Benchmarking of numerical integration methods for ODE models of biological systems

Abstract Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations i...

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Autores principales:	Philipp Städter, Yannik Schälte, Leonard Schmiester, Jan Hasenauer, Paul L. Stapor
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b16b0ae2bd97415eafd7326d2a4ff5ae
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spelling	oai:doaj.org-article:b16b0ae2bd97415eafd7326d2a4ff5ae2021-12-02T14:16:07ZBenchmarking of numerical integration methods for ODE models of biological systems10.1038/s41598-021-82196-22045-2322https://doaj.org/article/b16b0ae2bd97415eafd7326d2a4ff5ae2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82196-2https://doaj.org/toc/2045-2322Abstract Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 142 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models.Philipp StädterYannik SchälteLeonard SchmiesterJan HasenauerPaul L. StaporNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Philipp Städter Yannik Schälte Leonard Schmiester Jan Hasenauer Paul L. Stapor Benchmarking of numerical integration methods for ODE models of biological systems
description	Abstract Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 142 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models.
format	article
author	Philipp Städter Yannik Schälte Leonard Schmiester Jan Hasenauer Paul L. Stapor
author_facet	Philipp Städter Yannik Schälte Leonard Schmiester Jan Hasenauer Paul L. Stapor
author_sort	Philipp Städter
title	Benchmarking of numerical integration methods for ODE models of biological systems
title_short	Benchmarking of numerical integration methods for ODE models of biological systems
title_full	Benchmarking of numerical integration methods for ODE models of biological systems
title_fullStr	Benchmarking of numerical integration methods for ODE models of biological systems
title_full_unstemmed	Benchmarking of numerical integration methods for ODE models of biological systems
title_sort	benchmarking of numerical integration methods for ode models of biological systems
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b16b0ae2bd97415eafd7326d2a4ff5ae
work_keys_str_mv	AT philippstadter benchmarkingofnumericalintegrationmethodsforodemodelsofbiologicalsystems AT yannikschalte benchmarkingofnumericalintegrationmethodsforodemodelsofbiologicalsystems AT leonardschmiester benchmarkingofnumericalintegrationmethodsforodemodelsofbiologicalsystems AT janhasenauer benchmarkingofnumericalintegrationmethodsforodemodelsofbiologicalsystems AT paullstapor benchmarkingofnumericalintegrationmethodsforodemodelsofbiologicalsystems
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Benchmarking of numerical integration methods for ODE models of biological systems

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