55 research outputs found
Entropy production and coarse-graining in Markov processes
We study the large time fluctuations of entropy production in Markov
processes. In particular, we consider the effect of a coarse-graining procedure
which decimates {\em fast states} with respect to a given time threshold. Our
results provide strong evidence that entropy production is not directly
affected by this decimation, provided that it does not entirely remove loops
carrying a net probability current. After the study of some examples of random
walks on simple graphs, we apply our analysis to a network model for the
kinesin cycle, which is an important biomolecular motor. A tentative general
theory of these facts, based on Schnakenberg's network theory, is proposed.Comment: 18 pages, 13 figures, submitted for publicatio
Entropy production and coarse-graining in Markov processes
We study the large time fluctuations of entropy production in Markov
processes. In particular, we consider the effect of a coarse-graining procedure
which decimates {\em fast states} with respect to a given time threshold. Our
results provide strong evidence that entropy production is not directly
affected by this decimation, provided that it does not entirely remove loops
carrying a net probability current. After the study of some examples of random
walks on simple graphs, we apply our analysis to a network model for the
kinesin cycle, which is an important biomolecular motor. A tentative general
theory of these facts, based on Schnakenberg's network theory, is proposed.Comment: 18 pages, 13 figures, submitted for publicatio
Anomalies, absence of local equilibrium and universality in 1-d particles systems
One dimensional systems are under intense investigation, both from
theoretical and experimental points of view, since they have rather peculiar
characteristics which are of both conceptual and technological interest. We
analyze the dependence of the behaviour of one dimensional, time reversal
invariant, nonequilibrium systems on the parameters defining their microscopic
dynamics. In particular, we consider chains of identical oscillators
interacting via hard core elastic collisions and harmonic potentials, driven by
boundary Nos\'e-Hoover thermostats. Their behaviour mirrors qualitatively that
of stochastically driven systems, showing that anomalous properties are typical
of physics in one dimension. Chaos, by itslef, does not lead to standard
behaviour, since it does not guarantee local thermodynamic equilibrium. A
linear relation is found between density fluctuations and temperature profiles.
This link and the temporal asymmetry of fluctuations of the main observables
are robust against modifications of thermostat parameters and against
perturbations of the dynamics.Comment: 26 pages, 16 figures, revised text, two appendices adde
Statistics of trajectories in two-state master equations
We derive a simple expression for the probability of trajectories of a master
equation. The expression is particularly useful when the number of states is
small and permits the calculation of observables that can be defined as
functionals of whole trajectories. We illustrate the method with a two-state
master equation, for which we calculate the distribution of the time spent in
one state and the distribution of the number of transitions, each in a given
time interval. These two expressions are obtained analytically in terms of
modified Bessel functions.Comment: 4 pages, 3 figure
Boltzmann entropy and chaos in a large assembly of weakly interacting systems
We introduce a high dimensional symplectic map, modeling a large system
consisting of weakly interacting chaotic subsystems, as a toy model to analyze
the interplay between single-particle chaotic dynamics and particles
interactions in thermodynamic systems. We study the growth with time of the
Boltzmann entropy, S_B, in this system as a function of the coarse graining
resolution. We show that a characteristic scale emerges, and that the behavior
of S_B vs t, at variance with the Gibbs entropy, does not depend on the coarse
graining resolution, as far as it is finer than this scale. The interaction
among particles is crucial to achieve this result, while the rate of entropy
growth depends essentially on the single-particle chaotic dynamics (for t not
too small). It is possible to interpret the basic features of the dynamics in
terms of a suitable Markov approximation.Comment: 21 pages, 11 figures, submitted to Journal of Statistical Physic
A Universal Lifetime Distribution for Multi-Species Systems
Lifetime distributions of social entities, such as enterprises, products, and
media contents, are one of the fundamental statistics characterizing the social
dynamics. To investigate the lifetime distribution of mutually interacting
systems, simple models having a rule for additions and deletions of entities
are investigated. We found a quite universal lifetime distribution for various
kinds of inter-entity interactions, and it is well fitted by a
stretched-exponential function with an exponent close to 1/2. We propose a
"modified Red-Queen" hypothesis to explain this distribution. We also review
empirical studies on the lifetime distribution of social entities, and
discussed the applicability of the model.Comment: 10 pages, 6 figures, Proceedings of Social Modeling and Simulations +
Econophysics Colloquium 201
Scaling Laws in Human Language
Zipf's law on word frequency is observed in English, French, Spanish,
Italian, and so on, yet it does not hold for Chinese, Japanese or Korean
characters. A model for writing process is proposed to explain the above
difference, which takes into account the effects of finite vocabulary size.
Experiments, simulations and analytical solution agree well with each other.
The results show that the frequency distribution follows a power law with
exponent being equal to 1, at which the corresponding Zipf's exponent diverges.
Actually, the distribution obeys exponential form in the Zipf's plot. Deviating
from the Heaps' law, the number of distinct words grows with the text length in
three stages: It grows linearly in the beginning, then turns to a logarithmical
form, and eventually saturates. This work refines previous understanding about
Zipf's law and Heaps' law in language systems.Comment: 6 pages, 4 figure
Phase Synchronization in Railway Timetables
Timetable construction belongs to the most important optimization problems in
public transport. Finding optimal or near-optimal timetables under the
subsidiary conditions of minimizing travel times and other criteria is a
targeted contribution to the functioning of public transport. In addition to
efficiency (given, e.g., by minimal average travel times), a significant
feature of a timetable is its robustness against delay propagation. Here we
study the balance of efficiency and robustness in long-distance railway
timetables (in particular the current long-distance railway timetable in
Germany) from the perspective of synchronization, exploiting the fact that a
major part of the trains run nearly periodically. We find that synchronization
is highest at intermediate-sized stations. We argue that this synchronization
perspective opens a new avenue towards an understanding of railway timetables
by representing them as spatio-temporal phase patterns. Robustness and
efficiency can then be viewed as properties of this phase pattern
A self-organized model for cell-differentiation based on variations of molecular decay rates
Systemic properties of living cells are the result of molecular dynamics
governed by so-called genetic regulatory networks (GRN). These networks capture
all possible features of cells and are responsible for the immense levels of
adaptation characteristic to living systems. At any point in time only small
subsets of these networks are active. Any active subset of the GRN leads to the
expression of particular sets of molecules (expression modes). The subsets of
active networks change over time, leading to the observed complex dynamics of
expression patterns. Understanding of this dynamics becomes increasingly
important in systems biology and medicine. While the importance of
transcription rates and catalytic interactions has been widely recognized in
modeling genetic regulatory systems, the understanding of the role of
degradation of biochemical agents (mRNA, protein) in regulatory dynamics
remains limited. Recent experimental data suggests that there exists a
functional relation between mRNA and protein decay rates and expression modes.
In this paper we propose a model for the dynamics of successions of sequences
of active subnetworks of the GRN. The model is able to reproduce key
characteristics of molecular dynamics, including homeostasis, multi-stability,
periodic dynamics, alternating activity, differentiability, and self-organized
critical dynamics. Moreover the model allows to naturally understand the
mechanism behind the relation between decay rates and expression modes. The
model explains recent experimental observations that decay-rates (or turnovers)
vary between differentiated tissue-classes at a general systemic level and
highlights the role of intracellular decay rate control mechanisms in cell
differentiation.Comment: 16 pages, 5 figure
Fast flowing populations are not well mixed
In evolutionary dynamics, well-mixed populations are almost always associated
with all-to-all interactions; mathematical models are based on complete graphs.
In most cases, these models do not predict fixation probabilities in groups of
individuals mixed by flows. We propose an analytical description in the
fast-flow limit. This approach is valid for processes with global and local
selection, and accurately predicts the suppression of selection as competition
becomes more local. It provides a modelling tool for biological or social
systems with individuals in motion.Comment: 19 pages, 8 figure
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