Quantitative Biology

, Volume 3, Issue 4, pp 168–181 | Cite as

Quantitative analysis of gene expression systems

Review

Abstract

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.

Keywords

gene expression chemical master equation statistical quantities binomial moments expression noise 

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Copyright information

© Higher Education Press and Springer-Verlag GmbH 2015

Authors and Affiliations

  1. 1.School of Mathematics and Computational ScienceSun Yat-Sen UniversityGuangzhouChina

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