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Dynamical reduction of linearized metabolic networks through quasi steady state approximation

Claudia López Zazueta 1, 2 Olivier Bernard 2, 1 Jean-Luc Gouzé 2, 1 
1 BIOCORE - Biological control of artificial ecosystems
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique, LOV - Laboratoire d'océanographie de Villefranche
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.
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Submitted on : Thursday, November 15, 2018 - 6:43:36 PM
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Claudia López Zazueta, Olivier Bernard, Jean-Luc Gouzé. Dynamical reduction of linearized metabolic networks through quasi steady state approximation. AIChE Journal, Wiley, 2019, 65 (1), pp.18-31. ⟨10.1002/aic.16406⟩. ⟨hal-01924343⟩



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