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AMIGO, a toolbox for advanced model identification in systems biology using global optimization

AuthorsBalsa-Canto, Eva ; Banga, Julio R.
Parameter identification
Sytems biology
Issue Date17-Jun-2011
PublisherOxford University Press
CitationBioinformatics 2716): 2311-2313 (2011)
AbstractABSTRACT Motivation: Mathematical models of complex biological systems usually consist of sets of differential equations which depend on several parameters which are not accessible to experimentation. These parameters must be estimated by fitting the model to experimental data. This estimation problem is very challenging due to the non-linear character of the dynamics, the large number of parameters and the frequently poor information content of the experimental data (poor practical identifiability). The design of optimal (more informative) experiments is an associated problem of the highest interest. Results: This work presents AMIGO, a toolbox which facilitates parametric identification by means of advanced numerical techniques which cover the full iterative identification procedure putting especial emphasis on robust methods for parameter estimation and practical identifiability analyses, plus flexible capabilities for optimal experimental design.
Publisher version (URL)http://dx.doi.org/10.1093/bioinformatics/btr370
Appears in Collections:(IIM) Artículos
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