scholarly journals Contribution of RNA Degradation to Intrinsic and Extrinsic Noise in Gene Expression

Cell Reports ◽  
2019 ◽  
Vol 26 (13) ◽  
pp. 3752-3761.e5 ◽  
Author(s):  
Antoine Baudrimont ◽  
Vincent Jaquet ◽  
Sandrine Wallerich ◽  
Sylvia Voegeli ◽  
Attila Becskei
Keyword(s):  
Gene Expression ◽  
Rna Degradation ◽  
Extrinsic Noise ◽  
Noise In Gene Expression ◽  
Intrinsic And Extrinsic Noise

scholarly journals Intrinsic and extrinsic noise are distinguishable in a synthesis – export – degradation model of mRNA production

2020 ◽  
Author(s):  
Gennady Gorin ◽  
Lior Pachter
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
Downstream Processing ◽  
Mrna Splicing ◽  
Noise Sources ◽  
Extrinsic Noise ◽  
Degradation Model ◽  
Degenerate Cases ◽  
Intrinsic And Extrinsic Noise

AbstractIntrinsic and extrinsic noise sources in gene expression, originating respectively from transcriptional stochasticity and from differences between cells, complicate the determination of transcriptional models. In particularly degenerate cases, the two noise sources are altogether impossible to distinguish. However, the incorporation of downstream processing, such as the mRNA splicing and export implicated in gene expression buffering, recovers the ability to identify the relevant source of noise. We report analytical copy-number distributions, discuss the noise sources’ qualitative effects on lower moments, and provide simulation routines for both models.

scholarly journals Extrinsic noise and heavy-tailed laws in gene expression

2019 ◽  
Author(s):  
Lucy Ham ◽  
Rowan D. Brackston ◽  
Michael P.H. Stumpf
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Extrinsic Noise ◽  
Individual Gene ◽  
Copy Numbers ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Intrinsic Stochasticity ◽  
The Individual ◽  
Noise In Gene Expression

AbstractNoise in gene expression is one of the hallmarks of life at the molecular scale. Here we derive analytical solutions to a set of models describing the molecular mechanisms underlying transcription of DNA into RNA. Our Ansatz allows us to incorporate the effects of extrinsic noise – encompassing factors external to the transcription of the individual gene – and discuss the ramifications for heterogeneity in gene product abundance that has been widely observed in single cell data. Crucially, we are able to show that heavy-tailed distributions of RNA copy numbers cannot result from the intrinsic stochasticity in gene expression alone, but must instead reflect extrinsic sources of variability.

scholarly journals Inferring intrinsic and extrinsic noise from a dual fluorescent reporter

2016 ◽  
Author(s):  
Erik van Nimwegen
Keyword(s):  
Gene Expression ◽  
Bayesian Analysis ◽  
Ad Hoc ◽  
Intrinsic Noise ◽  
Extrinsic Noise ◽  
Fluorescent Reporter ◽  
Gene Expression Noise ◽  
Total Gene Expression ◽  
Using Data ◽  
Intrinsic And Extrinsic Noise

AbstractDual fluorescent reporter constructs, which measure gene expression from two identical promoters within the same cell, allow total gene expression noise to be decomposed into an extrinsic component, roughly associated with cell-to-cell fluctuations in cellular component concentrations, and intrinsic noise, roughly associated with inherent stochasticity of the biochemical reactions involved in gene expression [1]. A recent paper by Fu and Pachter presented frequentist statistical estimators for intrinsic and extrinsic noise using data from dual reporters [2]. For comparison, I here present results of a Bayesian analysis of this problem. I show that the orthodox estimators suffer from pathologies such as predicting negative values for a manifestly non-negative quantity, i.e. variance, and show that the Bayesian estimators do not suffer from such pathologies. In addition, I show that the Bayesian analysis automatically identifies that optimal estimates of intrinsic and extrinsic noise depend on a subtle combination of two statistics of the data, allowing for accuracies that are up to twice the accuracy of the orthodox estimators in some parameter regimes.I hope up this little worked out example contrasting orthodox statistical analysis based on ad hoc estimators with estimators resulting from a Bayesian analysis, will be educational for others in the field. I distribute a Mathematica Notebook with this paper that allows users to easily reproduce all results and figures of the paper.

scholarly journals How the extrinsic noise in gene expression can be controlled?

2017 ◽  
Vol 50 (1) ◽  
pp. 15092-15096 ◽  
Author(s):  
Aarón Vázquez-Jiménez ◽  
Moisés Santillán ◽  
Jesús Rodríguez-González
Keyword(s):  
Gene Expression ◽  
Extrinsic Noise ◽  
Noise In Gene Expression

scholarly journals Uncoupling gene expression noise along the central dogma using genome engineered human cell lines

2020 ◽  
Vol 48 (16) ◽  
pp. 9406-9413 ◽  
Author(s):  
Tyler Quarton ◽  
Taek Kang ◽  
Vasileios Papakis ◽  
Khai Nguyen ◽  
Chance Nowak ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Reporter System ◽  
Extrinsic Noise ◽  
Cell Content ◽  
Gene Expression Noise ◽  
Central Dogma ◽  
Expression Noise ◽  
Global Correlation ◽  
Noise In Gene Expression

Abstract Eukaryotic protein synthesis is an inherently stochastic process. This stochasticity stems not only from variations in cell content between cells but also from thermodynamic fluctuations in a single cell. Ultimately, these inherently stochastic processes manifest as noise in gene expression, where even genetically identical cells in the same environment exhibit variation in their protein abundances. In order to elucidate the underlying sources that contribute to gene expression noise, we quantify the contribution of each step within the process of protein synthesis along the central dogma. We uncouple gene expression at the transcriptional, translational, and post-translational level using custom engineered circuits stably integrated in human cells using CRISPR. We provide a generalized framework to approximate intrinsic and extrinsic noise in a population of cells expressing an unbalanced two-reporter system. Our decomposition shows that the majority of intrinsic fluctuations stem from transcription and that coupling the two genes along the central dogma forces the fluctuations to propagate and accumulate along the same path, resulting in increased observed global correlation between the products.

scholarly journals Population growth affects intrinsic and extrinsic noise in gene expression

2018 ◽  
Author(s):  
Philipp Thomas
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Intrinsic Noise ◽  
Integrated Approach ◽  
Time Lapse ◽  
Biochemical Networks ◽  
Intrinsic Variability ◽  
Extrinsic Noise ◽  
Growing Population ◽  
Intrinsic And Extrinsic Noise

Clonal cells of exponentially growing populations vary substantially from cell to cell. The main drivers of this heterogeneity are the population dynamics and stochasticity in the intracellular reactions, which are commonly studied separately. Here we develop an agent-based framework that allows tracking of the biochemical dynamics in every single cell of a growing population that accounts for both of these factors. Apart from the common intrinsic variability of the biochemical reactions, the framework also predicts extrinsic noise arising from fluctuations in the histories of cells without the need to introduce fluctuating rate constants. Instead, these extrinsic fluctuations are explained by cell cycle fluctuations and differences in cell age, which are ubiquitously observed in growing populations. We give explicit formulas to quantify mean molecule numbers, intrinsic and extrinsic noise statistics as measured in two-colour experiments. We find that these statistics may differ significantly depending on the experimental setup used to observe the cells. We illustrate this fact using (i) averages over an isolated cell lineage tracked over many generations as observed in the mother machine, (ii) snapshots of a growing population with known cell ages as recorded in time-lapse microscopy, and (iii) snapshots of unknown cell ages as measured from static images. Our integrated approach applies to arbitrary biochemical networks and generation time distributions. By employing models of stochastic gene expression and feedback regulation, we elucidate that isolated lineages, as compared to snapshot data, can significantly overestimate the mean number of molecules, overestimate extrinsic noise but underestimate intrinsic noise and have qualitatively different sensitivities to cell cycle fluctuations.

scholarly journals Estimating intrinsic and extrinsic noise from single-cell gene expression measurements

2016 ◽  
Vol 15 (6) ◽  
Author(s):  
Audrey Qiuyan Fu ◽  
Lior Pachter
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Hierarchical Model ◽  
Mean Squared Error ◽  
Single Cells ◽  
Geometric Interpretation ◽  
Published Data ◽  
Stochastic Gene Expression ◽  
Extrinsic Noise ◽  
Intrinsic And Extrinsic Noise

AbstractGene expression is stochastic and displays variation (“noise”) both within and between cells. Intracellular (intrinsic) variance can be distinguished from extracellular (extrinsic) variance by applying the law of total variance to data from two-reporter assays that probe expression of identically regulated gene pairs in single cells. We examine established formulas [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): “Stochastic gene expression in a single cell,” Science, 297, 1183–1186.] for the estimation of intrinsic and extrinsic noise and provide interpretations of them in terms of a hierarchical model. This allows us to derive alternative estimators that minimize bias or mean squared error. We provide a geometric interpretation of these results that clarifies the interpretation in [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): “Stochastic gene expression in a single cell,” Science, 297, 1183–1186.]. We also demonstrate through simulation and re-analysis of published data that the distribution assumptions underlying the hierarchical model have to be satisfied for the estimators to produce sensible results, which highlights the importance of normalization.

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