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This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

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SELEST is a procedure for identifiability of parameters in which selection and estimation steps are simultaneous, ensuring a well-conditioned estimation problem for a subset of identifiable parameters. Nevertheless, since SELEST is based on local sensitivity analysis, the identifiability criteria are dependent on the parameters initial values, requiring intensive parameters evaluation. In order to improve the convergence of the algorithm, we propose to update the values of the selected parameters and their sensitivity submatrix when re-ranking the remaining parameters. Therefore, the parameters estimations are performed using more appropriate values than the initial estimates. Two cases studies illustrate the performance of the proposed procedure: a hypothetical model, and an enzymatic hydrolysis model. Results demonstrate that the proposed modifications improved the performance of the algorithm, reducing the computational time significantly.