Stochastic simulation of biochemical systems with randomly fluctuating rate constants

TL;DR

This paper introduces a modified stochastic simulation algorithm based on Gillespie's method to model biochemical systems with randomly fluctuating rate constants, validated against experimental data.

Contribution

It develops a general framework for simulating biochemical reactions with fluctuating rate constants, including fast and slow fluctuation models, and applies it to enzyme reactions.

Findings

The modified algorithm accurately reproduces experimental results.

It can handle different fluctuation time scales in biochemical systems.

Potential for parameter estimation and system calibration.

Abstract

In an experimental study of single enzyme reactions, it has been proposed that the rate constants of the enzymatic reactions fluctuate randomly, according to a given distribution. To quantify the uncertainty arising from random rate constants, it is necessary to investigate how one can simulate such a biochemical system. To do this, we will take the Gillespie's stochastic simulation algorithm for simulating the evolution of the state of a chemical system, and study a modification of the algorithm that incorporates the random rate constants. In addition to simulating the waiting time of each reaction step, the modified algorithm also involves simulating the random fluctuation of the rate constant at each reaction time. We consider the modified algorithm in a general framework, then specialize it to two contrasting physical models, one in which the fluctuations occur on a much faster time scale than the reaction step, and the other in which the fluctuations occur much more slowly. The latter case was applied to the single enzyme reaction system, using in part the Metropolis-Hastings algorithm to enact the given distribution on the random rate constants. The modified algorithm is shown to produce simulation outputs that are corroborated by the experimental results. It is hoped that this modified algorithm can subsequently be used as a tool for the estimation or calibration of parameters in the system using experimental data.