21,313 research outputs found
Stochastic timing in gene expression for simple regulatory strategies
Timing is essential for many cellular processes, from cellular responses to
external stimuli to the cell cycle and circadian clocks. Many of these
processes are based on gene expression. For example, an activated gene may be
required to reach in a precise time a threshold level of expression that
triggers a specific downstream process. However, gene expression is subject to
stochastic fluctuations, naturally inducing an uncertainty in this
threshold-crossing time with potential consequences on biological functions and
phenotypes. Here, we consider such "timing fluctuations", and we ask how they
can be controlled. Our analytical estimates and simulations show that, for an
induced gene, timing variability is minimal if the threshold level of
expression is approximately half of the steady-state level. Timing fuctuations
can be reduced by increasing the transcription rate, while they are insensitive
to the translation rate. In presence of self-regulatory strategies, we show
that self-repression reduces timing noise for threshold levels that have to be
reached quickly, while selfactivation is optimal at long times. These results
lay a framework for understanding stochasticity of endogenous systems such as
the cell cycle, as well as for the design of synthetic trigger circuits.Comment: 10 pages, 5 figure
Strong negative self regulation of Prokaryotic transcription factors increases the intrinsic noise of protein expression
Background
Many prokaryotic transcription factors repress their own transcription. It is often asserted that such regulation enables a cell to homeostatically maintain protein abundance. We explore the role of negative self regulation of transcription in regulating the variability of protein abundance using a variety of stochastic modeling techniques.
Results
We undertake a novel analysis of a classic model for negative self regulation. We demonstrate that, with standard approximations, protein variance relative to its mean should be independent of repressor strength in a physiological range. Consequently, in that range, the coefficient of variation would increase with repressor strength. However, stochastic computer simulations demonstrate that there is a greater increase in noise associated with strong repressors than predicted by theory. The discrepancies between the mathematical analysis and computer simulations arise because with strong repressors the approximation that leads to Michaelis-Menten-like hyperbolic repression terms ceases to be valid. Because we observe that strong negative feedback increases variability and so is unlikely to be a mechanism for noise control, we suggest instead that negative feedback is evolutionarily favoured because it allows the cell to minimize mRNA usage. To test this, we used in silico evolution to demonstrate that while negative feedback can achieve only a modest improvement in protein noise reduction compared with the unregulated system, it can achieve good improvement in protein response times and very substantial improvement in reducing mRNA levels.
Conclusions
Strong negative self regulation of transcription may not always be a mechanism for homeostatic control of protein abundance, but instead might be evolutionarily favoured as a mechanism to limit the use of mRNA. The use of hyperbolic terms derived from quasi-steady-state approximation should also be avoided in the analysis of stochastic models with strong repressors
A quantitative comparison of sRNA-based and protein-based gene regulation
Small, non-coding RNAs (sRNAs) play important roles as genetic regulators in
prokaryotes. sRNAs act post-transcriptionally via complementary pairing with
target mRNAs to regulate protein expression. We use a quantitative approach to
compare and contrast sRNAs with conventional transcription factors (TFs) to
better understand the advantages of each form of regulation. In particular, we
calculate the steady-state behavior, noise properties, frequency-dependent gain
(amplification), and dynamical response to large input signals of both forms of
regulation. While the mean steady-state behavior of sRNA-regulated proteins
exhibits a distinctive tunable threshold-linear behavior, our analysis shows
that transcriptional bursting leads to significantly higher intrinsic noise in
sRNA-based regulation than in TF-based regulation in a large range of
expression levels and limits the ability of sRNAs to perform quantitative
signaling. Nonetheless, we find that sRNAs are better than TFs at filtering
noise in input signals. Additionally, we find that sRNAs allow cells to respond
rapidly to large changes in input signals. These features suggest a niche for
sRNAs in allowing cells to transition quickly yet reliably between distinct
states. This functional niche is consistent with the widespread appearance of
sRNAs in stress-response and quasi-developmental networks in prokaryotes.Comment: 26 pages, 8 figures; accepted for publication in Molecular Systems
Biolog
Interplay between pleiotropy and secondary selection determines rise and fall of mutators in stress response
Dramatic rise of mutators has been found to accompany adaptation of bacteria
in response to many kinds of stress. Two views on the evolutionary origin of
this phenomenon emerged: the pleiotropic hypothesis positing that it is a
byproduct of environmental stress or other specific stress response mechanisms
and the second order selection which states that mutators hitchhike to fixation
with unrelated beneficial alleles. Conventional population genetics models
could not fully resolve this controversy because they are based on certain
assumptions about fitness landscape. Here we address this problem using a
microscopic multiscale model, which couples physically realistic molecular
descriptions of proteins and their interactions with population genetics of
carrier organisms without assuming any a priori fitness landscape. We found
that both pleiotropy and second order selection play a crucial role at
different stages of adaptation: the supply of mutators is provided through
destabilization of error correction complexes or fluctuations of production
levels of prototypic mismatch repair proteins (pleiotropic effects), while rise
and fixation of mutators occur when there is a sufficient supply of beneficial
mutations in replication-controlling genes. This general mechanism assures a
robust and reliable adaptation of organisms to unforeseen challenges. This
study highlights physical principles underlying physical biological mechanisms
of stress response and adaptation
A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli
Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alon
Designer Gene Networks: Towards Fundamental Cellular Control
The engineered control of cellular function through the design of synthetic
genetic networks is becoming plausible. Here we show how a naturally occurring
network can be used as a parts list for artificial network design, and how
model formulation leads to computational and analytical approaches relevant to
nonlinear dynamics and statistical physics.Comment: 35 pages, 8 figure
Engineering stochasticity in gene expression
Stochastic fluctuations (noise) in gene expression can cause members of otherwise genetically identical populations to display drastically different phenotypes. An understanding of the sources of noise and the strategies cells employ to function reliably despite noise is proving to be increasingly important in describing the behavior of natural organisms and will be essential for the engineering of synthetic biological systems. Here we describe the design of synthetic constructs, termed ribosome competing RNAs (rcRNAs), as a means to rationally perturb noise in cellular gene expression. We find that noise in gene expression increases in a manner proportional to the ability of an rcRNA to compete for the cellular ribosome pool. We then demonstrate that operons significantly buffer noise between coexpressed genes in a natural cellular background and can even reduce the level of rcRNA enhanced noise. These results demonstrate that synthetic genetic constructs can significantly affect the noise profile of a living cell and, importantly, that operons are a facile genetic strategy for buffering against noise
Study of in vitro transcriptional binding effects and noise using constitutive promoters combined with UP element sequences in Escherichia coli
Background UP elements (upstream element) are DNA sequences upstream of a promoter that interact with the α-subunit of RNA polymerase (RNAP) and can affect transcription by altering the binding RNAP to DNA. However, details of UP element and binding affinity effects on transcriptional strength are unclear. Results Here, we investigated the effects of UP element sequences on gene transcription, binding affinity, and gene expression noise. Addition of UP elements resulted in increased gene expression (maximum 95.7-fold increase) and reduced gene expression noise (8.51-fold reduction). Half UP element sequences at the proximal subsite has little effect on transcriptional strength despite increasing binding affinity by 2.28-fold. In vitro binding assays were used to determine dissociation constants (Kd) and in the in vitro system, the full range of gene expression occurs in a small range of dissociation constants (25 nM \u3c Kd \u3c 45 nM) indicating that transcriptional strength is highly sensitive to small changes in binding affinity. Conclusions These results demonstrate the utility of UP elements and provide mechanistic insight into the functional relationship between binding affinity and transcription. Given the centrality of gene expression via transcription to biology, additional insight into transcriptional mechanisms can foster both fundamental and applied research. In particular, knowledge of the DNA sequence-specific effects on expression strength can aid in promoter engineering for different organisms and for metabolic engineering to balance pathway fluxes
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