DAISY: A new software tool to test global identifiability of biological and physiological systems. A priori global identifiability is a structural property of biological and physiological models. It is considered a prerequisite for well-posed estimation, since it concerns the possibility of recovering uniquely the unknown model parameters from measured input–output data, under ideal conditions (noise-free observations and error-free model structure). Of course, determining if the parameters can be uniquely recovered from observed data is essential before investing resources, time and effort in performing actual biomedical experiments. Many interesting biological models are nonlinear but identifiability analysis for nonlinear system turns out to be a difficult mathematical problem. Different methods have been proposed in the literature to test identifiability of nonlinear models but, to the best of our knowledge, so far no software tools have been proposed for automatically checking identifiability of nonlinear models. In this paper, we describe a software tool implementing a differential algebra algorithm to perform parameter identifiability analysis for (linear and) nonlinear dynamic models described by polynomial or rational equations. Our goal is to provide the biological investigator a completely automatized software, requiring minimum prior knowledge of mathematical modelling and no in-depth understanding of the mathematical tools. The DAISY (Differential Algebra for Identifiability of SYstems) software will potentially be useful in biological modelling studies, especially in physiology and clinical medicine, where research experiments are particularly expensive and/or difficult to perform. Practical examples of use of the software tool DAISY are presented. DAISY is available at the web site http://www.dei.unipd.it/∼pia/.

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  1. Janzén, David L.I.; Jirstrand, Mats; Chappell, Michael J.; Evans, Neil D.: Extending existing structural identifiability analysis methods to mixed-effects models (2018)
  2. Davidson, Shaun M.; Docherty, Paul D.; Murray, Rua: The dimensional reduction method for identification of parameters that trade-off due to similar model roles (2017)
  3. Eisenberg, Marisa C.; Jain, Harsh V.: A confidence building exercise in data and identifiability: modeling cancer chemotherapy as a case study (2017)
  4. Nimmegeers, Philippe; Lauwers, Joost; Telen, Dries; Logist, Filip; van Impe, Jan: Identifiability of large-scale non-linear dynamic network models applied to the ADM1-case study (2017)
  5. Joyner, Michele L.; Manning, Cammey C.; Forbes, Whitney; Maiden, Michelle; Nikas, Ariel N.: A physiologically-based pharmacokinetic model for the antibiotic ertapenem (2016)
  6. Letham, Benjamin; Letham, Portia A.; Rudin, Cynthia; Browne, Edward P.: Prediction uncertainty and optimal experimental design for learning dynamical systems (2016)
  7. Tuncer, Necibe; Gulbudak, Hayriye; Cannataro, Vincent L.; Martcheva, Maia: Structural and practical identifiability issues of immuno-epidemiological vector-host models with application to Rift Valley Fever (2016)
  8. Clermont, Gilles; Zenker, Sven: The inverse problem in mathematical biology (2015)
  9. Mansell, Erin J.; Docherty, Paul D.; Fisk, Liam M.; Chase, J.Geoffrey: Estimation of secondary effect parameters in glycaemic dynamics using accumulating data from a virtual type 1 diabetic patient (2015)
  10. Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa: Identifiability results for several classes of linear compartment models (2015)
  11. Wongvanich, N.; Hann, C.E.; Sirisena, H.R.: Robust global identifiability theory using potentials -- application to compartmental models (2015)
  12. Yang, Huan; Meijer, Hil G.E.; Doll, Robert J.; Buitenweg, Jan R.; van Gils, Stephan A.: Computational modeling of Adelta-fiber-mediated nociceptive detection of electrocutaneous stimulation (2015)
  13. Eisenberg, Marisa C.; Hayashi, Michael A. L.: Determining identifiable parameter combinations using subset profiling (2014)
  14. Berthoumieux, Sara; Brilli, Matteo; Kahn, Daniel; de Jong, Hidde; Cinquemani, Eugenio: On the identifiability of metabolic network models (2013)
  15. Petrov, Andrii Y.; Geoffrey Chase, J.; Sellier, Mathieu; Docherty, Paul D.: Non-identifiability of the Rayleigh damping material model in magnetic resonance elastography (2013)
  16. Meshkat, Nicolette; Anderson, Chris; DiStefano, Joseph J. III: Alternative to Ritt’s pseudodivision for finding the input-output equations of multi-output models (2012)
  17. Meshkat, Nicolette; Anderson, Chris; DiStefano, Joseph J.III: Finding identifiable parameter combinations in nonlinear ODE models and the rational reparameterization of their input-output equations (2011)
  18. Miao, Hongyu; Xia, Xiaohua; Perelson, Alan S.; Wu, Hulin: On identifiability of nonlinear ODE models and applications in viral dynamics (2011)
  19. Saccomani, Maria Pia: An effective automatic procedure for testing parameter identifiability of HIV/AIDS models (2011)
  20. Chase, J.Geoffrey; Mayntzhusen, Klaus; Docherty, Paul D.; Andreassen, Steen; McAuley, Kirsten A.; Lotz, Thomas F.; Hann, Christopher E.: A three-compartment model of the C-peptide-insulin dynamic during the DIST test (2010)

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