• Journal of Microbiology and Biotechnology
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• v.25 no.7
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• pp.963-977
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• 2015

The well-characterized gram-positive bacterium Bacillus subtilis is an outstanding industrial candidate for protein expression owing to its single membrane and high capacity of secretion, simplifying the downstream processing of secretory proteins. During the last few years, there has been continuous progress in the illustration of secretion mechanisms and application of this robust host in various fields of life science, such as enzyme production, feed additives, and food and pharmaceutical industries. Here, we review the developments of Bacillus subtilis as a highly promising expression system illuminating strong chemical- and temperatureinducible and other types of promoters, strategies for ribosome-binding-site utilization, and the novel approach of signal peptide selection. Furthermore, we outline the main steps of the Sec pathway and the relevant elements as well as their interactions. In addition, we introduce the latest discoveries of Tat-related complex structures and functions and the countless applications of this full-folded protein secretion pathway. This review also lists some of the current understandings of ATP-binding cassette transporters. According to the extensive knowledge on the genetic modification strategies and molecular biology of Bacillus subtilis, we propose some suggestions and strategies for improving the yield of intended productions. We expect this to promote striking future developments in the optimization and application of this bacterium.

• Journal of Microbiology and Biotechnology
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• v.22 no.9
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• pp.1279-1287
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• 2012

We established an efficient transformation method for thermophile Geobacillus kaustophilus HTA426 using conjugative transfer from Escherichia coli of host-mimicking plasmids that imitate DNA methylation of strain HTA426 to circumvent its DNA restriction barriers. Two conjugative plasmids, pSTE33T and pUCG18T, capable of shuttling between E. coli and Geobacillus spp., were constructed. The plasmids were first introduced into E. coli BR408, which expressed one inherent DNA methylase gene (dam) and two heterologous methylase genes from strain HTA426 (GK1380-GK1381 and GK0343-GK0344). The plasmids were then directly transferred from E. coli cells to strain HTA426 by conjugative transfer using pUB307 or pRK2013 as a helper plasmid. pUCG18T was introduced very efficiently (transfer efficiency, $10^{-5}-10^{-3}\;recipient^{-1}$). pSTE33T showed lower efficiency ($10^{-7}-10^{-6}\;recipient^{-1}$) but had a high copy number and high segregational stability. Methylase genes in the donor substantially affected the transfer efficiency, demonstrating that the host-mimicking strategy contributes to efficient transformation. The transformation method, along with the two distinguishing plasmids, increases the potential of G. kaustophilus HTA426 as a thermophilic host to be used in various applications and as a model for biological studies of this genus. Our results also demonstrate that conjugative transfer is a promising approach for introducing exogenous DNA into thermophiles.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.847-859
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• 2011

cDNA microarray-based comparative genomic hybridization(CGH) data includes low-intensity spots and thus a statistical strategy is needed to detect subtle differences between different cancer classes. In this study, genes displaying a high frequency of alteration in one of the different classes were selected among the pre-selected genes that show relatively large variations between genes compared to total variations. Utilizing copy-number changes of the selected genes, this study suggests a statistical approach to predict patients' classes with increased performance by pre-classifying patients with similar genetic alteration scores. Two-stage logistic regression model(TLRM) was suggested to pre-classify homogeneous patients and predict patients' classes for cancer prediction; a decision tree(DT) was combined with logistic regression on the set of informative genes. TLRM was constructed in cDNA microarray-based CGH data from the Cancer Metastasis Research Center(CMRC) at Yonsei University; it predicted the patients' clinical diagnoses with perfect matches (except for one patient among the high-risk and low-risk classified patients where the performance of predictions is critical due to the high sensitivity and specificity requirements for clinical treatments. Accuracy validated by leave-one-out cross-validation(LOOCV) was 83.3% while other classification methods of CART and DT performed as comparisons showed worse performances than TLRM.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.1
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• pp.58-66
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• 2011

Arsenic pollution is a serious global concern which affects all life forms. Being a toxic metalloid, the continued search for appropriate technologies for its remediation is needed. Phytoremediation, the use of green plants, is not only a low cost but also an environmentally friendly approach for metal uptake and stabilization. However, its application is limited by slow plant growth which is further aggravated by the phytotoxic effect of the pollutant. Attempts to address these constraints were done by exploiting plant-microbe interactions which offers more advantages for phytoremediation. Several bacterial mechanisms that can increase the efficiency of phytoremediation of As are nitrogen fixation, phosphate solubilization, siderophore production, ACC deaminase activity and growth regulator production. Many have been reported for other metals, but few for arsenic. This mini-review attempts to present what has been done so far in exploring plants and their rhizosphere microbiota and some genetic manipulations to increase the efficiency of arsenic soil phytoremediation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1534-1538
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• 2005

In this paper, we propose a method to detect contours of a face, eyes, and a mouth of a person in the color image in order to make an avatar automatically. First, we use the HSI color model to exclude the effect of various light conditions, and find skin regions in the input image by using the skin color defined on HS-plane. And then, we use deformable templates and genetic algorithm (GA) to detect contours of a face, eyes, and a mouth. Deformable templates consist of B-spline curves and control point vectors. Those represent various shapes of a face, eyes and a mouth. GA is a very useful search algorithm based on the principals of natural selection and genetics. Second, the avatar is automatically created by using GA-detected contours and Fuzzy C-Means clustering (FCM). FCM is used to reduce the number of face colors. In result, we could create avatars which look like handmade caricatures representing user's identity. Our approach differs from those generated by existing methods.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.256-265
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• 2014

Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using non-dominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

• Journal of Korea Game Society
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• v.2 no.2
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• pp.60-65
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• 2002

In the majority of today's computer games, the behaviour of characters are controlled by pre-defined game logic or pre-generated motion. As game developers strive for richer and more interactive games, they often encounter limitations with this approach. This paper attempts to construct a game model using Genetic Algorithms (GAs) in order to produce more intelligent and compelling computer games. Based on teaming ability, the use of GAs will enable the characters to continually evolve, providing a changing and dynamic game environment. A real-time game was implemented to investigate the performance and limitations of the system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.111-114
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• 1997

Usually, Evolutionary Algorithms are considered more efficient for optimal system design, However, the performance of the system is determined by fitness function and system environment. In this paper, in order to overcome the limitation of the performance by this factor, we propose a co-evolutionary method that two populations constantly interact and coevolve. In this paper, we apply coevolution to neural network's evolving. So, one population is composed of the structure of neural networks and other population is composed of training patterns. The structure of neural networks evolve to optimal structure and, at the same time, training patterns coevolve to feature patterns. This method prevent the system from the limitation of the performance by random design of neural network structure and inadequate selection of training patterns. In this time neural networks are trained by evolution strategies that are able to apply to the unsupervised learning. And in the coding of neural networks, we propose the method to maintain nonredundancy and character preservingness that are essential factor of genetic coding. We show the validity and the effectiveness of the proposed scheme by applying it to the visual servoing of RV-M2 robot manipulators.

• Genomics & Informatics
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• v.10 no.4
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• pp.244-248
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• 2012

Oxidative stress, which results in an excessive product of reactive oxygen species (ROS), is one of the fundamental mechanisms of the development of hypertension. In the vascular system, ROS have physical and pathophysiological roles in vascular remodeling and endothelial dysfunction. In this study, ROS-hypertension-related genes were collected by the biological literature-mining tools, such as SciMiner and gene2pubmed, in order to identify the genes that would cause hypertension through ROS. Further, single nucleotide polymorphisms (SNPs) located within these gene regions were examined statistically for their association with hypertension in 6,419 Korean individuals, and pathway enrichment analysis using the associated genes was performed. The 2,945 SNPs of 237 ROS-hypertension genes were analyzed, and 68 genes were significantly associated with hypertension (p < 0.05). The most significant SNP was rs2889611 within MAPK8 (p = $2.70{\times}10^{-5}$; odds ratio, 0.82; confidence interval, 0.75 to 0.90). This study demonstrates that a text mining approach combined with association analysis may be useful to identify the candidate genes that cause hypertension through ROS or oxidative stress.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.51-58
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• 2004

This paper proposes a new artificial immune approach to on-line hardware test which is the most indispensable technique for fault tolerant hardware. A novel algorithm of generating tolerance conditions is suggested based on the principle of the antibody diversity. Tolerance conditions in artificial immune system correspond to the antibody in biological immune system. In addition, antigen presenting cell (APC) is realized by Quine-McCluskey method in this algorithm and tolerance conditions are generated through GA (Genetic Algorithm). The suggested method is applied to the on-line monitoring of a typical FSM (a decade counter) and its effectiveness is demonstrated by the computer simulation.