University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Reducing the time complexity of chemical reaction-network simulations to O(N)

Reducing the time complexity of chemical reaction-network simulations to O(N)

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We present an alternative formulation of the exact stochastic simulation algorithm (SSA) for sampling trajectories of the chemical master equation for a well-stirred system of coupled chemical reactions. Our formulation is based on factored-out, partial reaction propensities. This novel exact SSA , called the partial propensity direct method (PDM), is highly efficient and has a computational cost that scales at most linearly with the number of chemical species, irrespective of the degree of coupling of the reaction network. In addition, we propose a sorting variant, SPDM , which is especially efficient for multiscale reaction networks.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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