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Reference: Villaverde, AF, Barreiro, A and Papachristodoulou, A, (2016). Structural identifiability of dynamic systems biology models. PLoS Computational Biology, 12 (10), Article: e1005153.Citable link to this page:


Structural identifiability of dynamic systems biology models

Abstract: A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model outputs. Structural identifiability is a prerequisite for parameter estimation, and should be assessed before exploiting a model. However, this analysis is seldom performed due to the high computational cost involved in the necessary symbolic calculations, which quickly becomes prohibitive as the problem size increases. In this paper we show how to analyse the structural identifiability of a very general class of nonlinear models by extending methods originally developed for studying observability. We present results about models whose identifiability had not been previously determined, report unidentifiabilities that had not been found before, and show how to modify those unidentifiable models to make them identifiable. This method helps prevent problems caused by lack of identifiability analysis, which can compromise the success of tasks such as experiment design, parameter estimation, and model-based optimization. The procedure is called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), and it is implemented in a MATLAB toolbox which is available as open source software. The broad applicability of this approach facilitates the analysis of the increasingly complex models used in systems biology and other areas.

Publication status:PublishedPeer Review status:Peer reviewedVersion:Publisher's versionDate of acceptance:19 September 2016Notes:Copyright © 2016 Villaverde et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Bibliographic Details

Publisher: Public Library of Science

Publisher Website:

Journal: PLoS Computational Biologysee more from them

Publication Website:

Volume: 12

Issue: 10

Extent: Article: e1005153

Issue Date: 2016

pages:Article: e1005153Identifiers

Issn: 1553-7358

Uuid: uuid:6643c54f-9471-4122-9267-c257fc5c3bbb

Urn: uri:6643c54f-9471-4122-9267-c257fc5c3bbb

Pubs-id: pubs:656106

Doi: Item Description

Type: journal-article;

Version: Publisher's version


Author: Villaverde, AF - - - Barreiro, A - - - Papachristodoulou, A - Oxford, MPLS, Engineering Science fundingEngineering and Physical S



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