• Molecules and Cells
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• v.27 no.3
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• pp.271-277
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• 2009

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

• Korean Journal of Environmental Biology
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• v.36 no.3
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• pp.370-376
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• 2018

It is important to consider interaction between species in understanding structure and function of the biological community. Current ecological issues such as climate change and habitat loss emphasize the significance of the concept of species interaction in that varying species' interaction across environmental gradients may lead to altered ecological function and services. However, most community studies have focused on species diversity through analysis of quantitative indices based on species composition and abundance data without considering species interactions in the community. 'Ecological network analysis' based on network theory enables exploration of structural and functional properties of ecosystems composed of various species and their interactions. In this paper, network analysis of Cheonggye stream as a case study was presented to promote uses of network analysis on ecological studies in Korea. Cheonggye stream has a simple biological structure with link density of 1.48, connectance 0.07, generality 4.43, and vulnerability 1.94. The ecological network analysis can be used to provide ecological interpretations of domestic long-term monitoring data and can contribute to conserving and managing species diversity in ecosystems.

• Korean Journal of Environmental Biology
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• v.26 no.4
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• pp.377-384
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• 2008

A cell is the product of a long period of evolution and can be represented as an optimized system (homeostasis). Stimuli from the outside environment are received by sensory apparatus on the surface of the cell and transferred through complicated pathways and eventually regulate gene expression. These signals affect cell physiology, growth, and development, and the interaction among genes in the signal transduction pathway is a critical part of the regulation. In this study, the interactions of deletion mutants and overexpression of the extracopies of the genes were used to understand their relationships to each other. Also, green fluorescent protein (GFP reporter gene) was fused to the regulatory genes to elucidate their interactions. Cooverexpression of the two genes in extracopy plasmids suggested that patS acts at the downstream of hetR in the regulatory network. The experiments using gfp fusion in different genetic background cells also indicated the epistasis relationships between the two genes. A model describing the regulatory network that controls cell development is presented.

• The Zoological Society Korea : Newsletter
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• v.15 no.1
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• pp.20-29
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• 1998

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.131-138
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• 2017

New drug development is time-consuming and costly. Hence, it is necessary to repurpose old drugs for finding new indication. We suggest the way that repurposing old drug using massive literature data and biological network. We supposed a disease-drug relationship can be available if signal pathways of the relationship include significant genes identified in literature data. This research is composed of three steps-identifying significant gene using co-occurrence in literature; analyzing the shortest path on biological network; and scoring a relationship with comparison between the significant genes and the shortest paths. Based on literatures, we identify significant genes based on the co-occurrence frequency between a gene and disease. With the network that include weight as possibility of interaction between genes, we use shortest paths on the network as signal pathways. We perform comparing genes that identified as significant gene and included on signal pathways, calculating the scores and then identifying the candidate drugs. With this processes, we show the drugs having new possibility of drug repurposing and the use of our method as the new method of drug repurposing.

• Journal of Ginseng Research
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• v.43 no.3
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• pp.475-481
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• 2019

Background: The ginsenoside Rg1 has been shown to exert various pharmacological activities with health benefits. Previously, we have reported that Rg1 promoted myogenic differentiation and myotube growth in C2C12 myoblasts. In this study, the in vivo effect of Rg1 on fiber-type composition and oxidative metabolism in skeletal muscle was examined. Methods: To examine the effect of Rg1 on skeletal muscle, 3-month-old mice were treated with Rg1 for 5 weeks. To assess muscle strength, grip strength tests were performed, and the lower hind limb muscles were harvested, followed by various detailed analysis, such as histological staining, immunoblotting, immunostaining, and real-time quantitative reverse transcription polymerase chain reaction. In addition, to verify the in vivo data, primary myoblasts isolated from mice were treated with Rg1, and the Rg1 effect on myotube growth was examined by immunoblotting and immunostaining analysis. Results: Rg1 treatment increased the expression of myosin heavy chain isoforms characteristic for both oxidative and glycolytic muscle fibers; increased myofiber sizes were accompanied by enhanced muscle strength. Rg1 treatment also enhanced oxidative muscle metabolism with elevated oxidative phosphorylation proteins. Furthermore, Rg1-treated muscles exhibited increased levels of anabolic S6 kinase signaling. Conclusion: Rg1 improves muscle functionality via enhancing muscle gene expression and oxidative muscle metabolism in mice.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.115-120
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• 2022

The main purpose of the study is to identify the key aspects of the use of innovative distance learning technologies in the training of biology students. Currently, there is a modernization, the evolution of the education system from a classical university to a virtual one, from lecture material teaching to computer educational programs, from a book library to a computer one, from multi-volume paper encyclopedias to modern search databases. During studies in higher education, distance learning ensures the delivery of information in an interactive mode through the use of information and communication technologies. The main disadvantage of distance learning is the emotional interaction of the teacher with students. It is necessary to increase the level of methodological developments for independent studies of students. The methodology includes a number of theoretical methods. Based on the results of the study, the main elements of the use of innovative distance learning technologies in the training of biology students were identified.

• BMB Reports
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• v.41 no.4
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• pp.287-293
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• 2008

In a yeast two-hybrid screen, we identified the microtubule-destabilizing protein SCG10 as a potential effector protein of $BRI_3$. The association was verified using GST pull-down, Co-IP, and their perinuclear co-localization. The analysis of in vitro microtubule polymerization/depolymerization showed that the binding of $BRI_3$ to SCG10 effectively blocked the ability of SCG10 to induce microtubule disassembly, as determined by turbidimetric assays. In intact PC12 cells, $BRI_3$ exhibited the ability to stabilize the microtubule network and attenuate the microtubule-destabilizing activity of SCG10. Furthermore, co-expression of $BRI_3$ with SCG10 attenuated SCG10-mediated PC12 cell neurite outgrowth induced by NGF. These results identify a novel connection between a neuron-specific BRI protein and the cytoskeletal network, suggesting possible roles of BRI3 in the process of neuronal differentiation.

• Genomics & Informatics
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• v.1 no.1
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• pp.1-6
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• 2003

In conclusion, the seemingly fuzzy and disorganized data of biology with thousands of different layers ranging from molecule to the Internet have refused so far to be mapped precisely and predicted successfully by mathematicians, physicists or computer scientists. Genomics and bioinformatics are the fields that process such complex data. The insights on the nature of biological entities as complex interaction networks are opening a door toward a generalization of the representation of biological entities. The main challenge of genomics and bioinformatics now lies in 1) how to data mine the networks of the domains of bioinformatics, namely, the literature, metabolic pathways, and proteome and structures, in terms of interaction; and 2) how to generalize the networks in order to integrate the information into computable genomic data for computers regardless of the levels of layer. Once bioinformatists succeed to find a general principle on the way components interact each other to form any organic interaction network at genomic scale, true simulation and prediction of life in silico will be possible.

• Proceedings of the Zoological Society Korea Conference
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• 2001.10a
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• pp.184.3-185
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• 2001

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