DETERMINACIÓN DE LAS EMPRESAS ELÉCTRICAS PARA APLICAR

Over the years, many researchers have been interested in the implications of the stochastic nature of gene expression [57, 71-74]. A classic and notable example is the lysis/lysogenic switch of lambda-phage infecting E.coli in which noise randomly dictates which phenotype the cell ultimately adopts [75, 76]. Noise is not only exhibited in cells with genetic differences but also in isogenic cell populations, even when the cells have been exposed to the same environments and have the same history [49, 51, 102]. Recent single-cell experiments, in which noise can be quantified, provide even more convincing evidence of the presence of stochastic fluctuations at the gene expression level ([46], [27], [26]). Typically, in these studies, small, simple and easily manipulated gene networks were artificially engineered in the cell, which were usually coupled with theoretical stochastic models formulated to explain the observations and furthermore, to give predictions. For example, Elowitz and co-workers [49] constructed strains of E.coli to detect noise using two different fluorescent proteins expressed from identical promoters. This study was the first quantitative demonstration and analysis of the existence of noise during gene expression in E.coli. In a complementary study, Ozbudak et al. [58] built a single-gene system in Bacillus subtilis in which they were able to vary

transcriptional and translational rates independently and to measure total noise. More recent single-cell experiments have provided further understanding of cell-to-cell variation by assessing noise dependence on the efficiency of gene expression processes such as

transcription and translation [51], examining the sources and measuring relative contributions of different types of noise in bacteria as well as eukaryotic gene expression [51, 91, 102, 103], investigating how noise propagates through synthetic cascades of regulatory genes [51, 92],

variation in a cell-fate decision [104], and quantifying the noise frequency content and its relation with gene circuit structure [93].

Many experimental and theoretical studies mentioned above have focused on investigating the relative contribution of intrinsic and extrinsic noise to the overall noise in gene expression in prokaryotes as well as eukaryotes. Elowitz and co-workers [49] showed that in bacteria, intrinsic noise decreases monotonically as transcription rate increases. This is not unexpected as higher transcription rate results in a higher number of corresponding molecules (e.g. mRNA transcripts), which tends to reduce fluctuation. However, under the same control condition, extrinsic noise initially increases, peaks at intermediate transcription level and then declines at higher transcription rate. Furthermore, it was revealed that extrinsic fluctuation is the

dominant source of noise in bacterial cells E.coli and Bacillus subtilis [49, 90]. Rosenfeld et al. [102] examined origin of variability of the gene regulation functions in bacteria E.coli and detected only a minor contribution from intrinsic factors, which also implied that extrinsic factors are the prominent noise sources. Similar conclusion was also arrived for the systems in eukaryotic organisms: for example, studies in Saccharomyces. cerevisiae indicated that gene expression is dominated by extrinsic noise [51, 91].

Several studies have gone further and looked into the origins of intrinsic and extrinsic stochasticity in gene expression [51, 90, 93, 105]. The question was which process,

transcription or translation or other processes (e.g. chromatin remodelling), is responsible for causing noise, and to what extent? The effects of built-in efficiency of transcription and translation on noise were examined. It is known that both transcription and translation occur in random size bursts [105]. The random activation and deactivation of a gene leads to

production of mRNA molecules in bursts of random sizes. In turn, translational bursting is due to the random lifetime of mRNA molecules during which several protein copies can be

produced. Transcriptional and translational burstings are thought to contribute to considerable fluctuations in the protein product.

Using experimental methods along with theoretical modelling, Ozbudak and colleagues [90] concluded that in prokaryotic gene expression, noise strength is more sensitive to variation in translational efficiency than transcriptional efficiency. This means for genes expressed at similar levels, low transcription rate coupled with the high translational efficiency result in more noise compared with a combination of high transcriptional rate and low translational efficiency. Because the average number of proteins and the cell volume are kept fixed, the increase in gene expression noise is attributed to the increased fluctuation in mRNA abundance, causing the increased fluctuation in the rate of protein synthesis [105]. This

concept of increased noise due to low transcription rate coupled with high translation rate is usually referred to as the translational bursting mechanism and has been confirmed in both bacteria and eukaryotes. In a recent large-scale study, Bar-Even et al. [106] measured the variation in abundance of 43 fluorescently labelled proteins expressed across 11 different environmental conditions in S.cerevisiae. They interestingly found in most cases that there is an inverse relationship between protein noise and mean protein abundance, with a

proportionality factor of ~ 1200. The authors further determined, after analyzing the large experimental data sets, that this factor is consistent with the number of proteins produced per mRNA, which suggests that translational bursting is a major source of protein noise that could explain the observed scaling behaviour.

On the other hand, it was shown that slow promoter activation (transcriptional bursting) owing to chromatin remodelling also has an important role in generating stochasticity in eukaryotic gene expression [91]. Furthermore, the position of the genes along the chromosome can be more significant than the number of transcripts or proteins in terms of giving rise to protein fluctuations [107].