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

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

Experimental Work Towards the Improvement of a Kinetic Model for Acetone-Butanol-Ethanol Pathway

In recent years, the production of waste materials because of population growth is increasing. Accordingly, finding new methods to convert such materials into fuel are becoming more popular. The objective of this study is to determine the potential of dried distiller`s grains and soluble (DDGS), a by-product of bioethanol industry, to produce butanol using strains of Clostridium saccharoperbutylacetonicum (C.SCC). In addition, this research is intended to develop the fermentation kinetic by improving the bacterial growth and medium optimisation while enhancing the process parameters based on the experimental data. Therefore, to find the most optimum condition for DDGS as the growth medium, the crucial effect of pH and various DDGS media supplementation were identified. Consequently, after performing several experiments in batch serum bottles without agitation, it had been determined that the best condition for DDGS as the growth medium is supplementing the autoclaved DDGS medium with 10 % (v/v) sucrose solution while pH is adjusted to 6.5. Thus, when the supplemented DDGS medium was inoculated with 10 % (v/v) of Clostridium saccharoperbutylacetonicum, the butanol concentration improved to approximately 7.2 (g/L) while this amount for the non-supplemented DDGS medium was approximately 4.4 (g/L). However, under the same conditions for DDGS as the growth medium, the butanol concentration decrease to 0.22 (g/L) while agitation was involved in the batch bioreactor. Based on the experimental results obtained from various experiments of this research, it is concluded that Clostridium saccharoperbutylacetonicum microorganism can ferment sucrose and other carbon sources available in DDGS such as glucose. Moreover, to improve the butanol concentration using mathematical modeling and computer simulation in ongoing studies, the collected preliminary data of this experimental research could be used in the proposed kinetic model by Shinto et al. (2007)