Case Study I: Chemical Reaction Networks

  • Roland Ewald


This chapter shows how the methods developed in chapter 4 to 8 can be applied to stochastic simulation algorithms (SSA), which have already been discussed briefly in section 1.3.1 (p. 7). JAMES II offers several kinds of simulators for the field of computational systems biology (see [308]). Some of them–e.g., those for models expressed in stochastic π-calculus [200]–rely on SSA implementations as well. At the same time, the performance of different SSA implementations is still fairly unexplored, particularly when it comes to different model properties and different sub-algorithms, e.g., RNGs or event queues (see discussion in [158]). Their relative merits are even debated in the literature (e.g., [281, p. 21]). Therefore, applying the algorithm selection methodology developed in part two of the thesis seems to be particularly beneficial in this setting.


Execution Time Algorithm Selection Portfolio Selection Average Execution Time Stochastic Simulation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden GmbH 2012

Authors and Affiliations

  • Roland Ewald

There are no affiliations available

Personalised recommendations