S-Leaping: An Adaptive, Accelerated Stochastic Simulation Algorithm, Bridging τ-Leaping and R-Leaping

We propose the S -leaping algorithm for the acceleration of Gillespie’s stochastic simulation algorithm that combines the advantages of the two main accelerated methods; the τ -leaping and R -leaping algorithms. These algorithms are known to be efficient under different conditions; the τ -leaping is...

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Bibliographic Details
Published in:Bulletin of mathematical biology Vol. 81; no. 8; pp. 3074 - 3096
Main Authors: Lipková, Jana, Arampatzis, Georgios, Chatelain, Philippe, Menze, Bjoern, Koumoutsakos, Petros
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2019
Springer Nature B.V
Subjects:
Acceleration
Adaptive algorithms
Algorithms
Bacillus subtilis - genetics
Bacillus subtilis - metabolism
Biochemical Phenomena
Cell Biology
Computer Simulation
Dimerization
Escherichia coli - genetics
Escherichia coli - metabolism
Escherichia coli Proteins - genetics
Escherichia coli Proteins - metabolism
Kinetics
Lac Operon
Life Sciences
Markov Chains
Mathematical and Computational Biology
Mathematical Concepts
Mathematics
Mathematics and Statistics
Models, Biological
Monosaccharide Transport Proteins - genetics
Monosaccharide Transport Proteins - metabolism
Sampling
Special Issue: Gillespie and His Algorithms
Stochastic Processes
Symporters - genetics
Symporters - metabolism
Systems Biology
Stochastic simulation algorithms
Stiff systems
Accelerated simulation
ISSN:0092-8240, 1522-9602, 1522-9602
Online Access:Get full text
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Summary:We propose the S -leaping algorithm for the acceleration of Gillespie’s stochastic simulation algorithm that combines the advantages of the two main accelerated methods; the τ -leaping and R -leaping algorithms. These algorithms are known to be efficient under different conditions; the τ -leaping is efficient for non-stiff systems or systems with partial equilibrium, while the R -leaping performs better in stiff system thanks to an efficient sampling procedure. However, even a small change in a system’s set up can critically affect the nature of the simulated system and thus reduce the efficiency of an accelerated algorithm. The proposed algorithm combines the efficient time step selection from the τ -leaping with the effective sampling procedure from the R -leaping algorithm. The S -leaping is shown to maintain its efficiency under different conditions and in the case of large and stiff systems or systems with fast dynamics, the S -leaping outperforms both methods. We demonstrate the performance and the accuracy of the S -leaping in comparison with the τ -leaping and R -leaping on a number of benchmark systems involving biological reaction networks.
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ISSN:0092-8240
1522-9602
1522-9602
DOI:10.1007/s11538-018-0464-9