  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, fluctuating reaction rates
induced by noisy light, etc) 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.
