7,960 research outputs found
Gene autoregulation via intronic microRNAs and its functions
Background: MicroRNAs, post-transcriptional repressors of gene expression,
play a pivotal role in gene regulatory networks. They are involved in core
cellular processes and their dysregulation is associated to a broad range of
human diseases. This paper focus on a minimal microRNA-mediated regulatory
circuit, in which a protein-coding gene (host gene) is targeted by a microRNA
located inside one of its introns. Results: Autoregulation via intronic
microRNAs is widespread in the human regulatory network, as confirmed by our
bioinformatic analysis, and can perform several regulatory tasks despite its
simple topology. Our analysis, based on analytical calculations and
simulations, indicates that this circuitry alters the dynamics of the host gene
expression, can induce complex responses implementing adaptation and Weber's
law, and efficiently filters fluctuations propagating from the upstream network
to the host gene. A fine-tuning of the circuit parameters can optimize each of
these functions. Interestingly, they are all related to gene expression
homeostasis, in agreement with the increasing evidence suggesting a role of
microRNA regulation in conferring robustness to biological processes. In
addition to model analysis, we present a list of bioinformatically predicted
candidate circuits in human for future experimental tests. Conclusions: The
results presented here suggest a potentially relevant functional role for
negative self-regulation via intronic microRNAs, in particular as a homeostatic
control mechanism of gene expression. Moreover, the map of circuit functions in
terms of experimentally measurable parameters, resulting from our analysis, can
be a useful guideline for possible applications in synthetic biology.Comment: 29 pages and 7 figures in the main text, 18 pages of Supporting
Informatio
Messenger RNA Fluctuations and Regulatory RNAs Shape the Dynamics of Negative Feedback Loop
Single cell experiments of simple regulatory networks can markedly differ
from cell population experiments. Such differences arise from stochastic events
in individual cells that are averaged out in cell populations. For instance,
while individual cells may show sustained oscillations in the concentrations of
some proteins, such oscillations may appear damped in the population average.
In this paper we investigate the role of RNA stochastic fluctuations as a
leading force to produce a sustained excitatory behavior at the single cell
level. Opposed to some previous models, we build a fully stochastic model of a
negative feedback loop that explicitly takes into account the RNA stochastic
dynamics. We find that messenger RNA random fluctuations can be amplified
during translation and produce sustained pulses of protein expression.
Motivated by the recent appreciation of the importance of non--coding
regulatory RNAs in post--transcription regulation, we also consider the
possibility that a regulatory RNA transcript could bind to the messenger RNA
and repress translation. Our findings show that the regulatory transcript helps
reduce gene expression variability both at the single cell level and at the
cell population level.Comment: 87.18.Vf --> Systems biology 87.10.Mn --> Stochastic models in
biological systems 87.18.Tt --> Noise in biological systems
http://www.ncbi.nlm.nih.gov/pubmed/20365787
http://www.weizmann.ac.il/complex/tlusty/papers/PhysRevE2010.pd
Optimal signal processing in small stochastic biochemical networks
We quantify the influence of the topology of a transcriptional regulatory
network on its ability to process environmental signals. By posing the problem
in terms of information theory, we may do this without specifying the function
performed by the network. Specifically, we study the maximum mutual information
between the input (chemical) signal and the output (genetic) response
attainable by the network in the context of an analytic model of particle
number fluctuations. We perform this analysis for all biochemical circuits,
including various feedback loops, that can be built out of 3 chemical species,
each under the control of one regulator. We find that a generic network,
constrained to low molecule numbers and reasonable response times, can
transduce more information than a simple binary switch and, in fact, manages to
achieve close to the optimal information transmission fidelity. These
high-information solutions are robust to tenfold changes in most of the
networks' biochemical parameters; moreover they are easier to achieve in
networks containing cycles with an odd number of negative regulators (overall
negative feedback) due to their decreased molecular noise (a result which we
derive analytically). Finally, we demonstrate that a single circuit can support
multiple high-information solutions. These findings suggest a potential
resolution of the "cross-talk" dilemma as well as the previously unexplained
observation that transcription factors which undergo proteolysis are more
likely to be auto-repressive.Comment: 41 pages 7 figures, 5 table
Functional characteristics of a double positive feedback loop coupled with autorepression
We study the functional characteristics of a two-gene motif consisting of a
double positive feedback loop and an autoregulatory negative feedback loop. The
motif appears in the gene regulatory network controlling the functional
activity of pancreatic -cells. The model exhibits bistability and
hysteresis in appropriate parameter regions. The two stable steady states
correspond to low (OFF state) and high (ON state) protein levels respectively.
Using a deterministic approach, we show that the region of bistability
increases in extent when the copy number of one of the genes is reduced from
two to one. The negative feedback loop has the effect of reducing the size of
the bistable region. Loss of a gene copy, brought about by mutations, hampers
the normal functioning of the -cells giving rise to the genetic
disorder, maturity-onset diabetes of the young (MODY). The diabetic phenotype
makes its appearance when a sizable fraction of the -cells is in the OFF
state. Using stochastic simulation techniques, we show that, on reduction of
the gene copy number, there is a transition from the monostable ON to the ON
state in the bistable region of the parameter space. Fluctuations in the
protein levels, arising due to the stochastic nature of gene expression, can
give rise to transitions between the ON and OFF states. We show that as the
strength of autorepression increases, the ONOFF state transitions become
less probable whereas the reverse transitions are more probable. The
implications of the results in the context of the occurrence of MODY are
pointed out..Comment: 9 pages 14 figure
Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks
The gene regulatory network (GRN) is the central decisionâmaking module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and largeâscale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation. DOI: http://dx.doi.org/10.7554/eLife.02863.00
Oscillation patterns in negative feedback loops
Organisms are equipped with regulatory systems that display a variety of
dynamical behaviours ranging from simple stable steady states, to switching and
multistability, to oscillations. Earlier work has shown that oscillations in
protein concentrations or gene expression levels are related to the presence of
at least one negative feedback loop in the regulatory network. Here we study
the dynamics of a very general class of negative feedback loops. Our main
result is that in these systems the sequence of maxima and minima of the
concentrations is uniquely determined by the topology of the loop and the
activating/repressing nature of the interaction between pairs of variables.
This allows us to devise an algorithm to reconstruct the topology of
oscillating negative feedback loops from their time series; this method applies
even when some variables are missing from the data set, or if the time series
shows transients, like damped oscillations. We illustrate the relevance and the
limits of validity of our method with three examples: p53-Mdm2 oscillations,
circadian gene expression in cyanobacteria, and cyclic binding of cofactors at
the estrogen-sensitive pS2 promoter.Comment: 10 pages, 8 figure
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