Stochastic Nature in Cellular Processes

The importance of stochasticity in cellular processes is increasingly recognized in both theoretical and experimental studies. General features of stochasticity in gene regulation and expression are briefly reviewed in this article, which include the main experimental phenomena, classification, quantization and regulation of noises. The correlation and transmission of noise in cascade networks are analyzed further and the stochastic simulation methods that can capture effects of intrinsic and extrinsic noise are described.     

Correlated Noises of the Stochastic Processes

The combined effect of several noises has been proposed recently. It has displayed that the presence of the correlation between noises can change the properties of the stochastic processes. In this paper, a general effective Fokker-Planck equation (FPE) with correlated noises is derived. The stationary distributions and the moments of two kinetic models under two correlated noise sources are obtained. The effects of the interference of additive and multiplicative noises are analyzed.     

On Maximal Hard-Core Thinnings of Stationary Particle Processes

The present paper studies existence and distributional uniqueness of subclasses of stationary hard-core particle systems arising as thinnings of stationary particle processes. These subclasses are defined by natural maximality criteria. We investigate two specific criteria, one related to the intensity of the hard-core particle process, the other one being a local optimality criterion on the level of realizations. In fact, the criteria are equivalent under suitable moment conditions. We show that stationary hard-core thinnings satisfying such criteria exist and are frequently distributionally unique. More precisely, distributional uniqueness holds in subcritical and barely supercritical regimes of continuum percolation. Additionally, based on the analysis of a specific example, we argue that fluctuations in grain sizes can play an important role for establishing distributional uniqueness at high intensities. Finally, we provide a family of algorithmically constructible approximations whose volume fractions are arbitrarily close to the maximum.     

The Three-Step Master Equation: Class of Parametric Stationary Solutions

We examine the three-step master equation from the standpoint of the general solution of the associated discrete Riccati equation. We report by this means stationary master solutions depending on a free constant parameter, denoted by D, that should be negative in order to assure the positivity of the solution. These solutions correspond to different discrete Markov processes characterized by the value of D, which is related to specific renormalizations of the transition rates of the chain of states.     

Filtration of Stochastic Processes Using First-Order Splines

In the present paper, the feasibility of filtration of a stationary stochastic process using first-order splines is examined when the moments of measurements form a Poisson stream of events with a constant intensity. The mean-square filtration error is determined and an approximate algorithm for estimating values of the process in spline nodes is described.     

Condensation in Stochastic Particle Systems with Stationary Product Measures

We study stochastic particle systems with stationary product measures that exhibit a condensation transition due to particle interactions or spatial inhomogeneities. We review previous work on the stationary behaviour and put it in the context of the equivalence of ensembles, providing a general characterization of the condensation transition for homogeneous and inhomogeneous systems in the thermodynamic limit. This leads to strengthened results on weak convergence for subcritical systems, and establishes the equivalence of ensembles for spatially inhomogeneous systems under very general conditions, extending previous results which focused on attractive and finite systems. We use relative entropy techniques which provide simple proofs, making use of general versions of local limit theorems for independent random variables.     

Information Length Analysis of Linear Autonomous Stochastic Processes

When studying the behaviour of complex dynamical systems, a statistical formulation can provide useful insights. In particular, information geometry is a promising tool for this purpose. In this paper, we investigate the information length for n-dimensional linear autonomous stochastic processes, providing a basic theoretical framework that can be applied to a large set of problems in engineering and physics. A specific application is made to a harmonically bound particle system with the natural oscillation frequency ω, subject to a damping γ and a Gaussian white-noise. We explore how the information length depends on ω and γ, elucidating the role of critical damping γ=2ω in information geometry. Furthermore, in the long time limit, we show that the information length reflects the linear geometry associated with the Gaussian statistics in a linear stochastic process.     

Asymptotic Equivalence of Probability Measures and Stochastic Processes

Let \(P_n\) and \(Q_n\) be two probability measures representing two different probabilistic models of some system (e.g., an n-particle equilibrium system, a set of random graphs with n vertices, or a stochastic process evolving over a time n) and let \(M_n\) be a random variable representing a “macrostate” or “global observable” of that system. We provide sufficient conditions, based on the Radon–Nikodym derivative of \(P_n\) and \(Q_n\), for the set of typical values of \(M_n\) obtained relative to \(P_n\) to be the same as the set of typical values obtained relative to \(Q_n\) in the limit \(n\rightarrow \infty \). This extends to general probability measures and stochastic processes the well-known thermodynamic-limit equivalence of the microcanonical and canonical ensembles, related mathematically to the asymptotic equivalence of conditional and exponentially-tilted measures. In this more general sense, two probability measures that are asymptotically equivalent predict the same typical or macroscopic properties of the system they are meant to model.     

Stationary States of Boundary Driven Exclusion Processes with Nonreversible Boundary Dynamics

We prove a law of large numbers for the empirical density of one-dimensional, boundary driven, symmetric exclusion processes with different types of non-reversible dynamics at the boundary. The proofs rely on duality techniques.     

Investigation of the Recombination Processes in Zinc Oxide with Stationary Photoexcitation

A model of recombination processes in zinc oxide powders with stationary photoexcitation of low intensity is suggested. According to this model, recombination proceeds through a system of local levels lying in the forbidden band below the Fermi level for a non-illuminated sample. The concentration of nonequilibrium electrons in the conduction band increases as the square root of the exciting radiation intensity. The concentration of holes in the valence band is proportional to the exciting radiation intensity. Based on this model, we successfully explained the lux-ampere characteristic of photoconductivity, lux-lux characteristic of luminescence, and some photocatalytic properties of zinc oxide.     

Stochastic Theory of Molecular Replication Processes with Selection Character

The replication of molecular sequences, e.g. of biopolymers, is considered as a stationary Markov process. Following Eigen, two types of competing constraints are discussed: 1. Condition of constant overall number;2. constant supply of raw material. For both cases the master equations in the occupation number space are given and the relation to Eigens deterministic theory is discussed. A few computer calculations for the evolution of molecular sequences are given.     

Transient Properties of One-Dimensional Stochastic Processes: Unified-Colored-Noise Approximation

By virtue of the unified-colored-noise approximation the formulas of mean, variance and skewness of first-passage-time distributions (FPTD) for the systems driven by colored noise are derived. The mean and variance of FPTD of bistable system driven by colored noise are calculated. The approximation expressions of the mean, variance and skewness of FPTD for the colored cubic model of a dye laser are obtained. The role of the "color" of pump noise in the dye laser transient is analyzed.     

Modeling Aggregation Processes of Lennard-Jones particles Via Stochastic Networks

We model an isothermal aggregation process of particles/atoms interacting according to the Lennard-Jones pair potential by mapping the energy landscapes of each cluster size N onto stochastic networks, computing transition probabilities from the network for an N-particle cluster to the one for \(N+1\), and connecting these networks into a single joint network. The attachment rate is a control parameter. The resulting network representing the aggregation of up to 14 particles contains 6427 vertices. It is not only time-irreversible but also reducible. To analyze its transient dynamics, we introduce the sequence of the expected initial and pre-attachment distributions and compute them for a wide range of attachment rates and three values of temperature. As a result, we find the configurations most likely to be observed in the process of aggregation for each cluster size. We examine the attachment process and conduct a structural analysis of the sets of local energy minima for every cluster size. We show that both processes taking place in the network, attachment and relaxation, lead to the dominance of icosahedral packing in small (up to 14 atom) clusters.