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 Allele-specific single-cell RNA sequencing reveals different architectures of intrinsic and extrinsic gene expression noises

2019 ◽  
Author(s):  
Mengyi Sun ◽  
Jianzhi Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Intrinsic Noise ◽  
Mouse Fibroblast ◽  
Extrinsic Noise ◽  
Gene Expression Noise ◽  
Cell State ◽  
Single Cell Rna Sequencing ◽  
Allele Specific

ABSTRACTGene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the low throughput of the existing method for measuring the two noise components, the architectures of intrinsic and extrinsic expression noises remain elusive. Using allele-specific single-cell RNA sequencing, we here estimate the two noise components of 3975 genes in mouse fibroblast cells. Our analyses verify predicted influences of several factors such as the TATA-box and microRNA targeting on intrinsic and extrinsic noises and reveal gene function-associated noise trends implicating the action of natural selection. These findings unravel differential regulations, optimizations, and biological consequences of intrinsic and extrinsic noises and can aid the construction of desired synthetic circuits.

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 Allele-specific single-cell RNA sequencing reveals different architectures of intrinsic and extrinsic gene expression noises

2019 ◽  
Vol 48 (2) ◽  
pp. 533-547 ◽  
Author(s):  
Mengyi Sun ◽  
Jianzhi Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Intrinsic Noise ◽  
Mouse Fibroblast ◽  
Extrinsic Noise ◽  
Gene Expression Noise ◽  
Cell State ◽  
Single Cell Rna Sequencing ◽  
Allele Specific

Abstract Gene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the low throughput of the existing method for measuring the two noise components, the architectures of intrinsic and extrinsic expression noises remain elusive. Using allele-specific single-cell RNA sequencing, we here estimate the two noise components of 3975 genes in mouse fibroblast cells. Our analyses verify predicted influences of several factors such as the TATA-box and microRNA targeting on intrinsic or extrinsic noises and reveal gene function-associated noise trends implicating the action of natural selection. These findings unravel differential regulations, optimizations, and biological consequences of intrinsic and extrinsic noises and can aid the construction of desired synthetic circuits.

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 Plasmids for Independently Tunable, Low-Noise Expression of Two Genes

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
João P. N. Silva ◽  
Soraia Vidigal Lopes ◽  
Diogo J. Grilo ◽  
Zach Hensel
Keyword(s):  
Gene Expression ◽  
Protein Expression ◽  
Low Noise ◽  
Expression Systems ◽  
Extrinsic Noise ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Foreign Proteins ◽  
Noise Limit ◽  
Cell Variation

ABSTRACTSome microbiology experiments and biotechnology applications can be improved if it is possible to tune the expression of two different genes at the same time with cell-to-cell variation at or below the level of genes constitutively expressed from the chromosome (the “extrinsic noise limit”). This was recently achieved for a single gene by exploiting negative autoregulation by the tetracycline repressor (TetR) and bicistronic gene expression to reduce gene expression noise. We report new plasmids that use the same principles to achieve simultaneous, low-noise expression for two genes inEscherichia coli. The TetR system was moved to a compatible plasmid backbone, and a system based on thelacrepressor (LacI) was found to also exhibit gene expression noise below the extrinsic noise limit. We characterized gene expression mean and noise across the range of induction levels for these plasmids, applied the LacI system to tune expression for single-molecule mRNA detection under two different growth conditions, and showed that two plasmids can be cotransformed to independently tune expression of two different genes.IMPORTANCEMicrobiologists often express foreign proteins in bacteria in order study them or to use bacteria as a microbial factory. Usually, this requires controlling the number of foreign proteins expressed in each cell, but for many common protein expression systems, it is difficult to “tune” protein expression without large cell-to-cell variation in expression levels (called “noise” in protein expression). This work describes two protein expression systems that can be combined in the same cell, with tunable expression levels and very low protein expression noise. One new system was used to detect single mRNA molecules by fluorescence microscopy, and the two systems were shown to be independent of each other. These protein expression systems may be useful in any experiment or biotechnology application that can be improved with low protein expression noise.

scholarly journals Plasmids for independently tunable, low-noise expression of two genes

2019 ◽  
Author(s):  
João P. N. Silva ◽  
Soraia Vidigal Lopes ◽  
Diogo J. Grilo ◽  
Zach Hensel
Keyword(s):  
Gene Expression ◽  
Single Molecule ◽  
Single Gene ◽  
Growth Conditions ◽  
Low Noise ◽  
Extrinsic Noise ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Compatible Plasmid ◽  
Noise Limit

AbstractSome microbiology experiments and biotechnology applications can be improved if it is possible to tune the expression of two different genes at the same time with cell-to-cell variation at or below the level of genes constitutively expressed from the chromosome (the “extrinsic noise limit”). This was recently achieved for a single gene by exploiting negative autoregulation by the tetracycline repressor (TetR) and bicistronic gene expression to reduce gene expression noise. We report new plasmids that use the same principles to achieve simultaneous, low-noise expression for two genes. The TetR system was moved to a compatible plasmid backbone, and a system based on the lac repressor (LacI) was found to also exhibit gene expression noise below the extrinsic noise limit. We characterize gene expression mean and noise across the range of induction levels for these plasmids, apply the LacI system to tune expression for single-molecule mRNA detection in two different growth conditions, and show that two plasmids can be co-transformed to independently tune expression of two different genes.

scholarly journals Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

2021 ◽  
Vol 18 (178) ◽  
pp. 20210274
Author(s):  
Philipp Thomas ◽  
Vahid Shahrezaei
Keyword(s):  
Gene Expression ◽  
Master Equation ◽  
Cell Size ◽  
Chemical Master Equation ◽  
Gillespie Algorithm ◽  
Agent Based Models ◽  
Extrinsic Noise ◽  
Gene Expression Noise ◽  
Agent Based ◽  
Expression Noise

The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation—including static extrinsic noise—exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis , a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.

scholarly journals Buffering gene expression noise by microRNA based feedforward regulation

2018 ◽  
Author(s):  
Pavol Bokes ◽  
Michal Hojcka ◽  
Abhyudai Singh
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Optimal Parameter ◽  
Kinetic Monte Carlo ◽  
Reaction Network ◽  
Fano Factor ◽  
Intrinsic Noise ◽  
Limit Function ◽  
Gene Expression Noise ◽  
Expression Noise

AbstractCells use various regulatory motifs, including feedforward loops, to control the intrinsic noise that arises in gene expression at low copy numbers. Here we study one such system, which is broadly inspired by the interaction between an mRNA molecule and an antagonistic microRNA molecule encoded by the same gene. The two reaction species are synchronously produced, individually degraded, and the second species (microRNA) exerts an antagonistic pressure on the first species (mRNA). Using linear-noise approximation, we show that the noise in the first species, which we quantify by the Fano factor, is sub-Poissonian, and exhibits a nonmonotonic response both to the species lifetime ratio and to the strength of the antagonistic interaction. Additionally, we use the Chemical Reaction Network Theory to prove that the first species distribution is Poissonian if the first species is much more stable than the second. Finally, we identify a special parametric regime, supporting a broad range of behaviour, in which the distribution can be analytically described in terms of the confluent hypergeometric limit function. We verify our analysis against large-scale kinetic Monte Carlo simulations. Our results indicate that, subject to specific physiological constraints, optimal parameter values can be found within the mRNA-microRNA motif that can benefit the cell by lowering the gene-expression 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 Limits of noise for autoregulated gene expression

2017 ◽  
Author(s):  
Peter Czuppon ◽  
Peter Pfaffelhuber
Keyword(s):  
Gene Expression ◽  
Negative Feedback ◽  
Positive Feedback ◽  
Fano Factor ◽  
Intrinsic Noise ◽  
Stochastic Averaging ◽  
Jump Processes ◽  
Markov Jump ◽  
Extrinsic Noise ◽  
Cellular Processes

AbstractGene expression is influenced by extrinsic noise (involving a fluctuating environment of cellular processes) and intrinsic noise (referring to fluctuations within a cell under constant environment). We study the standard model of gene expression including an (in-)active gene, mRNA and protein. Gene expression is regulated in the sense that the protein feeds back and either represses (negative feedback) or enhances (positive feedback) its production at the stage of transcription. While it is well-known that negative (positive) feedback reduces (increases) intrinsic noise, we give a precise result on the resulting fluctuations in protein numbers. The technique we use is an extension of the Langevin approximation and is an application of a central limit theorem under stochastic averaging for Markov jump processes (Kang, Kurtz and Popovic, 2014). We find that (under our scaling and in equilibrium), negative feedback leads to a reduction in the Fano factor of at most 2, while the noise under positive feedback is potentially unbounded. The fit with simulations is very good and improves on known approximations.

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