Research Article Simultaneous Parameters Identifiability and Estimation of an E. coli Metabolic Network Model Kese Pontes Freitas Alberton, 1 André Luís Alberton, 2 Jimena Andrea Di Maggio, 3 Vanina Gisela Estrada, 3 María Soledad Díaz, 3 and Argimiro Resende Secchi 1 1 Programa de Engenharia Qu´ ımica-COPPE, Universidade Federal do Rio de Janeiro, Cidade Universit´ aria, 21941-972 Rio de Janeiro, BR, Brazil 2 Instituto de Qu´ ımica, Universidade do Estado do Rio de Janeiro, S˜ ao Francisco Xavier 524, 20550-900 Rio de Janeiro, BR, Brazil 3 Planta Piloto de Ingenier´ ıa Qu´ ımica-CONICET, Universidad Nacional del Sur, Camino La Carrindanga, Km 7, 8000 Bah´ ıa Blanca, Argentina Correspondence should be addressed to Kese Pontes Freitas Alberton; kese@peq.coppe.ufrj.br Received 31 May 2014; Revised 29 August 2014; Accepted 5 September 2014 Academic Editor: Eug´ enio Ferreira Copyright © 2015 Kese Pontes Freitas Alberton et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis work proposes a procedure for simultaneous parameters identifability and estimation in metabolic networks in order to overcome difculties 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 identifability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 diferential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model ft was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. Te results indicate that simultaneous parameters identifability and estimation approach in metabolic networks is appealing, since model ft to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available. 1. Introduction Te development of mathematical model for metabolic net- works has been severely hampered by the lack of kinetic information [1–4]. Usually, available experimental data are obtained under diferent conditions using heterogeneous techniques, whose choice must be done according to the observation of a specifc phenomenon of interest on the pathways [2, 4–6]. In such systems, the type of experiment, sampling method, and the mathematical interpretation of the data depend on the desired experimental information [5]. However, as pointed out by Costa et al. [4], kinetic infor- mation presented in the literature about metabolic network models is scarce and ofen confuse; thus, other strategies are adopted in detriment to the dynamic simulation of such systems. Mathematically, metabolic networks are described by complex dynamics models, whose structure is composed by ordinary diferential equations that represent mass bal- ance of the substrate, biomass, products and intracellular metabolites crucial on the pathways, and numerous reaction rates regarding to the pathways. Such mathematical struc- ture presents a large number of parameters, for which the estimation procedure demands a considerable number of experimental data. Since experimentally metabolic networks are only partially observed, only a fraction of the intracellular metabolites considered in the mathematical model can be directly measured and thus initial conditions should also be estimated. Unfortunately, in metabolic network systems, lack of experimental data is almost unavoidable, which compromises the reliability of reactions rates proposition and makes the estimation of all parameters unfeasible. Tus, such Hindawi Publishing Corporation BioMed Research International Volume 2015, Article ID 454765, 21 pages http://dx.doi.org/10.1155/2015/454765