title: Effect of feedback regulation on stochastic gene expression.
abstract: Stochastic noise in gene expression arises as a result of species in small copy number undergoing transitions between discrete chemical states. Here the noise in a single gene network is investigated using the Omega-expansion techniques. We show that the linear noise approximation implies an invariant relationship between the normalized variances and normalized covariance in steady-state statistics. This invariant relationship provides an exactly statistical interpretation for why the stochastic noise in gene expression should be measured by the normalized variance. The nature of the normalized variance reveals the basic relationship between the stochasticity and system size in gene expression. The linear noise approximation implies also that for both mRNA and protein, the total noise can be decomposed into two basic components, one concerns the contribution of average number of molecules, and other the contribution of interactions between mRNA and protein. For the situation with linear feedback, our results clearly show that for two genes with the same average number of protein molecules, the gene with negative feedback will have a small protein noise, i.e., the negative feedback will reduce the protein noise. For the effect of the burst size on the protein noise, we show also that the protein intrinsic noise will decrease with the increase of the burst size, but the protein extrinsic noise is independent of the burst size.
authors.affiliation: Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100080, PR China. snowdiey@yahoo.com
authors.forename: Yi
authors.initials: Y
authors.lastname: Tao
authors.forename: Xiudeng
authors.initials: X
authors.lastname: Zheng
authors.forename: Yuehua
authors.initials: Y
authors.lastname: Sun
mesh_terms: D000818:Animals; D002477:Cells / Q000378:metabolism*; D003198:Computer Simulation; D025461:Feedback, Physiological; D015870:Gene Expression; D005786:Gene Expression Regulation; D008957:Models, Genetic; D014176:Protein Biosynthesis; D013269:Stochastic Processes