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RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats
Comprehensive, predictive computational models have significant potential for science, bioengineering, and medicine. One promising way to achieve more predictive models is to combine submodels of multiple subsystems. To capture the multiple scales of biology, these submodels will likely require mult...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262693/ https://www.ncbi.nlm.nih.gov/pubmed/34019658 http://dx.doi.org/10.1093/nar/gkab411 |
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author | Shaikh, Bilal Marupilla, Gnaneswara Wilson, Mike Blinov, Michael L Moraru, Ion I Karr, Jonathan R |
author_facet | Shaikh, Bilal Marupilla, Gnaneswara Wilson, Mike Blinov, Michael L Moraru, Ion I Karr, Jonathan R |
author_sort | Shaikh, Bilal |
collection | PubMed |
description | Comprehensive, predictive computational models have significant potential for science, bioengineering, and medicine. One promising way to achieve more predictive models is to combine submodels of multiple subsystems. To capture the multiple scales of biology, these submodels will likely require multiple modeling frameworks and simulation algorithms. Several community resources are already available for working with many of these frameworks and algorithms. However, the variety and sheer number of these resources make it challenging to find and use appropriate tools for each model, especially for novice modelers and experimentalists. To make these resources easier to use, we developed RunBioSimulations (https://run.biosimulations.org), a single web application for executing a broad range of models. RunBioSimulations leverages community resources, including BioSimulators, a new open registry of simulation tools. These resources currently enable RunBioSimulations to execute nine frameworks and 44 algorithms, and they make RunBioSimulations extensible to additional frameworks and algorithms. RunBioSimulations also provides features for sharing simulations and interactively visualizing their results. We anticipate that RunBioSimulations will foster reproducibility, stimulate collaboration, and ultimately facilitate the creation of more predictive models. |
format | Online Article Text |
id | pubmed-8262693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-82626932021-07-08 RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats Shaikh, Bilal Marupilla, Gnaneswara Wilson, Mike Blinov, Michael L Moraru, Ion I Karr, Jonathan R Nucleic Acids Res Web Server Issue Comprehensive, predictive computational models have significant potential for science, bioengineering, and medicine. One promising way to achieve more predictive models is to combine submodels of multiple subsystems. To capture the multiple scales of biology, these submodels will likely require multiple modeling frameworks and simulation algorithms. Several community resources are already available for working with many of these frameworks and algorithms. However, the variety and sheer number of these resources make it challenging to find and use appropriate tools for each model, especially for novice modelers and experimentalists. To make these resources easier to use, we developed RunBioSimulations (https://run.biosimulations.org), a single web application for executing a broad range of models. RunBioSimulations leverages community resources, including BioSimulators, a new open registry of simulation tools. These resources currently enable RunBioSimulations to execute nine frameworks and 44 algorithms, and they make RunBioSimulations extensible to additional frameworks and algorithms. RunBioSimulations also provides features for sharing simulations and interactively visualizing their results. We anticipate that RunBioSimulations will foster reproducibility, stimulate collaboration, and ultimately facilitate the creation of more predictive models. Oxford University Press 2021-05-21 /pmc/articles/PMC8262693/ /pubmed/34019658 http://dx.doi.org/10.1093/nar/gkab411 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://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/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Web Server Issue Shaikh, Bilal Marupilla, Gnaneswara Wilson, Mike Blinov, Michael L Moraru, Ion I Karr, Jonathan R RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats |
title | RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats |
title_full | RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats |
title_fullStr | RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats |
title_full_unstemmed | RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats |
title_short | RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats |
title_sort | runbiosimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262693/ https://www.ncbi.nlm.nih.gov/pubmed/34019658 http://dx.doi.org/10.1093/nar/gkab411 |
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