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Open Access Methodology article

Simulation methods with extended stability for stiff biochemical Kinetics

Pau Rué12, Jordi Villà-Freixa1* and Kevin Burrage34*

Author Affiliations

1 Computational Biochemistry and Biophysics Group, Research Unit on Biomedical Informatics, IMIM/Universitat Pompeu Fabra, c/Dr. Aiguader 88, 08003, Barcelona, Catalonia, Spain

2 Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Edifici GAIA, Rambla de Sant Nebridi s/n 08222, Terrassa, Barcelona, Spain

3 Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia

4 COMLAB and OCISB, University of Oxford, Oxford OX1 3QD, UK

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BMC Systems Biology 2010, 4:110  doi:10.1186/1752-0509-4-110

Published: 11 August 2010

Abstract

Background

With increasing computer power, simulating the dynamics of complex systems in chemistry and biology is becoming increasingly routine. The modelling of individual reactions in (bio)chemical systems involves a large number of random events that can be simulated by the stochastic simulation algorithm (SSA). The key quantity is the step size, or waiting time, τ, whose value inversely depends on the size of the propensities of the different channel reactions and which needs to be re-evaluated after every firing event. Such a discrete event simulation may be extremely expensive, in particular for stiff systems where τ can be very short due to the fast kinetics of some of the channel reactions. Several alternative methods have been put forward to increase the integration step size. The so-called τ-leap approach takes a larger step size by allowing all the reactions to fire, from a Poisson or Binomial distribution, within that step. Although the expected value for the different species in the reactive system is maintained with respect to more precise methods, the variance at steady state can suffer from large errors as τ grows.

Results

In this paper we extend Poisson τ-leap methods to a general class of Runge-Kutta (RK) τ-leap methods. We show that with the proper selection of the coefficients, the variance of the extended τ-leap can be well-behaved, leading to significantly larger step sizes.

Conclusions

The benefit of adapting the extended method to the use of RK frameworks is clear in terms of speed of calculation, as the number of evaluations of the Poisson distribution is still one set per time step, as in the original τ-leap method. The approach paves the way to explore new multiscale methods to simulate (bio)chemical systems.