Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samples

Jessica C. Mar, Renee Rubio, John Quackenbush

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Background: 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 low levels. 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.

Original languageEnglish (US)
Article numberR119
JournalGenome biology
Volume7
Issue number12
DOIs
StatePublished - Dec 14 2006

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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