Skip to Main content Skip to Navigation
Journal articles

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.
Document type :
Journal articles
Complete list of metadata

Cited literature [17 references]  Display  Hide  Download

https://hal.inria.fr/hal-01924343
Contributor : Jean-Luc Gouzé <>
Submitted on : Thursday, November 15, 2018 - 6:43:36 PM
Last modification on : Monday, December 21, 2020 - 11:45:26 AM

File

accepted_version_AIChE_Lopez20...
Files produced by the author(s)

Identifiers

Citation

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⟩

Share

Metrics

Record views

270

Files downloads

684