Performance of objective functions and optimisation procedures for parameter estimation in system biology models

Systems biology: Performance of parameter estimation procedures A systematic comparison of critical choices for faithful parameter-estimation identifies a combination of a hybrid optimisation algorithm (GLSDC) with data-driven normalisation of simulations (DNS) as the generally best option. Experime...

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Autores principales:	Andrea Degasperi, Dirk Fey, Boris N. Kholodenko
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f39d9d389cf242828144b415430ae66d
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spelling	oai:doaj.org-article:f39d9d389cf242828144b415430ae66d2021-12-02T12:33:54ZPerformance of objective functions and optimisation procedures for parameter estimation in system biology models10.1038/s41540-017-0023-22056-7189https://doaj.org/article/f39d9d389cf242828144b415430ae66d2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41540-017-0023-2https://doaj.org/toc/2056-7189Systems biology: Performance of parameter estimation procedures A systematic comparison of critical choices for faithful parameter-estimation identifies a combination of a hybrid optimisation algorithm (GLSDC) with data-driven normalisation of simulations (DNS) as the generally best option. Experimental data are often provided in relative, arbitrary units. To match simulations to data, two approaches are common: i) using scaling-factors that have to be estimated (SF); or ii) normalising the simulations in the same way as the data (DNS). Using three test-models of increasing complexity, we explored how this choice affects parameter identifiability and estimation performance. We show that in contrast to SF, DNS does not aggravate non-identifiability and a global-hybrid method combined with DNS outperformed local-multi-start methods. The advantage of DNS in terms of estimation speed was particularly pronounced for the most complex test-problem.Andrea DegasperiDirk FeyBoris N. KholodenkoNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 3, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Andrea Degasperi Dirk Fey Boris N. Kholodenko Performance of objective functions and optimisation procedures for parameter estimation in system biology models
description	Systems biology: Performance of parameter estimation procedures A systematic comparison of critical choices for faithful parameter-estimation identifies a combination of a hybrid optimisation algorithm (GLSDC) with data-driven normalisation of simulations (DNS) as the generally best option. Experimental data are often provided in relative, arbitrary units. To match simulations to data, two approaches are common: i) using scaling-factors that have to be estimated (SF); or ii) normalising the simulations in the same way as the data (DNS). Using three test-models of increasing complexity, we explored how this choice affects parameter identifiability and estimation performance. We show that in contrast to SF, DNS does not aggravate non-identifiability and a global-hybrid method combined with DNS outperformed local-multi-start methods. The advantage of DNS in terms of estimation speed was particularly pronounced for the most complex test-problem.
format	article
author	Andrea Degasperi Dirk Fey Boris N. Kholodenko
author_facet	Andrea Degasperi Dirk Fey Boris N. Kholodenko
author_sort	Andrea Degasperi
title	Performance of objective functions and optimisation procedures for parameter estimation in system biology models
title_short	Performance of objective functions and optimisation procedures for parameter estimation in system biology models
title_full	Performance of objective functions and optimisation procedures for parameter estimation in system biology models
title_fullStr	Performance of objective functions and optimisation procedures for parameter estimation in system biology models
title_full_unstemmed	Performance of objective functions and optimisation procedures for parameter estimation in system biology models
title_sort	performance of objective functions and optimisation procedures for parameter estimation in system biology models
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/f39d9d389cf242828144b415430ae66d
work_keys_str_mv	AT andreadegasperi performanceofobjectivefunctionsandoptimisationproceduresforparameterestimationinsystembiologymodels AT dirkfey performanceofobjectivefunctionsandoptimisationproceduresforparameterestimationinsystembiologymodels AT borisnkholodenko performanceofobjectivefunctionsandoptimisationproceduresforparameterestimationinsystembiologymodels
_version_	1718393895118700544

Performance of objective functions and optimisation procedures for parameter estimation in system biology models

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