Signatures of gene expression noise in cellular systems

Abstract

Noise in gene expression, either due to inherent stochasticity or to varying inter-and intracellular environment, can generate significant cell-to-cell variability of protein levels in clonal populations. We present a theoretical framework, based on stochastic processes, to quantify the different sources of gene expression noise taking cell division explicitly into account. Analytical, time-dependent solutions for the noise contributions arising from the major steps involved in protein synthesis are derived.The analysis shows that the induction level of the activator or transcription factor is crucial for the characteristic signature of the dominant source of gene expression noise and thus bridges the gap between seemingly contradictory experimental results. Furthermore, on the basis of experimentally measured cell distributions, our simulations suggest that transcription factor binding and promoter activation can be modelled independently of each other with sufficient accuracy.

Method for physiologic phenotype characterization at the single-cell level in non-interacting and interacting cells

“…Although genetic heterogeneity results from alterations in the genomic DNA sequence, nongenetic intercellular heterogeneity is caused by intrinsic (e.g., differences in gene expression) and/or extrinsic (e.g., microenvironmental factors) noise in gene transcription machinery, which results in differential gene expression levels that can lead to phenotypic alterations in a cell. [1][2][3] Under stress conditions, this phenotypic heterogeneity can, in turn, lead to changes in a cell's homeostasis, survival, or death because of a transiently high or low abundance of relevant proteins, imparting to the cell a selective advantage over other cells under given conditions. 4 Studies of intercellular variability are therefore critical as they can help reveal novel details as to why and how cells alter their phenotypes from benign to malignant.…”

Localized mRNA translation and protein association

“…This approach also forms a basis for analysis of genetic networks in ensembles of cells with intercellular communication. The kinetic models of this category are abundant (see, e.g., recent reviews, focused on stochastic effects [1], oscillations [2], ncRNAs [3], and complex genetic networks [4]). …”

Two novel aspects of the kinetics of gene expression including miRNAs

“…[11][12][13] and references therein). The interplay of various steps has also been extensively studied as reviewed with emphasis on stochastic bursts and bistability in simple mRNA-protein networks [14][15][16], kinetic oscillations in such networks [17,18], complex mRNA-protein networks [19][20][21][22], and networks including mRNAs, proteins and non-coding RNAs [23].…”