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Stochastic simulation and analysis of biomolecular reaction networks

BACKGROUND: In recent years, several stochastic simulation algorithms have been developed to generate Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks. However, the effects of various stochastic simulation and data analysis conditions on the...

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Detalles Bibliográficos
Autores principales: Frazier, John M, Chushak, Yaroslav, Foy, Brent
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2708125/
https://www.ncbi.nlm.nih.gov/pubmed/19534796
http://dx.doi.org/10.1186/1752-0509-3-64
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author Frazier, John M
Chushak, Yaroslav
Foy, Brent
author_facet Frazier, John M
Chushak, Yaroslav
Foy, Brent
author_sort Frazier, John M
collection PubMed
description BACKGROUND: In recent years, several stochastic simulation algorithms have been developed to generate Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks. However, the effects of various stochastic simulation and data analysis conditions on the observed dynamics of complex biomolecular reaction networks have not recieved much attention. In order to investigate these issues, we employed a a software package developed in out group, called Biomolecular Network Simulator (BNS), to simulate and analyze the behavior of such systems. The behavior of a hypothetical two gene in vitro transcription-translation reaction network is investigated using the Gillespie exact stochastic algorithm to illustrate some of the factors that influence the analysis and interpretation of these data. RESULTS: Specific issues affecting the analysis and interpretation of simulation data are investigated, including: (1) the effect of time interval on data presentation and time-weighted averaging of molecule numbers, (2) effect of time averaging interval on reaction rate analysis, (3) effect of number of simulations on precision of model predictions, and (4) implications of stochastic simulations on optimization procedures. CONCLUSION: The two main factors affecting the analysis of stochastic simulations are: (1) the selection of time intervals to compute or average state variables and (2) the number of simulations generated to evaluate the system behavior.
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spelling pubmed-27081252009-07-09 Stochastic simulation and analysis of biomolecular reaction networks Frazier, John M Chushak, Yaroslav Foy, Brent BMC Syst Biol Research Article BACKGROUND: In recent years, several stochastic simulation algorithms have been developed to generate Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks. However, the effects of various stochastic simulation and data analysis conditions on the observed dynamics of complex biomolecular reaction networks have not recieved much attention. In order to investigate these issues, we employed a a software package developed in out group, called Biomolecular Network Simulator (BNS), to simulate and analyze the behavior of such systems. The behavior of a hypothetical two gene in vitro transcription-translation reaction network is investigated using the Gillespie exact stochastic algorithm to illustrate some of the factors that influence the analysis and interpretation of these data. RESULTS: Specific issues affecting the analysis and interpretation of simulation data are investigated, including: (1) the effect of time interval on data presentation and time-weighted averaging of molecule numbers, (2) effect of time averaging interval on reaction rate analysis, (3) effect of number of simulations on precision of model predictions, and (4) implications of stochastic simulations on optimization procedures. CONCLUSION: The two main factors affecting the analysis of stochastic simulations are: (1) the selection of time intervals to compute or average state variables and (2) the number of simulations generated to evaluate the system behavior. BioMed Central 2009-06-17 /pmc/articles/PMC2708125/ /pubmed/19534796 http://dx.doi.org/10.1186/1752-0509-3-64 Text en Copyright © 2009 Frazier et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Frazier, John M
Chushak, Yaroslav
Foy, Brent
Stochastic simulation and analysis of biomolecular reaction networks
title Stochastic simulation and analysis of biomolecular reaction networks
title_full Stochastic simulation and analysis of biomolecular reaction networks
title_fullStr Stochastic simulation and analysis of biomolecular reaction networks
title_full_unstemmed Stochastic simulation and analysis of biomolecular reaction networks
title_short Stochastic simulation and analysis of biomolecular reaction networks
title_sort stochastic simulation and analysis of biomolecular reaction networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2708125/
https://www.ncbi.nlm.nih.gov/pubmed/19534796
http://dx.doi.org/10.1186/1752-0509-3-64
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