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. 2011 Aug 15;27(16):2311-3.
doi: 10.1093/bioinformatics/btr370. Epub 2011 Jun 17.

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

Eva Balsa-Canto  1 , Julio R Banga
Affiliations

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

Eva Balsa-Canto et al. Bioinformatics. .

Abstract

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.

Availability: The toolbox and the corresponding documentation may be downloaded from: http://www.iim.csic.es/~amigo

Contact: ebalsa@iim.csic.es.

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Figures

Fig. 1.
Fig. 1.
Illustrative example of the experimental scheme and data.
Fig. 2.
Fig. 2.
AMIGO structure.
See this image and copyright information in PMC

References

    1. Balsa-Canto E., et al. Dynamic optimization of single-and multi-stage systems using a hybrid stochastic-deterministic method. Ind. Eng. Chem. Res. 2005;44:1514–1523.
    1. Balsa-Canto E., et al. Computational procedures for optimal experimental design in biological systems. IET Syst. Biol. 2008a;2:163–172. - PubMed
    1. Balsa-Canto E., et al. Hybrid optimization method with general switching strategy for parameter estimation. BMC Syst. Biol. 2008b;2:26. - PMC - PubMed
    1. Balsa-Canto E., et al. An iterative identification procedure for dynamic modeling of biochemical networks. BMC Syst. Biol. 2010;4:11. - PMC - PubMed
    1. Csendes T., et al. The GLOBAL optimization method revisited. Optim. Lett. 2008;2:445–454.

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