A description of the noise associated with gene expression is presented, based on a simplified form of the combined multistep processes of transcription and translation. These processes are influenced by numerous factors, including the accessibility of promoter regions to the transcriptional machinery, the kinetics of assembly of the transcription complexes, and the synthesis and degradation of both mRNA and proteins, among others. Ultimately, stochasticity in cellular processes results in variation in protein levels. Here we constructed a rationally designed RNA-based transcriptional activator to reduce these variables and provide a cleaner, more detailed portrayal of cellular noise. Functioning at a level comparable to natural transcription activation, this activator is isolated to a lacZ reporter gene in yeast cells to quantitatively describe the efficiency of the combined processes of transcription and translation. By employing single-cell array techniques to monitor individual cells simultaneously and in real time, a statistical approach to investigate noise inherent in gene expression is possible. Live cell arrays enabled cell populations to be characterized temporally at the individual cell level. The array platform allowed for a relative measure of protein production in real time and could characterize protein bursts with variable size and random timing, such that bursts occurred in a temporally indiscriminate fashion. The inherent variability and randomness of these processes is characterized, with almost half (47%) of cells experiencing bursting behavior at least once over the course of the experiment. We demonstrate that cells identified on the upper periphery of activity exhibit behaviors that are substantially different from the majority of the population, and such variable activities within a population will provide a more accurate characterization of the population.

• PMID: 22324657
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Journal Article | Research Support, Non-U.S. Gov't | Research Support, U.S. Gov't, Non-P.H.S.

Authors

Walling MA, Shi H, Shepard JR

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