• BMB Reports
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• v.54 no.9
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• pp.464-469
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• 2021

Cardiomyocyte differentiation occurs through complex and finely regulated processes including cardiac lineage commitment and maturation from pluripotent stem cells (PSCs). To gain some insight into the genome-wide characteristics of cardiac lineage commitment, we performed transcriptome analysis on both mouse embryonic stem cells (mESCs) and human induced PSCs (hiPSCs) at specific stages of cardiomyocyte differentiation. Specifically, the gene expression profiles and the protein-protein interaction networks of the mESC-derived platelet-derived growth factor receptor-alpha (PDGFRα)⁺ cardiac lineage-committed cells (CLCs) and hiPSC-derived kinase insert domain receptor (KDR)⁺ and PDGFRα⁺ cardiac progenitor cells (CPCs) at cardiac lineage commitment were compared with those of mesodermal cells and differentiated cardiomyocytes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that the genes significantly upregulated at cardiac lineage commitment were associated with responses to organic substances and external stimuli, extracellular and myocardial contractile components, receptor binding, gated channel activity, PI3K-AKT signaling, and cardiac hypertrophy and dilation pathways. Protein-protein interaction network analysis revealed that the expression levels of genes that regulate cardiac maturation, heart contraction, and calcium handling showed a consistent increase during cardiac differentiation; however, the expression levels of genes that regulate cell differentiation and multicellular organism development decreased at the cardiac maturation stage following lineage commitment. Additionally, we identified for the first time the protein-protein interaction network connecting cardiac development, the immune system, and metabolism during cardiac lineage commitment in both mESC-derived PDGFRα⁺ CLCs and hiPSC-derived KDR⁺PDGFRα⁺ CPCs. These findings shed light on the regulation of cardiac lineage commitment and the pathogenesis of cardiometabolic diseases.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.274-276
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• 2005

본 논문에서는 단백질 상호작용 네트워크의 복잡성을 제어하고 생물학자가 자신이 설정한 조건을 만족시키는 환경에서 추가적인 다양한 제약 조건을 가하면서 원하는 상호작용 네트워크를 구성하고 조작할 수 있도록 지원하는 Constraints Based Dynamic Protein Interaction Network 이라는 새로운 개념의 단백질 상호작용 네트워크를 소개한다. 본 기법에서는 기존의 단백질 상호작용 네트워크에서 주로 사용하는 단백질 상호작용 정보뿐 아니라 단백질 상호작용에 영향을 미칠 수 있는 개개 단백질의 물리 화학적 특성 및 위치 정보와 상호작용의 환경 정보도 단백질 상호작용 네트워크 구성에 활용한다. 제안된 네트워크상에서 생물학자는 단백질 상호작용 네트워크 구성 조건을 변경하거나 얻어진 네트워크에 변경을 가하면서 점차 자신이 원하는 의미 일은 대사경로 모델을 찾거나, 제약조건의 다양한 조작을 통하여 생물학적 실험을 통하여 얻어진 대사모델의 유효성을 검증하는 것도 가능하다.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.4
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• pp.508-518
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• 2019

Objective: This research aimed to determine biological pathways and protein-protein interaction (PPI) networks for 305-d milk yield (MY), 305-d fat yield (FY), and age at first calving (AFC) in the Thai multibreed dairy population. Methods: Genotypic information contained 75,776 imputed and actual single nucleotide polymorphisms (SNP) from 2,661 animals. Single-step genomic best linear unbiased predictions were utilized to estimate SNP genetic variances for MY, FY, and AFC. Fixed effects included herd-year-season, breed regression and heterosis regression effects. Random effects were animal additive genetic and residual. Individual SNP explaining at least 0.001% of the genetic variance for each trait were used to identify nearby genes in the National Center for Biotechnology Information database. Pathway enrichment analysis was performed. The PPI of genes were identified and visualized of the PPI network. Results: Identified genes were involved in 16 enriched pathways related to MY, FY, and AFC. Most genes had two or more connections with other genes in the PPI network. Genes associated with MY, FY, and AFC based on the biological pathways and PPI were primarily involved in cellular processes. The percent of the genetic variance explained by genes in enriched pathways (303) was 2.63% for MY, 2.59% for FY, and 2.49% for AFC. Genes in the PPI network (265) explained 2.28% of the genetic variance for MY, 2.26% for FY, and 2.12% for AFC. Conclusion: These sets of SNP associated with genes in the set enriched pathways and the PPI network could be used as genomic selection targets in the Thai multibreed dairy population. This study should be continued both in this and other populations subject to a variety of environmental conditions because predicted SNP values will likely differ across populations subject to different environmental conditions and changes over time.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3E
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• pp.126-131
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• 2002

Ultrasonic measurements are made in egg white to study the properties of the solution of the natural protein. The high-Q ultrasonic resonator method is used to get the ultrasonic absorption spectra over the range 0.2-10 ㎒ at 20℃. It is proportional to the 1.25th power of the frequency. The gelation process caused by heat is studied from the change in the velocity and the absorption. at 3 ㎒ using the pulse echo overlap technique over the range of 10-80℃. The absorption decreases with increasing temperature up to 60℃ where it turns up sharply and rapidly increases thereafter. The strong absorption in the gel region is described by the interaction between the solution and the network structure made of protein. Very slow variation in time elapse is observed after the temperature is quickly raised. It would be a real-time observation of the network building process and the characteristic time for the process is shown to be 400 min. A hysteresis phenomenon with respect to the temperature is observed. This phenomenon is associated with the memorizing effect of the network structure of protein of the gel.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.2
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• pp.60-67
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• 2009

Analyzing protein-protein interactions(PPI) is an important task in bioinformatics as it can help in new drugs' discovery process. However, due to vast amount of PPI data and their complexity, efficient visualization of the data is still remained as a challenging problem. We have developed efficient and effective visualization system that integrates Gene Ontology(GO) and PPI network to provide better insights to scientists. To provide efficient data visualization, we have employed dynamic interactive graph drawing methods and context-based browsing strategy. In addition, quick and flexible cross-reference system between GO and PPI; LCA(Least Common Ancestor) finding for GO; and etc are supported as special features. In terms of interface, our visualization system provides two separate graphical windows side-by-side for GO graphs and PPI network, and also provides cross-reference functions between them.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.529-535
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• 2015

Biological networks have been handled with the static concept. However, life phenomena in cells occur depending on the cellular state and the external environment, and only a few proteins and their interactions are selectively activated. Therefore, we should adopt the dynamic network concept that the structure of a biological network varies along the flow of time. This concept is effective to analyze the progressive transition of the disease. In this paper, we applied the proposed method to Alzheimer's disease to analyze the structural and functional characteristics of the disease network. Using gene expression data and protein-protein interaction data, we constructed the sub-networks in accordance with the progress of disease (normal, early, middle and late). Based on this, we analyzed structural properties of the network. Furthermore, we found module structures in the network to analyze the functional properties of the sub-networks using the gene ontology analysis (GO). As a result, it was shown that the functional characteristics of the dynamics network is well compatible with the stage of the disease which shows that it can be used to describe important biological events of the disease. Via the proposed approach, it is possible to observe the molecular network change involved in the disease progression which is not generally investigated, and to understand the pathogenesis and progression mechanism of the disease at a molecular level.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.31-40
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• 2005

In the post-genomic era, researches on proteins as well as genes have been increasingly required. Particularly, work on protein-protein interaction and protein network construction have been recently establishing. Most biologists publish their research results through papers or other media. However, biologists do not use the information effectively, because the published research results are very large. As the growth of internet field, it becomes easy to access these research results. It is important to extract information with a biological meaning from various media. Therefore, In this paper, we efficiently extract the protein information from many open papers or other media and construct the database of the extracted information. We build a protein network from the established database and then design and implement various pathway analysis algorithms which find biological meaning from the protein network.

• Molecules and Cells
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• v.39 no.9
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• pp.692-698
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• 2016

Advances in next generation sequencing (NGS) technologies have enabled population-level studies for many animals to unravel the relationships between genotypic differences and traits of specific populations. The objective of this study was to perform evolutionary analysis of single nucleotide polymorphisms (SNP) in genes of Korean native cattle Hanwoo in comparison to SNP data from four other cattle breeds (Jersey, Simmental, Angus, and Holstein) and four related species (pig, horse, human, and mouse) obtained from public databases through NGS-based resequencing. We analyzed population structures and differentiation levels for the five cattle breeds and estimated species-specific SNPs with their origins and phylogenetic relationships among species. In addition, we identified Hanwoo-specific genes and proteins, and determined distinct changes in protein-protein interactions among five species (cattle, pig, horse, human, mouse) in the STRING network database by additionally considering indirect protein interactions. We found that the Hanwoo population was clearly different from the other four cattle populations. There were Hanwoo-specific genes related to its meat trait. Protein interaction rewiring analysis also confirmed that there were Hanwoo-specific protein-protein interactions that might have contributed to its unique meat quality.

• Biotechnology and Bioprocess Engineering:BBE
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• v.6 no.5
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• pp.326-331
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• 2001

With the aim of developing of pH-sensitive controlled drug release system, a poly(Llysine) (PLL) based cationic semi-interpenetrating polymer network (semi-IPN) has been synthesized. This cationic hydrogel was designed to swell at lower pH and de-swell at higher pH and therefore be applicable for achieving regulated drug release at a specific pH range. In addition to the pH sensitivity, this hydrogel was anticipated to interact with an ionic drug, providing another means to regulate the release rate of ionic drugs. This semi-IPN hydrogel was prepared using a free-radical polymerization method and by crosslinking of the polyethylene glycol (PEG)-methacrylate polymer through the PLL network. The two polymers were penetrated with each other via interpolymer complexation to yield the semi-IPN structures. The PLL hydrogel thus prepared showed dynamic swelling/de-swelling behavior in response to pH change, and such a behavior was influenced by both the concentrations of PLL and PEG-methacrylate. Drug release from this semi-IPN hydrogel was also investigated using a model protein drug, streptokinase. Streptokinase release was found to be dependent on its ionic interaction with the PLL backbones as well as on the swelling of the semi-IPN hydrogel. These results suggest that a PLL semi-IPN hydrogel could potentially be used as a drug delivery platform to modulate drug release by pH-sensitivity and ionic interaction.

• Genomics & Informatics
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• v.19 no.4
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• pp.42.1-42.17
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• 2021

Salivary gland carcinoma (SGC) is rare cancer, constituting 6% of neoplasms in the head and neck area. The most responsible genes and pathways involved in the pathology of this disorder have not been fully understood. We aimed to identify differentially expressed genes (DEGs), the most critical hub genes, transcription factors, signaling pathways, and biological processes (BPs) associated with the pathogenesis of primary SGC. The mRNA dataset GSE153283 in the Gene Expression Omnibus database was re-analyzed for determining DEGs in cancer tissue of patients with primary SGC compared to the adjacent normal tissue (adjusted p-value < 0.001; |Log2 fold change| > 1). A protein interaction map (PIM) was built, and the main modules within the network were identified and focused on the different pathways and BP analyses. The hub genes of PIM were discovered, and their associated gene regulatory network was built to determine the master regulators involved in the pathogenesis of primary SGC. A total of 137 genes were found to be differentially expressed in primary SGC. The most significant pathways and BPs that were deregulated in the primary disease condition were associated with the cell cycle and fibroblast proliferation procedures. TP53, EGF, FN1, NOTCH1, EZH2, COL1A1, SPP1, CDKN2A, WNT5A, PDGFRB, CCNB1, and H2AFX were demonstrated to be the most critical genes linked with the primary SGC. SPIB, FOXM1, and POLR2A significantly regulate all the hub genes. This study illustrated several hub genes and their master regulators that might be appropriate targets for the therapeutic aims of primary SGC.