A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kineti

Background: Dynamic modeling of metabolic reaction networks under in vivo conditions is a crucial step in order to obtain a better understanding of the (dis)functioning of living cells. So far dynamic metabolic models generally have been based on mechanistic rate equations which often contain so many parameters that their identifiability from experimental data forms a serious problem. Recently, approximative rate equations, based on the linear logarithmic (linlog) format have been proposed as a suitable alternative with fewer parameters. Results: In this paper we present a method for estimation of the kinetic model parameters, which are equal to the elasticities defined in Metabolic Control Analysis, from metabolite data obtained from dynamic as well as steady state perturbations, using the linlog kinetic format. Additionally, we address the question of parameter identifiability from dynamic perturbation data in the presence of noise. The method is illustrated using metabolite data ge...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: I. Emrah Nikerel, Wouter A. van Winden, Walter M. van Gulik, Joseph J. Heijnen
• Keywords: BMCBI 2006, Mechanistic Rate Equations, Parameter, Rate Equations