However, flux constraints are excluded from the input data which can allow for a zero flux solution to be obtained even in non-equilibrium conditions. Since fluxes are not explicitly expressed as model elements, constraining parameters using those software is still not straightforward. Dynamic flux estimation shows that by verifying mass conservation in metabolic time-series data and integrating fluxes in the estimation of kinetic Inhibitors,research,lifescience,medical parameters values, the redundancy in model parameters can be reduced [25]. GRaPe uses a genetic

algorithm to estimate kinetic parameters using flux values to constrain kinetic parameters. Figure 1 illustrates the process undertaken to reconstruct our kinetic model of M. tuberculosis. Other data sets can also be introduced into the parameter estimation process for constraining purposes, however the availability of comprehensive datasets Inhibitors,research,lifescience,medical on a large-scale is often lacking. Figure 1 Schematic overview of the model development process. 2.3. Parameter Variability Analysis (PVA) One of the issues relating to parameter estimation is that of mathematical redundancy. The redundancy results in multiple

sets of parameter values that can fit equally well to an experimental data set. A simple example Inhibitors,research,lifescience,medical of redundancy is when two parameters, a and b, are part of an equation in the form of a + b or a * b; if only their sum or product is known it is impossible to identify the value of a and b individually; if both the sum and product are known, then the value of a and b can be calculated.

This example illustrates that the level of redundancy is dependent on the amount of experimental Inhibitors,research,lifescience,medical data used to constrain the estimation. When there is redundancy, the parameter values found in several runs of the estimation algorithm are likely to be different. In this article, we analysed the redundancy or ‘sloppiness’ Inhibitors,research,lifescience,medical in parameter estimation using parameter variability analysis (PVA). PVA allowed us to measure the range of changes in a set of parameter values when the estimation is MK1775 repeated multiple times. Once a model has been constructed or uploaded in GRaPe, PVA can be performed using the same data required to estimate parameter values for the model. PD184352 (CI-1040) The PVA algorithm works by repeating the estimation of kinetic parameters for the model multiple times using a genetic algorithm (GA). The GA works by populating a set of random initial parameter values; this is why results may differ after each run of the algorithm when there is redundancy. These estimated values are then optimised in an iterative manner until the maximum number of iterations is reached or a suitable solution is found. In GRaPe, GA uses flux and metabolic data to constrain parameters as illustrated in Figure 1.