Efficient implementation of the hybrid method for stochastic simulation of biochemical systems

TL;DR

This paper presents an efficient implementation of the hybrid stochastic-deterministic simulation method for biochemical systems, improving computational performance and comparing different ODE solvers through numerical experiments.

Contribution

It introduces a new efficient implementation approach for the hybrid method coupled with traditional ODE solvers, enhancing simulation efficiency.

Findings

Hybrid method outperforms traditional stochastic simulation in efficiency.

DLSODAR shows superior performance among tested ODE solvers.

Numerical experiments validate the effectiveness of the proposed implementation.

Abstract

Stochastic effect in cellular systems has been an important topic in systems biology. Stochastic modeling and simulation methods are important tools to study stochastic effect. Given the low efficiency of stochastic simulation algorithms, the hybrid method, which combines an ordinary differential equation (ODE) system with a stochastic chemically reacting system, shows its unique advantages in the modeling and simulation of biochemical systems. The efficiency of hybrid method is usually limited by reactions in the stochastic subsystem, which are modeled and simulated using Gillespie's framework and frequently interrupt the integration of the ODE subsystem. In this paper we develop an efficient implementation approach for the hybrid method coupled with traditional ODE solvers. We also compare the efficiency of hybrid methods with three widely used ODE solvers RADAU5, DASSL, and DLSODAR. Numerical experiments with three biochemical models are presented. A detailed discussion is presented for the performances of three ODE solvers.