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Briefings in Bioinformatics Advance Access originally published online on November 14, 2006
Briefings in Bioinformatics 2006 7(4):364-374; doi:10.1093/bib/bbl040
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© The Author 2006. Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments

Natal A.W. van Riel

Natal A.W. van Riel, Biomodeling and Bioinformatics, Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands. Tel.: +31-40-2475506; Fax: +31-40-2472740; E-mail: N.A.W.v.Riel{at}tue.nl

Systems biology applies quantitative, mechanistic modelling to study genetic networks, signal transduction pathways and metabolic networks. Mathematical models of biochemical networks can look very different. An important reason is that the purpose and application of a model are essential for the selection of the best mathematical framework. Fundamental aspects of selecting an appropriate modelling framework and a strategy for model building are discussed.

Concepts and methods from system and control theory provide a sound basis for the further development of improved and dedicated computational tools for systems biology. Identification of the network components and rate constants that are most critical to the output behaviour of the system is one of the major problems raised in systems biology. Current approaches and methods of parameter sensitivity analysis and parameter estimation are reviewed. It is shown how these methods can be applied in the design of model-based experiments which iteratively yield models that are decreasingly wrong and increasingly gain predictive power.

Keywords: systems biology, parameter sensitivity analysis, parameter estimation, optimal experiment design, differential equations


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