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code implementing gillespie’s stochastic simulation algorithm  (MathWorks Inc)


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    MathWorks Inc code implementing gillespie’s stochastic simulation algorithm
    Code Implementing Gillespie’s Stochastic Simulation Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/gillespie+algorithm+implemented+in/pmc11869391-619-39-35
    Average 90 stars, based on 1 article reviews
    code implementing gillespie’s stochastic simulation algorithm - by Bioz Stars, 2026-09
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    Related Articles

    Mutagenesis:

    Article Title: Genetic dominance governs the evolution and spread of mobile genetic elements in bacteria
    Article Snippet: .. We explicitly consider random mutations occurring during replication events, so if μ > 0 denotes the probability of a mutation occurring in a plasmid, the per-cell mutation rate is μ ⋅ n . Stochastic simulations of mutation and replication dynamics were performed using a Gillespie algorithm implemented in MATLAB with propensities determined from the distribution of plasmid copies of each allele carried by the cell (code can be downloaded from GitHub; https://github.com/ccg-esb/dominatrix ) ( SI Appendix , Fig. S2 ). ..

    Article Title: Genetic dominance governs the evolution and spread of mobile genetic elements in bacteria.
    Article Snippet: .. Stochastic simulations of mutation and replication dynamics were performed using a Gillespie algorithm implemented in MATLAB with propensities determined from the distribution of plasmid copies of each allele carried by the cell (code can be downloaded from GitHub; https://github. com/ccg-esb/dominatrix) (SI Appendix, Fig. S2). ..

    Plasmid Preparation:

    Article Title: Genetic dominance governs the evolution and spread of mobile genetic elements in bacteria
    Article Snippet: .. We explicitly consider random mutations occurring during replication events, so if μ > 0 denotes the probability of a mutation occurring in a plasmid, the per-cell mutation rate is μ ⋅ n . Stochastic simulations of mutation and replication dynamics were performed using a Gillespie algorithm implemented in MATLAB with propensities determined from the distribution of plasmid copies of each allele carried by the cell (code can be downloaded from GitHub; https://github.com/ccg-esb/dominatrix ) ( SI Appendix , Fig. S2 ). ..

    Article Title: Genetic dominance governs the evolution and spread of mobile genetic elements in bacteria.
    Article Snippet: .. Stochastic simulations of mutation and replication dynamics were performed using a Gillespie algorithm implemented in MATLAB with propensities determined from the distribution of plasmid copies of each allele carried by the cell (code can be downloaded from GitHub; https://github. com/ccg-esb/dominatrix) (SI Appendix, Fig. S2). ..



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    Timing comparisons. ( A – C ) Time taken to simulate a given number of realisations for a single core of an Intel Core i7-975 Extreme Edition Processor 3.33 GHz (solid line) and one Tesla C2050 GPU (dashed line) for (A) the <t>LSODA</t> (B) the Euler–Maruyama and (C) <t>the</t> <t>Gillespie</t> algorithm, respectively. The relative speed-ups for given numbers of simulations are indicated next to the GPU simulation results. ( D ) Summary of the relative speed-up of the three different algorithms.
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    Timing comparisons. ( A – C ) Time taken to simulate a given number of realisations for a single core of an Intel Core i7-975 Extreme Edition Processor 3.33 GHz (solid line) and one Tesla C2050 GPU (dashed line) for (A) the LSODA (B) the Euler–Maruyama and (C) the Gillespie algorithm, respectively. The relative speed-ups for given numbers of simulations are indicated next to the GPU simulation results. ( D ) Summary of the relative speed-up of the three different algorithms.

    Journal: Bioinformatics

    Article Title: GPU accelerated biochemical network simulation

    doi: 10.1093/bioinformatics/btr015

    Figure Lengend Snippet: Timing comparisons. ( A – C ) Time taken to simulate a given number of realisations for a single core of an Intel Core i7-975 Extreme Edition Processor 3.33 GHz (solid line) and one Tesla C2050 GPU (dashed line) for (A) the LSODA (B) the Euler–Maruyama and (C) the Gillespie algorithm, respectively. The relative speed-ups for given numbers of simulations are indicated next to the GPU simulation results. ( D ) Summary of the relative speed-up of the three different algorithms.

    Article Snippet: But since in most applications of these algorithms, either in order to explore the parameter space or to perform inference, at least thousands of simulations will be needed for which the GPU outperforms the CPU even for the rather simple p53-Mdm2 model. We also compared the cuda-sim implementations of the LSODA and Gillespie algorithms with implementations in the Matlab package SBTOOLBOX2 ( ) and our Euler–Maruyama implementation with the native sde function within Matlab.

    Techniques: