Literature DB >> 19913563

Structural identifiability of polynomial and rational systems.

Jana Nemcová1.   

Abstract

Since analysis and simulation of biological phenomena require the availability of their fully specified models, one needs to be able to estimate unknown parameter values of the models. In this paper we deal with identifiability of parametrizations which is the property of one-to-one correspondence of parameter values and the corresponding outputs of the models. Verification of identifiability of a parametrization precedes estimation of numerical values of parameters, and thus determination of a fully specified model of a considered phenomenon. We derive necessary and sufficient conditions for the parametrizations of polynomial and rational systems to be structurally or globally identifiable. The results are applied to investigate the identifiability properties of the system modeling a chain of two enzyme-catalyzed irreversible reactions. The other examples deal with the phenomena modeled by using Michaelis-Menten kinetics and the model of a peptide chain elongation. Copyright (c) 2009 Elsevier Inc. All rights reserved.

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Year:  2009        PMID: 19913563     DOI: 10.1016/j.mbs.2009.11.002

Source DB:  PubMed          Journal:  Math Biosci        ISSN: 0025-5564            Impact factor:   2.144


  7 in total

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Journal:  J Pharmacokinet Pharmacodyn       Date:  2018-02-02       Impact factor: 2.745

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Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2017-05-19

4.  Dynamic compensation, parameter identifiability, and equivariances.

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Journal:  PLoS Comput Biol       Date:  2017-04-06       Impact factor: 4.475

5.  Structural identifiability of equilibrium ligand-binding parameters.

Authors:  Thomas R Middendorf; Richard W Aldrich
Journal:  J Gen Physiol       Date:  2016-12-19       Impact factor: 4.086

6.  Parameter estimation and identifiability in a neural population model for electro-cortical activity.

Authors:  Agus Hartoyo; Peter J Cadusch; David T J Liley; Damien G Hicks
Journal:  PLoS Comput Biol       Date:  2019-05-30       Impact factor: 4.475

Review 7.  Reverse engineering and identification in systems biology: strategies, perspectives and challenges.

Authors:  Alejandro F Villaverde; Julio R Banga
Journal:  J R Soc Interface       Date:  2013-12-04       Impact factor: 4.118

  7 in total

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