Adaptive stochastic-deterministic chemical kinetic simulations.
Bioinformatics 20(1):78-84, 2004
Authors:

Karan Vasudeva: National Centre for Biological Sciences

Upinder S. Bhalla: National Centre for Biological Sciences

• Constants and database of references
• Downloading GENESIS and Kinetikit.

Kinetikit 9 and recent releases of GENESIS incorporate the adaptive method described in this paper, as well as three exact stochastic methods.

• Test suite of reaction cases
• Supplementary tables 1 and 2

Abstract

Biochemical signaling pathways and genetic circuits often involve very small numbers of key signaling molecules. Computationally expensive stochastic methods are necessary to simulate such chemical situations. Single-molecule chemical events often coexist with much larger numbers of signaling molecules where mass-action kinetics is a reasonable approximation. Here we describe an adaptive stochastic method which dynamically chooses between deterministic and stochastic calculations depending on molecular count and propensity of forward reactions. We compare the efficiency and accuracy of this method with exact stochastic methods. For typical biologically constrained reaction schemes, the method is faster than exact stochastic methods for reaction volumes greater than 10 cubic microns. We have developed a test suite of reaction cases to test the accuracy of mixed simulation methods.