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 Coordination of gene expression noise with cell size: extrinsic noise versus agent-based models of growing cell populations

2020 ◽  
Author(s):  
Philipp Thomas ◽  
Vahid Shahrezaei
Keyword(s):  
Gene Expression ◽  
Master Equation ◽  
Cell Size ◽  
Chemical Master Equation ◽  
Simulation Algorithm ◽  
Agent Based Models ◽  
Extrinsic Noise ◽  
Gene Expression Noise ◽  
Agent Based ◽  
Expression Noise

The chemical master equation and the stochastic simulation algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled for 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 in a growing population. We find that the solution of the chemical master equation – including static extrinsic noise – exactly agrees with the one of the agent-based formulation when a simple condition on the network’s topology is met. We illustrate this result for a range of common gene expression networks. When these conditions are not met, we demonstrate using analytical solutions of the agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the effective master equation. Surprisingly, the latter distorts total noise in gene regulatory networks by at most 8% independently of network parameters. Our results highlight the accuracy of extrinsic noise modelling within the chemical master equation framework.

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 Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits

2017 ◽  
Author(s):  
François Bertaux ◽  
Samuel Marguerat ◽  
Vahid Shahrezaei
Keyword(s):  
Gene Expression ◽  
Cell Division ◽  
Cell Size ◽  
Global Changes ◽  
Noise Levels ◽  
Division Rate ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Cell Division Rate ◽  
Degradation Rates

AbstractThe cell division rate, size, and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli d other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persistors cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding of circuits’ robustness across growth conditions is key for the effective design of synthetic biological systems.

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 Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits

2018 ◽  
Vol 5 (3) ◽  
pp. 172234 ◽  
Author(s):  
François Bertaux ◽  
Samuel Marguerat ◽  
Vahid Shahrezaei
Keyword(s):  
Gene Expression ◽  
Cell Division ◽  
Cell Size ◽  
Global Changes ◽  
Noise Levels ◽  
Division Rate ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Cell Division Rate ◽  
Degradation Rates

The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli , for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.

scholarly journals Fitness costs of noise in biochemical reaction networks and the evolutionary limits of cellular robustness

2016 ◽  
Author(s):  
J. David Van Dyken
Keyword(s):  
Gene Expression ◽  
Cell Size ◽  
Cell Function ◽  
Noise Control ◽  
Average Rate ◽  
Transcriptional Noise ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Regulatory Complexity ◽  
Gene Regulatory

ABSTRACTGene expression is inherently noisy, but little is known about whether noise affects cell function or, if so, how and by how much. Here I present a theoretical framework to quantify the fitness costs of gene expression noise and identify the evolutionary and synthetic targets of noise control. I find that gene expression noise reduces fitness by slowing the average rate of nutrient uptake and protein synthesis. This is a direct consequence of the hyperbolic (Michaelis-Menten) kinetics of most biological reactions, which I show cause “hyperbolic filtering”, a process that diminishes both the average rate and noise propagation of stochastic reactions. Interestingly, I find that transcriptional noise directly slows growth by slowing the average translation rate. Perhaps surprisingly, this is the largest fitness cost of transcriptional noise since translation strongly filters mRNA noise, making protein noise largely independent of transcriptional noise, consistent with empirical data. Translation, not transcription, then, is the primary target of protein noise control. Paradoxically, selection for protein-noise control favors increased ribosome-mRNA binding affinity, even though this increases translational bursting. However, I find that the efficacy of selection to suppress noise decays faster than linearly with increasing cell size. This predicts a stark, cell-size-mediated taxonomic divide in selection pressures for noise control: small unicellular species, including most prokaryotes, face fairly strong selection to suppress gene expression noise, whereas larger unicells, including most eukaryotes, experience extremely weak selection. I suggest that this taxonomic discrepancy in selection efficacy contributed to the evolution of greater gene-regulatory complexity in eukaryotes.ARTICLE SUMMARYGene expression is a probabilistic process, resulting in random variation in mRNA and protein abundance among cells called “noise”. Understanding how noise affects cell function is a major problem in biology. Here I present theory demonstrating that gene expression noise slows the average rate of cell division. Furthermore, by modeling stochastic gene expression with non-linearity, I identify novel mechanisms of cellular robustness. However, I find that the cost of noise, and therefore the strength of selection favoring robustness, decays faster than linearly with increasing cell size. This may help explain the vast differences in gene-regulatory complexity between prokaryotes and eukaryotes.

scholarly journals Extrinsic noise prevents the independent tuning of gene expression noise and protein mean abundance in bacteria

2020 ◽  
Vol 6 (41) ◽  
pp. eabc3478
Author(s):  
A. Deloupy ◽  
V. Sauveplane ◽  
J. Robert ◽  
S. Aymerich ◽  
M. Jules ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fluorescent Protein ◽  
Biological Engineering ◽  
Translation Control ◽  
Extrinsic Noise ◽  
Translation Rate ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Noise Optimization ◽  
Custom Made

It is generally accepted that prokaryotes can tune gene expression noise independently of protein mean abundance by varying the relative levels of transcription and translation. Here, we address this question quantitatively, using a custom-made library of 40 Bacillus subtilis strains expressing a fluorescent protein under the control of different transcription and translation control elements. We quantify noise and mean protein abundance by fluorescence microscopy and show that for most of the natural transcription range of B. subtilis, expression noise is equally sensitive to variations in the transcription or translation rate because of the prevalence of extrinsic noise. In agreement, analysis of whole-genome transcriptomic and proteomic datasets suggests that noise optimization through transcription and translation tuning during evolution may only occur in a regime of weak transcription. Therefore, independent control of mean abundance and noise can rarely be achieved, which has strong implications for both genome evolution and biological engineering.

Reduction in gene expression noise by targeted increase in accessibility at gene loci

2021 ◽  
Vol 118 (42) ◽  
pp. e2018640118
Author(s):  
LaTasha C. R. Fraser ◽  
Ryan J. Dikdan ◽  
Supravat Dey ◽  
Abhyudai Singh ◽  
Sanjay Tyagi
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Molecule ◽  
Single Cells ◽  
Global Changes ◽  
Messenger Rnas ◽  
Histone Acetyl Transferase ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Eukaryotic Genes

Many eukaryotic genes are expressed in randomly initiated bursts that are punctuated by periods of quiescence. Here, we show that the intermittent access of the promoters to transcription factors through relatively impervious chromatin contributes to this “noisy” transcription. We tethered a nuclease-deficient Cas9 fused to a histone acetyl transferase at the promoters of two endogenous genes in HeLa cells. An assay for transposase-accessible chromatin using sequencing showed that the activity of the histone acetyl transferase altered the chromatin architecture locally without introducing global changes in the nucleus and rendered the targeted promoters constitutively accessible. We measured the gene expression variability from the gene loci by performing single-molecule fluorescence in situ hybridization against mature messenger RNAs (mRNAs) and by imaging nascent mRNA molecules present at active gene loci in single cells. Because of the increased accessibility of the promoter to transcription factors, the transcription from two genes became less noisy, even when the average levels of expression did not change. In addition to providing evidence for chromatin accessibility as a determinant of the noise in gene expression, our study offers a mechanism for controlling gene expression noise which is otherwise unavoidable.

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