1. Use of noise in gene expression as an experimental parameter to test phenotypic effects
- Author
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Jean-Pascal Capp, Jian Liu, and Jean-Marie François
- Subjects
0301 basic medicine ,Genetics ,Regulation of gene expression ,education.field_of_study ,Genetic heterogeneity ,Population ,Bioengineering ,Biology ,Applied Microbiology and Biotechnology ,Biochemistry ,Phenotype ,03 medical and health sciences ,Noise ,030104 developmental biology ,Expression (architecture) ,Gene expression ,education ,Organism ,Biotechnology - Abstract
During the last decade, the molecular basis for gene expression noise has been mostly deciphered, helping understanding of how gene regulation is controlled and how the generation of cell-cell non-genetic heterogeneity is modulated through noise. In the same period, the functional importance of phenotypic heterogeneity among cell populations has been recognized and widely involved in major biological phenomena. Surprisingly, only a few studies connect these two highly active research fields, most of them having been obtained using the yeast Saccharomyces cerevisiae. This organism has long been the preferred model for studying many aspects of gene expression noise, especially revealing that evolution seems to act to either increase or decrease gene expression noise, depending on whether the associated phenotypic heterogeneity is beneficial or deleterious to the population. Nevertheless, direct evidences of phenotypic consequences of noise differences are often lacking, in spite of this evolutionary tendency. This rarity is probably due to the complex relationships between mean and noise levels, making the study of the sole effect of noise difficult, and also to problems caused by the detection of cell-cell expression variability of native functional proteins, allowing the testing of specific phenotypic effects. Despite these difficulties, the widespread use of gene expression noise as an experimental parameter at equal mean expression levels to test phenotypic consequences would often help to change explanations of cell population behaviour beyond the simple consideration of average expression levels, and constitute a major step towards single-cell biology. Copyright © 2016 John Wiley & Sons, Ltd.
- Published
- 2016