Structural identifiability for a class of non-linear compartmental systems using linear/non-linear splitting and symbolic computation

2003 ◽  
Vol 183 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Michael J. Chapman ◽  
Keith R. Godfrey ◽  
Michael J. Chappell ◽  
Neil D. Evans
Keyword(s):  
Symbolic Computation ◽  
Structural Identifiability ◽  
Compartmental Systems ◽  
Non Linear

A study on structural identifiability of compartmental systems

1981 ◽  
Vol 64 (3) ◽  
pp. 1-9
Author(s):  
Shigetoshi Okano ◽  
Hajime Maeda ◽  
Hideo Kusuoka ◽  
Shinzo Kodama
Keyword(s):  
Structural Identifiability ◽  
Compartmental Systems

3 Structural Identifiability of Linear Compartmental Systems

2015 ◽  
pp. 36-46
Author(s):  
Hideo Kusuoka ◽  
Hajime Maeda ◽  
Shinzo Kodama
Keyword(s):  
Structural Identifiability ◽  
Compartmental Systems

scholarly journals A Graphical Aspect of Structural Identifiability of Compartmental Systems

1981 ◽  
Vol 17 (4) ◽  
pp. 455-460
Author(s):  
Hajime MAEDA ◽  
Shigetoshi OKANO ◽  
Shinzo KODAMA ◽  
Hideo KUSUOKA
Keyword(s):  
Structural Identifiability ◽  
Compartmental Systems

scholarly journals Testing structural identifiability by a simple scaling method

2020 ◽  
Vol 16 (11) ◽  
pp. e1008248
Author(s):  
Mario Castro ◽  
Rob J. de Boer
Keyword(s):  
Mathematical Modeling ◽  
General Practitioner ◽  
Empirical Data ◽  
Linear Models ◽  
Biological Processes ◽  
Structural Identifiability ◽  
Scaling Method ◽  
Partially Observed ◽  
Non Linear ◽  
Simple Scaling

Successful mathematical modeling of biological processes relies on the expertise of the modeler to capture the essential mechanisms in the process at hand and on the ability to extract useful information from empirical data. A model is said to be structurally unidentifiable, if different quantitative sets of parameters provide the same observable outcome. This is typical (but not exclusive) of partially observed problems in which only a few variables can be experimentally measured. Most of the available methods to test the structural identifiability of a model are either too complex mathematically for the general practitioner to be applied, or require involved calculations or numerical computation for complex non-linear models. In this work, we present a new analytical method to test structural identifiability of models based on ordinary differential equations, based on the invariance of the equations under the scaling transformation of its parameters. The method is based on rigorous mathematical results but it is easy and quick to apply, even to test the identifiability of sophisticated highly non-linear models. We illustrate our method by example and compare its performance with other existing methods in the literature.

scholarly journals On testing structural identifiability by a simple scaling method: relying on scaling symmetries can be misleading

2020 ◽  
Author(s):  
Alejandro F. Villaverde ◽  
Gemma Massonis Feixas
Keyword(s):  
Computational Biology ◽  
Dynamic Model ◽  
Symbolic Computation ◽  
Structural Identifiability ◽  
Scaling Method ◽  
Scaling Invariance ◽  
Simple Scaling ◽  
Local Identifiability ◽  
Possible Cause

AbstractA recent paper (Castro M, de Boer RJ, “Testing structural identifiability by a simple scaling method”, PLOS Computational Biology, 2020, 16(11):e1008248) introduces the Scaling Invariance Method (SIM) for analysing structural local identifiability and observability. These two properties define mathematically the possibility of determining the values of the parameters (identifiability) and states (observability) of a dynamic model by observing its output. In this note we warn that SIM considers scaling symmetries as the only possible cause of non-identifiability and non-observability. We show that other types of symmetries can cause the same problems without being detected by SIM, and that in those cases the method may yield a wrong result. Finally, we demonstrate how to analyse structural local identifiability and observability with symbolic computation tools that do not exhibit those issues.

Sign in / Sign up

Export Citation Format

Share Document