Premium
Dynamical reduction of linearized metabolic networks through quasi steady state approximation
Author(s) -
López Zazueta Claudia,
Bernard Olivier,
Gouzé JeanLuc
Publication year - 2019
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16406
Subject(s) - flux balance analysis , reduction (mathematics) , steady state (chemistry) , metabolic network , dimension (graph theory) , control theory (sociology) , mathematics , flux (metallurgy) , ordinary differential equation , minification , dimensionality reduction , metabolic control analysis , statistical physics , biological system , computer science , mathematical optimization , differential equation , control (management) , physics , chemistry , mathematical analysis , biochemistry , geometry , organic chemistry , artificial intelligence , pure mathematics , biology , medicine , endocrinology , insulin
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