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Lazy Updating increases the speed of stochastic simulations

Banff International Research Station for Mathematical Innovation and Discovery

Many biological reaction networks contain molecules involved in many reactions. For example, ATP is often consumed or produced. When reaction networks contain molecules like ATP, they are difficult to efficiently simulate, because every time such a molecule is consumed or produced, many propensity updates need to occur. In order to increase the speed of simulations, we developed the “Lazy Updating” method, which postpones propensity updates. Lazy Updating can be used in conjunction with many stochastic simulation algorithms, including Gillespie’s direct method and the Next Reaction Method.\\r\\n\\r\\nWe tested Lazy Updating on two example systems and found that it substantially increased the speed of simulations. We derived a formula predicting the expected speed increase and showed that the empirical speed increase matches our expectation closely. According to our results, Lazy Updating trades off a small amount of accuracy for a large speed increase. Since Lazy Updating enhances our ability to quickly simulate large reaction networks, it is a useful add-on to existing stochastic simulation algorithms.

Mathematics; Biology and other natural sciences; Systems theory; control; Mathematical biology

video/mp4

Author affiliation: University of Wisconsin-Madison

2014-08-07

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/49622

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/