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• W2567163025 abstract "This contribution illustrates a framework for reconstructing and verifying large-scale kinetic metabolic network models in a semi-automated way. The experimental basis of this approach is provided by a stimulus response experiment. Parameterizing a large-scale kinetic network model then consists of three steps. In a first step, metabolic fluxes are identified. Subsequently, the dynamic network model is set-up automatically using canonical enzyme kinetics. In a third step, the kinetic parameters of the model are estimated from time-series metabolite data through integrating an evolutionary algorithm with a highperformance dynamic simulation platform. In this study, the time-series metabolite data were collected from HepG2 cells and analysed in the range of 0 to 180 minutes after depriving glucose from the culture medium. In total, more than 6 million simulation runs were performed. The in silico metabolite dynamics were in accordance with the experimental data." @default.
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• W2567163025 date "2010-01-01" @default.
• W2567163025 modified "2023-09-24" @default.
• W2567163025 title "Parameterization of large-scale autonomous network models from time-series metabolite data" @default.
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