24,921 research outputs found
Pattern formation at cellular membranes by phosphorylation and dephosphorylation of proteins
We consider a classical model on activation of proteins, based in two reciprocal enzymatic biochemical reactions. The combination of phosphorylation and dephosphorylation reactions of proteins is a well established mechanism for protein activation in cell signalling. We introduce different affinity of the two versions of the proteins to the membrane and to the cytoplasm. The difference in the diffusion coefficient at the membrane and in the cytoplasm together with the high density of proteins at the membrane which reduces the accessible area produces domain formation of protein concentration at the membrane. We differentiate two mechanisms responsible for the pattern formation inside of living cells and discuss the consequences of these models for cell biology.Peer ReviewedPreprin
Universal features of cell polarization processes
Cell polarization plays a central role in the development of complex
organisms. It has been recently shown that cell polarization may follow from
the proximity to a phase separation instability in a bistable network of
chemical reactions. An example which has been thoroughly studied is the
formation of signaling domains during eukaryotic chemotaxis. In this case, the
process of domain growth may be described by the use of a constrained
time-dependent Landau-Ginzburg equation, admitting scale-invariant solutions
{\textit{\`a la}} Lifshitz and Slyozov. The constraint results here from a
mechanism of fast cycling of molecules between a cytosolic, inactive state and
a membrane-bound, active state, which dynamically tunes the chemical potential
for membrane binding to a value corresponding to the coexistence of different
phases on the cell membrane. We provide here a universal description of this
process both in the presence and absence of a gradient in the external
activation field. Universal power laws are derived for the time needed for the
cell to polarize in a chemotactic gradient, and for the value of the smallest
detectable gradient. We also describe a concrete realization of our scheme
based on the analysis of available biochemical and biophysical data.Comment: Submitted to Journal of Statistical Mechanics -Theory and Experiment
Information processing in biological molecular machines
Biological molecular machines are bi-functional enzymes that simultaneously
catalyze two processes: one providing free energy and second accepting it.
Recent studies show that most protein enzymes have a rich dynamics of
stochastic transitions between the multitude of conformational substates that
make up their native state. It often manifests in fluctuating rates of the
catalyzed processes and the presence of short-term memory resulting from the
preference of selected conformations. For any stochastic protein machine
dynamics we proved a generalized fluctuation theorem that leads to the
extension of the second law of thermodynamics. Using them to interpret the
results of random walk on a complex model network, we showed the possibility of
reducing free energy dissipation at the expense of creating some information
stored in memory. The subject of our analysis is the time course of the
catalyzed processes expressed by sequences of jumps at random moments of time.
Since similar signals can be registered in the observation of real systems, all
theses of the paper are open to experimental verification. From a broader
physical point of view, the division of free energy into the operation and
organization energies is worth emphasizing. Information can be assigned a
physical meaning of a change in the value of both these functions of state.Comment: The manuscript contains 14 pages, 7 figure
A variational approach to the stochastic aspects of cellular signal transduction
Cellular signaling networks have evolved to cope with intrinsic fluctuations,
coming from the small numbers of constituents, and the environmental noise.
Stochastic chemical kinetics equations govern the way biochemical networks
process noisy signals. The essential difficulty associated with the master
equation approach to solving the stochastic chemical kinetics problem is the
enormous number of ordinary differential equations involved. In this work, we
show how to achieve tremendous reduction in the dimensionality of specific
reaction cascade dynamics by solving variationally an equivalent quantum field
theoretic formulation of stochastic chemical kinetics. The present formulation
avoids cumbersome commutator computations in the derivation of evolution
equations, making more transparent the physical significance of the variational
method. We propose novel time-dependent basis functions which work well over a
wide range of rate parameters. We apply the new basis functions to describe
stochastic signaling in several enzymatic cascades and compare the results so
obtained with those from alternative solution techniques. The variational
ansatz gives probability distributions that agree well with the exact ones,
even when fluctuations are large and discreteness and nonlinearity are
important. A numerical implementation of our technique is many orders of
magnitude more efficient computationally compared with the traditional Monte
Carlo simulation algorithms or the Langevin simulations.Comment: 15 pages, 11 figure
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
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