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Large-Scale Design Space Exploration of SSA

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Computational Methods in Systems Biology (CMSB 2008)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 5307))

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Abstract

Stochastic simulation algorithms (SSA) are popular methods for the simulation of chemical reaction networks, so that various enhancements have been introduced and evaluated over the years. However, neither theoretical analysis nor empirical comparisons of single implementations suffice to capture the general performance of a method. This makes choosing an appropriate algorithm very hard for anyone who is not an expert in the field, especially if the system provides many alternative implementations. We argue that this problem can only be solved by thoroughly exploring the design spaces of such algorithms. This paper presents the results of an empirical study, which subsumes several thousand simulation runs. It aims at exploring the performance of different SSA implementations and comparing them to an approximation via τ-Leaping, while using different event queues and random number generators.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Modelling and Simulation Group, University of Rostock, Albert-Einstein-Str. 21, 18059, Rostock, Germany

    Matthias Jeschke & Roland Ewald

Authors
  1. Matthias Jeschke
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  2. Roland Ewald
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Editors and Affiliations

  1. Department of Computer Science, Brandenburg University of Technology, Postbox 10 13 44, 03013, Cottbus, Germany

    Monika Heiner

  2. University of Rostock, Albert-Einstein-Str. 21, D-18059, Rostock, Germany

    Adelinde M. Uhrmacher

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Jeschke, M., Ewald, R. (2008). Large-Scale Design Space Exploration of SSA. In: Heiner, M., Uhrmacher, A.M. (eds) Computational Methods in Systems Biology. CMSB 2008. Lecture Notes in Computer Science(), vol 5307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88562-7_17

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  • DOI: https://doi.org/10.1007/978-3-540-88562-7_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88561-0

  • Online ISBN: 978-3-540-88562-7

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