• Journal of Biosystems Engineering
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• v.43 no.1
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• pp.45-58
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• 2018

Background: Metabolic modeling has been an essential tool in metabolic reconstruction, which has dramatically advanced in the last decades as a part of systems biology. At present, the protocol for metabolic reconstruction has been systematically established, and it provides the basis for the analysis of complex systems, which has been limited in the past. Therefore, metabolic reconstruction can be adapted to analyze agricultural systems whose metabolic data has been accumulated recently. Purpose: The aim of this review is to suggest the suitability of metabolic modeling for understanding agricultural metabolic data and to encourage the potential use of this modeling in the field of agriculture. Review: We reviewed the procedure of metabolic reconstruction using computational modeling with applicable strategies and software tools. Additionally, we presented the initial attempts of metabolic reconstruction in the field of agriculture and proposed further applications.

• 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.

• Journal of Biosystems Engineering
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• v.37 no.4
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• pp.258-264
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• 2012

Purpose: This review aims at introducing 3 modeling approaches classified into 3 categories based on the purpose (estimation or prediction), structure (linear or non-linear) and phase (steady-state or dynamic-state); 1) statistical approaches, 2) kinetic modeling and 3) mechanistic modeling. We hope that this review can be a useful guide in the model-based approach of calcium metabolism as well as illustrates an application of engineering tools in studying biosystems. Background: The meaning of biosystems has been expanded, including agricultural/food system as well as biological systems like genes, cells and metabolisms. This expansion has required a useful tool for assessing the biosystems and modeling has arisen as a method that satisfies the current inquiry. To suit for the flow of the era, examining the system which is a little bit far from the traditional biosystems may be interesting issue, which can enlarge our insights and provide new ideas for prospective biosystem-researches. Herein, calcium metabolic models reviewed as an example of application of modeling approaches into the biosystems. Review: Calcium is an essential nutrient widely involved in animal and human metabolism including bone mineralization and signaling pathways. For this reason, the calcium metabolic system has been studied in various research fields of academia and industries. To study calcium metabolism, model-based system analyses have been utilized according to the purpose, subject characteristics, metabolic sites of interest, and experimental design. Either individual metabolic pathways or a whole homeostasis has been modeled in a number of studies.

• 한국생물공학회:학술대회논문집
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• 2001.11a
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• pp.61-62
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• 2001

• Journal of the Korean Dietetic Association
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• v.25 no.2
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• pp.142-155
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• 2019

The aim of this study was to construct and test a structural equation model for the risk factors of metabolic syndrome in Korean adults. The structural equation model hypothesizes that eating alone and feeling depressed is a risk factor for metabolic syndrome. The data of this study were obtained from the Sixth Korea National Health and Nutrition Examination Survey which was cross-sectional data from the representative national survey. A total of 4,013 subjects replied to the survey item of lifestyle and completed the physical examinations among adults aged 19 years or older in South Korea was in 2015. The structural model in this study was composed of four latent variables: eating alone, depression, negative health behavior, and metabolic syndrome. Two variables, the rate of eating alone and depression, were exogenous variables. Negative health behavior was both a mediating variable and endogenous variable, and metabolic syndrome was the final endogenous variable. The data were analyzed using the Maximum Likelihood method and bootstrapping. The structural model was appropriate for the data based on the model fit indices. The results of this study can be summarized as follows: Eating alone is a direct risk factor of metabolic syndrome in Korean women. Depression can mediate metabolic syndrome through negative health behaviors. Negative health behavior had a direct impact on metabolic syndrome in both men and women. This study may be a guideline for interventions and strategies to reduce the incidence of metabolic syndrome in Korean adults.

• 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.

• 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.

• BMB Reports
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• v.37 no.1
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• pp.83-92
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• 2004

As a result of the enormous amount of information that has been collected with E. coli over the past half century (e.g. genome sequence, mutant phenotypes, metabolic and regulatory networks, etc.), we now have detailed knowledge about gene regulation, protein activity, several hundred enzyme reactions, metabolic pathways, macromolecular machines, and regulatory interactions for this model organism. However, understanding how all these processes interact to form a living cell will require further characterization, quantification, data integration, and mathematical modeling, systems biology. No organism can rival E. coli with respect to the amount of available basic information and experimental tractability for the technologies needed for this undertaking. A focused, systematic effort to understand the E. coli cell will accelerate the development of new post-genomic technologies, including both experimental and computational tools. It will also lead to new technologies that will be applicable to other organisms, from microbes to plants, animals, and humans. E. coli is not only the best studied free-living model organism, but is also an extensively used microbe for industrial applications, especially for the production of small molecules of interest. It is an excellent representative of Gram-negative commensal bacteria. E. coli may represent a perfect model organism for systems biology that is aimed at elucidating both its free-living and commensal life-styles, which should open the door to whole-cell modeling and simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.843-850
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• 2005

FBA(flux balance analysis) with Boolean rules for representing regulatory events has correctly predicted cellular behaviors, such as optimal flux distribution, maximal growth rate, metabolic by-product, and substrate concentration changes, with various environmental conditions. However, until now, since FBA has not taken into account a hierarchical regulatory network, it has limited the representation of the whole transcriptional regulation mechanism and interactions between specific regulatory proteins and genes. In this paper, in order to solve these problems, we describe the construction of hierarchical regulatory network with defined symbols and the introduction of a weight for representing interactions between symbols. Finally, the whole cellular behaviors with time were simulated through the linkage of a hierarchical regulatory network module and dynamic simulation module including FBA. The central metabolic network of E. coli was chosen as the basic model to identify our suggested modeling method.

• 한국생물공학회:학술대회논문집
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• 2005.10a
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• pp.793-794
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• 2005