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SELANSI: a toolbox for simulation of stochastic gene regulatory networks

MOTIVATION: Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stoch...

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Autores principales: Pájaro, Manuel, Otero-Muras, Irene, Vázquez, Carlos, Alonso, Antonio A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030881/
https://www.ncbi.nlm.nih.gov/pubmed/29040384
http://dx.doi.org/10.1093/bioinformatics/btx645
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author Pájaro, Manuel
Otero-Muras, Irene
Vázquez, Carlos
Alonso, Antonio A
author_facet Pájaro, Manuel
Otero-Muras, Irene
Vázquez, Carlos
Alonso, Antonio A
author_sort Pájaro, Manuel
collection PubMed
description MOTIVATION: Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort. RESULTS: This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a software toolbox for the simulation of stochastic multidimensional gene regulatory networks. SELANSI exploits intrinsic structural properties of gene regulatory networks to accurately approximate the corresponding Chemical Master Equation with a partial integral differential equation that is solved by a semi-lagrangian method with high efficiency. Networks under consideration might involve multiple genes with self and cross regulations, in which genes can be regulated by different transcription factors. Moreover, the validity of the method is not restricted to a particular type of kinetics. The tool offers total flexibility regarding network topology, kinetics and parameterization, as well as simulation options. AVAILABILITY AND IMPLEMENTATION: SELANSI runs under the MATLAB environment, and is available under GPLv3 license at https://sites.google.com/view/selansi.
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spelling pubmed-60308812018-07-10 SELANSI: a toolbox for simulation of stochastic gene regulatory networks Pájaro, Manuel Otero-Muras, Irene Vázquez, Carlos Alonso, Antonio A Bioinformatics Applications Notes MOTIVATION: Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort. RESULTS: This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a software toolbox for the simulation of stochastic multidimensional gene regulatory networks. SELANSI exploits intrinsic structural properties of gene regulatory networks to accurately approximate the corresponding Chemical Master Equation with a partial integral differential equation that is solved by a semi-lagrangian method with high efficiency. Networks under consideration might involve multiple genes with self and cross regulations, in which genes can be regulated by different transcription factors. Moreover, the validity of the method is not restricted to a particular type of kinetics. The tool offers total flexibility regarding network topology, kinetics and parameterization, as well as simulation options. AVAILABILITY AND IMPLEMENTATION: SELANSI runs under the MATLAB environment, and is available under GPLv3 license at https://sites.google.com/view/selansi. Oxford University Press 2018-03-01 2017-10-11 /pmc/articles/PMC6030881/ /pubmed/29040384 http://dx.doi.org/10.1093/bioinformatics/btx645 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Pájaro, Manuel
Otero-Muras, Irene
Vázquez, Carlos
Alonso, Antonio A
SELANSI: a toolbox for simulation of stochastic gene regulatory networks
title SELANSI: a toolbox for simulation of stochastic gene regulatory networks
title_full SELANSI: a toolbox for simulation of stochastic gene regulatory networks
title_fullStr SELANSI: a toolbox for simulation of stochastic gene regulatory networks
title_full_unstemmed SELANSI: a toolbox for simulation of stochastic gene regulatory networks
title_short SELANSI: a toolbox for simulation of stochastic gene regulatory networks
title_sort selansi: a toolbox for simulation of stochastic gene regulatory networks
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030881/
https://www.ncbi.nlm.nih.gov/pubmed/29040384
http://dx.doi.org/10.1093/bioinformatics/btx645
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