Extending existing structural identifiability analysis methods to mixed-effects models

DOI10.1016/j.mbs.2017.10.009zbMath1380.92020OpenAlexW2766985004WikidataQ47377637 ScholiaQ47377637MaRDI QIDQ1697297

Neil D. Evans, Mats Jirstrand, David L. I. Janzén, Michael J. Chappell

Publication date: 15 February 2018

Published in: Mathematical Biosciences (Search for Journal in Brave)

Full work available at URL: http://wrap.warwick.ac.uk/94847/1/WRAP-extending-existing-structural-analysis-models-Evans-2018.pdf

zbMATH Keywords

structural identifiabilityinput-output form approachmixed-effects modellingTaylor series expansion approach

Mathematics Subject Classification ID

Biochemistry, molecular biology (92C40)

Related Items (1)

Experimental design for parameter estimation in steady-state linear models of metabolic networks

Uses Software

• DAISY

Cites Work

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• Similarity transformation approach to identifiability analysis of nonlinear compartmental models
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• Identifiability of linear mixed effects models
• On the identifiability and distinguishability of nonlinear parametric models
• The identifiability of parameters of models used in biomedicine
• Some effective approaches to check the identifiability of uncontrolled nonlinear systems
• Differential algebra methods for the study of the structural identifiability of rational function state-space models in the biosciences

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