• Journal of Microbiology and Biotechnology
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• v.16 no.7
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• pp.1139-1143
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• 2006

Various techniques and strategies have been developed for the identification of intracellular metabolic conditions, and among them, isotope balance-based flux analysis with gas chromatography/mass spectrometry (GC/ MS) has recently become popular. Even though isotope balance-based flux analysis allows a more accurate estimation of intracellular fluxes, its application has been restricted to relatively small metabolic systems because of the limited number of measurable metabolites. In this paper, a strategy for incorporating isotope balance-based flux data obtained for a small network into metabolic flux analysis was examined as a feasible alternative allowing more accurate quantification of intracellular flux distribution in a large metabolic system. To impose GC/MS based data into a large metabolic network and obtain optimum flux distribution profile, data reconciliation procedure was applied. As a result, metabolic flux values of 308 intracellular reactions could be estimated from 29 GC/ MS based fluxes with higher accuracy.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.425-431
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• 2005

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution resides in silico genome-scale metabolic model, In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.

• 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.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.1
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• pp.91-98
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• 2005

In order to investigate central metabolic changes in Beijerinckia indica, cells were grown on different carbon sources and intracellular flux distributions were studied under varying concentrations of nitrogen. Metabolic fluxes were estimated by combining material balances with extracellular substrate uptake rate, biomass formation rate, and exopolysaccharide (EPS) accumulation rate. Thirty-one metabolic reactions and 30 intracellular metabolites were considered for the flux analysis. The results revealed that most of the carbon source was directed into the Entner-Doudoroff pathway, followed by the recycling of triose-3-phosphate back to Hexose­6-phosphate. The pentose phosphate pathway was operated at a minimal level to supply the precursors for biomass formation. The different metabolic behaviors under varying nitrogen concentrations were observed with flux analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.823-829
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• 2005

We have newly constructed an in silico model of fermentative metabolism for Lactococcus lactis in order to analyze the characteristics of metabolite flux for dynamic network. A rigorous mathematical model for metabolic flux has been developed and simulation researches have been performed by using GEPASI program. In this simulation task, we were able to predict the whole flux distribution trend for lactate metabolism and analyze the flux ratio on the pyruvate branch point by using metabolic flux analysis(MFA). And we have studied flux control coefficients of key reaction steps in the model by using metabolic control analysis(MCA). The role of pyruvate branch seems to be essential for the secretion of lactate and other organic byproducts. Then we have made an effort to elucidate its metabolic regulation characteristics and key reaction steps, and find an optimal condition for the production of lactate.

• Journal of Microbiology and Biotechnology
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• v.13 no.4
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• pp.571-577
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• 2003

The intracellular metabolic fluxes can be calculated by metabolic flux analysis, which uses a stoichiometric model for the intracellulal reactions along with mass balances around the intracellular metabolites. In this study, metabolic flux analyses were carried out to estimate flux distributions for the maximum in silico yields of various metabolites in Escherichia coli. The maximum in silico yields of acetic acid and lactic acid were identical to their theoretical yields. On the other hand, the in silico yields of succinic acid and ethanol were only 83% and 6.5% of their theoretical yields, respectively. The lower in silico yield of succinic acid was found to be due to the insufficient reducing power. but this lower yield could be increased to its theoretical yield by supplying more reducing power. The maximum theoretical yield of ethanol could be achieved, when a reaction catalyzed by pyruvate decarboxylase was added in the metabolic network. Futhermore, optimal metabolic pathways for the production of various metabolites could be proposed, based on the results of metabolic flux analyses. In the case of succinic acid production, it was found that the pyruvate carboxylation pathway should be used for its optimal production in E. coli rather than the phosphoenolpyruvate carboxylation pathway.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.462-469
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• 2005

A top-down approach is known to be a useful and effective technique for the design and analysis of metabolic systems. In this Study, we have constructed a grouped metabolic network for Lactococcus lactis under aerobic conditions using grouped enzyme kinetics. To test the usefulness of grouping work, a non-grouped system and grouped systems were compared quantitatively with each other. Here, grouped Systems were designed as two groups according to the extent of grouping. The overall simulated flux values in grouped and non-grouped models had pretty similar distribution trends, but the details on flux ratio at the pyruvate branch point showed a little difference. This result indicates that our grouping technique can be used as a good model for complicated metabolic networks, however, for detailed analysis of metabolic network, a more robust mechanism Should be considered. In addition to the data for the pyruvate branch point analysis, Some major flux control coefficients were obtained in this research.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.57.3-57
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• 2002

1. Introduction 2. General flux balance model 3. MetaFluxNet 3.1 Overview of MetaFluxNet 3.2 Project file format 3.3 Construction of metabolite reaction model 3.4 Metabolic flux analysis using linear programming 3.5 Visualization of MFA results 4. Conclusion and plan 5. Acknowledgement. References.

• Journal of Microbiology and Biotechnology
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• v.21 no.2
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• pp.162-169
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• 2011

A dynamic model of lactic acid fermentation using Lactococcus lactis was constructed, and a metabolic flux analysis (MFA) and metabolic control analysis (MCA) were performed to reveal an intensive metabolic understanding of lactic acid bacteria (LAB). The parameter estimation was conducted with COPASI software to construct a more accurate metabolic model. The experimental data used in the parameter estimation were obtained from an LC-MS/MS analysis and time-course simulation study. The MFA results were a reasonable explanation of the experimental data. Through the parameter estimation, the metabolic system of lactic acid bacteria can be thoroughly understood through comparisons with the original parameters. The coefficients derived from the MCA indicated that the reaction rate of L-lactate dehydrogenase was activated by fructose 1,6-bisphosphate and pyruvate, and pyruvate appeared to be a stronger activator of L-lactate dehydrogenase than fructose 1,6-bisphosphate. Additionally, pyruvate acted as an inhibitor to pyruvate kinase and the phosphotransferase system. Glucose 6-phosphate and phosphoenolpyruvate showed activation effects on pyruvate kinase. Hexose transporter was the strongest effector on the flux through L-lactate dehydrogenase. The concentration control coefficient (CCC) showed similar results to the flux control coefficient (FCC).

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.408-417
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• 2005

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput 'omics' data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.