• The Korea Genome Conference
• /
• 2004.07a
• /
• pp.49.2-49.2
• /
• 2004

• Journal of Microbiology and Biotechnology
• /
• v.16 no.5
• /
• pp.651-660
• /
• 2006

Natural products are a rich source of biologically active small molecules and a fertile area for lead discovery of new drugs [10, 52]. For instance, 5% of the 1,031 new chemical entities approved as drugs by the US Food and Drug Administration (FDA) were natural products between 1981 and 2002, and another 23% were natural product-derived molecules [53]. These molecules have evolved through millions of years of natural selection to interact with biomolecules in the cells or organisms and offer unrivaled chemical and structural diversity [14, 37]. Nonetheless, a large percentage of nature remains unexplored, in particular, in the marine and microbial environments. Therefore, natural products are still major valuable sources of innovative therapeutic agents for human diseases. However, even when a natural product is found to exhibit biological activity, the cellular target and mode of action of the compound are mostly mysterious. This is also true of many natural products that are currently under clinical trials or have already been approved as clinical drugs [11]. The lack of information on a definitive cellular target for a biologically active natural product prevents the rational design and development of more potent therapeutics. Therefore, there is a great need for new techniques to expedite the rapid identification and validation of cellular targets for biologically active natural products. Chemical genomics is a new integrated research engine toward functional studies of genome and drug discovery [40, 69]. The identification and validation of cellular receptors of biologically active small molecules is one of the key goals of the discipline. This eventually facilitates subsequent rational drug design, and provides valuable information on the receptors in cellular processes. Indeed, several biologically crucial proteins have already been identified as targets for natural products using chemical genomics approach (Table 1). Herein, the representative case studies of chemical genomics using natural products derived from microbes, marine sources, and plants will be introduced.

• Proceedings of the KSRS Conference
• /
• 2000.04a
• /
• pp.9-9
• /
• 2000

For the planning of future land use for economic activities, an essential component is the identification of the vulnerable areas for natural hazard and environmental impacts from the activities. Also, exploration for mineral and energy resources is carried out by a step by step approach. At each step, a selection of the target area for the next exploration strategy is made based on all the data harnessed from the previous steps. The uncertainty of the selected target area containing undiscovered resources is a critical factor for estimating the exploration risk. We have developed not only spatial prediction models based on adapted artificial intelligence techniques to predict target and vulnerable areas but also validation techniques to estimate the uncertainties associated with the predictions. The prediction models will assist the scientists and decision-makers to make two critical decisions: (i) of the selections of the target or vulnerable areas, and (ii) of estimating the risks associated with the selections.

• Smart Structures and Systems
• /
• v.26 no.6
• /
• pp.735-751
• /
• 2020

Identification of structure parameters is crucial in Structural Health Monitoring (SHM) context for activities such as model validation, damage assessment and signal processing of structure response. In this paper, guided waves generated by piezoelectric transducers are used for in-situ and non-destructive structural parameter estimation based on Bayesian approach. As Bayesian approach needs iterative process, which is computationally expensive, this paper proposes a method in which an analytical model is selected and developed in order to decrease computational time and complexity of modeling. An experimental set-up is implemented to estimate three target elastic and geometrical parameters: Young's modulus, Poisson ratio and thickness of aluminum and steel plates. Experimental and simulated data are combined in a Bayesian framework for parameter identification. A significant accuracy is achieved regarding estimation of target parameters with maximum error of 8, 11 and 17 percent respectively. Moreover, the limitation of analytical model concerning boundary reflections is addressed and managed experimentally. Pulse excitation is selected as it can excite the structure in a wide frequency range contrary to conventional tone burst excitation. The results show that the proposed non-destructive method can be used in service for estimation of material and geometrical properties of structure in industrial applications.

• Proceedings of the PSK Conference
• /
• 2003.10a
• /
• pp.92-92
• /
• 2003

The drug discovery landscape is changing rapidly in the post-genomic era. Mapping of the human genome has led to an abundance of potential drug targets. Drug discovery times and costs can be significantly reduced by developing methods for high throughput target identification/ validation, multiplexed assay development and high efficient combinatorial chemistry. (omitted)

• Journal of Microbiology and Biotechnology
• /
• v.31 no.8
• /
• pp.1154-1162
• /
• 2021

The transcriptional capacities of target genes are strongly influenced by promoters, whereas few studies have focused on the development of robust, high-performance and cross-species promoters for wide application in different bacteria. In this work, four novel promoters (P_k.rtufB, P_k.r1, P_k.r2, and P_k.r3) were predicted from Ketogulonicigenium robustum and their inconsistency in the -10 and -35 region nucleotide sequences indicated they were different promoters. Their activities were evaluated by using green fluorescent protein (gfp) as a reporter in different species of bacteria, including K. vulgare SPU B805, Pseudomonas putida KT2440, Paracoccus denitrificans PD1222, Bacillus licheniformis and Raoultella ornithinolytica, due to their importance in metabolic engineering. Our results showed that the four promoters had different activities, with P_k.r1 showing the strongest activity in almost all of the experimental bacteria. By comparison with the commonly used promoters of E. coli (tufB, lac, lacUV5), K. vulgare (Psdh, Psndh) and P. putida KT2440 (JE111411), the four promoters showed significant differences due to only 12.62% nucleotide similarities, and relatively higher ability in regulating target gene expression. Further validation experiments confirmed their ability in initiating the target minCD cassette because of the shape changes under the promoter regulation. The overexpression of sorbose dehydrogenase and cytochrome c551 by P_k.r1 and P_k.r2 resulted in a 22.75% enhancement of 2-KGA yield, indicating their potential for practical application in metabolic engineering. This study demonstrates an example of applying bioinformatics to find new biological components for gene operation and provides four novel promoters with broad suitability, which enriches the usable range of promoters to realize accurate regulation in different genetic backgrounds.

• Nuclear Engineering and Technology
• /
• v.54 no.1
• /
• pp.269-274
• /
• 2022

An artificial neural network (ANN) that identifies radionuclides from low-count gamma spectra of a NaI scintillator is proposed. The ANN was trained and tested using simulated spectra. 14 target nuclides were considered corresponding to the requisite radionuclide library of a radionuclide identification device mentioned in IEC 62327-2017. The network shows an average identification accuracy of 98.63% on the validation dataset, with the gross counts in each spectrum N_c = 100~10000 and the signal to noise ratio SNR = 0.05-1. Most of the false predictions come from nuclides with low branching ratio and/or similar decay energies. If the N_c>1000 and SNR>0.3, which is defined as the minimum identifiable condition, the averaged identification accuracy is 99.87%. Even when the source and the detector are covered with lead bricks and the response function of the detector thus varies, the ANN which was trained using non-shielding spectra still shows high accuracy as long as the minimum identifiable condition is satisfied. Among all the considered nuclides, only the identification accuracy of ²³⁵U is seriously affected by the shielding. Identification of other nuclides shows high accuracy even the shielding condition is changed, which indicates that the ANN has good generalization performance.

• Journal of Microbiology and Biotechnology
• /
• v.33 no.1
• /
• pp.83-95
• /
• 2023

These days, bacterial detection methods have some limitations in sensitivity, specificity, and multiple detection. To overcome these, novel detection and identification method is necessary to be developed. Recently, NGS panel method has been suggested to screen, detect, and even identify specific foodborne pathogens in one reaction. In this study, new NGS panel primer sets were developed to target 13 specific virulence factor genes from five types of pathogenic Escherichia coli, Listeria monocytogenes, and Salmonella enterica serovar Typhimurium, respectively. Evaluation of the primer sets using singleplex PCR, crosscheck PCR and multiplex PCR revealed high specificity and selectivity without interference of primers or genomic DNAs. Subsequent NGS panel analysis with six artificially contaminated food samples using those primer sets showed that all target genes were multi-detected in one reaction at 10⁸-10⁵ CFU of target strains. However, a few false-positive results were shown at 10⁶-10⁵ CFU. To validate this NGS panel analysis, three sets of qPCR analyses were independently performed with the same contaminated food samples, showing the similar specificity and selectivity for detection and identification. While this NGS panel still has some issues for detection and identification of specific foodborne pathogens, it has much more advantages, especially multiple detection and identification in one reaction, and it could be improved by further optimized NGS panel primer sets and even by application of a new real-time NGS sequencing technology. Therefore, this study suggests the efficiency and usability of NGS panel for rapid determination of origin strain in various foodborne outbreaks in one reaction.

• Food Science and Biotechnology
• /
• v.17 no.5
• /
• pp.1038-1046
• /
• 2008

This study was carried out to develop a small-sized biosensor based on surface plasmon resonance (SPR) for the rapid identification of insecticide residues for food safety. The SPR biosensor module consists of a single 770 nm-light emitting diodes (LED) light source, several optical lenses for transferring light, a hemisphere sensor chip, photo detector, A/D converter, power source, and software for signal processing using a computer. Except for the computer, the size and weight of the sensor module are 150 (L)$\times$70 (W)$\times$120 (H) mm and 828 g, respectively. Validation and application procedures were designed to assess refractive index analysis, affinity properties, sensitivity, linearity, limits of detection, and robustness which includes an analysis of baseline stability and reproducibility of ligand immobilization using carbamate (carbofuran and carbaryl) and organophosphate (cadusafos, ethoprofos, and chlorpyrifos) insecticide residues. With direct binding analysis, insecticide residues were detected at less than the minimum 0.01 ppm and analyzed in less than 100 sec with a good linear relationship. Based on these results, we find that the binding interaction with active target groups in enzymes using the miniaturized SPR biosensor could detect low concentrations which satisfy the maximum residue limits for pesticide tolerance in Korea, Japan, and the USA.

• Biomolecules & Therapeutics
• /
• v.22 no.4
• /
• pp.267-274
• /
• 2014

Drug development and preclinical trials are challenging processes and more than 80% to 90% of drug candidates fail to gain approval from the United States Food and Drug Administration. Predictive and efficient tools are required to discover high quality targets and increase the probability of success in the process of new drug development. One such solution to the challenges faced in the development of new drugs and combination therapies is the use of low-cost and experimentally manageable in vivo animal models. Since the 1980's, scientists have been able to genetically modify the mouse genome by removing or replacing a specific gene, which has improved the identification and validation of target genes of interest. Now genetically engineered mouse models (GEMMs) are widely used and have proved to be a powerful tool in drug discovery processes. This review particularly covers recent fascinating technologies for drug discovery and preclinical trials, targeted transgenesis and RNAi mouse, including application and combination of inducible system. Improvements in technologies and the development of new GEMMs are expected to guide future applications of these models to drug discovery and preclinical trials.