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Briefings in Bioinformatics Advance Access published online on November 14, 2006
Briefings in Bioinformatics, 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
Received August 28, 2006
Accepted October 4, 2006

Original Papers

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

Natal A.W. van Riel *

* To whom correspondence should be addressed.
Natal A.W. van Riel, E-mail: N.A.W.v.Riel{at}tue.nl


  Abstract

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.

Natal van Riel is Assistant Professor of Biomodeling and Systems Biology in the Department of Biomedical Engineering at Eindhoven University of Technology and Principal Investigator of Eindhoven Biomedical Systems Biology. His research interests include mathematical modelling and identification of biological systems.


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