Genetic negative feedback circuits for filtering stochasticity in gene expression

The inherent stochastic nature of biochemical reactions coupled with low copy numbers of many mRNA species can create large stochastic fluctuations in protein levels over time. These fluctuations in protein molecular counts are referred to as gene-expression noise and are known to profoundly effect biological function and phenotype. Cells often encode negative feedback circuits to suppress or filter gene-expression noise and reduce intercellular variability in protein levels. We here compare and contrast the noise suppression ability of different negative feedback architectures. Using stochastic models of gene-expression we derive analytical formulas that quantify the extent of stochastic fluctuations in protein levels corresponding to different negative feedback circuits. These formulas reveal that some feedback architectures are inherently better at noise suppression, while the performance of others is dependent on the parameters of gene-expression. More specifically, our results show that among different negative feedback architectures, negative feedback through the mRNA provides the best filtering of gene-expression noise in a mathematically controlled comparison. Finally, we discuss potential ways these negative feedback circuits can be implemented within the process of gene-expression.