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• W1652041254 abstract "In order to improve understanding of metabolic systems there have been attempts to construct S-system models from time courses. Conventionally, non-linear curve-fitting algorithms have been used for modelling, because of the non-linear properties of parameter estimation from time series. However, the huge iterative calculations required have hindered the development of large-scale metabolic pathway models. To solve this problem we propose a novel method involving power-law modelling of metabolic pathways from the Jacobian of the targeted system and the steady-state flux profiles by linearization of S-systems.The results of two case studies modelling a straight and a branched pathway, respectively, showed that our method reduced the number of unknown parameters needing to be estimated. The time-courses simulated by conventional kinetic models and those described by our method behaved similarly under a wide range of perturbations of metabolite concentrations.The proposed method reduces calculation complexity and facilitates the construction of large-scale S-system models of metabolic pathways, realizing a practical application of reverse engineering of dynamic simulation models from the Jacobian of the targeted system and steady-state flux profiles." @default.
• W1652041254 created "2016-06-24" @default.
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• W1652041254 date "2006-07-17" @default.
• W1652041254 modified "2023-09-27" @default.
• W1652041254 title "A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles" @default.
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• W1652041254 doi "https://doi.org/10.1186/1742-4682-3-24" @default.
• W1652041254 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/1550393" @default.
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