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A Critical Comparison of Rejection-Based Algorithms for Simulation of Large Biochemical Reaction Networks

  • Special Issue: Gillespie and His Algorithms
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Abstract

The rejection-based simulation technique has been applying to improve the computational efficiency of the stochastic simulation algorithm (SSA) in simulating large reaction networks, which are required for a thorough understanding of biological systems. We compare two recently proposed simulation methods, namely the composition–rejection algorithm (SSA-CR) and the rejection-based SSA (RSSA), aiming for this purpose. We discuss the right interpretation of the rejection-based technique used in these algorithms in order to make an informed choice when dealing with different aspects of biochemical networks. We provide the theoretical analysis as well as the detailed runtime comparison of these algorithms on concrete biological models. We highlight important factors that are omitted in previous analysis of these algorithms. The numerical comparison shows that for reaction networks where the search cost is expensive then SSA-CR is more efficient, and for reaction networks where the update cost is dominant, often the case in practice, then RSSA should be the choice.

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Notes

  1. We often use \(a_j\) instead of \(a_j(X(t))\) for a shorthand when the context is clear.

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Acknowledgements

This work has been partially done when VHT was at the Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Italy. The research supported by Academy of Finland grant 311639, “Algorithmic Designs for Biomolecular Nanostructures (ALBION)”.

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Authors and Affiliations

  1. Department of Computer Science, Aalto University, Espoo, Finland

    Vo Hong Thanh

  2. The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy

    Vo Hong Thanh

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  1. Vo Hong Thanh
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Correspondence to Vo Hong Thanh.

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Thanh, V.H. A Critical Comparison of Rejection-Based Algorithms for Simulation of Large Biochemical Reaction Networks. Bull Math Biol 81, 3053–3073 (2019). https://doi.org/10.1007/s11538-018-0462-y

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  • DOI: https://doi.org/10.1007/s11538-018-0462-y

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