• Proceedings of the Zoological Society Korea Conference
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• 2003.08a
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• pp.10-10
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• 2003

No Abstract, See Full Text

• BMB Reports
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• v.39 no.5
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• pp.560-570
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• 2006

Mistletoe (Viscum album) lectins, which are classified as a type II ribosome-inactivating protein (RIP) due to their unique biological function and the potential medical and therapeutic application in cancer cells, receive a rising attention. The heterodimeric glycoproteins contain the A-chain with catalytic activity and the B-chain with sugar binding properties. In recent years, studies involving the lectins from the white berry European mistletoe (Viscum album) and the yellow berry Korean mistletoe (Viscum album coloratum) have been described. However, the detailed mechanism in exerting unique cytotoxic effect on cancer cells still remains unclear. Here, we aim to understand and define the molecular basis and biological effects of the type II RIPs, through the studies of the recombinant Korean mistletoe lectin. To this end, we expressed, purified the recombinant Korean mistletoe lectin (rKML), and investigated its molecular characteristics in vitro, its cytotoxicity and ability to induce apoptotic cell death in cancer cells. To gain structural basis for its catalytic activity and sugar binding properties, we performed homology modeling studies based on the high degree of sequence identity and conserved secondary structure prediction between Korean and European, Himalayan mistletoe lectins, and Ricin.

• Molecules and Cells
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• v.22 no.1
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• pp.13-20
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• 2006

Hepatitis C virus (HCV) non-structural protein 5A protein (NS5A), which consists of three functional domains, is involved in regulating viral replication, interferon resistance, and apoptosis. Recently, the three-dimensional structure of the domain 1 was determined. However, currently the molecular basis for the domains 2 and 3 of HCV NS5A is yet to be defined. Toward this end, we expressed, purified the domain 2 of the NS5A (NS5A-D2), and then performed biochemical and structural studies. The purified domain 2 was active and was able to bind NS5B and PKR, biological partners of NS5A. The results from gel filtration, CD analysis, 1D $^1H$ NMR and 2D $^1H-^{15}N$ heteronuclear single quantum correlation (HSQC) spectroscopy indicate that the domain 2 of NS5A appears to be flexible and disordered.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.830-836
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• 2005

Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

• Molecules and Cells
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• v.42 no.2
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• pp.104-112
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• 2019

Tracking the fate of individual cells and their progeny through lineage tracing has been widely used to investigate various biological processes including embryonic development, homeostatic tissue turnover, and stem cell function in regeneration and disease. Conventional lineage tracing involves the marking of cells either with dyes or nucleoside analogues or genetic marking with fluorescent and/or colorimetric protein reporters. Both are imaging-based approaches that have played a crucial role in the field of developmental biology as well as adult stem cell biology. However, imaging-based lineage tracing approaches are limited by their scalability and the lack of molecular information underlying fate transitions. Recently, computational biology approaches have been combined with diverse tracing methods to overcome these limitations and so provide high-order scalability and a wealth of molecular information. In this review, we will introduce such novel computational methods, starting from single-cell RNA sequencing-based lineage analysis to DNA barcoding or genetic scar analysis. These novel approaches are complementary to conventional imaging-based approaches and enable us to study the lineage relationships of numerous cell types during vertebrate, and in particular human, development and disease.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• Journal of Preventive Medicine and Public Health
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• v.48 no.4
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• pp.203-215
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• 2015

Objectives: This study was performed to investigate the relationship between the incidence of national notifiable infectious diseases (NNIDs) and meteorological factors, air pollution levels, and hospital resources in Korea. Methods: We collected and stored 660 000 pieces of publicly available data associated with infectious diseases from public data portals and the Diseases Web Statistics System of Korea. We analyzed correlations between the monthly incidence of these diseases and monthly average temperatures and monthly average relative humidity, as well as vaccination rates, number of hospitals, and number of hospital beds by district in Seoul. Results: Of the 34 NNIDs, malaria showed the most significant correlation with temperature (r=0.949, p<0.01) and concentration of nitrogen dioxide (r=-0.884, p<0.01). We also found a strong correlation between the incidence of NNIDs and the number of hospital beds in 25 districts in Seoul (r=0.606, p<0.01). In particular, Geumcheon-gu was found to have the lowest incidence rate of NNIDs and the highest number of hospital beds per patient. Conclusions: In this study, we conducted a correlational analysis of public data from Korean government portals that can be used as parameters to forecast the spread of outbreaks.

• Journal of Microbiology and Biotechnology
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• v.32 no.3
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• pp.365-377
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• 2022

Mammalian target of rapamycin (mTOR) is a serine-threonine kinase member of the cellular phosphatidylinositol 3-kinase (PI3K) pathway, which is involved in multiple biological functions by transcriptional and translational control. mTOR is a downstream mediator in the PI3K/Akt signaling pathway and plays a critical role in cell survival. In cancer, this pathway can be activated by membrane receptors, including the HER (or ErbB) family of growth factor receptors, the insulin-like growth factor receptor, and the estrogen receptor. In the present work, we congregated an electronic network of mTORC1 built on an assembly of data using natural language processing, consisting of 470 edges (activations/interactions and/or inhibitions) and 206 nodes representing genes/proteins, using the Cytoscape 3.6.0 editor and its plugins for analysis. The experimental design included the extraction of gene expression data related to five distinct types of cancers, namely, pancreatic ductal adenocarcinoma, hepatic cirrhosis, cervical cancer, glioblastoma, and anaplastic thyroid cancer from Gene Expression Omnibus (NCBI GEO) followed by pre-processing and normalization of the data using R & Bioconductor. ExprEssence plugin was used for network condensation to identify differentially expressed genes across the gene expression samples. Gene Ontology (GO) analysis was performed to find out the over-represented GO terms in the network. In addition, pathway enrichment and functional module analysis of the protein-protein interaction (PPI) network were also conducted. Our results indicated NOTCH1, NOTCH3, FLCN, SOD1, SOD2, NF1, and TLR4 as upregulated proteins in different cancer types highlighting their role in cancer progression. The MCODE analysis identified gene clusters for each cancer type with MYC, PCNA, PARP1, IDH1, FGF10, PTEN, and CCND1 as hub genes with high connectivity. MYC for cervical cancer, IDH1 for hepatic cirrhosis, MGMT for glioblastoma and CCND1 for anaplastic thyroid cancer were identified as genes with prognostic importance using survival analysis.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.428-432
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• 2005

This paper has studied the concept and technical factors of e-Learning system to which we intend to apply domestic biodiversity information. This article describes how we analyzed and designed the e-learning system to serve biodiversity information as e-Learning contents. It would be useful for the public and students if this information are organized and provided as e-learning contents especially in our country which has well-established network infrastructure considering the limited land space. It is expected that the establishment of e-Learning system based on this proposed design will help students and public to access and team biodiversity on cyber space.

• Journal of Integrative Natural Science
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• v.17 no.1
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• pp.13-20
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• 2024

Female breast cancer is the fifth highest cause of mortality. Breast cancer is the most prevalent type of cancer in women globally, while it can also affect men. STAT5A plays a role in its development and progression. Given that activation of STAT5a is frequently linked to the growth and progression of tumors, STAT5a has been identified as a possible target for the therapy of several cancers. STAT5A, in particular, has proven to be overexpressed in various breast cancer cell lines and tumors, and it has been associated to the promotion of tumour cell proliferation and survival. STAT5A inhibition has been shown in vitro and in vivo to reduce the development of breast cancer cells. As a result, we have screened compounds from the FDA database that might serve as potential inhibitors of STAT5a through virtual screening, docking, DFT and MD simulation approaches. The drug Nilotinib has shown promising results inhibiting STAT5a. Further, in-vitro analysis will be carried forward to understand the anti-cancer activity.