Effective Computation of Stochastic Protein Kinetic Equation by Reducing Stiffness via Variable Transformation

TL;DR

This paper introduces a variable transformation method to reduce stiffness in stochastic protein kinetic equations, improving numerical simulation efficiency and accuracy, with theoretical and numerical validation and potential generalization to other stochastic models.

Contribution

The paper presents a novel variable transformation technique to effectively reduce stiffness in stochastic protein kinetic equations, enhancing computational efficiency and accuracy.

Findings

Stiffness in stochastic protein kinetic equations can be mitigated by variable transformation.

The method improves numerical stability and reduces computational cost.

The approach is validated through theoretical analysis and numerical experiments.

Abstract

The stochastic protein kinetic equations can be stiff for certain parameters, which makes their numerical simulation rely on very small time step sizes, resulting in large computational cost and accumulated round-off errors. For such situation, we provide a method of reducing stiffness of the stochastic protein kinetic equation by means of a kind of variable transformation. Theoretical and numerical analysis show effectiveness of this method. Its generalization to a more general class of stochastic differential equation models is also discussed.