Open Access Highly Accessed Open Badges Methodology article

Towards a genome-scale kinetic model of cellular metabolism

Kieran Smallbone12, Evangelos Simeonidis13*, Neil Swainston14 and Pedro Mendes145

Author Affiliations

1 Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, 131 Princess Street, Manchester, M1 7DN, UK

2 School of Mathematics, The University of Manchester, Oxford Road, Manchester M13 9PL, UK

3 School of Chemical Engineering and Analytical Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK

4 School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK

5 Virginia Bioinformatics Institute, Virginia Tech, Washington Street 0499, Virginia 24061, USA

For all author emails, please log on.

BMC Systems Biology 2010, 4:6  doi:10.1186/1752-0509-4-6

Published: 28 January 2010



Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour.


We describe a method for building a parameterized genome-scale kinetic model of a metabolic network. Simplified linlog kinetics are used and the parameters are extracted from a kinetic model repository. We demonstrate our methodology by applying it to yeast metabolism. The resultant model has 956 metabolic reactions involving 820 metabolites, and, whilst approximative, has considerably broader remit than any existing models of its type. Control analysis is used to identify key steps within the system.


Our modelling framework may be considered a stepping-stone toward the long-term goal of a fully-parameterized model of yeast metabolism. The model is available in SBML format from the BioModels database (BioModels ID: MODEL1001200000) and at webcite.