A gene is often regulated by a variety of transcription factors, leading to complex promoter structure. However, how this structure affects gene expression remains elusive. Here, this paper studies a stochastic gene m...A gene is often regulated by a variety of transcription factors, leading to complex promoter structure. However, how this structure affects gene expression remains elusive. Here, this paper studies a stochastic gene model with the promoter containing arbitrarily many active and inactive states.First, the authors use the binomial moment method to derive analytical steady-state distributions of the mRNA and protein numbers. Then, the authors analytically investigate how the promoter structure impacts the mean expression levels and the expression noise. Third, numerical simulation finds interesting phenomena, e.g., the common on-off model overestimates the expression noise in contrast to multiple-state models; the multi-on mechanism can reduce the expression noise more than the multi-off mechanism if the mean expression level is kept the same; and multiple exits of transcription can result in multimodal distributions.展开更多
基金supported by Science and Technology Department under Grant No.2014CB964703the Natural Science Foundation under Grant Nos.91530320,11401448,61573011+1 种基金the Hubei Province Education Department under Grant No.B2016062the Science and Technology Department of Hubei Province under Grant Nos.2017CFB682 and 2018CFB688
文摘A gene is often regulated by a variety of transcription factors, leading to complex promoter structure. However, how this structure affects gene expression remains elusive. Here, this paper studies a stochastic gene model with the promoter containing arbitrarily many active and inactive states.First, the authors use the binomial moment method to derive analytical steady-state distributions of the mRNA and protein numbers. Then, the authors analytically investigate how the promoter structure impacts the mean expression levels and the expression noise. Third, numerical simulation finds interesting phenomena, e.g., the common on-off model overestimates the expression noise in contrast to multiple-state models; the multi-on mechanism can reduce the expression noise more than the multi-off mechanism if the mean expression level is kept the same; and multiple exits of transcription can result in multimodal distributions.
