This paper investigates the intrinsic and extrinsic sources of stochasticity in gene expression. Gene expression is inherently noisy due to the stochastic nature of biochemical processes like transcription and translation, which generate intrinsic noise. Additionally, fluctuations in other cellular components indirectly affect gene expression, contributing to extrinsic noise. The authors show how the total variation in gene expression can be decomposed into intrinsic and extrinsic components. They demonstrate that simultaneous measurement of two identical genes per cell allows discrimination between these two types of noise. Analytical expressions for intrinsic noise are derived for a model involving all major steps in transcription and translation. These expressions quantify the deviation from Poisson statistics and provide a way to fit experimental data. Transcription dominates intrinsic noise when the average number of proteins per mRNA transcript is greater than approximately 2, while translational effects become important below this number. Gene replication and cell division cause protein numbers to tend toward a limit cycle. The authors also calculate a general form for extrinsic noise, illustrating it with the example of a repressor protein. All results are confirmed by stochastic simulations using plausible parameters for *E. coli*. The paper distinguishes between intrinsic and extrinsic noise sources and integrates both within a single framework. It models intrinsic noise at a level that allows direct connection with biochemical parameters, including those related to cell growth. The authors suggest an experimental method to discriminate and quantify the two components of noise in living cells. The approach is general enough to allow comparison with experimental data. The paper defines protein noise as the size of protein fluctuations compared to their mean concentration. It shows that the total noise in gene expression is a direct sum of intrinsic and extrinsic contributions. Intrinsic noise is proportional to the variance of the intrinsic distribution, while extrinsic noise vanishes as extrinsic distributions become more spiked. The paper also discusses how to experimentally discriminate between intrinsic and extrinsic contributions. Two identical copies of the gene in the same cell allow for the measurement of intrinsic noise, while extrinsic noise can be extracted from the total noise. The paper presents expressions for intrinsic noise, showing that transcription dominates intrinsic noise when the number of proteins per transcript is greater than two. The authors also discuss the role of the cell cycle in protein numbers and intrinsic noise, showing that protein numbers tend to a limit cycle. The paper also discusses the role of extrinsic noise, showing that it can be a linear sum of the noise in each extrinsic variable. The authors verify their analytical expressions by simulating a repressed gene, where the repressor number is the only fluctuating extrinsic variable. The paper concludes that intrinsic noise is the quantity of interest for a given gene, which can be measured by monitoring expression from two identical copies of the same gene integrated into each cell. The theoretical framework supports experimental research aimed at understanding the role of noise in gene expression and its potential evolutionary advantages.This paper investigates the intrinsic and extrinsic sources of stochasticity in gene expression. Gene expression is inherently noisy due to the stochastic nature of biochemical processes like transcription and translation, which generate intrinsic noise. Additionally, fluctuations in other cellular components indirectly affect gene expression, contributing to extrinsic noise. The authors show how the total variation in gene expression can be decomposed into intrinsic and extrinsic components. They demonstrate that simultaneous measurement of two identical genes per cell allows discrimination between these two types of noise. Analytical expressions for intrinsic noise are derived for a model involving all major steps in transcription and translation. These expressions quantify the deviation from Poisson statistics and provide a way to fit experimental data. Transcription dominates intrinsic noise when the average number of proteins per mRNA transcript is greater than approximately 2, while translational effects become important below this number. Gene replication and cell division cause protein numbers to tend toward a limit cycle. The authors also calculate a general form for extrinsic noise, illustrating it with the example of a repressor protein. All results are confirmed by stochastic simulations using plausible parameters for *E. coli*. The paper distinguishes between intrinsic and extrinsic noise sources and integrates both within a single framework. It models intrinsic noise at a level that allows direct connection with biochemical parameters, including those related to cell growth. The authors suggest an experimental method to discriminate and quantify the two components of noise in living cells. The approach is general enough to allow comparison with experimental data. The paper defines protein noise as the size of protein fluctuations compared to their mean concentration. It shows that the total noise in gene expression is a direct sum of intrinsic and extrinsic contributions. Intrinsic noise is proportional to the variance of the intrinsic distribution, while extrinsic noise vanishes as extrinsic distributions become more spiked. The paper also discusses how to experimentally discriminate between intrinsic and extrinsic contributions. Two identical copies of the gene in the same cell allow for the measurement of intrinsic noise, while extrinsic noise can be extracted from the total noise. The paper presents expressions for intrinsic noise, showing that transcription dominates intrinsic noise when the number of proteins per transcript is greater than two. The authors also discuss the role of the cell cycle in protein numbers and intrinsic noise, showing that protein numbers tend to a limit cycle. The paper also discusses the role of extrinsic noise, showing that it can be a linear sum of the noise in each extrinsic variable. The authors verify their analytical expressions by simulating a repressed gene, where the repressor number is the only fluctuating extrinsic variable. The paper concludes that intrinsic noise is the quantity of interest for a given gene, which can be measured by monitoring expression from two identical copies of the same gene integrated into each cell. The theoretical framework supports experimental research aimed at understanding the role of noise in gene expression and its potential evolutionary advantages.