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Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems

Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system a...

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Autores principales: Alden, Kieran, Read, Mark, Timmis, Jon, Andrews, Paul S., Veiga-Fernandes, Henrique, Coles, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3585389/
https://www.ncbi.nlm.nih.gov/pubmed/23468606
http://dx.doi.org/10.1371/journal.pcbi.1002916
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author Alden, Kieran
Read, Mark
Timmis, Jon
Andrews, Paul S.
Veiga-Fernandes, Henrique
Coles, Mark
author_facet Alden, Kieran
Read, Mark
Timmis, Jon
Andrews, Paul S.
Veiga-Fernandes, Henrique
Coles, Mark
author_sort Alden, Kieran
collection PubMed
description Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis R Toolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and http://www.cs.york.ac.uk/spartan. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website.
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spelling pubmed-35853892013-03-06 Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems Alden, Kieran Read, Mark Timmis, Jon Andrews, Paul S. Veiga-Fernandes, Henrique Coles, Mark PLoS Comput Biol Research Article Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis R Toolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and http://www.cs.york.ac.uk/spartan. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website. Public Library of Science 2013-02-28 /pmc/articles/PMC3585389/ /pubmed/23468606 http://dx.doi.org/10.1371/journal.pcbi.1002916 Text en © 2013 Alden et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Alden, Kieran
Read, Mark
Timmis, Jon
Andrews, Paul S.
Veiga-Fernandes, Henrique
Coles, Mark
Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems
title Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems
title_full Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems
title_fullStr Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems
title_full_unstemmed Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems
title_short Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems
title_sort spartan: a comprehensive tool for understanding uncertainty in simulations of biological systems
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3585389/
https://www.ncbi.nlm.nih.gov/pubmed/23468606
http://dx.doi.org/10.1371/journal.pcbi.1002916
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