Selection and control of pathways by using externally adjustable noise on a stochastic cubic autocatalytic chemical system

Abstract

We investigate the effect of noisy feed rates on the behavior of a cubic autocatalytic chemical reaction model. By combining the renormalization group and stoichiometric network analysis, we demonstrate how externally adjustable random perturbations (extrinsic noise) can be used to select reaction pathways and therefore control reaction yields. This method is general and provides the means to explore the impact that changing statistical parameters in a noisy external environment (such as noisy feed rates and fluctuating reaction rates induced by noisy light) has on chemical fluxes and pathways, thus demonstrating how external noise may be used to control, promote, direct, and optimize chemical progress through a given reaction pathway.