Abstract
The nonlinear character and the usually large number of parameters in biological mathematical models make model identification from experimental data a rather complex task. The origin of such complexity is often related with the lack of identifiability.This work presents a model identification procedure and the corresponding numerical techniques to iteratively improve model predictive capabilities in the context of systems biology. The procedure involves several steps such as identifiability analysis, global ranking of parameters, parameter estimation and optimal experimental design. Most of these steps are being incorporated in a MATLAB based toolbox, AMIGO (Advanced Model Identification using Global Optimization).To illustrate the performance of the proposed procedure we considered a mathematical model that describes the NF-κB regulatory module involving several unknown parameters.
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