Extending the Multi-level Method for the Simulation of Stochastic Biological Systems

The multi-level method for discrete-state systems, first introduced by Anderson and Higham (SIAM Multiscale Model Simul 10(1):146–179, 2012 ), is a highly efficient simulation technique that can be used to elucidate statistical characteristics of biochemical reaction networks. A single point estimat...

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Bibliographic Details
Published in:Bulletin of mathematical biology Vol. 78; no. 8; pp. 1640 - 1677
Main Authors: Lester, Christopher, Baker, Ruth E., Giles, Michael B., Yates, Christian A.
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2016
Springer Nature B.V
Subjects:
Algorithms
Biochemical Phenomena
Cell Biology
Computer Simulation
Gene Regulatory Networks
Life Sciences
MAP Kinase Signaling System
Mathematical and Computational Biology
Mathematical Concepts
Mathematics
Mathematics and Statistics
Models, Biological
Models, Chemical
Models, Genetic
Monte Carlo Method
Original Article
Poisson Distribution
Stochastic Processes
Stochastic simulation
Gene regulatory networks
Multi-level
Tau-leaping
Gillespie algorithm
ISSN:0092-8240, 1522-9602, 1522-9602
Online Access:Get full text
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Summary:The multi-level method for discrete-state systems, first introduced by Anderson and Higham (SIAM Multiscale Model Simul 10(1):146–179, 2012 ), is a highly efficient simulation technique that can be used to elucidate statistical characteristics of biochemical reaction networks. A single point estimator is produced in a cost-effective manner by combining a number of estimators of differing accuracy in a telescoping sum, and, as such, the method has the potential to revolutionise the field of stochastic simulation. In this paper, we present several refinements of the multi-level method which render it easier to understand and implement, and also more efficient. Given the substantial and complex nature of the multi-level method, the first part of this work reviews existing literature, with the aim of providing a practical guide to the use of the multi-level method. The second part provides the means for a deft implementation of the technique and concludes with a discussion of a number of open problems.
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ISSN:0092-8240
1522-9602
1522-9602
DOI:10.1007/s11538-016-0178-9