Gene expression is a complex biochemical process, involving many specific processes such as transcription, translation, switching between promoter states, and regulation. All these biochemical processes inevitably lea...Gene expression is a complex biochemical process, involving many specific processes such as transcription, translation, switching between promoter states, and regulation. All these biochemical processes inevitably lead to fluctuations in mRNA and protein abundances. This noise has been identified as an important factor underlying the observed phenotypic variability of genetically identical cells in homogeneous environments. Quantifying the contributions of different sources of noise using stochastic models of gene expression is an important step towards understanding fundamental cellular processes and cell-to-cell variability in expression levels. In this paper, we review progresses in quantitative study of simple gene expression systems, including some results that we have not published. We analytically show how specific processes associated with gene expression affect expression levels. In particular, we derive the analytical decomposition of expression noise, which is important for understanding the roles of the factorial noise in controlling phenotypic variability. We also introduce a new index （called attribute factor） to quantify expression noise, which has more advantages than the commonly-used noise indices such as noise intensity and Fano factor.展开更多
Stochasticity in gene expression can result in fluctuations in gene product levels. Recent experiments indicated that feedback regulation plays an important role in controlling the noise in gene expression.A quantitat...Stochasticity in gene expression can result in fluctuations in gene product levels. Recent experiments indicated that feedback regulation plays an important role in controlling the noise in gene expression.A quantitative understanding of the feedback effect on gene expression requires analysis of the corresponding stochastic model. However, for stochastic models of gene expression with general regulation functions, exact analytical results for gene product distributions have not been given so far. Here, we propose a technique to solve a generalized ON-OFF model of stochastic gene expression with arbitrary(positive or negative, linear or nonlinear) feedbacks including posttranscriptional or posttranslational regulation. The obtained results, which generalize results obtained previously, provide new insights into the role of feedback in regulating gene expression. The proposed analytical framework can easily be extended to analysis of more complex models of stochastic gene expression.展开更多
Expression of cellular genes is regulated by binding of transcription factors to their promoter, either activating or inhibiting transcription of a gene. Particularly interesting is the case when the expressed protein...Expression of cellular genes is regulated by binding of transcription factors to their promoter, either activating or inhibiting transcription of a gene. Particularly interesting is the case when the expressed protein regulates its own transcription. In this paper, the features of this self-regulating process are investigated. In the presented model here, the gene can be in two states. Either a protein is bound to its promoter or not. The steady state distributions of protein during and just before switching from one state to the next state are analyzed. Moreover, a powerful numerical method based on the corresponding master equation to compute the protein distribution in the steady state is presented and compared to an already-existing method. Additionally the special case of self-regulation, in which protein can only be produced, if one of these proteins is bound to the promoter region, is analyzed. Furthermore, a self-regulating gene is compared to a similar gene, which also has two states and produces the same amount of proteins but is not regulated by its protein-product.展开更多
文摘Gene expression is a complex biochemical process, involving many specific processes such as transcription, translation, switching between promoter states, and regulation. All these biochemical processes inevitably lead to fluctuations in mRNA and protein abundances. This noise has been identified as an important factor underlying the observed phenotypic variability of genetically identical cells in homogeneous environments. Quantifying the contributions of different sources of noise using stochastic models of gene expression is an important step towards understanding fundamental cellular processes and cell-to-cell variability in expression levels. In this paper, we review progresses in quantitative study of simple gene expression systems, including some results that we have not published. We analytically show how specific processes associated with gene expression affect expression levels. In particular, we derive the analytical decomposition of expression noise, which is important for understanding the roles of the factorial noise in controlling phenotypic variability. We also introduce a new index （called attribute factor） to quantify expression noise, which has more advantages than the commonly-used noise indices such as noise intensity and Fano factor.
基金supported by National Natural Science Foundation of China (Grant Nos. 11931019, 11775314 and 91530320)
文摘Stochasticity in gene expression can result in fluctuations in gene product levels. Recent experiments indicated that feedback regulation plays an important role in controlling the noise in gene expression.A quantitative understanding of the feedback effect on gene expression requires analysis of the corresponding stochastic model. However, for stochastic models of gene expression with general regulation functions, exact analytical results for gene product distributions have not been given so far. Here, we propose a technique to solve a generalized ON-OFF model of stochastic gene expression with arbitrary(positive or negative, linear or nonlinear) feedbacks including posttranscriptional or posttranslational regulation. The obtained results, which generalize results obtained previously, provide new insights into the role of feedback in regulating gene expression. The proposed analytical framework can easily be extended to analysis of more complex models of stochastic gene expression.
文摘Expression of cellular genes is regulated by binding of transcription factors to their promoter, either activating or inhibiting transcription of a gene. Particularly interesting is the case when the expressed protein regulates its own transcription. In this paper, the features of this self-regulating process are investigated. In the presented model here, the gene can be in two states. Either a protein is bound to its promoter or not. The steady state distributions of protein during and just before switching from one state to the next state are analyzed. Moreover, a powerful numerical method based on the corresponding master equation to compute the protein distribution in the steady state is presented and compared to an already-existing method. Additionally the special case of self-regulation, in which protein can only be produced, if one of these proteins is bound to the promoter region, is analyzed. Furthermore, a self-regulating gene is compared to a similar gene, which also has two states and produces the same amount of proteins but is not regulated by its protein-product.
