Open AccessBiochemical complexity drives log‐normal variation in genetic expression
Author(s) -
Beal Jacob
Publication year - 2017
Publication title -
engineering biology
Language(s) - English
Resource type - Journals
ISSN - 2398-6182
DOI - 10.1049/enb.2017.0004
Subject(s) - expression (computer science) , biology , gene expression , gene , variation (astronomy) , repressor , bursting , genetics , homogeneous , computational biology , normal distribution , evolutionary biology , statistical physics , mathematics , computer science , statistics , neuroscience , physics , astrophysics , programming language
Cells exhibit a high degree of variation in levels of gene expression, even within otherwise homogeneous populations. The standard model to describe this variation centres on a gamma distribution driven by stochastic bursts of translation. Stochastic bursting, however, cannot account for the well‐established behaviour of strong transcriptional repressors. Instead, it can be shown that the very complexity of the biochemical processes involved in gene expression drives an emergent log‐normal distribution of expression levels. Emergent log‐normal distributions can account for the observed behaviour of transcriptional repressors, are still compatible with stochastically constrained distributions, and have important implications for both analysis of gene expression data and the engineering of biological organisms.