It is worth noting that when glycerol uptake is set to zero the bacteria are still able to growth using glucose, albeit at a slow rate. For any given condition, the FBA solution is not unique as there are many alternative flux distributions that can sustain the same objective function, but only a particular solution is needed to provide a feasible flux distribution. Flux distribution data obtained under each #Epigenetic inhibitor manufacturer keyword# experimental condition was

then used as an input data source to estimate the parameters of our kinetic model. The precision of values in each dataset was limited to three decimal places for faster computing. A major difficulty in building genome-scale kinetic models is the lack of quantitative data available to fully define the model [21]; as a consequence, we set the initial concentrations of metabolites and enzyme species to an arbitrary unit of 1 by default. We performed three separate Inhibitors,research,lifescience,medical parameter estimations for each of the three glycerol consumption rates. The kinetic parameters for each reaction in the model were estimated using GRaPe’s genetic algorithm. Model 1, with a glycerol consumption rate at 0 mmol/gDW/h, had 2297 kinetic parameters after parameter estimation; Model 2 had 2537 parameters

with a glycerol consumption rate at 0.5 mmol/gDW/h, Inhibitors,research,lifescience,medical and Model 3 had 2931 parameters after parameter estimation with a glycerol consumption rate at 1 mmol/gDW/h. The difference in the number of parameters after each estimation Inhibitors,research,lifescience,medical was due to different numbers of reactions having a zero flux in each case. Furthermore, reactions with negative fluxes had their substrates and

products swapped around to prevent having negative kinetic parameter values. The three models are provided in SBML format in Supplementary File 1, 2 and 3 respectively. Inhibitors,research,lifescience,medical 3.3. Model Validation We performed a steady-state analysis for Model 1, 2 and 3 using COPASI. The results were then compared with the FBA flux distribution obtained from the Beste model under the same experimental conditions. Our verification analysis showed a near-perfect agreement between the results obtained from our models and the respective FBA simulation. Figure 2 shows the flux distributions in part of the central metabolic pathways; the complete comparisons of flux distributions for Model 1, 2 and 3 are provided in Supplementary File 4. These comparisons demonstrate the ability to accurately reproduce a steady-state flux distribution at the genome-scale using our model building approach. Figure 2 Main isothipendyl response of Mycobacterium tuberculosis to glycerol uptake rates at 0, 0.5 and 1.0 mmol/gDW/h. The network shows a selected set of reactions in the central metabolic pathways of M. tuberculosis. Reactions are represented using arrows and the positive … We identified reactions that showed the greatest change in flux with respect to change in glycerol consumption rate (Figure 3). We calculated the relative change in fluxes between glycerol consumption rates at 0 and 0.