[en] For the purpose of precise mathematical modelling of chemical reaction networks, useful techniques for estimating their parameters from experimental data are necessary. In this manuscript, we propose a new parameter estimation method for enzymatic chemical reaction networks from time-series experimental data of reaction rates. The main idea is based on retrieving time-series data of the species' concentrations from the available experimental data of reaction rates by making use of parametric Bézier curves. The least-squares method is applied to these retrieved data in order to determine the best-fitting values of the parameters in the corresponding mathematical model. Subsequently, we demonstrate the applicability of our parameter estimation method on three examples of enzymatic chemical reaction networks, including a model of ryanodine receptor adaptation and a model of protein kinase cascades. We also address the issue of identifiability of chemical reaction network models from reaction rates.
Disciplines :
Mathématiques Sciences du vivant: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
Gasparyan, Manvel
Van Messem, Arnout ; Université de Liège - ULiège > Département de mathématique > Statistique applquée aux sciences
Rao, Shodhan
Langue du document :
Anglais
Titre :
Parameter estimation for chemical reaction networks from experimental data of reaction rates
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