• KIEAE Journal
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• v.17 no.5
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• pp.95-100
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• 2017

Purpose: The purpose of this study is to investigate the prior art based on deep learning to objectively calculate the metabolic rate which is the subjective factor for the PMV optimum control and to make a plan for future research based on this study. Methods: For this purpose, the theoretical and technical review and applicability analysis were conducted through various documents and data both in domestic and foreign. Results: As a result of the prior art research, the machine learning model of artificial neural network and deep learning has been used in various fields such as speech recognition, scene recognition, and image restoration. As a representative case, OpenCV Background Subtraction is a technique to separate backgrounds from objects or people. PASCAL VOC and ILSVRC are surveyed as representative technologies that can recognize people, objects, and backgrounds. Based on the results of previous researches on deep learning based on metabolic rate for occupational metabolic rate, it was found out that basic technology applicable to occupational metabolic rate calculation technology to be developed in future researches. It is considered that the study on the development of the activity quantity calculation model with high accuracy will be done.

• Journal of Microbiology and Biotechnology
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• v.13 no.4
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• pp.571-577
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• 2003

The intracellular metabolic fluxes can be calculated by metabolic flux analysis, which uses a stoichiometric model for the intracellulal reactions along with mass balances around the intracellular metabolites. In this study, metabolic flux analyses were carried out to estimate flux distributions for the maximum in silico yields of various metabolites in Escherichia coli. The maximum in silico yields of acetic acid and lactic acid were identical to their theoretical yields. On the other hand, the in silico yields of succinic acid and ethanol were only 83% and 6.5% of their theoretical yields, respectively. The lower in silico yield of succinic acid was found to be due to the insufficient reducing power. but this lower yield could be increased to its theoretical yield by supplying more reducing power. The maximum theoretical yield of ethanol could be achieved, when a reaction catalyzed by pyruvate decarboxylase was added in the metabolic network. Futhermore, optimal metabolic pathways for the production of various metabolites could be proposed, based on the results of metabolic flux analyses. In the case of succinic acid production, it was found that the pyruvate carboxylation pathway should be used for its optimal production in E. coli rather than the phosphoenolpyruvate carboxylation pathway.

• Genomics & Informatics
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• v.6 no.2
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• pp.68-71
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• 2008

The static approach of representing metabolic pathway diagrams offers no flexibility. Thus, many systems adopt automatic graph layout techniques to visualize the topological architecture of pathways. There are weaknesses, however, because automatically drawn figures are generally difficult to understand. The problem becomes even more serious when we attempt to visualize all of the information in a single, big picture, which usually results in a confusing diagram. To provide a partial solution to this thorny issue, we propose J2dpathway, a metabolic pathway atlas viewer that has node-abstracting features.

• IMMUNE NETWORK
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• v.11 no.2
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• pp.95-99
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• 2011

Type 1 diabetes is one of the classical examples of organ-specific autoimmune diseases characterized by lymphocytic infiltration or inflammation in pancreatic islets called 'insulitis'. In contrast, type 2 diabetes has been traditionally regarded as a metabolic disorder with a pathogenesis that is totally different from that of type 1 diabetes. However, recent investigation has revealed contribution of chronic inflammation in the pathogenesis of type 2 diabetes. In addition to type 2 diabetes, the role of chronic inflammation is being appreciated in a wide variety of metabolic disorders such as obesity, metabolic syndrome, and atherosclerosis. In this review, we will cover the role of innate immunity in the pathogenesis of metabolic disorders with an emphasis on NLRP3.

• Journal of the Korean Magnetic Resonance Society
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• v.21 no.1
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• pp.31-43
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• 2017

Together with radiotherapy, chemotherapy using cytotoxic agents is one of the most common therapies in cancer. Metabolic changes in cancer cells are drawing much attention recently, but the metabolic alterations by anticancer agents have not been much studied. Here, we investigated the effects of commonly used cytotoxic agents on lung normal cell MRC5 and lung cancer cell A549. We employed cis-plastin, doxorubicin, and 5-Fluorouracil and compared their effects on the viability and metabolism of the normal and cancer cell lines. We first established the concentration of the cytotoxic reagents that give differences in the viabilities of normal and cancer cell lines. In those conditions, the viability of A549 decreased significantly, whereas that of MRC5 remained unchanged. To study the metabolic alterations implicated in the viability differences, we obtained the metabolic profiles using $^1H$-NMR spectrometry. The $^1H$-NMR data showed that the metabolic changes of A549 cells are more remarkable than that of MRC5 cells and the effect of 5-FU on the A549 cells is the most distinct compared to other treatments. Heat map analysis showed that metabolic alterations under treatment of cytotoxic agents are totally different between normal and cancer cells. Multivariate analysis and weighted correlation network analysis (WGCNA) revealed a distinctive metabolite signature and hub metabolites. Two different analysis tools revealed that the changes of cell metabolism in response to cytotoxic agents were highly correlated with the Warburg effect and Reductive lipogenesis, two pathways having important effects on the cell survival. Taken together, our study addressed the correlation between the viability and metabolic profiles of MRC5 and A549 cells upon the treatment of cytotoxic anticancer agents.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.385-399
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• 2005

The phenotypic response of a cell results from a well orchestrated web of complex interactions which propagate from the genetic architecture through the metabolic flux network. To rationally design cell factories which carry out specific functional objectives by controlling this hierarchical system is a challenge. Transcriptome analysis, the most mature high-throughput measurement technology, has been readily applied In strain improvement programs in an attempt to Identify genes involved in expressing a given phenotype. Unfortunately, while differentially expressed genes may provide targets for metabolic engineering, phenotypic responses are often not directly linked to transcriptional patterns, This limits the application of genome-wide transcriptional analysis for the design of cell factories. However, improved tools for integrating transcriptional data with other high-throughput measurements and known biological interactions are emerging. These tools hold significant promise for providing the framework to comprehensively dissect the regulatory mechanisms that identify the cellular control mechanisms and lead to more effective strategies to rewire the cellular control elements for metabolic engineering.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.28-37
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• 2006

Recent studies in molecular biology and proteomics have identified a significant number of novel diagnostic, prognostic, and therapeutic disease markers. However, validation of these markers in clinical specimens with traditional histopathological techniques involves low throughput and is time consuming and labor intensive. Tissue microarrays (TMAs) offer a means of combining tens to hundreds of specimens of tissue onto a single slide for simultaneous analysis. This capability is particularly pertinent in the field of cancer for target verification of data obtained from cDNA micro arrays and protein expression profiling of tissues, as well as in epidemiology-based investigations using histochemical/immunohistochemical staining or in situ hybridization. In combination with automated image analysis, TMA technology can be used in the global cellular network analysis of tissues. In particular, this potential has generated much excitement in cardiovascular disease research. The following review discusses recent advances in the construction and application of TMAs and the opportunity for developing novel, highly sensitive diagnostic tools for the early detection of cardiovascular disease.

• 한국생물공학회:학술대회논문집
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• 2003.10a
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• pp.783-784
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• 2003

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.875-890
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• 2009

Metabolimics interprets an organism as a network of functional units and an organism is represented by a metabolic pathway i.e., well-displayed graph. So a software tool for drawing pathway is necessary to understand it comprehensively. These tools have a problem that edge-crossings exponentially increase as the number of nodes grows. To apply automatic graph layout techniques to the genome-scale metabolic flow, it is very important to reduce unnecessary edge-crossing on a metabolic pathway layout. In this paper, we design and implement 2.5D metabolic pathway layout modules. Metabolic pathways are represented hierarchically by making use of the '2-layered layout algorithm' in 3D. It enhances the readability and reduces unnecessary edge-crossings by using 3D layout modules instead of 2D layout algorithms.

• Journal of Ginseng Research
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• v.48 no.1
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• pp.103-111
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• 2024

Background: Ginseng (Panax ginseng Mayer) is an important natural medicine. However, a long culture period and challenging quality control requirements limit its further use. Although artificial cultivation can yield a sustainable medicinal supply, research on the association between the transplantation and chaining of metabolic networks, especially the regulation of ginsenoside biosynthetic pathways, is limited. Methods: Herein, we performed Liquid chromatography tandem mass spectrometry based metabolomic measurements to evaluate ginsenoside accumulation and categorise differentially abundant metabolites (DAMs). Transcriptome measurements using an Illumina Platform were then conducted to probe the landscape of genetic alterations in ginseng at various ages in transplantation mode. Using pathway data and crosstalk DAMs obtained by MapMan, we constructed a metabolic profile of transplantation Ginseng. Results: Accumulation of active ingredients was not obvious during the first 4 years (in the field), but following transplantation, the ginsenoside content increased significantly from 6-8 years (in the wild). Glycerolipid metabolism and Glycerophospholipid metabolism were the most significant metabolic pathways, as Lipids and lipid-like molecule affected the yield of ginsenosides. Starch and sucrose were the most active metabolic pathways during transplantation Ginseng growth. Conclusion: This study expands our understanding of metabolic network features and the accumulation of specific compounds during different growth stages of this perennial herbaceous plant when growing in transplantation mode. The findings provide a basis for selecting the optimal transplanting time.