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

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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Bibliographic Details
Main Authors: Abhishekh Gupta, Pedro Mendes
Format: Article
Language:English
Published: MDPI AG 2018-01-01
Series:Computation
Subjects:
stochastic simulation
modeling
network-based
network-free
rule-based modeling
systems biology
Online Access:http://www.mdpi.com/2079-3197/6/1/9
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spelling doaj-dcab3856fe0f46b6a9ff04cffee237e52020-11-24T21:17:43ZengMDPI AGComputation2079-31972018-01-0161910.3390/computation6010009computation6010009An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical SystemsAbhishekh Gupta0Pedro Mendes1Center for Quantitative Medicine and Department of Cell Biology, University of Connecticut School of Medicine, 263 Farmington Av., Farmington, CT 06030-6033, USACenter for Quantitative Medicine and Department of Cell Biology, University of Connecticut School of Medicine, 263 Farmington Av., Farmington, CT 06030-6033, USAStochastic 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.http://www.mdpi.com/2079-3197/6/1/9stochastic simulationmodelingnetwork-basednetwork-freerule-based modelingsystems biology
collection DOAJ
language English
format Article
sources DOAJ
author Abhishekh Gupta
Pedro Mendes
spellingShingle Abhishekh Gupta
Pedro Mendes
An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems
Computation
stochastic simulation
modeling
network-based
network-free
rule-based modeling
systems biology
author_facet Abhishekh Gupta
Pedro Mendes
author_sort Abhishekh Gupta
title 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_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_sort overview of network-based and -free approaches for stochastic simulation of biochemical systems
publisher MDPI AG
series Computation
issn 2079-3197
publishDate 2018-01-01
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.
topic stochastic simulation
modeling
network-based
network-free
rule-based modeling
systems biology
url http://www.mdpi.com/2079-3197/6/1/9
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AT pedromendes anoverviewofnetworkbasedandfreeapproachesforstochasticsimulationofbiochemicalsystems
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