The input-output relationship approach to structural identifiability analysis

Bearup, Daniel and Evans, N.D. and Chappell, M.J. (2010) The input-output relationship approach to structural identifiability analysis. In: IET Seminar Digest. UKACC International Conference on Control 2010 Proceedings. IEEE pp. 132-137. ISBN 978-1-84600-038-6. (doi:https://doi.org/10.1049/ic.2010.0269) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1049/ic.2010.0269

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. In order 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 either using the built-in Rosenfeld_Groebner algorithm or via the observability normal form, an alternative representation of the model derived from observability criteria. These techniques are applied to analyse models of two reaction schemes. Such systems form the building blocks of metabolic pathway models which are increasingly used in drug discovery and development.

Item Type: Conference or workshop item (Proceeding)
Additional information: cited By 0
Uncontrolled keywords: Differential algebra, Computational methods, Identifiability, Observability
Subjects: Q Science > QA Mathematics (inc Computing science)
Q Science > QA Mathematics (inc Computing science) > QA150 Algebra
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Applied Mathematics
Depositing User: Daniel Bearup
Date Deposited: 30 Nov 2017 11:24 UTC
Last Modified: 01 Dec 2017 12:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/64324 (The current URI for this page, for reference purposes)
Bearup, Daniel: https://orcid.org/0000-0001-8524-7659
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