1,036 research outputs found
Genetic noise control via protein oligomerization
Gene expression in a cell entails random reaction events occurring over
disparate time scales. Thus, molecular noise that often results in phenotypic
and population-dynamic consequences sets a fundamental limit to biochemical
signaling. While there have been numerous studies correlating the architecture
of cellular reaction networks with noise tolerance, only a limited effort has
been made to understand the dynamic role of protein-protein interactions. Here
we have developed a fully stochastic model for the positive feedback control of
a single gene, as well as a pair of genes (toggle switch), integrating
quantitative results from previous in vivo and in vitro studies. We find that
the overall noise-level is reduced and the frequency content of the noise is
dramatically shifted to the physiologically irrelevant high-frequency regime in
the presence of protein dimerization. This is independent of the choice of
monomer or dimer as transcription factor and persists throughout the multiple
model topologies considered. For the toggle switch, we additionally find that
the presence of a protein dimer, either homodimer or heterodimer, may
significantly reduce its random switching rate. Hence, the dimer promotes the
robust function of bistable switches by preventing the uninduced (induced)
state from randomly being induced (uninduced). The specific binding between
regulatory proteins provides a buffer that may prevent the propagation of
fluctuations in genetic activity. The capacity of the buffer is a non-monotonic
function of association-dissociation rates. Since the protein oligomerization
per se does not require extra protein components to be expressed, it provides a
basis for the rapid control of intrinsic or extrinsic noise
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
Intrinsic noise profoundly alters the dynamics and steady state of morphogen-controlled bistable genetic switches
During tissue development, patterns of gene expression determine the spatial
arrangement of cell types. In many cases, gradients of secreted signaling
molecules - morphogens - guide this process. The continuous positional
information provided by the gradient is converted into discrete cell types by
the downstream transcriptional network that responds to the morphogen. A
mechanism commonly used to implement a sharp transition between two adjacent
cell fates is the genetic toggle switch, composed of cross-repressing
transcriptional determinants. Previous analyses emphasize the steady state
output of these mechanisms. Here, we explore the dynamics of the toggle switch
and use exact numerical simulations of the kinetic reactions, the Chemical
Langevin Equation, and Minimum Action Path theory to establish a framework for
studying the effect of gene expression noise on patterning time and boundary
position. This provides insight into the time scale, gene expression
trajectories and directionality of stochastic switching events between cell
states. Taking gene expression noise into account predicts that the final
boundary position of a morphogen-induced toggle switch, although robust to
changes in the details of the noise, is distinct from that of the deterministic
system. Moreover, stochastic switching introduces differences in patterning
time along the morphogen gradient that result in a patterning wave propagating
away from the morphogen source. The velocity of this wave is influenced by
noise; the wave sharpens and slows as it advances and may never reach steady
state in a biologically relevant time. This could explain experimentally
observed dynamics of pattern formation. Together the analysis reveals the
importance of dynamical transients for understanding morphogen-driven
transcriptional networks and indicates that gene expression noise can
qualitatively alter developmental patterning
The interplay of intrinsic and extrinsic bounded noises in genetic networks
After being considered as a nuisance to be filtered out, it became recently
clear that biochemical noise plays a complex role, often fully functional, for
a genetic network. The influence of intrinsic and extrinsic noises on genetic
networks has intensively been investigated in last ten years, though
contributions on the co-presence of both are sparse. Extrinsic noise is usually
modeled as an unbounded white or colored gaussian stochastic process, even
though realistic stochastic perturbations are clearly bounded. In this paper we
consider Gillespie-like stochastic models of nonlinear networks, i.e. the
intrinsic noise, where the model jump rates are affected by colored bounded
extrinsic noises synthesized by a suitable biochemical state-dependent Langevin
system. These systems are described by a master equation, and a simulation
algorithm to analyze them is derived. This new modeling paradigm should enlarge
the class of systems amenable at modeling.
We investigated the influence of both amplitude and autocorrelation time of a
extrinsic Sine-Wiener noise on: the Michaelis-Menten approximation of
noisy enzymatic reactions, which we show to be applicable also in co-presence
of both intrinsic and extrinsic noise, a model of enzymatic futile cycle
and a genetic toggle switch. In and we show that the
presence of a bounded extrinsic noise induces qualitative modifications in the
probability densities of the involved chemicals, where new modes emerge, thus
suggesting the possibile functional role of bounded noises
Effects of cell cycle noise on excitable gene circuits
We assess the impact of cell cycle noise on gene circuit dynamics. For
bistable genetic switches and excitable circuits, we find that transitions
between metastable states most likely occur just after cell division and that
this concentration effect intensifies in the presence of transcriptional delay.
We explain this concentration effect with a 3-states stochastic model. For
genetic oscillators, we quantify the temporal correlations between daughter
cells induced by cell division. Temporal correlations must be captured properly
in order to accurately quantify noise sources within gene networks.Comment: 15 pages, 8 figure
Mathematical model of a serine integrase-controlled toggle switch with a single input
Dual-state genetic switches that can change their state in response to input signals can be used in synthetic biology to encode memory and control gene expression. A transcriptional toggle switch (TTS), with two mutually repressing transcription regulators, was previously used for switching between two expression states. In other studies, serine integrases have been used to control DNA inversion switches that can alternate between two different states. Both of these switches use two different inputs to switch ON or OFF. Here, we use mathematical modelling to design a robust one-input binary switch, which combines a TTS with a DNA inversion switch. This combined circuit switches between the two states every time it receives a pulse of a single-input signal. The robustness of the switch is based on the bistability of its TTS, while integrase recombination allows single-input control. Unidirectional integrase-RDF-mediated recombination is provided by a recently developed integrase-RDF fusion protein. We show that the switch is stable against parameter variations and molecular noise, making it a promising candidate for further use as a basic element of binary counting devices
Transcriptional delay stabilizes bistable gene networks
Transcriptional delay can significantly impact the dynamics of gene networks.
Here we examine how such delay affects bistable systems. We investigate several
stochastic models of bistable gene networks and find that increasing delay
dramatically increases the mean residence times near stable states. To explain
this, we introduce a non-Markovian, analytically tractable reduced model. The
model shows that stabilization is the consequence of an increased number of
failed transitions between stable states. Each of the bistable systems that we
simulate behaves in this manner
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