis.

• Interdisciplinary Bio Central
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• v.1 no.1
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• pp.4.1-4.7
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• 2009

Introduction: Histone modifications and DNA methylation are the major factors in epigenetic gene regulation. Especially, revealing how histone modifications are related to DNA methylation is one of the challenging problems in this field. In this paper, we address this issue and propose several plausible mechanisms for precise controlling of DNA methylation status at CpG islands. Materials and Methods: To establish the regulatory relationships, we used 38 histone modification types including H2A.Z and CTCF, and DNA methylation status at CpG islands across chromosome 6, 20, and 22 of human CD4+ T cell. We utilized Bayesian network to construct regulatory network. Results and Discussion: We found several meaningful relationships supported by previous studies. In addition, our results show that histone modifications can be clustered into several groups with different regulatory properties. Based on those findings we predicted the status of methylation level at CpG islands with high accuracy, and suggested core-regulatory network to control DNA methylation status.

• Genomics & Informatics
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• v.18 no.3
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• pp.26.1-26.6
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• 2020

Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type-specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request.

• Korean Journal of Microbiology
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• v.30 no.5
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• pp.347-354
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• 1992

New regulatory, loci which participate in the regulation of anaerobic inducible gene expression in Salmonella typhimurium were identified. We observed the regulatory network of new regulator mutations to various anaerobic inducible gene (1). Some anaerobic inducible lac fusions were also induced at low pH condition which was severe environment to withstand for its virulence at the place like phagolysosome. Sic oxygen-regulated regulatory mutants (oxr) isolated by Tn10 mutagenesis were divided into two groups. Five of them were found to show negative effect on the regulation of anaerobic gene expression, while on e showed positive effect on the regulation. Genetic loci of four oxr were identified with 54 Mud-P22 lysogens covering the whole chromosome of S. typhimurium, in the nearby region of map unit 87 min (oxr101), 63 min (oxr104), 97 min (oxr 105), and 57 min (oxr 106), respectively. Two oxr mutants were subjected to two-dimensional polyacrylamide electrophoretic analysis of anaerobic inducible proteins for searching the control circuitry of our oxr mutants.

• Journal of Microbiology and Biotechnology
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• v.15 no.3
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• pp.551-559
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• 2005

The recent rapid increase in genomic data related to many microorganisms and the development of computational tools to accurately analyze large amounts of data have enabled us to design several kinds of simulation approaches for the complex behaviors of cells. Among these approaches, dFBA (dynamic flux balance analysis), which utilizes FBA, differential equations, and regulatory events, has correctly predicted cellular behaviors under given environmental conditions. However, until now, dFBA has centered on substrate concentration, cell growth, and gene on/off, but a detailed hierarchical structure of a regulatory network has not been taken into account. The use of Boolean rules for regulatory events in dFBA has limited the representation of interactions between specific regulatory proteins and genes and the whole transcriptional regulation mechanism with environmental change. In this paper, we adopted the operon as the basic structure, constructed a hierarchical structure for a regulatory network with defined fundamental symbols, and introduced a weight between symbols in order to solve the above problems. Finally, the total control mechanism of regulatory elements (operons, genes, effectors, etc.) with time was simulated through the linkage of dFBA with regulatory network modeling. The lac operon, trp operon, and tna operon in the central metabolic network of E. coli were chosen as the basic models for control patterns. The suggested modeling method in this study can be adopted as a basic framework to describe other transcriptional regulations, and provide biologists and engineers with useful information on transcriptional regulation mechanisms under extracellular environmental change.