Journal Article

Streamlining the construction of large-scale dynamic models using generic kinetic equations

Delali A. Adiamah, Julia Handl and Jean-Marc Schwartz

in Bioinformatics

Volume 26, issue 10, pages 1324-1331
Published in print May 2010 | ISSN: 1367-4803
Published online March 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq136
Streamlining the construction of large-scale dynamic models using generic kinetic equations

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Motivation: Studying biological systems, not just at an individual component level but at a system-wide level, gives us great potential to understand fundamental functions and essential biological properties. Despite considerable advances in the topological analysis of metabolic networks, inadequate knowledge of the enzyme kinetic rate laws and their associated parameter values still hampers large-scale kinetic modelling. Furthermore, the integration of gene expression and protein levels into kinetic models is not straightforward.

Results: The focus of our research is on streamlining the construction of large-scale kinetic models. A novel software tool was developed, which enables the generation of generic rate equations for all reactions in a model. It encompasses an algorithm for estimating the concentration of proteins for a reaction to reach a particular steady state when kinetic parameters are unknown, and two robust methods for parameter estimation. It also allows for the seamless integration of gene expression or protein levels into a reaction and can generate equations for both transcription and translation. We applied this methodology to model the yeast glycolysis pathway; our results show that the behaviour of the system can be accurately described using generic kinetic equations.

Availability and implementation: The software tool, together with its source code in Java, is available from our project web site at http://www.bioinf.manchester.ac.uk/schwartz/grape

Contact: jean-marc.schwartz@manchester.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6285 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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