This paper investigates the intrinsic noise in gene regulatory networks, focusing on how noise is generated and controlled in genetic systems. The authors use an analytic model to study the noise properties of genetic systems, showing that for a single gene, noise is primarily determined at the translational level. They find that the mean and variance of protein concentration can be independently controlled, and that the noise strength immediately after gene induction is almost twice the final steady-state value. Fluctuations in regulatory proteins can propagate through a genetic cascade, and translational noise control may explain the inefficient translation rates observed for genes encoding such proteins. For autoregulatory proteins, negative feedback efficiently reduces system noise. The model can predict the noise characteristics of arbitrary gene networks, and is illustrated for an autocatalytic protein and a bistable genetic switch. The analysis of intrinsic noise reveals biological roles of gene network structures and can lead to a deeper understanding of their evolutionary origin. The paper also discusses the importance of noise in biological systems, noting that while noise is often seen as undesirable, it is essential for function and can be exploited by organisms to introduce diversity. The authors also discuss the limitations of their model, including the effects of cell division and the random partitioning of proteins, and the nonlinearity of biochemical systems. They conclude that the model provides a quick and accurate estimate of the emergent noise properties of genetic networks, which is preferable to long numerical simulations. The results show that noise can be significantly higher in out-of-equilibrium systems, and that the burst size of proteins plays an essential role in determining noise levels. The paper also discusses the importance of autoregulation in reducing noise and the trade-offs between energy efficiency and noise reduction in gene regulatory networks. The authors conclude that the study of intrinsic noise in gene regulatory networks is essential for understanding the design principles of stable and robust synthetic biochemical systems.This paper investigates the intrinsic noise in gene regulatory networks, focusing on how noise is generated and controlled in genetic systems. The authors use an analytic model to study the noise properties of genetic systems, showing that for a single gene, noise is primarily determined at the translational level. They find that the mean and variance of protein concentration can be independently controlled, and that the noise strength immediately after gene induction is almost twice the final steady-state value. Fluctuations in regulatory proteins can propagate through a genetic cascade, and translational noise control may explain the inefficient translation rates observed for genes encoding such proteins. For autoregulatory proteins, negative feedback efficiently reduces system noise. The model can predict the noise characteristics of arbitrary gene networks, and is illustrated for an autocatalytic protein and a bistable genetic switch. The analysis of intrinsic noise reveals biological roles of gene network structures and can lead to a deeper understanding of their evolutionary origin. The paper also discusses the importance of noise in biological systems, noting that while noise is often seen as undesirable, it is essential for function and can be exploited by organisms to introduce diversity. The authors also discuss the limitations of their model, including the effects of cell division and the random partitioning of proteins, and the nonlinearity of biochemical systems. They conclude that the model provides a quick and accurate estimate of the emergent noise properties of genetic networks, which is preferable to long numerical simulations. The results show that noise can be significantly higher in out-of-equilibrium systems, and that the burst size of proteins plays an essential role in determining noise levels. The paper also discusses the importance of autoregulation in reducing noise and the trade-offs between energy efficiency and noise reduction in gene regulatory networks. The authors conclude that the study of intrinsic noise in gene regulatory networks is essential for understanding the design principles of stable and robust synthetic biochemical systems.