Bioinformatics Advance Access originally published online on February 10, 2008
Bioinformatics 2008 24(6):848-854; doi:10.1093/bioinformatics/btn035
Complexity reduction of biochemical rate expressions
1Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany, 2Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark and 3Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
*To whom correspondence should be addressed.
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Abstract |
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Motivation: The current trend in dynamical modelling of biochemical systems is to construct more and more mechanistically detailed and thus complex models. The complexity is reflected in the number of dynamic state variables and parameters, as well as in the complexity of the kinetic rate expressions. However, a greater level of complexity, or level of detail, does not necessarily imply better models, or a better understanding of the underlying processes. Data often does not contain enough information to discriminate between different model hypotheses, and such overparameterization makes it hard to establish the validity of the various parts of the model. Consequently, there is an increasing demand for model reduction methods.
Results: We present a new reduction method that reduces complex rational rate expressions, such as those often used to describe enzymatic reactions. The method is a novel term-based identifiability analysis, which is easy to use and allows for user-specified reductions of individual rate expressions in complete models. The method is one of the first methods to meet the classical engineering objective of improved parameter identifiability without losing the systems biology demand of preserved biochemical interpretation.
Availability: The method has been implemented in the Systems Biology Toolbox 2 for MATLAB, which is freely available from http://www.sbtoolbox2.org. The Supplementary Material contains scripts that show how to use it by applying the method to the example models, discussed in this article.
Contact: henning.schmidt{at}uni-rostock.de
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Jonathan Wren
Present address: Topsoe Fuel Cell, Nymøllevej 66, DK-2800 Lyngby, Denmark.
Received on September 28, 2007; revised on December 17, 2007; accepted on January 22, 2008
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