Bioinformatics Advance Access published online on February 10, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn035
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Complexity Reduction of Biochemical Rate Expressions
aSystems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany
bDepartment of Biomedical Sciences, University of Copenhagen, Denmark
cDepartment of Cellular Biology, Linköping University, Sweden
+To whom correspondence should be addressed. Dr. Henning Schmidt, E-mail: henning{at}hschmidt.de, henning{at}hschmidt.de
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Abstract |
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Motivation: The current trend in dynamical modeling 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 does often 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 paper.
Contact: henning.schmidt{at}uni-rostock.de
Supplementary information: The supplementary material is available on Bioinformatics online.
Associate Editor: Dr. Jonathan Wren
Work partly performed at the Fraunhofer-Chalmers Research Centre, Gothenburg, Sweden
Present address: Topsoe Fuel Cell, Nymøllevej 55, DK-2800 Lyngby, Denmark
Received on September 28, 2007; revised on December 17, 2007; accepted on January 22, 2008
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