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GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism

Dany JV Beste1, Tracy Hooper1, Graham Stewart1, Bhushan Bonde1, Claudio Avignone-Rossa1, Michael E Bushell1, Paul Wheeler2, Steffen Klamt3, Andrzej M Kierzek1 and Johnjoe McFadden1*

Author Affiliations

1 School of Biomedical and Molecular Sciences, University of Surrey, Stag Hill, Guildford, Surrey, GU2 7XH, UK

2 Tuberculosis Research Group, Veterinary Laboratories Agency (Weybridge), New Haw, Addlestone KT15 3NB, UK

3 Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse, D-39106 Magdeburg, Germany

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Genome Biology 2007, 8:R89  doi:10.1186/gb-2007-8-5-r89

Published: 23 May 2007



An impediment to the rational development of novel drugs against tuberculosis (TB) is a general paucity of knowledge concerning the metabolism of Mycobacterium tuberculosis, particularly during infection. Constraint-based modeling provides a novel approach to investigating microbial metabolism but has not yet been applied to genome-scale modeling of M. tuberculosis.


GSMN-TB, a genome-scale metabolic model of M. tuberculosis, was constructed, consisting of 849 unique reactions and 739 metabolites, and involving 726 genes. The model was calibrated by growing Mycobacterium bovis bacille Calmette Guérin in continuous culture and steady-state growth parameters were measured. Flux balance analysis was used to calculate substrate consumption rates, which were shown to correspond closely to experimentally determined values. Predictions of gene essentiality were also made by flux balance analysis simulation and were compared with global mutagenesis data for M. tuberculosis grown in vitro. A prediction accuracy of 78% was achieved. Known drug targets were predicted to be essential by the model. The model demonstrated a potential role for the enzyme isocitrate lyase during the slow growth of mycobacteria, and this hypothesis was experimentally verified. An interactive web-based version of the model is available.


The GSMN-TB model successfully simulated many of the growth properties of M. tuberculosis. The model provides a means to examine the metabolic flexibility of bacteria and predict the phenotype of mutants, and it highlights previously unexplored features of M. tuberculosis metabolism.