%0 Journal Article
%T An iterative identification procedure for dynamic modeling of biochemical networks
%A Eva Balsa-Canto
%A Antonio A Alonso
%A Julio R Banga
%J BMC Systems Biology
%D 2010
%I BioMed Central
%R 10.1186/1752-0509-4-11
%X We propose a novel iterative identification procedure for detecting and dealing with the lack of identifiability. The procedure involves the following steps: 1) performing a structural identifiability analysis to detect identifiable parameters; 2) globally ranking the parameters to assist in the selection of the most relevant parameters; 3) calibrating the model using global optimization methods; 4) conducting a practical identifiability analysis consisting of two (a priori and a posteriori) phases aimed at evaluating the quality of given experimental designs and of the parameter estimates, respectively and 5) optimal experimental design so as to compute the scheme of experiments that maximizes the quality and quantity of information for fitting the model.The presented procedure was used to iteratively identify a mathematical model that describes the NF-¦ĘB regulatory module involving several unknown parameters. We demonstrated the lack of identifiability of the model under typical experimental conditions and computed optimal dynamic experiments that largely improved identifiability properties.Biological systems are mainly composed of genes that encode the molecular machines that execute the functions of life and networks of regulatory interactions specifying how genes are expressed, with both operating on multiple, hierarchical levels of organization [1]. Systems biology aims at understanding how such systems are organized by combining experimental data with mathematical modeling and computer-aided analysis techniques [1,2].The modeling and simulation of biochemical networks (e.g. metabolic or signaling pathways) has recently received a great deal of attention [3-5]. The modeling framework selected depends both on the properties of the studied system and the modeling goals. Lauffenburger et al. [4,6] organized the models in terms of three main groups, depending on their level of detail: deterministic, probabilistic and statistical.Currently, the most typical approac
%U http://www.biomedcentral.com/1752-0509/4/11