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JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language

BACKGROUND: Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model...

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Autores principales: Bassen, David M., Vilkhovoy, Michael, Minot, Mason, Butcher, Jonathan T., Varner, Jeffrey D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5264316/
https://www.ncbi.nlm.nih.gov/pubmed/28122561
http://dx.doi.org/10.1186/s12918-016-0380-2
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author Bassen, David M.
Vilkhovoy, Michael
Minot, Mason
Butcher, Jonathan T.
Varner, Jeffrey D.
author_facet Bassen, David M.
Vilkhovoy, Michael
Minot, Mason
Butcher, Jonathan T.
Varner, Jeffrey D.
author_sort Bassen, David M.
collection PubMed
description BACKGROUND: Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. RESULTS: In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. CONCLUSIONS: JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open source, available under an MIT license, and can be installed using the Julia package manager from the JuPOETs GitHub repository
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spelling pubmed-52643162017-01-30 JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language Bassen, David M. Vilkhovoy, Michael Minot, Mason Butcher, Jonathan T. Varner, Jeffrey D. BMC Syst Biol Software BACKGROUND: Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. RESULTS: In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. CONCLUSIONS: JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open source, available under an MIT license, and can be installed using the Julia package manager from the JuPOETs GitHub repository BioMed Central 2017-01-25 /pmc/articles/PMC5264316/ /pubmed/28122561 http://dx.doi.org/10.1186/s12918-016-0380-2 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 Software
Bassen, David M.
Vilkhovoy, Michael
Minot, Mason
Butcher, Jonathan T.
Varner, Jeffrey D.
JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language
title JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language
title_full JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language
title_fullStr JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language
title_full_unstemmed JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language
title_short JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language
title_sort jupoets: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the julia programming language
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5264316/
https://www.ncbi.nlm.nih.gov/pubmed/28122561
http://dx.doi.org/10.1186/s12918-016-0380-2
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