Abstract
Mathematical models of complex biological systems often consist of sets of differential equations which depend on several non measurable parameters that must be estimated by fitting the model to experimental data. However the nonlinear character and the usually large number of parameters make model identification from experimental data a rather complex task due to the multimodal character of the problem and/or the poor practical identifiability.This work presents a MATLAB based toolbox, AMIGO (Advanced Model Identification using Global Optimization), which is devoted to facilitate parametric identification. With this aim it covers all steps within a complete iterative identification procedure: sensitivity analysis, rank of parameters, practical identifiability analysis, parameter estimation and optimal experimental design.
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