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

References in zbMATH (referenced in 10 articles )

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  1. Abdulla, Ugur G.; Poteau, Roby: Identification of parameters for large-scale kinetic models (2021)
  2. Leonard Schmiester, Yannik Schälte, Frank T. Bergmann, Tacio Camba, Erika Dudkin, Janine Egert, Fabian Fröhlich, Lara Fuhrmann, Adrian L. Hauber, Svenja Kemmer, Polina Lakrisenko, Carolin Loos, Simon Merkt, Wolfgang Müller, Dilan Pathirana, Elba Raimúndez, Lukas Refisch, Marcus Rosenblatt, Paul L. Stapor, Philipp Städter, Dantong Wang, Franz-Georg Wieland, Julio R. Banga, Jens Timmer, Alejandro F. Villaverde, Sven Sahle, Clemens Kreutz, Jan Hasenauer, Daniel Weindl: PEtab - interoperable specification of parameter estimation problems in systems biology (2020) arXiv
  3. Paul F. Lang, Sungho Shin, Victor M. Zavala: SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization (2020) arXiv
  4. Schmiester, Leonard; Weindl, Daniel; Hasenauer, Jan: Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach (2020)
  5. Abdulla, Ugur G.; Poteau, Roby: Identification of parameters in systems biology (2018)
  6. Saccomani, Maria Pia; Thomaseth, Karl: The union between structural and practical identifiability makes strength in reducing oncological model complexity: a case study (2018)
  7. Balsa-Canto, Eva; Alonso, Antonio A.; Arias-Méndez, Ana; García, Miriam R.; López-Núñez, A.; Mosquera-Fernández, Maruxa; Vázquez, C.; Vilas, Carlos: Modeling and optimization techniques with applications in food processes, bio-processes and bio-systems (2016)
  8. Chis, Oana-Teodora; Villaverde, Alejandro F.; Banga, Julio R.; Balsa-Canto, Eva: On the relationship between sloppiness and identifiability (2016)
  9. Clermont, Gilles; Zenker, Sven: The inverse problem in mathematical biology (2015)
  10. Jose A Egea, David Henriques, Thomas Cokelaer, Alejandro F Villaverde, Julio R Banga, Julio Saez-Rodriguez: MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics (2013) arXiv