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Stochasticity in gene expression: from theories to phenotypes


Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.

Key Points

  • Stochasticity in gene expression is manifested as fluctuations in the abundance of expressed molecules at the single-cell level, and variability and heterogeneity within populations of genetically identical cells.

  • Analyses of simple models indicate that stochasticity in gene expression is dominated by translational bursting, arising from a low number of expressed mRNAs, and transcriptional bursting, arising from slow transitions between promoter states.

  • Transcriptional bursting, which arises from random transitions between chromatin states, might cause stochastic all-or-nothing responses in eukaryotic cells and lead to the emergence of populations that contain a mixture of expressing and non-expressing cells.

  • Experimental evidence indicates that translational bursting is a dominant source of stochasticity in prokaryote gene expression, and that both translational and transcriptional bursting contribute to stochasticity in eukaryote gene expression.

  • Evidence also indicates that translational bursting in eukaryotes is an evolvable trait that is subject to natural selection. Transcriptional bursting has been implicated in syndromes that are associated with haploinsufficiency.

  • Sources that are extrinsic to the process of gene expression, such as fluctuations in regulatory signals, also contribute significantly to stochasticity in gene expression. Gene-intrinsic and gene-extrinsic noise can be distinguished experimentally using a two-reporter assay.

  • Fluctuations in regulatory signals are important for the function of transcriptional regulatory networks. In genetic cascades, such fluctuations lead to increased population variability at intermediate expression levels and an initial population asynchrony that increases with cascade length. Increased variability in a regulatory signal might also cause the emergence of mixed populations, containing cells that show either high or low expression levels of the target gene.

  • Negative and positive feedback typically leads to reduction and amplification, respectively, of fluctuations and population heterogeneity. Positive feedback can yield unique or multiple cellular-expression states, depending on the strength of the feedback.

  • Stochasticity in gene expression might provide microorganisms with the flexibility required to respond and adapt to environmental changes and stresses, and can prevent cells from being trapped in suboptimal epigenetic states and phenotypes.

  • Stochastic mechanisms have also been implicated in cellular differentiation and development. They provide a means of generating the initial population heterogeneity on which regulatory mechanisms can function to establish and propagate the expression of cell-type-specific genes.

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We would like to thank M. Elowitz, J. Hasty, F. Isaacs, E. O'Shea, A. van Oudenaarden, J. Paulsson, J. Raser, M. Simpson, P. Swain and R. Weiss for useful discussions and insights. We apologize to the authors whose contributions could not be discussed owing to space limitations. This work was supported by the Canada Research Chair programme (M.K.), Defence Advanced Research Projects Agency (T.C.E.) and the National Institutes of Health (W.J.B. and J.J.C.).

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The authors declare no competing financial interests.

Correspondence to Mads Kærn.

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Genetically identical. Individual cells within an isogenic population are typically the progeny of a single ancestor.


The fundamental unit into which DNA and histones are packaged in eukaryotic cells. It is the basic structural subunit of chromatin and consists of 200 bp of DNA and an octamer of histone proteins.


Codons that have different nucleotide triplets, but which encode the signal for incorporation of the same amino-acid residue during translation. Differences in synonymous codon usage can result in differences in translation rates because codon-specific tRNAs have different abundances.


A consensus sequence within promoters that is enriched in thymine and adenine residues, and is generally important for the recruitment of the transcriptional machinery.


A sequence that is located upstream of a promoter at which transcriptional activators bind and subsequently facilitate the expression of downstream genes.


One of the best-studied negative-feedback regulatory networks in human cells. The tumour-suppressor p53 activates the synthesis of Mdm2, which in turn targets p53 for degradation.


The gene-regulatory network that allows the periodic and population-synchronous expression of genes in the primitive streak and posterior presomitic mesoderm of developing vertebrates. This allows the formation of a periodic pattern of gene expression in the anterior presomitic mesoderm.


The gene-regulatory network that allows bacteriophage-λ to switch between a dormant (lysogenic) state, in which phage DNA is integrated into the chromosome of the host cell, and an active (lytic) state, in which the cellular machinery of the host is used to rapidly produce phage progeny.


The ability to transfer information through successive generations without modification of the DNA sequence. Common mechanisms of epigenetic inheritance are covalent modifications of DNA and altered chromatin structure that affects gene expression.


The inactivation of one of two alleles in diploid cells to produce a heterozygote that is insufficient to assure normal function.


Short, tree-like extensions that are features of many neurons and allow the transmittance of nerve impulses between cells.


An emerging technology for temporal single-cell fluorescence measurements. Each cell is contained within a well etched into the tip of a single optical fibre, with thousands of such fibres arranged in an array. This allows simultaneous measurements across large cell populations.

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Figure 1: A model of the expression of a single gene.
Figure 2: Finite-number effects and translational bursting.
Figure 3: Slow promoter transitions and transcriptional bursting.
Figure 4: Measuring gene-intrinsic noise.
Figure 5: Noise in gene networks.