Andreas Raue1, Johan Karlsson, Maria Pia Saccomani, Mats Jirstrand, Jens Timmer. 1. University of Freiburg, Institute for Physics, 79104 Freiburg, Germany, Merrimack Pharmaceuticals Inc., 02139 Cambridge, MA, USA, Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Chalmers Science Park, SE-412 88 Göteborg, Sweden, Department of Information Engineering, University of Padova, 35131 Padova, Italy, BIOSS Centre for Biological Signalling Studies and Zentrum für Biosystemanalyse (ZBSA), University of Freiburg, 79104 Freiburg, Germany.
Abstract
MOTIVATION: Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of Systems Biology. The amount of experimental data that are used to build and calibrate these models is often limited. In this setting, the model parameters may not be uniquely determinable. Structural or a priori identifiability is a property of the system equations that indicates whether, in principle, the unknown model parameters can be determined from the available data. RESULTS: We performed a case study using three current approaches for structural identifiability analysis for an application from cell biology. The approaches are conceptually different and are developed independently. The results of the three approaches are in agreement. We discuss strength and weaknesses of each of them and illustrate how they can be applied to real world problems. AVAILABILITY AND IMPLEMENTATION: For application of the approaches to further applications, code representations (DAISY, Mathematica and MATLAB) for benchmark model and data are provided on the authors webpage. CONTACT: andreas.raue@fdm.uni-freiburg.de.
MOTIVATION: Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of Systems Biology. The amount of experimental data that are used to build and calibrate these models is often limited. In this setting, the model parameters may not be uniquely determinable. Structural or a priori identifiability is a property of the system equations that indicates whether, in principle, the unknown model parameters can be determined from the available data. RESULTS: We performed a case study using three current approaches for structural identifiability analysis for an application from cell biology. The approaches are conceptually different and are developed independently. The results of the three approaches are in agreement. We discuss strength and weaknesses of each of them and illustrate how they can be applied to real world problems. AVAILABILITY AND IMPLEMENTATION: For application of the approaches to further applications, code representations (DAISY, Mathematica and MATLAB) for benchmark model and data are provided on the authors webpage. CONTACT: andreas.raue@fdm.uni-freiburg.de.
Authors: Ahmed Ramadan; Jongeun Choi; Jacek Cholewicki; N Peter Reeves; John M Popovich; Clark J Radcliffe Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2019-01-10 Impact factor: 3.802
Authors: Alexander P Browning; David J Warne; Kevin Burrage; Ruth E Baker; Matthew J Simpson Journal: J R Soc Interface Date: 2020-12-16 Impact factor: 4.118
Authors: Eyan Yeung; Sarah McFann; Lewis Marsh; Emilie Dufresne; Sarah Filippi; Heather A Harrington; Stanislav Y Shvartsman; Martin Wühr Journal: Curr Biol Date: 2020-02-13 Impact factor: 10.834