2,975 research outputs found
Stochastic sequence-level model of coupled transcription and translation in prokaryotes
<p>Abstract</p> <p>Background</p> <p>In prokaryotes, transcription and translation are dynamically coupled, as the latter starts before the former is complete. Also, from one transcript, several translation events occur in parallel. To study how events in transcription elongation affect translation elongation and fluctuations in protein levels, we propose a delayed stochastic model of prokaryotic transcription and translation at the nucleotide and codon level that includes the promoter open complex formation and alternative pathways to elongation, namely pausing, arrests, editing, pyrophosphorolysis, RNA polymerase traffic, and premature termination. Stepwise translation can start after the ribosome binding site is formed and accounts for variable codon translation rates, ribosome traffic, back-translocation, drop-off, and trans-translation.</p> <p>Results</p> <p>First, we show that the model accurately matches measurements of sequence-dependent translation elongation dynamics. Next, we characterize the degree of coupling between fluctuations in RNA and protein levels, and its dependence on the rates of transcription and translation initiation. Finally, modeling sequence-specific transcriptional pauses, we find that these affect protein noise levels.</p> <p>Conclusions</p> <p>For parameter values within realistic intervals, transcription and translation are found to be tightly coupled in <it>Escherichia coli</it>, as the noise in protein levels is mostly determined by the underlying noise in RNA levels. Sequence-dependent events in transcription elongation, e.g. pauses, are found to cause tangible effects in the degree of fluctuations in protein levels.</p
Dynamics of Stochastic Sequence-Level Models of Transcription and Translation in Prokaryotes
In prokaryotes, transcription and translation are dynamically coupled, as the latter starts before the former is completed. Also, from one transcript, several translation events occur in parallel. To study how events in transcription elongation affect translation elongation and fluctuations in protein levels, we propose a delayed stochastic model of prokaryotic transcription and translation at the nucleotide and codon level that includes the promoter open complex formation and alternative pathways to elongation, namely pausing, arrests, editing, pyrophosphorolysis, RNA polymerase traffic, and premature termination. Stepwise translation can start after the ribosome binding site is formed and accounts for variable codon translation rates, ribosome traffic, back-translocation, drop-off, and trans-translation.
The recent development of measurement techniques in genetics promises better un-derstanding of the functioning of biological systems. To attain the most out of these techniques, new methods are needed of interpreting the data, since most existent me-thods have been developed to analyze population level measurements, rather than ex-tracting information from single cell dynamics. For example, one needs accurate estima-tion of the measurement noise from single cell measurements of gene expression. We use recently developed methods to measure gene expression in vivo in individual cells, at the single RNA and protein molecule levels. Such measurements of gene expression, attained in various conditions, as well as the proposed modeling strategy, are used to study and model the dynamics of gene expression at the single event level and to esti-mate noise sources in the processes.
First, the model is shown to accurately match the measurements of sequence-dependent translation elongation dynamics. Next, the degree of coupling between fluc-tuations in RNA and protein levels, and its dependence on the rates of transcription and translation initiation is characterized. Finally, sequence-specific transcriptional pauses are found to have an effect on protein noise levels. For parameter values within realistic intervals, transcription and translation are found to be tightly coupled in Escherichia coli, as the noise in protein levels is mostly determined by the underlying noise in RNA levels. Sequence-dependent events in transcription elongation, e.g. pauses, are found to cause tangible effects in the degree of fluctuations in protein levels, implying that these are evolvable. /Kir1
Molecular Distributions in Gene Regulatory Dynamics
We show how one may analytically compute the stationary density of the
distribution of molecular constituents in populations of cells in the presence
of noise arising from either bursting transcription or translation, or noise in
degradation rates arising from low numbers of molecules. We have compared our
results with an analysis of the same model systems (either inducible or
repressible operons) in the absence of any stochastic effects, and shown the
correspondence between behaviour in the deterministic system and the stochastic
analogs. We have identified key dimensionless parameters that control the
appearance of one or two steady states in the deterministic case, or unimodal
and bimodal densities in the stochastic systems, and detailed the analytic
requirements for the occurrence of different behaviours. This approach
provides, in some situations, an alternative to computationally intensive
stochastic simulations. Our results indicate that, within the context of the
simple models we have examined, bursting and degradation noise cannot be
distinguished analytically when present alone.Comment: 14 pages, 12 figures. Conferences: "2010 Annual Meeting of The
Society of Mathematical Biology", Rio de Janeiro (Brazil), 24-29/07/2010.
"First International workshop on Differential and Integral Equations with
Applications in Biology and Medicine", Aegean University, Karlovassi, Samos
island (Greece), 6-10/09/201
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
Theory on the Dynamics of Oscillatory Loops in the Transcription Factor Networks
We develop a detailed theoretical framework for various types of
transcription factor gene oscillators. We further demonstrate that one can
build genetic-oscillators which are tunable and robust against perturbations in
the critical control parameters by coupling two or more independent
Goodwin-Griffith oscillators through either -OR- or -AND- type logic. Most of
the coupled oscillators constructed in the literature so far seem to be of -OR-
type. When there are transient perturbations in one of the -OR- type
coupled-oscillators, then the overall period of the system remains constant
(period-buffering) whereas in case of -AND- type coupling the overall period of
the system moves towards the perturbed oscillator. Though there is a
period-buffering, the amplitudes of oscillators coupled through -OR- type logic
are more sensitive to perturbations in the parameters associated with the
promoter state dynamics than -AND- type. Further analysis shows that the period
of -AND- type coupled dual-feedback oscillators can be tuned without conceding
on the amplitudes. Using these results we derive the basic design principles
governing the robust and tunable synthetic gene oscillators without
compromising on their amplitudes.Comment: 37 pages, 13 figures, 2 table
Intrinsic noise of microRNA-regulated genes and the ceRNA hypothesis
MicroRNAs are small noncoding RNAs that regulate genes post-transciptionally
by binding and degrading target eukaryotic mRNAs. We use a quantitative model
to study gene regulation by inhibitory microRNAs and compare it to gene
regulation by prokaryotic small non-coding RNAs (sRNAs). Our model uses a
combination of analytic techniques as well as computational simulations to
calculate the mean-expression and noise profiles of genes regulated by both
microRNAs and sRNAs. We find that despite very different molecular machinery
and modes of action (catalytic vs stoichiometric), the mean expression levels
and noise profiles of microRNA-regulated genes are almost identical to genes
regulated by prokaryotic sRNAs. This behavior is extremely robust and persists
across a wide range of biologically relevant parameters. We extend our model to
study crosstalk between multiple mRNAs that are regulated by a single microRNA
and show that noise is a sensitive measure of microRNA-mediated interaction
between mRNAs. We conclude by discussing possible experimental strategies for
uncovering the microRNA-mRNA interactions and testing the competing endogenous
RNA (ceRNA) hypothesis.Comment: 32 pages, 11 figure
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