75 research outputs found
The Measure-theoretic Identity Underlying Transient Fluctuation Theorems
We prove a measure-theoretic identity that underlies all transient
fluctuation theorems (TFTs) for entropy production and dissipated work in
inhomogeneous deterministic and stochastic processes, including those of Evans
and Searles, Crooks, and Seifert. The identity is used to deduce a tautological
physical interpretation of TFTs in terms of the arrow of time, and its
generality reveals that the self-inverse nature of the various trajectory and
process transformations historically relied upon to prove TFTs, while necessary
for these theorems from a physical standpoint, is not necessary from a
mathematical one. The moment generating functions of thermodynamic variables
appearing in the identity are shown to converge in general only in a vertical
strip in the complex plane, with the consequence that a TFT that holds over
arbitrary timescales may fail to give rise to an asymptotic fluctuation theorem
for any possible speed of the corresponding large deviation principle. The case
of strongly biased birth-death chains is presented to illustrate this
phenomenon. We also discuss insights obtained from our measure-theoretic
formalism into the results of Saha et. al. on the breakdown of TFTs for driven
Brownian particles
Fluctuation Theorems for Entropy Production and Heat Dissipation in Periodically Driven Markov Chains
Asymptotic fluctuation theorems are statements of a Gallavotti-Cohen symmetry
in the rate function of either the time-averaged entropy production or heat
dissipation of a process. Such theorems have been proved for various general
classes of continuous-time deterministic and stochastic processes, but always
under the assumption that the forces driving the system are time independent,
and often relying on the existence of a limiting ergodic distribution. In this
paper we extend the asymptotic fluctuation theorem for the first time to
inhomogeneous continuous-time processes without a stationary distribution,
considering specifically a finite state Markov chain driven by periodic
transition rates. We find that for both entropy production and heat
dissipation, the usual Gallavotti-Cohen symmetry of the rate function is
generalized to an analogous relation between the rate functions of the original
process and its corresponding backward process, in which the trajectory and the
driving protocol have been time-reversed. The effect is that spontaneous
positive fluctuations in the long time average of each quantity in the forward
process are exponentially more likely than spontaneous negative fluctuations in
the backward process, and vice-versa, revealing that the distributions of
fluctuations in universes in which time moves forward and backward are related.
As an additional result, the asymptotic time-averaged entropy production is
obtained as the integral of a periodic entropy production rate that generalizes
the constant rate pertaining to homogeneous dynamics
A Taxonomy of Causality-Based Biological Properties
We formally characterize a set of causality-based properties of metabolic
networks. This set of properties aims at making precise several notions on the
production of metabolites, which are familiar in the biologists' terminology.
From a theoretical point of view, biochemical reactions are abstractly
represented as causal implications and the produced metabolites as causal
consequences of the implication representing the corresponding reaction. The
fact that a reactant is produced is represented by means of the chain of
reactions that have made it exist. Such representation abstracts away from
quantities, stoichiometric and thermodynamic parameters and constitutes the
basis for the characterization of our properties. Moreover, we propose an
effective method for verifying our properties based on an abstract model of
system dynamics. This consists of a new abstract semantics for the system seen
as a concurrent network and expressed using the Chemical Ground Form calculus.
We illustrate an application of this framework to a portion of a real
metabolic pathway
Non-equilibrium statistical mechanics: From a paradigmatic model to biological transport
Unlike equilibrium statistical mechanics, with its well-established
foundations, a similar widely-accepted framework for non-equilibrium
statistical mechanics (NESM) remains elusive. Here, we review some of the many
recent activities on NESM, focusing on some of the fundamental issues and
general aspects. Using the language of stochastic Markov processes, we
emphasize general properties of the evolution of configurational probabilities,
as described by master equations. Of particular interest are systems in which
the dynamics violate detailed balance, since such systems serve to model a wide
variety of phenomena in nature. We next review two distinct approaches for
investigating such problems. One approach focuses on models sufficiently simple
to allow us to find exact, analytic, non-trivial results. We provide detailed
mathematical analyses of a one-dimensional continuous-time lattice gas, the
totally asymmetric exclusion process (TASEP). It is regarded as a paradigmatic
model for NESM, much like the role the Ising model played for equilibrium
statistical mechanics. It is also the starting point for the second approach,
which attempts to include more realistic ingredients in order to be more
applicable to systems in nature. Restricting ourselves to the area of
biophysics and cellular biology, we review a number of models that are relevant
for transport phenomena. Successes and limitations of these simple models are
also highlighted.Comment: 72 pages, 18 figures, Accepted to: Reports on Progress in Physic
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