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

S. Wang

Departments of Computer Science, Virginia Tech, Blacksburg, VA 24061, US

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M. Chen

Departments of Computer Science, Virginia Tech, Blacksburg, VA 24061, US

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L. T. Watson

Departments of Computer Science, Virginia Tech, Blacksburg, VA 24061, US

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, and

Y. Cao

Departments of Computer Science, Virginia Tech, Blacksburg, VA 24061, US

E-mail Address: ycao@cs.vt.edu

Corresponding author.

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https://doi.org/10.1142/S2424913017500060Cited by:6 (Source: Crossref)

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 the 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 the 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.

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