Accuracy Analysis of Hybrid Stochastic Simulation Algorithm on Linear Chain Reaction Systems

Noise in cellular systems is often modeled and simulated with Gillespie’s stochastic simulation algorithm (SSA), but the low efficiency of the SSA limits its application to large biochemical networks. To improve the efficiency of stochastic simulations, Haseltine and Rawlings (HR) proposed a hybrid...

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Vydané v:Bulletin of mathematical biology Ročník 81; číslo 8; s. 3024 - 3052
Hlavní autori: Chen, Minghan, Wang, Shuo, Cao, Yang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.08.2019
Springer Nature B.V
Predmet:
Algorithms
Cell Biology
Chains
Computer simulation
Differential equations
Life Sciences
Mathematical and Computational Biology
Mathematical models
Mathematics
Mathematics and Statistics
Model accuracy
Molecular chains
Ordinary differential equations
Organic chemistry
Special Issue: Gillespie and His Algorithms
Stochastic models
SSA
Accuracy analysis
Next slow reaction firing time
Linear chain reaction systems
Hybrid stochastic algorithm
ISSN:0092-8240, 1522-9602, 1522-9602
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Popis
Shrnutí:Noise in cellular systems is often modeled and simulated with Gillespie’s stochastic simulation algorithm (SSA), but the low efficiency of the SSA limits its application to large biochemical networks. To improve the efficiency of stochastic simulations, Haseltine and Rawlings (HR) proposed a hybrid algorithm, which combines ordinary differential equations for traditional deterministic models and the SSA for stochastic models. In this paper, accuracy of the HR hybrid method is studied based on a linear chain reaction system. Mathematical analysis and numerical results both show that the HR hybrid method is accurate if either the quantity of reactant molecules in fast reactions is above a certain threshold, or the reaction rates of fast reactions are much larger than those of slow reactions. This analysis also shows that the HR hybrid method approximates the chemical master equation well for a much greater region in system parameter space than the slow-scale SSA and the stochastic quasi-steady-state assumption methods.
Bibliografia:ObjectType-Article-1
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ISSN:0092-8240
1522-9602
1522-9602
DOI:10.1007/s11538-018-0461-z