On linear models and parameter identifiability in experimental biological systems

Lamberton, Timothy O., Condon, Nicholas D., Stow, Jennifer L. and Hamilton, Nicholas A. (2014) On linear models and parameter identifiability in experimental biological systems. Journal of Theoretical Biology, 358 102-121. doi:10.1016/j.jtbi.2014.05.028

Author Lamberton, Timothy O.
Condon, Nicholas D.
Stow, Jennifer L.
Hamilton, Nicholas A.
Title On linear models and parameter identifiability in experimental biological systems
Journal name Journal of Theoretical Biology   Check publisher's open access policy
ISSN 1095-8541
Publication date 2014-10-07
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.jtbi.2014.05.028
Open Access Status
Volume 358
Start page 102
End page 121
Total pages 20
Place of publication London, United Kingdom
Publisher Academic Press
Collection year 2015
Language eng
Abstract A key problem in the biological sciences is to be able to reliably estimate model parameters from experimental data. This is the well-known problem of parameter identifiability. Here, methods are developed for biologists and other modelers to design optimal experiments to ensure parameter identifiability at a structural level. The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function - the transfer function - and a framework for the identifiability analysis of complex model structures using linked models. Linked models are composed by letting the output of one model become the input to another model which is then experimentally measured. The linked model framework is shown to be applicable to designing experiments to identify the measured sub-model and recover the input from the unmeasured sub-model, even in cases that the unmeasured sub-model is not identifiable. Applications for a set of common model features are demonstrated, and the results combined in an example application to a real-world experimental system. These applications emphasize the insight into answering "where to measure" and "which experimental scheme" questions provided by both the parameter extraction methodology and the linked model framework. The aim is to demonstrate the tools[U+05F3] usefulness in guiding experimental design to maximize parameter information obtained, based on the model structure.
Keyword Modeling
Experimental design
Protein trafficking
Ordinary differential equations
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
Institute for Molecular Bioscience - Publications
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