Abstract
This chapter introduces the reader to the principles underlying Gillespie’s widely-used stochastic simulation algorithm (SSA), for the exact stochastic modeling of chemical reactions involving relatively small numbers of molecules. We also look at Gibson and Bruck’s improvements to the SSA, in order to support larger numbers of reactions, as well as the more recent variation by Slepoy, Thompson and Plimpton. All of these techniques are illustrated with Java implementations and a discussion of their complexity. We also introduce the Dizzy and SGNSim toolkits, which implement some of these approaches, along with tau-leap approximation and reaction delays.
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Notes
- 1.
We ignore here the indirect dependencies introduced through non-constant reaction probabilities that are affected by non-reacting molecular species, although this is handled in the full version of the code provided for this chapter.
- 2.
Note that we ignore here the possibility that a new is zero, although this is handled in the full version of the code provided for this chapter.
- 3.
In measurements with our implementation, using a large set of random reactions, the average number of iterations was around 1.4.
- 4.
If the model has been defined directly in the editor window rather than loaded from file, it may be necessary to specify a parser; select command-language.
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Barnes, D.J., Chu, D. (2010). Simulating Biochemical Systems. In: Introduction to Modeling for Biosciences. Springer, London. https://doi.org/10.1007/978-1-84996-326-8_7
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DOI: https://doi.org/10.1007/978-1-84996-326-8_7
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