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Structural Identifiability of Dynamic Systems Biology Models
- Source :
- PLOS Computational Biology, PLoS Computational Biology, PLoS Computational Biology, Vol 12, Iss 10, p e1005153 (2016), Digital.CSIC. Repositorio Institucional del CSIC, instname
- Publication Year :
- 2016
-
Abstract
- 22 páginas, 5 figuras, 2 tablas.-- 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.<br />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<br />AFV acknowledges funding from the Galician government (Xunta de Galiza, Consellería de Cultura, Educación e Ordenación Universitaria http://www.edu.xunta.es/portal/taxonomy/term/206) through the I2C postdoctoral program, fellowship ED481B2014/133-0. AB and AFV were partially supported by grant DPI2013-47100-C2-2-P from the Spanish Ministry of Economy and Competitiveness (MINECO). AFV acknowledges additional funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 686282 (CanPathPro). AP was partially supported through EPSRC projects EP/M002454/1 and EP/J012041/1.
- Subjects :
- 0301 basic medicine
0209 industrial biotechnology
Cell signaling
Computer science
02 engineering and technology
Signal transduction
Systems Science
020901 industrial engineering & automation
Drug Metabolism
Statistics
Decomposition (computer science)
Medicine and Health Sciences
lcsh:QH301-705.5
Class (computer programming)
Ecology
Estimation theory
Design of experiments
Systems Biology
Signaling cascades
CHO cells
Plants
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Cell lines
Biological cultures
Algorithms
Research Article
Optimization
Mathematical optimization
Cell biology
Computer and Information Sciences
MAPK signaling cascades
Systems biology
Arabidopsis Thaliana
Brassica
Research and Analysis Methods
Models, Biological
03 medical and health sciences
Cellular and Molecular Neuroscience
Model Organisms
Plant and Algal Models
Genetics
Animals
Humans
Pharmacokinetics
Computer Simulation
Observability
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Pharmacology
Biology and life sciences
Organisms
Nonlinear system
030104 developmental biology
lcsh:Biology (General)
Nonlinear Dynamics
Identifiability
Programming Languages
Mathematics
Nonlinear Systems
Software
Subjects
Details
- ISSN :
- 15537358
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....c172e3d97c3da9e8c3999aebeb2cef49
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1005153