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Title

Finding and Breaking Lie Symmetries: Implications for Structural Identifiability and Observability in Biological Modelling

AuthorsMassonis, Gemma; Villaverde, A. F.
KeywordsDynamic modelling
Nonlinear systems
Observability
Structural identifiability
Lie symmetries
Issue Date2020
PublisherMultidisciplinary Digital Publishing Institute
CitationSymmetry 12(3): 469 (2020)
AbstractA dynamic model is structurally identifiable (respectively, observable) if it is theoretically possible to infer its unknown parameters (respectively, states) by observing its output over time. The two properties, structural identifiability and observability, are completely determined by the model equations. Their analysis is of interest for modellers because it informs about the possibility of gaining insight into a model’s unmeasured variables. Here we cast the problem of analysing structural identifiability and observability as that of finding Lie symmetries. We build on previous results that showed that structural unidentifiability amounts to the existence of Lie symmetries. We consider nonlinear models described by ordinary differential equations and restrict ourselves to rational functions. We revisit a method for finding symmetries by transforming rational expressions into linear systems. We extend the method by enabling it to provide symmetry-breaking transformations, which allows for a semi-automatic model reformulation that renders a non-observable model observable. We provide a MATLAB implementation of the methodology as part of the STRIKE-GOLDD toolbox for observability and identifiability analysis. We illustrate the use of the methodology in the context of biological modelling by applying it to a set of problems taken from the literature.
Description© 2020 by the authors.
Publisher version (URL)https://doi.org/10.3390/sym12030469
URIhttp://hdl.handle.net/10261/205625
DOI10.3390/sym12030469
ISSN2073-8994
E-ISSN2073-8994
Appears in Collections:(IIM) Artículos
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