• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.69.3-69.3
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• 2019

The CHIMPS2 survey is to extend the JCMT HARP $^{13}CO/C^{18}O$ J=3-2 Inner Milky-Way Plane Survey (CHIMPS) and the ${12}^CO$ J=3-2 survey (COHRS) into the inner Galactic Plane, the Central Molecular Zone (CMZ), and a section of the Outer Plane. When combined with the complementary $^{12}CO/^{13}CO/C^{18}O$ J=1-0 survey at the Nobeyama 45m (FUGIN) at matching 15" resolution and sensitivity, and other current CO surveys, the results will provide a complete set of transition data with which to calculate accurate column densities, gas temperatures and turbulent Mach numbers. These will be used to: analyze molecular cloud properties across a range of Galactic environments; map the star-formation efficiency (SFE) and dense-gas mass fraction (DGMF) in molecular gas as a function of position in the Galaxy and its relation to the nature of the turbulence within molecular clouds; determine Galactic structure as traced by molecular gas and star formation; constrain cloud-formation models; study the relationship of filaments to star formation; test current models of the gas kinematics and stability in the Galactic center region and the flow of gas from the disc. It will also provide an invaluable legacy data set for JCMT that will not be superseded for several decades. In this poster, we will present the current status of the CHIMPS2.

• Bulletin of the Korean Chemical Society
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• v.34 no.9
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• pp.2680-2684
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• 2013

Protein kinases (PKs) are an important source of drug targets, especially in oncology. With 500 or more kinases in the human genome and only few kinase inhibitors approved, kinase inhibitor discovery is becoming more and more valuable. Because the discovery of kinase inhibitors with an increased selectivity is an important therapeutic concept, many researchers have been trying to address this issue with various methodologies. Although many attempts to predict the activity and selectivity of kinase inhibitors have been made, the issue of selectivity has not yet been resolved. Here, we studied kinase selectivity by generating predictive models and analyzing their descriptors by using kinase-profiling data. The 5-fold cross-validation accuracies for the 51 models were between 72.4% and 93.7% and the ROC values for all the 51 models were over 0.7. The phylogenetic tree based on the descriptor distance is quite different from that generated on the basis of sequence alignment.

• Genomics & Informatics
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• v.15 no.4
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• pp.142-146
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• 2017

More effective production of human insulin is important, because insulin is the main medication that is used to treat multiple types of diabetes and because many people are suffering from diabetes. The current system of insulin production is based on recombinant DNA technology, and the expression vector is composed of a preproinsulin sequence that is a fused form of an artificial leader peptide and the native proinsulin. It has been reported that the sequence of the leader peptide affects the production of insulin. To analyze how the leader peptide affects the maturation of insulin structurally, we adapted several in silico simulations using 13 artificial proinsulin sequences. Three-dimensional structures of models were predicted and compared. Although their sequences had few differences, the predicted structures were somewhat different. The structures were refined by molecular dynamics simulation, and the energy of each model was estimated. Then, protein-protein docking between the models and trypsin was carried out to compare how efficiently the protease could access the cleavage sites of the proinsulin models. The results showed some concordance with experimental results that have been reported; so, we expect our analysis will be used to predict the optimized sequence of artificial proinsulin for more effective production.

• Tribology and Lubricants
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• v.32 no.1
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• pp.9-17
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• 2016

Contact between solid surfaces is one of the most important factors that influence dynamic behavior in micro/nanoscale. Although numerous theories and experimental results on contact behavior have been proposed, a thorough investigation for nanomaterials is still not available owing to technical difficulties. Therefore, molecular dynamics simulation was performed to investigate the contact behavior of nanomaterials, and the application of conventional contact theories to nanoscale was assessed in this work. Particularly, the contact characteristics of cylindrical nanowires were examined via simulation and contact theories. For theoretical analysis, various contact models were utilized and work of adhesion, Hamaker constant and elastic modulus those are required for calculation of the models were obtained from both indentation simulation and tensile simulation. The contact area of the cylindrical nanowire was assessed directly through molecular dynamics simulation and compared with the results obtained from the theories. Determination of the contact area of the nanowires was carried out via simulation by counting each atom, which is within the equilibrium length. The results of the simulation and theoretical calculations were compared, and it was estimated that the discrepancy in the results calculated between the simulation and the theories was less than 10 except in the case of the smallest nanowires. As the result, it was revealed that contact models can be effectively utilized to assess the contact area of nanomaterials.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.5
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• pp.414-422
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• 2005

This paper shows the results of atomistic modeling for the Interaction between spherical nano abrasive and substrate In chemical mechanical polishing processes. Atomistic modeling was achieved from 2-dimensional molecular dynamics simulations using the Lennard-jones 12-6 potentials. We proposed and investigated three mechanical models: (1) Constant Force Model; (2) Constant Depth Model, (3) Variable Force Model, and three chemical models, such as (1) Chemically Reactive Surface Model, (2) Chemically Passivating Surface Model, and (3) Chemically Passivating-reactive Surface Model. From the results obtained from classical molecular dynamics simulations for these models, we concluded that atomistic chemical mechanical polishing model based on both Variable Force Model and Chemically Passivating-reactive Surface Model were the most suitable for realistic simulation of chemical mechanical polishing in the atomic scale. The proposed model can be extended to investigate the 3-dimensional chemical mechanical polishing processes in the atomic scale.

• Molecules and Cells
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• v.37 no.10
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• pp.713-718
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• 2014

Alteration in chromosome numbers and structures instigate and foster massive genetic instability. As Boveri has seen a hundred years ago (Boveri, 1914; 2008), aneuploidy is hall-mark of many cancers. However, whether aneuploidy is the cause or the result of cancer is still at debate. The molecular mechanism behind aneuploidy includes the chromosome mis-segregation in mitosis by the compromise of spindle assembly checkpoint (SAC). SAC is an elaborate network of proteins, which monitor that all chromosomes are bipolarly attached with the spindles. Therefore, the weakening of the SAC is the major reason for chromosome number instability, while complete compromise of SAC results in detrimental death, exemplified in natural abortion in embryonic stage. Here, I will review on the recent progress on the understanding of chromosome missegregation and cancer, based on the comparison of different mouse models of BubR1, the core component of SAC.

• Korean Membrane Journal
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• v.1 no.1
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• pp.8-24
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• 1999

free volume in polymers is defined as the difference of the specific volume and the volume which is not available for the particular molecular motion which is responsible or the process that is considered . Relations between free volume and viscosity free volume and diffusion coefficient are pre-sented both in the case of simple low molecular weight liquids and in the case of polymers. Molecular models and free volume models are reminded starting from the equilibrium state equation of Simha and Somcynski. The non equilibrium situations of specific volume of glass polymers below Tg are shown introducing different relaxation volume equations which involve different material's parameters and con-cept of the fictitious temperature. The diffusivity equations of Vrentas and Duda are introduced both for the glassy and rubbery states. The possibility of introducing time relaxation functions is also suggested. The importance of finding experimental evidences of the free volume is stressed. highlights of the free volume measurement methods are given in particular as to dilatometry photocromy fluorescence electron spin resonance small angle X-ray scattering positron annihilation spectroscopy.

• BMB Reports
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• v.47 no.7
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• pp.369-375
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• 2014

Pituitary adenylate cyclase-activating polypeptide (PACAP) is a pleiotropic bioactive peptide that was first isolated from an ovine hypothalamus in 1989. PACAP belongs to the secretin/glucagon/vasoactive intestinal polypeptide (VIP) superfamily. PACAP is widely distributed in the central and peripheral nervous systems and acts as a neurotransmitter, neuromodulator, and neurotrophic factor via three major receptors (PAC1, VPAC1, and VPAC2). Recent studies have shown a neuroprotective role of PACAP using in vitro and in vivo models. In this review, we briefly summarize the current findings on the neurotrophic and neuroprotective effects of PACAP in different brain injury models, such as cerebral ischemia, Parkinson's disease (PD), and Alzheimer's disease (AD). This review will provide information for the future development of therapeutic strategies in treatment of these neurodegenerative diseases.

• BMB Reports
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• v.56 no.1
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• pp.24-31
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• 2023

Urological cancers such as kidney, bladder, prostate, and testicular cancers are the most common types of cancers worldwide with high mortality and morbidity. To date, traditional cell lines and animal models have been broadly used to study pre-clinical applications and underlying molecular mechanisms of urological cancers. However, they cannot reflect biological phenotypes of real tissues and clinical diversities of urological cancers in vitro system. In vitro models cannot be utilized to reflect the tumor microenvironment or heterogeneity. Cancer organoids in three-dimensional culture have emerged as a promising platform for simulating tumor microenvironment and revealing heterogeneity. In this review, we summarize recent advances in prostate and kidney cancer organoids regarding culture conditions, advantages, and applications of these cancer organoids.

• 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.