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• W2890862116 abstract "Metabolic modeling has gained accuracy in the last decades, but the resulting models are of high dimension and difficult to use for control purpose. Here we propose a mathematical approach to reduce high dimensional linearized metabolic models, which relies on time scale separation and the quasi steady state assumption. Contrary to the flux balance analysis assumption that the whole system reaches an equilibrium, our reduced model depends on a small system of differential equations which represents the slow variables dynamics. Moreover, we prove that the concentration of metabolites in quasi steady state is one order of magnitude lower than the concentration of metabolites with slow dynamics (under some flux conditions). Also, we propose a minimization strategy to estimate the reduced system parameters. The reduction of a toy network with the method presented here is compared with other approaches. Finally, our reduction technique is applied to an autotrophic microalgae metabolic network. © 2018 American Institute of Chemical Engineers AIChE J , 65: 18–31, 2019" @default.
• W2890862116 created "2018-09-27" @default.
• W2890862116 creator A5012671543 @default.
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• W2890862116 date "2018-10-12" @default.
• W2890862116 modified "2023-10-05" @default.
• W2890862116 title "Dynamical reduction of linearized metabolic networks through quasi steady state approximation" @default.
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• W2890862116 doi "https://doi.org/10.1002/aic.16406" @default.
• W2890862116 hasPublicationYear "2018" @default.
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