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. Bortner, Cashous; Meshkat, Nicolette: Identifiable paths and cycles in linear compartmental models (2022)
  2. Pieschner, Susanne; Hasenauer, Jan; Fuchs, Christiane: Identifiability analysis for models of the translation kinetics after mRNA transfection (2022)
  3. van der Vegt, Solveig A.; Polonchuk, Liudmila; Wang, Ken; Waters, Sarah L.; Baker, Ruth E.: Mathematical modelling of autoimmune myocarditis and the effects of immune checkpoint inhibitors (2022)
  4. Wang, Yanbing; Monache, Maria Laura Delle; Work, Daniel B.: Identifiability of car-following dynamics (2022)
  5. Ilmer, Ilia; Ovchinnikov, Alexey; Pogudin, Gleb: Web-based structural identifiability analyzer (2021)
  6. Kumbhari, Adarsh; Rose, Danya; Lee, Peter P.; Kim, Peter S.: A minimal model of T cell avidity may identify subtherapeutic vaccine schedules (2021)
  7. Lichtblau, Daniel: Symbolic analysis of multiple steady states in a MAPK chemical reaction network (2021)
  8. Szlobodnyik, Gergely; Szederkényi, Gábor: Structural identifiability analysis of nonlinear time delayed systems with generalized frequency response functions. (2021)
  9. Tuncer, Necibe; Martcheva, Maia: Determining reliable parameter estimates for within-host and within-vector models of Zika virus (2021)
  10. Gross, Elizabeth; Harrington, Heather; Meshkat, Nicolette; Shiu, Anne: Joining and decomposing reaction networks (2020)
  11. Kabanikhin, S. I.; Krivorotko, O. I.: Mathematical modeling of the Wuhan COVID-2019 epidemic and inverse problems (2020)
  12. Kabanikhin, S. I.; Krivorotko, O. I.: Optimization methods for solving inverse immunology and epidemiology problems (2020)
  13. Müller, Christian; Diedam, Holger; Mrziglod, Thomas; Schuppert, Andreas: A neural network assisted Metropolis adjusted Langevin algorithm (2020)
  14. Gross, Elizabeth; Harrington, Heather; Meshkat, Nicolette; Shiu, Anne: Linear compartmental models: input-output equations and operations that preserve identifiability (2019)
  15. Jeronimo, Gabriela; Pérez Millán, Mercedes; Solernó, Pablo: Identifiability from a few species for a class of biochemical reaction networks (2019)
  16. Lund, Alana; Dyke, Shirley J.; Song, Wei; Bilionis, Ilias: Global sensitivity analysis for the design of nonlinear identification experiments (2019)
  17. Saccomani, Maria Pia; Thomaseth, Karl: Calculating all multiple parameter solutions of ODE models to avoid biological misinterpretations (2019)
  18. Saccomani, M. P.; Bellu, G.; Audoly, S.; d’Angió, L.: A new version of DAISY to test structural identifiability of biological models (2019)
  19. Verdière, N.; Orange, S.: A systematic approach for doing an a priori identifiability study of dynamical nonlinear models (2019)
  20. Villaverde, Alejandro F.: Observability and structural identifiability of nonlinear biological systems (2019)

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