Modeling gene expression variability to understand stem cell regulation

Mar JC1,2,3

  1. Australian Institute for Bioengineering and Nanotechnology.
  2. University of Queensland.
  3. Albert Einstein College of Medicine.

When studying the transcriptome and its contribution to the regulation of stem cells, our inferences typically revolve around changes in average gene expression. For a wide range of stem cell populations, heterogeneity in gene expression is a recognized part of transcriptomic data, and recent studies have demonstrated how modeling variability has revealed more information than following average trends alone. This talk outlines some of the approaches my group has developed to investigate how variability of gene expression contributes to our understanding of transcriptional regulation using examples from human stem cell populations.