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lsoda algorithm  (The BioAnalytics Group LLC)

 
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    The BioAnalytics Group LLC lsoda algorithm
    Lsoda Algorithm, supplied by The BioAnalytics Group LLC, 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/lsoda/lsoda+algorithm/pmc04106706-99-9-22
    Average 90 stars, based on 1 article reviews
    lsoda algorithm - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Taking Advantage: High Affinity B cells in the Germinal Center Have Lower Death Rates, But Similar Rates of Division Compared to Low Affinity Cells
    Article Snippet: Integration of the model equations was performed with the LSODA algorithm ( 25 ) with algorithmic enhancements and Matlab interface developed by The BioAnalytics Group LLC.



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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 <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 <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: