Mycobacterium tuberculosis GSMN-TB

Recent refinement of the GSMN-TB (Beste et al. 2007) model reproducing slow growing, chemostat culture of M. tuberculosis.

Simulation methods

Simulations are based on the principle of Flux Balance Analysis. All intracellular metabolites are assumed to be at steady state, where their concentration is constant. Extracellular metabolites are not required to be balanced. In all models available on the system the names of extracellular metabolites end with "xt" string. The variables of the model are reaction fluxes. The media conditions for a particular simulation are defined as bounds on extracellular metabolite transport reactions. If particular metabolite is absent from the medium the transport reaction for this metabolite is bound to (0, 0) otherwise it is allowed to assume positive (transport) or negative (secretion) flux. Nutritional conditions are defined within model interface by typing reaction name, lower flux bound, upper flux bound (white-space separated) into "Edit media conditions field" form. This form may be used not only to specify active transport reactions, but also to constraint any reaction in the model.

Flux Balance Analysis simulation results in the maximal value of the selected flux under given media conditions. The optimisation target may be specified as a reaction or metabolite name. If optimisation target is a reaction, maximal value of the reaction flux is calculated. If the metabolite name is given the system calculates maximal production rate of the metabolite. The usual optimisation target is a BIOMASS metabolite. Biomass synthesis rate models growth rate. The linear programming algorithm guarantees that the unique, maximal value of the optimised flux is found. However, the flux distribution is not unique. System reports one of many possible distributions sustaining maximal flux towards optimisation target. The Flux Variability Analysis allows exploration of the flux ranges consistent with the maximal flux towards optimisation target. The Flux Balance Analysis is performed first and the target flux is constrained to the maximal value. Subsequently, each reaction becomes an optimisation target and the minimal and maximal flux through this reaction is calculated. The Reaction Essentiality Scan is another iterative simulation protocol. Each reaction in the model is removed and the maximal flux towards optimisation target is re-calculated. This allows identification of reactions essential for the particular metabolic function. Finally, the program allows single Gene Essentiality Prediction. All reactions that require selected gene are constrained to (0, 0) and the maximal flux towards optimisation target is calculated. Therefore, the method predicts essentiality of a particular gene, for metabolic function of interest, under specified media conditions.

Flux Balance Analysis

Flux Variability Analysis

Reaction Essentiality Scan

Gene Essentiality Prediction