This study investigates the regulation of noise in gene expression, focusing on the contribution of biochemical processes to phenotypic noise in genetically identical bacterial populations. The researchers used a fluorescent reporter gene, green fluorescent protein (GFP), in *Bacillus subtilis* to measure phenotypic noise. They found that increased translational efficiency is the main source of increased phenotypic noise, consistent with a stochastic model of gene expression where proteins are produced in random bursts. The study shows that the level of phenotypic variation in an isogenic population can be regulated by genetic parameters. The researchers used a single-copy chromosomal gene with an inducible promoter to minimize additional noise sources. They varied transcriptional and translational efficiencies by adjusting IPTG concentration and introducing mutations in the ribosome binding site (RBS) and initiation codon of the GFP gene. They measured phenotypic noise strength, defined as the variance of protein expression levels divided by the mean, and found a strong positive correlation with translational efficiency. The study also presents a stochastic model of gene expression, where the noise strength is greater than Poissonian and increases with translational efficiency. The model predicts that the noise strength is a function of the average number of proteins synthesized per mRNA transcript and the burst size of protein production. The results show that the phenotypic noise strength is more sensitive to translational efficiency than transcriptional efficiency. The study has broad implications for understanding the role of noise in biological systems. It shows that noise can be harmful, affecting cell signaling and development, but can also be beneficial in creating individuality in populations. The findings suggest that naturally selected genes with low translation rates have evolved to minimize noise, even though efficient translation is energetically favorable. The study also highlights the potential of translational noise control in the development of artificial genetic networks.This study investigates the regulation of noise in gene expression, focusing on the contribution of biochemical processes to phenotypic noise in genetically identical bacterial populations. The researchers used a fluorescent reporter gene, green fluorescent protein (GFP), in *Bacillus subtilis* to measure phenotypic noise. They found that increased translational efficiency is the main source of increased phenotypic noise, consistent with a stochastic model of gene expression where proteins are produced in random bursts. The study shows that the level of phenotypic variation in an isogenic population can be regulated by genetic parameters. The researchers used a single-copy chromosomal gene with an inducible promoter to minimize additional noise sources. They varied transcriptional and translational efficiencies by adjusting IPTG concentration and introducing mutations in the ribosome binding site (RBS) and initiation codon of the GFP gene. They measured phenotypic noise strength, defined as the variance of protein expression levels divided by the mean, and found a strong positive correlation with translational efficiency. The study also presents a stochastic model of gene expression, where the noise strength is greater than Poissonian and increases with translational efficiency. The model predicts that the noise strength is a function of the average number of proteins synthesized per mRNA transcript and the burst size of protein production. The results show that the phenotypic noise strength is more sensitive to translational efficiency than transcriptional efficiency. The study has broad implications for understanding the role of noise in biological systems. It shows that noise can be harmful, affecting cell signaling and development, but can also be beneficial in creating individuality in populations. The findings suggest that naturally selected genes with low translation rates have evolved to minimize noise, even though efficient translation is energetically favorable. The study also highlights the potential of translational noise control in the development of artificial genetic networks.