Abstract
Often ODE models in systems biology, medical research, epidemiology, ecology and many other areas, contain unknown parameters which need to be estimated from experimental data. Identifiability deals with the uniqueness of the relation between model parameters and ODE solution thus being a prerequisite for the well-posedness of parameter estimation. In this paper a novel extension of the software tool DAISY (Differential Algebra for Identifiability of SYstems) is presented. DAISY performs structural identifiability analysis for linear and nonlinear dynamic models described by polynomial or rational ODE’s. The major upgrades of this new version regard the ability to include in the identifiability analysis either known and unknown model initial conditions, the possibility of entering a parameter estimate to calculate all the equivalent parameter solutions, the portability to MacOS platforms and an user-friendly interface. These upgrades make DAISY surely more general and easy to use. Practical examples are presented. DAISY is available at the web site daisy.dei.unipd.it.
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Saccomani, M.P., Bellu, G., Audoly, S., d’Angió, L. (2019). A New Version of DAISY to Test Structural Identifiability of Biological Models. In: Bortolussi, L., Sanguinetti, G. (eds) Computational Methods in Systems Biology. CMSB 2019. Lecture Notes in Computer Science(), vol 11773. Springer, Cham. https://doi.org/10.1007/978-3-030-31304-3_21
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DOI: https://doi.org/10.1007/978-3-030-31304-3_21
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