Non-linear reduction for kinetic models of metabolic reaction networks.
Kinetic models of metabolic networks are essential for predicting and optimizing the transient behavior of cells in culture. However, such models are inherently high dimensional and stiff due to the large number of species and reactions involved and to kinetic rate constants of widely different orders of magnitude. In this paper we address the problem of deriving non-stiff, reduced-order non-linear models of the dominant dynamics of metabolic networks with fast and slow reactions. We present a method, based on singular perturbation analysis, which allows the systematic identification of quasi-steady-state conditions for the fast reactions, and the derivation of explicit non-linear models of the slow dynamics independent of the fast reaction rate expressions. The method is successfully applied to detailed models of metabolism in human erythrocytes and Saccharomyces cerevisiae.
Gerdtzen
Ziomara P
ZP
Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN 55455-0132, USA.
Daoutidis
Prodromos
P
Hu
Wei-Shou
WS
D002244:Carbon / Q000378:metabolism; D003198:Computer Simulation; D004912:Erythrocytes / Q000378:metabolism*; D006019:Glycolysis / Q000502:physiology; D006801:Humans; D008660:Metabolism / Q000502:physiology*; D008954:Models, Biological; D008956:Models, Chemical; D009097:Multienzyme Complexes / Q000378:metabolism; D017711:Nonlinear Dynamics; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology*
D009097:Multienzyme Complexes; D029701:Saccharomyces cerevisiae Proteins; D002244:Carbon