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An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems

Stochastic simulation has been widely used to model the dynamics of biochemical reaction networks. Several algorithms have been proposed that are exact solutions of the chemical master equation, following the work of Gillespie. These stochastic simulation approaches can be broadly classified into tw...

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Detalles Bibliográficos
Autores principales: Gupta, Abhishekh, Mendes, Pedro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6013266/
https://www.ncbi.nlm.nih.gov/pubmed/29938118
http://dx.doi.org/10.3390/computation6010009
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author Gupta, Abhishekh
Mendes, Pedro
author_facet Gupta, Abhishekh
Mendes, Pedro
author_sort Gupta, Abhishekh
collection PubMed
description Stochastic simulation has been widely used to model the dynamics of biochemical reaction networks. Several algorithms have been proposed that are exact solutions of the chemical master equation, following the work of Gillespie. These stochastic simulation approaches can be broadly classified into two categories: network-based and -free simulation. The network-based approach requires that the full network of reactions be established at the start, while the network-free approach is based on reaction rules that encode classes of reactions, and by applying rule transformations, it generates reaction events as they are needed without ever having to derive the entire network. In this study, we compare the efficiency and limitations of several available implementations of these two approaches. The results allow for an informed selection of the implementation and methodology for specific biochemical modeling applications.
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spelling pubmed-60132662018-06-21 An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems Gupta, Abhishekh Mendes, Pedro Computation (Basel) Article Stochastic simulation has been widely used to model the dynamics of biochemical reaction networks. Several algorithms have been proposed that are exact solutions of the chemical master equation, following the work of Gillespie. These stochastic simulation approaches can be broadly classified into two categories: network-based and -free simulation. The network-based approach requires that the full network of reactions be established at the start, while the network-free approach is based on reaction rules that encode classes of reactions, and by applying rule transformations, it generates reaction events as they are needed without ever having to derive the entire network. In this study, we compare the efficiency and limitations of several available implementations of these two approaches. The results allow for an informed selection of the implementation and methodology for specific biochemical modeling applications. 2018-01-31 2018-03 /pmc/articles/PMC6013266/ /pubmed/29938118 http://dx.doi.org/10.3390/computation6010009 Text en http://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gupta, Abhishekh
Mendes, Pedro
An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems
title An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems
title_full An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems
title_fullStr An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems
title_full_unstemmed An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems
title_short An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems
title_sort overview of network-based and -free approaches for stochastic simulation of biochemical systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6013266/
https://www.ncbi.nlm.nih.gov/pubmed/29938118
http://dx.doi.org/10.3390/computation6010009
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