title: Asynchronous τ-leaping.
abstract: Stochastic simulation of cell signaling pathways and genetic regulatory networks has contributed to the understanding of cell function; however, investigation of larger, more complicated systems requires computationally efficient algorithms. τ-leaping methods, which improve efficiency when some molecules have high copy numbers, either use a fixed leap size, which does not adapt to changing state, or recalculate leap size at a heavy computational cost. We present a hybrid simulation method for reaction-diffusion systems which combines exact stochastic simulation and τ-leaping in a dynamic way. Putative times of events are stored in a priority queue, which reduces the cost of each step of the simulation. For every reaction and diffusion channel at each step of the simulation the more efficient of an exact stochastic event or a τ-leap is chosen. This new approach removes the inherent trade-off between speed and accuracy in stiff systems which was present in all τ-leaping methods by allowing each reaction channel to proceed at its own pace. Both directions of reversible reactions and diffusion are combined in a single event, allowing bigger leaps to be taken. This improves efficiency for systems near equilibrium where forward and backward events are approximately equally frequent. Comparison with existing algorithms and behaviour for five test cases of varying complexity shows that the new method is almost as accurate as exact stochastic simulation, scales well for large systems, and for various problems can be significantly faster than τ-leaping.
authors.affiliation: Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia 22030, USA.
authors.forename: Zbigniew
authors.initials: Z
authors.lastname: Jȩdrzejewski-Szmek
authors.affiliation: Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia 22030, USA.
authors.forename: Kim T
authors.initials: KT
authors.lastname: Blackwell
mesh_terms: D000465:Algorithms; D004058:Diffusion; D013269:Stochastic Processes