Non-linear reduction for kinetic models of metabolic reaction networks

Ziomara P. Gerdtzen, Prodromos Daoutidis, Wei Shou Hu

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)140-154
Number of pages15
JournalMetabolic Engineering
Issue number2
StatePublished - Apr 2004

Bibliographical note

Funding Information:
This work was supported in part by the Biotechnology Institute (BTI) at the University of Minnesota, the Minnesota Supercomputing Institute (MSI) and the ACS Petroleum Research Fund.

Copyright 2017 Elsevier B.V., All rights reserved.


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