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

Background: 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. Constraints-based modelling provides a novel approach to investigating microbial metabolism but has not so far been applied to genome-scale modelling of M. tuberculosis. Results: GSMN-TB, a genome-scale metabolic model of M. tuberculosis, was constructed, consisting of 849 unique reactions, 739 metabolites and involving 726 genes. The model was calibrated by growing M. bovis BCG in continuous culture and steady state growth parameters were measured. Flux balance analysis ( FBA) 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 FBA simulation and were compared with global mutagenesis data for in vitro-grown M. tuberculosis. A prediction accuracy of 78% was obtained. 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. Conclusions: 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 bacterium, predict the phenotype of mutants, and it highlights previously unexplored features of M. tuberculosis metabolism.