Efficient Attenuation of Stochasticity in Gene Expression Through Post-transcriptional Control

Thermal fluctuations can lead to significant, unpredictable concentration changes in intracellular molecules, potentially disrupting the functioning of cellular networks and challenging cellular efficiency. Biochemical systems might therefore be expected to have evolved network architectures and motifs that limit the effects of stochastic disturbances. During gene expression itself, stochasticity, or “noise”, in protein concentrations is believed to be determined mostly by mRNA, rather than protein, levels. Here, we demonstrate in silico, and analytically, how a number of commonly occurring network architectures in bacteria use mRNA to efficiently attenuate fluctuations. Genes coded in operons share mRNA, which we show generates strongly correlated expression despite multiple ribosome binding sites. For autogeneous control, we provide general analytic expressions using Langevin theory, and demonstrate that negative translational feedback has a much greater efficiency at reducing stochasticity than negative transcriptional feedback. Using the ribosomal proteins as an example, we also show that translational, rather than transcriptional, feedback best coordinates gene expression during assembly of macromolecular complexes. Our findings suggest that selection of a gene controlled post-transcriptionally may be for the resulting low stochasticity in its expression. Such low noise genes can be speculated to play a central role in the local gene network.