[15], which was shown to successfully simulate many of the growth properties

of the bacterium. Tuberculosis (TB) remains one of the largest killer infectious diseases [16,17], and although significant advances were achieved in understanding the biology of M. tuberculosis, no new drug to treat tuberculosis has been developed in the last 30 years, making this organism Inhibitors,research,lifescience,medical an important subject for systems biology studies [18,19]. Our results show that an excellent agreement with flux values is obtained under several growth conditions, although kinetic parameters may vary in different conditions. Parameter variability analysis indicates that a high degree of redundancy remains present in model parameters when fluxes are the only constraining input. 2. Methods 2.1. Enzyme Kinetics and Rate Equations We used the GRaPe software [7] to build a genome-scale kinetic model of M. tuberculosis. Inhibitors,research,lifescience,medical Rate equations for all reactions in the model are automatically generated by GRaPe based on the stoichiometry of the reaction.

Reactions in the model assume a random-order mechanism as the sequential order of binding and releasing Inhibitors,research,lifescience,medical of substrates is often unknown. A key advantage of GRaPe over other tools is its ability to automatically generate rate equations for reactions, making it less error-prone and more time-efficient in building large-scale models. The King-Altman method [20] was used to derive rate equations based on the stoichiometry of a reaction and the enzyme mechanism. The generic rate equations provided in [7] were used for all reactions of up to two substrates or products; these reactions can be of type uni-uni, uni-bi, bi-uni or bi-bi. For reactions of more than two substrates Inhibitors,research,lifescience,medical of products, the convenience kinetics was used [12]. The convenience kinetics equation, a generalised form of Michaelis-Menten kinetics, assumes a Inhibitors,research,lifescience,medical random-order mechanism and implements enzyme saturation and regulation. It can cover all possible reaction stoichiometries. For a reaction of type A1 + A2+ … ↔ B1 + B2 + …, the concentrations of substrates are represented by a vector

a = (a1, a2, …) and the concentrations of products are represented by a vector b = (b1, b2, …). The flux v(a, b) is defined as:       (1) where i is the substrate index, j is the product index, KAi represents the parameter (substrate constants) of the ith substrate and KBj that of the jth product of the reaction, from e0 is the concentration of enzyme, V+ is the substrate turnover rate and V- is the product turnover rate. 2.2. Parameter Estimation Kinetic models have been shown to produce accurate and testable results [21], but due to the enormous number of kinetic parameters needed to MEK activity define the system, the number of large-scale kinetic models remains relatively low. Furthermore, it was observed by Teusink et al. [22] that in vitro measurements of kinetic constants may not necessarily be representative of their numerical values in vivo.