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Benchmark problems for dynamic modeling of intracellular processes

MOTIVATION: Dynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as in...

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Autores principales: Hass, Helge, Loos, Carolin, Raimúndez-Álvarez, Elba, Timmer, Jens, Hasenauer, Jan, Kreutz, Clemens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6735869/
https://www.ncbi.nlm.nih.gov/pubmed/30624608
http://dx.doi.org/10.1093/bioinformatics/btz020
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author Hass, Helge
Loos, Carolin
Raimúndez-Álvarez, Elba
Timmer, Jens
Hasenauer, Jan
Kreutz, Clemens
author_facet Hass, Helge
Loos, Carolin
Raimúndez-Álvarez, Elba
Timmer, Jens
Hasenauer, Jan
Kreutz, Clemens
author_sort Hass, Helge
collection PubMed
description MOTIVATION: Dynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A multitude of computational approaches, e.g. for parameter estimation or uncertainty analysis have been developed within recent years. However, many of these approaches lack proper testing in application settings because a comprehensive set of benchmark problems is yet missing. RESULTS: We present a collection of 20 benchmark problems in order to evaluate new and existing methodologies, where an ODE model with corresponding experimental data is referred to as problem. In addition to the equations of the dynamical system, the benchmark collection provides observation functions as well as assumptions about measurement noise distributions and parameters. The presented benchmark models comprise problems of different size, complexity and numerical demands. Important characteristics of the models and methodological requirements are summarized, estimated parameters are provided, and some example studies were performed for illustrating the capabilities of the presented benchmark collection. AVAILABILITY AND IMPLEMENTATION: The models are provided in several standardized formats, including an easy-to-use human readable form and machine-readable SBML files. The data is provided as Excel sheets. All files are available at https://github.com/Benchmarking-Initiative/Benchmark-Models, including step-by-step explanations and MATLAB code to process and simulate the models. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-67358692019-09-16 Benchmark problems for dynamic modeling of intracellular processes Hass, Helge Loos, Carolin Raimúndez-Álvarez, Elba Timmer, Jens Hasenauer, Jan Kreutz, Clemens Bioinformatics Original Papers MOTIVATION: Dynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A multitude of computational approaches, e.g. for parameter estimation or uncertainty analysis have been developed within recent years. However, many of these approaches lack proper testing in application settings because a comprehensive set of benchmark problems is yet missing. RESULTS: We present a collection of 20 benchmark problems in order to evaluate new and existing methodologies, where an ODE model with corresponding experimental data is referred to as problem. In addition to the equations of the dynamical system, the benchmark collection provides observation functions as well as assumptions about measurement noise distributions and parameters. The presented benchmark models comprise problems of different size, complexity and numerical demands. Important characteristics of the models and methodological requirements are summarized, estimated parameters are provided, and some example studies were performed for illustrating the capabilities of the presented benchmark collection. AVAILABILITY AND IMPLEMENTATION: The models are provided in several standardized formats, including an easy-to-use human readable form and machine-readable SBML files. The data is provided as Excel sheets. All files are available at https://github.com/Benchmarking-Initiative/Benchmark-Models, including step-by-step explanations and MATLAB code to process and simulate the models. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2019-09-01 2019-01-08 /pmc/articles/PMC6735869/ /pubmed/30624608 http://dx.doi.org/10.1093/bioinformatics/btz020 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Hass, Helge
Loos, Carolin
Raimúndez-Álvarez, Elba
Timmer, Jens
Hasenauer, Jan
Kreutz, Clemens
Benchmark problems for dynamic modeling of intracellular processes
title Benchmark problems for dynamic modeling of intracellular processes
title_full Benchmark problems for dynamic modeling of intracellular processes
title_fullStr Benchmark problems for dynamic modeling of intracellular processes
title_full_unstemmed Benchmark problems for dynamic modeling of intracellular processes
title_short Benchmark problems for dynamic modeling of intracellular processes
title_sort benchmark problems for dynamic modeling of intracellular processes
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6735869/
https://www.ncbi.nlm.nih.gov/pubmed/30624608
http://dx.doi.org/10.1093/bioinformatics/btz020
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