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 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 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 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.

Transcriptional profiling of iPSC-derived neurons with reciprocal monogenic CNV in 3p26.3 region

2020 ◽  
pp. 10-11
Author(s):  
М.Е. Лопаткина ◽  
В.С. Фишман ◽  
М.М. Гридина ◽  
Н.А. Скрябин ◽  
Т.В. Никитина ◽  
...  
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
System Development ◽  
Transcriptional Profiling ◽  
Global Gene Expression ◽  
Nervous System Development ◽  
Number Variation ◽  
The Central Nervous System ◽  
Cntn6 Gene ◽  
Copy Number Changes

Проведен анализ генной экспрессии в нейронах, дифференцированных из индуцированных плюрипотентных стволовых клеток пациентов с идиопатическими интеллектуальными нарушениями и реципрокными хромосомными мутациями в регионе 3p26.3, затрагивающими единственный ген CNTN6. Для нейронов с различным типом хромосомных аберраций была показана глобальная дисрегуляция генной экспрессии. В нейронах с вариациями числа копий гена CNTN6 была снижена экспрессия генов, продукты которых вовлечены в процессы развития центральной нервной системы. The gene expression analysis of iPSC-derived neurons, obtained from patients with idiopathic intellectual disability and reciprocal microdeletion and microduplication in 3p26.3 region affecting the single CNTN6 gene was performed. The global gene expression dysregulation was demonstrated for cells with CNTN6 copy number variation. Gene expression in neurons with CNTN6 copy number changes was downregulated for genes, whose products are involved in the central nervous system development.

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