English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/170913
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Title

Sufficiently exciting inputs for structurally identifiable systems biology models

AuthorsVillaverde, A. F. ; Evans, Neil D.; Chappell, Michael J. ; Banga, Julio R.
KeywordsIdentifiability
Structural Identifiability
Structural properties
Observability
Parameter identification
Parameter estimation
Inputs
Issue Date2018
PublisherInternational Federation of Automatic Control
CitationIFAC-PapersOnLine 51(19): 16-19 (2018)
AbstractA parameter is structurally identifiable if its value can theoretically be estimated by observing the model output. Structural identifiability is a desirable property in biological modelling: if a parameter is structurally unidentifiable, its estimated numerical value is meaningless, and model predictions of unmeasured state variables can be wrong, compromising the ability of the model to provide biological insight. Structural identifiability depends on the system dynamics, observation function (model output), initial conditions, and external inputs. In this paper we focus on the last factor. Methods for structural identifiability analysis typically classify a model as identifiable provided that it is fed with sufficiently exciting inputs. For example, a given model may require a time-varying input to be structurally identifiable, while for another model a constant non-zero input may be enough. Here we present a method that determines how sufficiently exciting an input should be. The approach builds on the STRIKE-GOLDD toolbox, which considers structural identifiability as generalized observability. The approach incorporates extended Lie derivatives, which correctly assess structural identifiability in the case of time-varying inputs. The procedure can also be used to determine the type of input profile that is required to make the parameters identifiable. This capability is helpful when designing new experiments for the purpose of parameter estimation
Description4 pages, 1 figure
Publisher version (URL)https://doi.org/10.1016/j.ifacol.2018.09.015
URIhttp://hdl.handle.net/10261/170913
DOI10.1016/j.ifacol.2018.09.015
ISSN1474-6670
Appears in Collections:(IIM) Artículos
Files in This Item:
File Description SizeFormat 
Sufficiently_exciting_2018.pdf456,57 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.