Universality and flexibility in gene expression from bacteria to human

Abstract

Highly parallel experimental biology is offering opportunities to not just accomplish work more easily, but to explore for underlying governing principles. Recent analysis of the large-scale organization of gene expression has revealed its complex and dynamic nature. However, the underlying dynamics that generate complex gene expression and cellular organization are not yet understood. To comprehensively and quantitatively elucidate these underlying gene expression dynamics, we have analyzed genome-wide gene expression in many experimental conditions in Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, Drosophila melanogaster, Mus musculus, and Homo sapiens. Here we demonstrate that the gene expression dynamics follows the same and surprisingly simple principle from E. coli to human, where gene expression changes are proportional to their expression levels, and show that this ''proportional'' dynamics or ''rich-travel-more'' mechanism can regenerate the observed complex and dynamic organization of the transcriptome. These findings provide a universal principle in the regulation of gene expression, show how complex and dynamic organization can emerge from simple underlying dynamics, and demonstrate the flexibility of transcription across a wide range of expression levels.systems biology ͉ DNA microarray ͉ gene expression dynamics ͉ transcription ͉ transcriptional organization

Physically grounded approach for estimating gene expression from microarray data

“…2B is expressed as a function of ðF i − B i Þ. For the Arabidopsis thaliana chips we considered, our results suggest that p E ðEÞ follows a power law decay with α ≈ 1.7, consistent with previous reports (12).…”

Дробно-Устойчивая Статистика Экспрессии Генов В Экспериментальных Данных Секвенирования Нового Поколения

“…It is well established that the empirical distributions are one-sided distributions, they manifest the power-law asymptotics and character of these distributions is the same for any tissues and organisms from bacteria to mammal [9]. Similar results have been reported by other authors [10,11,12,13,14].…”

“…In the above work of [9], the same distribution was applied for approximation of the profiles of the gene expression in various organisms. The parameter α was shown to vary within the limits from 0.69 to 1.09.…”

Gene-Sharing Networks Reveal Organizing Principles of Transcriptomes in Arabidopsis and Other Multicellular Organisms

“…Coexpression analysis can also be used to create tissue networks; in that case, a pair of tissues is connected if the Pearson correlation coefficient of the expression levels of all genes in the pair of tissues exceeds a predetermined threshold (Ueda et al, 2004). As a comparison to our GSN, we analyzed guard cell neighbor tissues identified using the coexpression network approach (Stuart et al, 2003).…”