Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samples
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CitationMar, Jessica C., Renee Rubio, and John Quackenbush. 2006. Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samples. Genome Biology 7(12): R119.
AbstractBackground: A great deal of interest has been generated by systems biology approaches that
attempt to develop quantitative, predictive models of cellular processes. However, the starting
point for all cellular gene expression, the transcription of RNA, has not been described and
measured in a population of living cells. Results: Here we present a simple model for transcript levels based on Poisson statistics and
provide supporting experimental evidence for genes known to be expressed at high, moderate, and
Conclusion: Although the model describes a microscopic process occurring at the level of an
individual cell, the supporting data we provide uses a small number of cells where the echoes of the
underlying stochastic processes can be seen. Not only do these data confirm our model, but this
general strategy opens up a potential new approach, Mesoscopic Biology, that can be used to assess
the natural variability of processes occurring at the cellular level in biological systems.
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