Biochemical complexity drives log-normal variation in genetic expression
- Author(s): Jacob Beal 1
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Volume 1, Issue 1,
June 2017,
p.
55 – 60
DOI: 10.1049/enb.2017.0004 , Online ISSN 2398-6182
This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received
24/02/2017,
Accepted
30/05/2017,
Revised
12/05/2017,
Published
11/07/2017
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.
Inspec keywords: cellular biophysics; genetics; gamma distribution; stochastic processes
Other keywords: biochemical complexity; transcriptional repressors; stochastically constrained distributions; stochastic bursting; log-normal variation; genetic expression; gene expression; gamma distribution
Subjects: Physics of subcellular structures; Probability theory, stochastic processes, and statistics; Biomolecular dynamics, molecular probes, molecular pattern recognition
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