title: Quantifying intrinsic and extrinsic variability in stochastic gene expression models.
abstract: Genetically identical cell populations exhibit considerable intercellular variation in the level of a given protein or mRNA. Both intrinsic and extrinsic sources of noise drive this variability in gene expression. More specifically, extrinsic noise is the expression variability that arises from cell-to-cell differences in cell-specific factors such as enzyme levels, cell size and cell cycle stage. In contrast, intrinsic noise is the expression variability that is not accounted for by extrinsic noise, and typically arises from the inherent stochastic nature of biochemical processes. Two-color reporter experiments are employed to decompose expression variability into its intrinsic and extrinsic noise components. Analytical formulas for intrinsic and extrinsic noise are derived for a class of stochastic gene expression models, where variations in cell-specific factors cause fluctuations in model parameters, in particular, transcription and/or translation rate fluctuations. Assuming mRNA production occurs in random bursts, transcription rate is represented by either the burst frequency (how often the bursts occur) or the burst size (number of mRNAs produced in each burst). Our analysis shows that fluctuations in the transcription burst frequency enhance extrinsic noise but do not affect the intrinsic noise. On the contrary, fluctuations in the transcription burst size or mRNA translation rate dramatically increase both intrinsic and extrinsic noise components. Interestingly, simultaneous fluctuations in transcription and translation rates arising from randomness in ATP abundance can decrease intrinsic noise measured in a two-color reporter assay. Finally, we discuss how these formulas can be combined with single-cell gene expression data from two-color reporter experiments for estimating model parameters.
authors.affiliation: Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, United States of America ; Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America ; Department of Mathematical Sciences, University of Delaware, Newark, Delaware, United States of America.
authors.forename: Abhyudai
authors.initials: A
authors.lastname: Singh
authors.affiliation: Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, United States of America.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Soltani
mesh_terms: D005786:Gene Expression Regulation / Q000235:genetics*; D059647:Gene-Environment Interaction; D008954:Models, Biological; D014176:Protein Biosynthesis / Q000502:physiology*; D013269:Stochastic Processes; D014158:Transcription, Genetic / Q000502:physiology*