Literature DB >> 23228562

The input-output relationship approach to structural identifiability analysis.

Daniel J Bearup1, Neil D Evans, Michael J Chappell.   

Abstract

Analysis of the identifiability of a given model system is an essential prerequisite to the determination of model parameters from physical data. However, the tools available for the analysis of non-linear systems can be limited both in applicability and by computational intractability for any but the simplest of models. The input-output relation of a model summarises the input-output structure of the whole system and as such provides the potential for an alternative approach to this analysis. However for this approach to be valid it is necessary to determine whether the monomials of a differential polynomial are linearly independent. A simple test for this property is presented in this work. The derivation and analysis of this relation can be implemented symbolically within Maple. These techniques are applied to analyse classical models from biomedical systems modelling and those of enzyme catalysed reaction schemes.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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Year:  2012        PMID: 23228562     DOI: 10.1016/j.cmpb.2012.10.012

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  8 in total

1.  Structural identifiability and sensitivity.

Authors:  Athanassios Iliadis
Journal:  J Pharmacokinet Pharmacodyn       Date:  2019-03-20       Impact factor: 2.745

2.  Structural identifiability for mathematical pharmacology: models of myelosuppression.

Authors:  Neil D Evans; S Y Amy Cheung; James W T Yates
Journal:  J Pharmacokinet Pharmacodyn       Date:  2018-02-02       Impact factor: 2.745

3.  Parameter Identifiability of Fundamental Pharmacodynamic Models.

Authors:  David L I Janzén; Linnéa Bergenholm; Mats Jirstrand; Joanna Parkinson; James Yates; Neil D Evans; Michael J Chappell
Journal:  Front Physiol       Date:  2016-12-05       Impact factor: 4.566

4.  Structural identifiability analysis of age-structured PDE epidemic models.

Authors:  Marissa Renardy; Denise Kirschner; Marisa Eisenberg
Journal:  J Math Biol       Date:  2022-01-04       Impact factor: 2.259

5.  Analysis of cellular kinetic models suggest that physiologically based model parameters may be inherently, practically unidentifiable.

Authors:  Liam V Brown; Mark C Coles; Mark McConnell; Alexander V Ratushny; Eamonn A Gaffney
Journal:  J Pharmacokinet Pharmacodyn       Date:  2022-08-06       Impact factor: 2.410

6.  Prediction of Clinical Transporter-Mediated Drug-Drug Interactions via Comeasurement of Pitavastatin and Eltrombopag in Human Hepatocyte Models.

Authors:  Simon J Carter; Bhavik Chouhan; Pradeep Sharma; Michael J Chappell
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2020-04

7.  Identifiability and numerical algebraic geometry.

Authors:  Daniel J Bates; Jonathan D Hauenstein; Nicolette Meshkat
Journal:  PLoS One       Date:  2019-12-13       Impact factor: 3.240

8.  Structural identifiability of the generalized Lotka-Volterra model for microbiome studies.

Authors:  Christopher H Remien; Mariah J Eckwright; Benjamin J Ridenhour
Journal:  R Soc Open Sci       Date:  2021-07-21       Impact factor: 2.963

  8 in total

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