A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks

TL;DR

This paper introduces the partial propensity direct method (PDM), a highly efficient exact stochastic simulation algorithm for chemical reaction networks that scales linearly with the number of species, improving simulation speed especially for complex multiscale systems.

Contribution

The paper presents a novel exact SSA based on factored-out partial propensities, significantly enhancing efficiency and scalability for simulating chemical reaction networks.

Findings

PDM scales linearly with the number of chemical species.

SPDM variant improves efficiency for multiscale networks.

The methods outperform existing algorithms in computational speed.

Abstract

We introduce 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.