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Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems

BACKGROUND: Kinetic models of biochemical systems usually consist of ordinary differential equations that have many unknown parameters. Some of these parameters are often practically unidentifiable, that is, their values cannot be uniquely determined from the available data. Possible causes are lack...

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Autores principales: Gábor, Attila, Villaverde, Alejandro F., Banga, Julio R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5420165/
https://www.ncbi.nlm.nih.gov/pubmed/28476119
http://dx.doi.org/10.1186/s12918-017-0428-y
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author Gábor, Attila
Villaverde, Alejandro F.
Banga, Julio R.
author_facet Gábor, Attila
Villaverde, Alejandro F.
Banga, Julio R.
author_sort Gábor, Attila
collection PubMed
description BACKGROUND: Kinetic models of biochemical systems usually consist of ordinary differential equations that have many unknown parameters. Some of these parameters are often practically unidentifiable, that is, their values cannot be uniquely determined from the available data. Possible causes are lack of influence on the measured outputs, interdependence among parameters, and poor data quality. Uncorrelated parameters can be seen as the key tuning knobs of a predictive model. Therefore, before attempting to perform parameter estimation (model calibration) it is important to characterize the subset(s) of identifiable parameters and their interplay. Once this is achieved, it is still necessary to perform parameter estimation, which poses additional challenges. METHODS: We present a methodology that (i) detects high-order relationships among parameters, and (ii) visualizes the results to facilitate further analysis. We use a collinearity index to quantify the correlation between parameters in a group in a computationally efficient way. Then we apply integer optimization to find the largest groups of uncorrelated parameters. We also use the collinearity index to identify small groups of highly correlated parameters. The results files can be visualized using Cytoscape, showing the identifiable and non-identifiable groups of parameters together with the model structure in the same graph. RESULTS: Our contributions alleviate the difficulties that appear at different stages of the identifiability analysis and parameter estimation process. We show how to combine global optimization and regularization techniques for calibrating medium and large scale biological models with moderate computation times. Then we evaluate the practical identifiability of the estimated parameters using the proposed methodology. The identifiability analysis techniques are implemented as a MATLAB toolbox called VisId, which is freely available as open source from GitHub (https://github.com/gabora/visid). CONCLUSIONS: Our approach is geared towards scalability. It enables the practical identifiability analysis of dynamic models of large size, and accelerates their calibration. The visualization tool allows modellers to detect parts that are problematic and need refinement or reformulation, and provides experimentalists with information that can be helpful in the design of new experiments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-017-0428-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-54201652017-05-08 Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems Gábor, Attila Villaverde, Alejandro F. Banga, Julio R. BMC Syst Biol Methodology Article BACKGROUND: Kinetic models of biochemical systems usually consist of ordinary differential equations that have many unknown parameters. Some of these parameters are often practically unidentifiable, that is, their values cannot be uniquely determined from the available data. Possible causes are lack of influence on the measured outputs, interdependence among parameters, and poor data quality. Uncorrelated parameters can be seen as the key tuning knobs of a predictive model. Therefore, before attempting to perform parameter estimation (model calibration) it is important to characterize the subset(s) of identifiable parameters and their interplay. Once this is achieved, it is still necessary to perform parameter estimation, which poses additional challenges. METHODS: We present a methodology that (i) detects high-order relationships among parameters, and (ii) visualizes the results to facilitate further analysis. We use a collinearity index to quantify the correlation between parameters in a group in a computationally efficient way. Then we apply integer optimization to find the largest groups of uncorrelated parameters. We also use the collinearity index to identify small groups of highly correlated parameters. The results files can be visualized using Cytoscape, showing the identifiable and non-identifiable groups of parameters together with the model structure in the same graph. RESULTS: Our contributions alleviate the difficulties that appear at different stages of the identifiability analysis and parameter estimation process. We show how to combine global optimization and regularization techniques for calibrating medium and large scale biological models with moderate computation times. Then we evaluate the practical identifiability of the estimated parameters using the proposed methodology. The identifiability analysis techniques are implemented as a MATLAB toolbox called VisId, which is freely available as open source from GitHub (https://github.com/gabora/visid). CONCLUSIONS: Our approach is geared towards scalability. It enables the practical identifiability analysis of dynamic models of large size, and accelerates their calibration. The visualization tool allows modellers to detect parts that are problematic and need refinement or reformulation, and provides experimentalists with information that can be helpful in the design of new experiments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-017-0428-y) contains supplementary material, which is available to authorized users. BioMed Central 2017-05-05 /pmc/articles/PMC5420165/ /pubmed/28476119 http://dx.doi.org/10.1186/s12918-017-0428-y Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Gábor, Attila
Villaverde, Alejandro F.
Banga, Julio R.
Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems
title Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems
title_full Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems
title_fullStr Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems
title_full_unstemmed Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems
title_short Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems
title_sort parameter identifiability analysis and visualization in large-scale kinetic models of biosystems
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5420165/
https://www.ncbi.nlm.nih.gov/pubmed/28476119
http://dx.doi.org/10.1186/s12918-017-0428-y
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