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Complexity reduction of biochemical rate expressions
Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany.
Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
Danø, S., Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Cell Biology.
2008 (English)In: Bioinformatics, ISSN 1367-4803, Vol. 24, no 6, 848-854 p.Article in journal (Refereed) Published
Abstract [en]

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. © The Author 2008. Published by Oxford University Press. All rights reserved.

Place, publisher, year, edition, pages
2008. Vol. 24, no 6, 848-854 p.
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-46767DOI: 10.1093/bioinformatics/btn035OAI: diva2:267663
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2011-01-10

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