80 research outputs found
Brownian Simulations and Uni-Directional Flux in Diffusion
Brownian dynamics simulations require the connection of a small discrete
simulation volume to large baths that are maintained at fixed concentrations
and voltages. The continuum baths are connected to the simulation through
interfaces, located in the baths sufficiently far from the channel. Average
boundary concentrations have to be maintained at their values in the baths by
injecting and removing particles at the interfaces. The particles injected into
the simulation volume represent a unidirectional diffusion flux, while the
outgoing particles represent the unidirectional flux in the opposite direction.
The classical diffusion equation defines net diffusion flux, but not
unidirectional fluxes. The stochastic formulation of classical diffusion in
terms of the Wiener process leads to a Wiener path integral, which can split
the net flux into unidirectional fluxes. These unidirectional fluxes are
infinite, though the net flux is finite and agrees with classical theory. We
find that the infinite unidirectional flux is an artifact caused by replacing
the Langevin dynamics with its Smoluchowski approximation, which is classical
diffusion. The Smoluchowski approximation fails on time scales shorter than the
relaxation time of the Langevin equation. We find the unidirectional
flux (source strength) needed to maintain average boundary concentrations in a
manner consistent with the physics of Brownian particles. This unidirectional
flux is proportional to the concentration and inversely proportional to
to leading order. We develop a BD simulation that maintains
fixed average boundary concentrations in a manner consistent with the actual
physics of the interface and without creating spurious boundary layers
Emergence of fractal behavior in condensation-driven aggregation
We investigate a model in which an ensemble of chemically identical Brownian
particles are continuously growing by condensation and at the same time undergo
irreversible aggregation whenever two particles come into contact upon
collision. We solved the model exactly by using scaling theory for the case
whereby a particle, say of size , grows by an amount over the
time it takes to collide with another particle of any size. It is shown that
the particle size spectra of such system exhibit transition to dynamic scaling
accompanied by the emergence of fractal of
dimension . One of the remarkable feature of this
model is that it is governed by a non-trivial conservation law, namely, the
moment of is time invariant regardless of the choice of the
initial conditions. The reason why it remains conserved is explained by using a
simple dimensional analysis. We show that the scaling exponents and
are locked with the fractal dimension via a generalized scaling relation
.Comment: 8 pages, 6 figures, to appear in Phys. Rev.
Molecular Motor Constructed from a Double-Walled Carbon Nanotube Driven by Axially Varying Voltage
A new molecular motor is conceptually constructed from a double-walled carbon
nanotube (DWNT) consisting of a long inner single-walled carbon nanotube (SWNT)
and a short outer SWNT with different chirality. The interaction between inner
and outer tubes is the sum of the Lennard-Jones potentials between carbon atoms
in inner tube and those in outer one. Within the framework of
Smoluchowski-Feynman ratchet, it is theoretically shown that this system in an
isothermal bath will exhibit a unidirectional rotation in the presence of a
varying axial electrical voltage.Comment: 11 pages + 3 figure
Modeling long-range memory with stationary Markovian processes
In this paper we give explicit examples of power-law correlated stationary
Markovian processes y(t) where the stationary pdf shows tails which are
gaussian or exponential. These processes are obtained by simply performing a
coordinate transformation of a specific power-law correlated additive process
x(t), already known in the literature, whose pdf shows power-law tails 1/x^a.
We give analytical and numerical evidence that although the new processes (i)
are Markovian and (ii) have gaussian or exponential tails their autocorrelation
function still shows a power-law decay =1/T^b where b grows with a
with a law which is compatible with b=a/2-c, where c is a numerical constant.
When a<2(1+c) the process y(t), although Markovian, is long-range correlated.
Our results help in clarifying that even in the context of Markovian processes
long-range dependencies are not necessarily associated to the occurrence of
extreme events. Moreover, our results can be relevant in the modeling of
complex systems with long memory. In fact, we provide simple processes
associated to Langevin equations thus showing that long-memory effects can be
modeled in the context of continuous time stationary Markovian processes.Comment: 5 figure
The Physics of Maxwell's demon and information
Maxwell's demon was born in 1867 and still thrives in modern physics. He
plays important roles in clarifying the connections between two theories:
thermodynamics and information. Here, we present the history of the demon and a
variety of interesting consequences of the second law of thermodynamics, mainly
in quantum mechanics, but also in the theory of gravity. We also highlight some
of the recent work that explores the role of information, illuminated by
Maxwell's demon, in the arena of quantum information theory.Comment: 24 pages, 13 figures. v2: some refs added, figs improve
Universal features of the order-parameter fluctuations : reversible and irreversible aggregation
We discuss the universal scaling laws of order parameter fluctuations in any
system in which the second-order critical behaviour can be identified. These
scaling laws can be derived rigorously for equilibrium systems when combined
with the finite-size scaling analysis. The relation between order parameter,
criticality and scaling law of fluctuations has been established and the
connexion between the scaling function and the critical exponents has been
found. We give examples in out-of-equilibrium aggregation models such as the
Smoluchowski kinetic equations, or of at-equilibrium Ising and percolation
models.Comment: 19 pages, 10 figure
A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation
Many important cellular processes are regulated by reaction-diffusion (RD) of molecules that takes place both in the cytoplasm and on the membrane. To model and analyze such multicompartmental processes, we developed a lattice-based Monte Carlo method, Spatiocyte that supports RD in volume and surface compartments at single molecule resolution. Stochasticity in RD and the excluded volume effect brought by intracellular molecular crowding, both of which can significantly affect RD and thus, cellular processes, are also supported. We verified the method by comparing simulation results of diffusion, irreversible and reversible reactions with the predicted analytical and best available numerical solutions. Moreover, to directly compare the localization patterns of molecules in fluorescence microscopy images with simulation, we devised a visualization method that mimics the microphotography process by showing the trajectory of simulated molecules averaged according to the camera exposure time. In the rod-shaped bacterium _Escherichia coli_, the division site is suppressed at the cell poles by periodic pole-to-pole oscillations of the Min proteins (MinC, MinD and MinE) arising from carefully orchestrated RD in both cytoplasm and membrane compartments. Using Spatiocyte we could model and reproduce the _in vivo_ MinDE localization dynamics by accounting for the established properties of MinE. Our results suggest that the MinE ring, which is essential in preventing polar septation, is largely composed of MinE that is transiently attached to the membrane independently after recruited by MinD. Overall, Spatiocyte allows simulation and visualization of complex spatial and reaction-diffusion mediated cellular processes in volumes and surfaces. As we showed, it can potentially provide mechanistic insights otherwise difficult to obtain experimentally
Applications of Field-Theoretic Renormalization Group Methods to Reaction-Diffusion Problems
We review the application of field-theoretic renormalization group (RG)
methods to the study of fluctuations in reaction-diffusion problems. We first
investigate the physical origin of universality in these systems, before
comparing RG methods to other available analytic techniques, including exact
solutions and Smoluchowski-type approximations. Starting from the microscopic
reaction-diffusion master equation, we then pedagogically detail the mapping to
a field theory for the single-species reaction k A -> l A (l < k). We employ
this particularly simple but non-trivial system to introduce the
field-theoretic RG tools, including the diagrammatic perturbation expansion,
renormalization, and Callan-Symanzik RG flow equation. We demonstrate how these
techniques permit the calculation of universal quantities such as density decay
exponents and amplitudes via perturbative eps = d_c - d expansions with respect
to the upper critical dimension d_c. With these basics established, we then
provide an overview of more sophisticated applications to multiple species
reactions, disorder effects, L'evy flights, persistence problems, and the
influence of spatial boundaries. We also analyze field-theoretic approaches to
nonequilibrium phase transitions separating active from absorbing states. We
focus particularly on the generic directed percolation universality class, as
well as on the most prominent exception to this class: even-offspring branching
and annihilating random walks. Finally, we summarize the state of the field and
present our perspective on outstanding problems for the future.Comment: 10 figures include
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