• Biotechnology and Bioprocess Engineering:BBE
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• v.7 no.5
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• pp.237-251
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• 2002

The recent progress on metabolic systems engineering was reviewed based on our recent research results in terms of (1) metabolic signal flow diagram approach, (2) metabolic flux analysis (MFA) in particular with intracellular isotopomer distribution using NMR and/or GC-MS, (3) synthesis and optimization of metabolic flux distribution (MFD), (4) modification of MFD by gene manipulation and by controlling culture environment, (5) metabolic control analysis (MCA), (6) design of metabolic regulation structure, and (7) identification of unknown pathways with isotope tracing by NMR. The main characteristics of metabolic engineering is to treat metabolism as a network or entirety instead of individual reactions. The applications were made for poly-3-hydroxybutyrate (PHB) production using Ralstonia eutropha and recombinant Escherichia coli, lactate production by recombinant Saccharomyces cerevisiae, pyruvate production by vitamin auxotrophic yeast Toluropsis glabrata, lysine production using Corynebacterium glutamicum, and energetic analysis of photosynthesic microorganisms such as Cyanobateria. The characteristics of each approach were reviewed with their applications. The approach based on isotope labeling experiments gives reliable and quantitative results for metabolic flux analysis. It should be recognized that the next stage should be toward the investigation of metabolic flux analysis with gene and protein expressions to uncover the metabolic regulation in relation to genetic modification and/ or the change in the culture condition.

• Journal of Microbiology and Biotechnology
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• v.8 no.1
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• pp.42-48
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• 1998

In order to maximize the secretory expression of human serum albumin (HSA) in the yeast Saccharomyces cerevisiae, a series of HSA expression vectors were constructed with a combination of different promoters, 5' untranslated regions (5'UTR), and secretion signal sequences. The expression vector composed of the galactose-inducible promoter GALl0, the natural 5'UTR, and the natural signal sequence of HSA directed the most efficient expression and secretion of HSA among the constructed vectors when introduced into several S. cerevisiae strains. Although the major form of HSA expressed and secreted in the yeast transformants was the mature form of 66 kDa, the truncated form of 45 kDa was also detected both in the cell extract and in the culture supernatant. The level of the intact HSA protein in the culture supernatant reached up to 30 mg/l at 24 h of cultivation in a shake-flask culture but began to decrease afterwards, indicating that the secreted HSA protein was unstable in a prolonged culture of yeast.

• The Korean Journal of Physiology and Pharmacology
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• v.19 no.5
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• pp.389-399
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• 2015

Zinc has been considered as a vital constituent of proteins, including enzymes. Mobile reactive zinc ($Zn^{2+}$) is the key form of zinc involved in signal transductions, which are mainly driven by its binding to proteins or the release of zinc from proteins, possibly via a redox switch. There has been growing evidence of zinc's critical role in cell signaling, due to its flexible coordination geometry and rapid shifts in protein conformation to perform biological reactions. The importance and complexity of $Zn^{2+}$ activity has been presumed to parallel the degree of calcium's participation in cellular processes. Whole body and cellular $Zn^{2+}$ levels are largely regulated by metallothioneins (MTs), $Zn^{2+}$ importers (ZIPs), and $Zn^{2+}$ transporters (ZnTs). Numerous proteins involved in signaling pathways, mitochondrial metabolism, and ion channels that play a pivotal role in controlling cardiac contractility are common targets of $Zn^{2+}$. However, these regulatory actions of $Zn^{2+}$ are not limited to the function of the heart, but also extend to numerous other organ systems, such as the central nervous system, immune system, cardiovascular tissue, and secretory glands, such as the pancreas, prostate, and mammary glands. In this review, the regulation of cellular $Zn^{2+}$ levels, $Zn^{2+}$-mediated signal transduction, impacts of $Zn^{2+}$ on ion channels and mitochondrial metabolism, and finally, the implications of $Zn^{2+}$ in health and disease development were outlined to help widen the current understanding of the versatile and complex roles of $Zn^{2+}$.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.991-992
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• 2010

• Investigative Magnetic Resonance Imaging
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• v.26 no.2
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• pp.125-134
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• 2022

Purpose: The aim of this study was to investigate the change in signal sensitivity over different acquisition start times and optimize the scanning window to provide the maximal signal sensitivity of [1-¹³C]pyruvate and its metabolic products, lactate and alanine, using spatially localized hyperpolarized 3D ¹³C magnetic resonance spectroscopic imaging (MRSI). Materials and Methods: We acquired 3D ¹³C MRSI data from the brain (n = 3), kidney (n = 3), and liver (n = 3) of rats using a 3T clinical scanner and a custom RF coil after the injection of hyperpolarized [1-¹³C]pyruvate. For each organ, we obtained three consecutive 3D ¹³C MRSI datasets with different acquisition start times per animal from a total of three animals. The mean signal-to-noise ratios (SNRs) of pyruvate, lactate, and alanine were calculated and compared between different acquisition start times. Based on the SNRs of lactate and alanine, we identified the optimal acquisition start timing for each organ. Results: For the brain, the acquisition start time of 18 s provided the highest mean SNR of lactate. At 18 s, however, the lactate signal predominantly originated from not the brain, but the blood vessels; therefore, the acquisition start time of 22 s was recommended for 3D ¹³C MRSI of the rat brain. For the kidney, all three metabolites demonstrated the highest mean SNR at the acquisition start time of 32 s. Similarly, the acquisition start time of 22 s provided the highest SNRs for all three metabolites in the liver. Conclusion: In this study, the acquisition start timing was optimized in an attempt to maximize metabolic signals in hyperpolarized 3D ¹³C MRSI examination with [1-¹³C] pyruvate as a substrate. We investigated the changes in metabolic signal sensitivity in the brain, kidney, and liver of rats to establish the optimal acquisition start time for each organ. We expect the results from this study to be of help in future studies.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.7 no.1
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• pp.79-86
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• 2003

A myoelectric signal, under sustained isometric contraction of muscle the modelled as the output of a linear time-varying system whose input is constant number of pulse train. The proposed model considered localized muscle fatigue by metabolic by-products during sustained fatiguing contraction. To characterize muscle fatiguing model of myoelectric signal, We calculated median frequency of generated signal as fatiguing index of muscle during sustained isometric contraction.

• Korean Journal of Clinical Pharmacy
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• v.28 no.4
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• pp.273-278
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• 2018

Bile acids are major constituents of bile and known to help absorb dietary fat and fat-soluble vitamins in the gastrointestinal tract. In the past few decades, many studies have shown that bile acids not only play a role in fat digestion but also function as broad range of signal transduction hormones by binding to various receptors present in cell membranes or nuclei. Bile acid receptors are distributed in a wide range of organs and tissues in the human body. They perform multitudes of physiological functions with complex mechanisms. When bile acids bind to their receptors, they regulate fat and glucose metabolism in a tissue-specific way. In addition, bile acids are shown to inhibit inflammation and fibrosis in the liver. Considering the roles of bile acids as metabolic regulators, bile acids and their receptors can be very attractive targets in treating metabolic disorders. In the future, if roles of bile acids and their receptors are further clarified, they will be the novel target of drugs in the treatment of various metabolic diseases.

• Proceedings of the Korean Society of Sericultural Science Conference
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• 2006.04a
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• pp.6-6
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• 2006

• Molecules and Cells
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• v.41 no.4
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• pp.264-270
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• 2018

Cells cope with diverse intrinsic and extrinsic stimuli in order to make adaptations for survival. The epigenetic landscape plays a crucial role in cellular adaptation, as it integrates the information generated from stimuli. Signaling pathways induced by stimuli communicate with chromatin to change the epigenetic landscape through regulation of epigenetic modifiers. Metabolic dynamics altered by these stimuli also affect the activity of epigenetic modifiers. Here, I review the current understanding of epigenetic regulation via signaling and metabolic pathways. In addition, I will discuss possible ways to achieve specificity of epigenetic modifications through the cooperation of stimuli-induced signal transduction and metabolic reprogramming.

• Genomics & Informatics
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• v.4 no.3
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• pp.118-124
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• 2006

For the direct understanding of flow, pathway data are usually represented as directed graphs in biological journals and texts. Databases of metabolic pathways or signal transduction pathways inevitably contain these kinds of graphs to show the flow. KEGG, one of the representative pathway databases, uses the manually drawn figure which can not be easily maintained. Graph layout algorithms are applied for visualizing metabolic pathways in some databases, such as EcoCyc. Although these can express any changes of data in the real time, it exponentially increases the edge crossings according to the increase of nodes. For the understanding of genome scale flow of metabolism, it is very important to reduce the unnecessary edge crossings which exist in the automatic graph layout. We propose a metabolic pathway drawing algorithm for reducing the number of edge crossings by considering the fact that metabolic pathway graph is scale-free network. The experimental results show that the number of edge crossings is reduced about $37{\sim}40%$ by the consideration of scale-free network in contrast with non-considering scale-free network. And also we found that the increase of nodes do not always mean that there is an increase of edge crossings.