Composing problem solvers for simulation experimentation: a case study on steady state estimation.

Simulation experiments involve various sub-tasks, e.g., parameter optimization, simulation execution, or output data analysis. Many algorithms can be applied to such tasks, but their performance depends on the given problem. Steady state estimation in systems biology is a typical example for this: s...

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Autores principales:	Stefan Leye, Roland Ewald, Adelinde M Uhrmacher
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a1a137a9b9b44d01b80f8a72646f22c4
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spelling	oai:doaj.org-article:a1a137a9b9b44d01b80f8a72646f22c42021-11-18T08:24:50ZComposing problem solvers for simulation experimentation: a case study on steady state estimation.1932-620310.1371/journal.pone.0091948https://doaj.org/article/a1a137a9b9b44d01b80f8a72646f22c42014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24705453/?tool=EBIhttps://doaj.org/toc/1932-6203Simulation experiments involve various sub-tasks, e.g., parameter optimization, simulation execution, or output data analysis. Many algorithms can be applied to such tasks, but their performance depends on the given problem. Steady state estimation in systems biology is a typical example for this: several estimators have been proposed, each with its own (dis-)advantages. Experimenters, therefore, must choose from the available options, even though they may not be aware of the consequences. To support those users, we propose a general scheme to aggregate such algorithms to so-called synthetic problem solvers, which exploit algorithm differences to improve overall performance. Our approach subsumes various aggregation mechanisms, supports automatic configuration from training data (e.g., via ensemble learning or portfolio selection), and extends the plugin system of the open source modeling and simulation framework James II. We show the benefits of our approach by applying it to steady state estimation for cell-biological models.Stefan LeyeRoland EwaldAdelinde M UhrmacherPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 4, p e91948 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Stefan Leye Roland Ewald Adelinde M Uhrmacher Composing problem solvers for simulation experimentation: a case study on steady state estimation.
description	Simulation experiments involve various sub-tasks, e.g., parameter optimization, simulation execution, or output data analysis. Many algorithms can be applied to such tasks, but their performance depends on the given problem. Steady state estimation in systems biology is a typical example for this: several estimators have been proposed, each with its own (dis-)advantages. Experimenters, therefore, must choose from the available options, even though they may not be aware of the consequences. To support those users, we propose a general scheme to aggregate such algorithms to so-called synthetic problem solvers, which exploit algorithm differences to improve overall performance. Our approach subsumes various aggregation mechanisms, supports automatic configuration from training data (e.g., via ensemble learning or portfolio selection), and extends the plugin system of the open source modeling and simulation framework James II. We show the benefits of our approach by applying it to steady state estimation for cell-biological models.
format	article
author	Stefan Leye Roland Ewald Adelinde M Uhrmacher
author_facet	Stefan Leye Roland Ewald Adelinde M Uhrmacher
author_sort	Stefan Leye
title	Composing problem solvers for simulation experimentation: a case study on steady state estimation.
title_short	Composing problem solvers for simulation experimentation: a case study on steady state estimation.
title_full	Composing problem solvers for simulation experimentation: a case study on steady state estimation.
title_fullStr	Composing problem solvers for simulation experimentation: a case study on steady state estimation.
title_full_unstemmed	Composing problem solvers for simulation experimentation: a case study on steady state estimation.
title_sort	composing problem solvers for simulation experimentation: a case study on steady state estimation.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/a1a137a9b9b44d01b80f8a72646f22c4
work_keys_str_mv	AT stefanleye composingproblemsolversforsimulationexperimentationacasestudyonsteadystateestimation AT rolandewald composingproblemsolversforsimulationexperimentationacasestudyonsteadystateestimation AT adelindemuhrmacher composingproblemsolversforsimulationexperimentationacasestudyonsteadystateestimation
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Composing problem solvers for simulation experimentation: a case study on steady state estimation.

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