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

MPG-Autoren
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Klamt,  S.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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GenomeBiology_8_R89.pdf
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Zitation

Beste, D. J., Hooper, T., Stewart, G., Bonde, B., Avignone-Rossa, C., Bushell, M. E., et al. (2007). GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biology, 8: R89. doi:10.1186/gb-2007-8-5-r89.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-98E0-E
Zusammenfassung
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. Results 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. Conclusion 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.