Narrow log-periodic modulations in non-Markovian random walks.

What are the necessary ingredients for log-periodicity to appear in the dynamics of a random walk model? Can they be subtle enough to be overlooked? Previous studies suggest that long-range damaged memory and negative feedback together are necessary conditions for the emergence of log-periodic oscillations. The role of negative feedback would then be crucial, forcing the system to change direction. In this paper we show that small-amplitude log-periodic oscillations can emerge when the system is driven by positive feedback. Due to their very small amplitude, these oscillations can easily be mistaken for numerical finite-size effects. The models we use consist of discrete-time random walks with strong memory correlations where the decision process is taken from memory profiles based either on a binomial distribution or on a delta distribution. Anomalous superdiffusive behavior and log-periodic modulations are shown to arise in the large time limit for convenient choices of the models parameters.

Scaling analysis of random walks with persistence lengths: Application to self-avoiding walks.

We develop an approach for performing scaling analysis of N-step random walks (RWs). The mean square end-to-end distance, 〈R[over ⃗]_{N}^{2}〉, is written in terms of inner persistence lengths (IPLs), which we define by the ensemble averages of dot products between the walker's position and displacement vectors, at the jth step. For RW models statistically invariant under orthogonal transformations, we analytically introduce a relation between 〈R[over ⃗]_{N}^{2}〉 and the persistence length, λ_{N}, which is defined as the mean end-to-end vector projection in the first step direction. For self-avoiding walks (SAWs) on 2D and 3D lattices we introduce a series expansion for λ_{N}, and by Monte Carlo simulations we find that λ_{∞} is equal to a constant; the scaling corrections for λ_{N} can be second- and higher-order corrections to scaling for 〈R[over ⃗]_{N}^{2}〉. Building SAWs with typically 100 steps, we estimate the exponents ν_{0} and Δ_{1} from the IPL behavior as function of j. The obtained results are in excellent agreement with those in the literature. This shows that only an ensemble of paths with the same length is sufficient for determining the scaling behavior of 〈R[over ⃗]_{N}^{2}〉, being that the whole information needed is contained in the inner part of the paths.

The universal growth rate behavior and regime transition in adherent cell colonies.

In this work, we used five cell lineages, cultivated in vitro, to show they follow a common functional form to the growth rate: a sigmoidal curve, suggesting that competition and cooperation (usual mechanisms for systems with this behavior) might be present. Both theoretical and experimental investigations, on the causes of this behavior, are challenging for the research field; since the sigmoidal form to the growth rate seems to absorb important properties of such systems, e.g., cell deformation and statistical interactions. We shed some light on this subject by showing how cell spreading affects the radius behavior of the growing colonies. Doing numerical time derivatives of the experimental data, we obtained the growth rates. Using reduced variables for the time and rates, we obtained the collapse of all colonies growth rates onto one curve with sigmoidal shape. This suggests a universal-type behavior, with regime transition related to a morphological transition of adherent cell colonies.

Ultraslow diffusion in an exactly solvable non-Markovian random walk.

We study a one-dimensional discrete-time non-Markovian random walk with strong memory correlations subjected to pauses. Unlike the Scher-Montroll continuous-time random walk, which can be made Markovian by defining an operational time equal to the random-walk step number, the model we study keeps a record of the entire history of the walk. This new model is closely related to the one proposed recently by Kumar, Harbola, and Lindenberg [Phys. Rev. E 82, 021101 (2010)], with the difference that in our model the stochastic dynamics does not stop even in the extreme limit of subdiffusion. Surprisingly, this small difference leads to large consequences. The main results we report here are exact results showing ultraslow diffusion and a stationary diffusion regime (i.e., localization). Specifically, the equations of motion are solved analytically for the first two moments, allowing the determination of the Hurst exponent. Several anomalous diffusion regimes are apparent, ranging from superdiffusion to subdiffusion, as well as ultraslow and stationary regimes. We present the complete phase diffusion diagram, along with a study of the persistence and the statistics in the regions of interest.

Non-Gaussian propagator for elephant random walks.

For almost a decade the consensus has held that the random walk propagator for the elephant random walk (ERW) model is a Gaussian. Here we present strong numerical evidence that the propagator is, in general, non-Gaussian and, in fact, non-Lévy. Motivated by this surprising finding, we seek a second, non-Gaussian solution to the associated Fokker-Planck equation. We prove mathematically, by calculating the skewness, that the ERW Fokker-Planck equation has a non-Gaussian propagator for the superdiffusive regime. Finally, we discuss some unusual aspects of the propagator in the context of higher order terms needed in the Fokker-Planck equation.

Basic ingredients for mathematical modeling of tumor growth in vitro: cooperative effects and search for space.

Based on the literature data from HT-29 cell monolayers, we develop a model for its growth, analogous to an epidemic model, mixing local and global interactions. First, we propose and solve a deterministic equation for the progress of these colonies. Thus, we add a stochastic (local) interaction and simulate the evolution of an Eden-like aggregate by using dynamical Monte Carlo methods. The growth curves of both deterministic and stochastic models are in excellent agreement with the experimental observations. The waiting times distributions, generated via our stochastic model, allowed us to analyze the role of mesoscopic events. We obtain log-normal distributions in the initial stages of the growth and Gaussians at long times. We interpret these outcomes in the light of cellular division events: in the early stages, the phenomena are dependent each other in a multiplicative geometric-based process, and they are independent at long times. We conclude that the main ingredients for a good minimalist model of tumor growth, at mesoscopic level, are intrinsic cooperative mechanisms and competitive search for space.

Alzheimer random walk model: two previously overlooked diffusion regimes.

A non-Markovian one-dimensional random walk model is studied with emphasis on the phase-diagram, showing all the diffusion regimes, along with the exactly determined critical lines. The model, known as the Alzheimer walk, is endowed with memory-controlled diffusion, responsible for the model's long-range correlations, and is characterized by a rich variety of diffusive regimes. The importance of this model is that superdiffusion arises due not to memory per se, but rather also due to loss of memory. The recently reported numerically and analytically estimated values for the Hurst exponent are hereby reviewed. We report the finding of two, previously overlooked, phases, namely, evanescent log-periodic diffusion and log-periodic diffusion with escape, both with Hurst exponent H=1/2. In the former, the log-periodicity gets damped, whereas in the latter the first moment diverges. These phases further enrich the already intricate phase diagram. The results are discussed in the context of phase transitions, aging phenomena, and symmetry breaking.

Weakly anomalous diffusion with non-Gaussian propagators.

A poorly understood phenomenon seen in complex systems is diffusion characterized by Hurst exponent H ≈ 1/2 but with non-Gaussian statistics. Motivated by such empirical findings, we report an exact analytical solution for a non-Markovian random walk model that gives rise to weakly anomalous diffusion with H = 1/2 but with a non-Gaussian propagator.

Effect of local thermal fluctuations on folding kinetics: a study from the perspective of nonextensive statistical mechanics.

The search through the proteins conformational space is thought as an early independent stage of the folding process, governed mainly by the hydrophobic effect. Because of the nanoscopic size of proteins, we assume that the effects of local thermal fluctuations work like folding assistants, managed by the nonextensive parameter q. Using a 27-mer heteropolymer on a cubic lattice, we obtained--by Monte Carlo simulations--kinetic and thermodynamic amounts (such as the characteristic folding time and the native stability) as a function of temperature T and q for a few distinct native targets. We found that for each native structure, at a specific system temperature T, there exists an optimum q* that minimizes the folding characteristic time τ(min); for T=1, it is found that q* lies in the interval 1.15±0.05, even for native structures presenting significantly different topological complexities. The distribution of τ(min) obtained for specific q>1 (nonextensive approach) and temperature T can be fully reproduced for q=1 (Boltzmann approach), but only at higher temperatures T'>T. However, assuming that the complete set of proteins of each organism is optimized to work in a narrow range of temperature, we conclude that--for the present problem--the two approaches, namely, (T,q>1) and (T>T',q=1), cannot be equivalent; it is not a simple matter of reparametrization. Finally, by associating the nonextensive parameter q with the instantaneous degree of compactness of the globule, q becomes a dynamic variable, self-adjusted along the simulation. The results obtained through the q-variable approach are utterly consistent with those obtained by using a target-tuned parameter q*. However, in the former approach, q is automatically adjusted by the chain conformational evolution, eliminating the need to seek for a specific optimized value of q for each case. Besides, using the q-variable approach, different target structures are promptly characterized by inherent distributions of q, which reflect the overall complexity of their corresponding native topologies and energy landscapes.

Stochastic modeling approach to the incubation time of prionic diseases.

Transmissible spongiform encephalopathies are neurodegenerative diseases for which prions are the attributed pathogenic agents. A widely accepted theory assumes that prion replication is due to a direct interaction between the pathologic (PrP(Sc)) form and the host-encoded (PrP(C)) conformation, in a kind of autocatalytic process. Here we show that the overall features of the incubation time of prion diseases are readily obtained if the prion reaction is described by a simple mean-field model. An analytical expression for the incubation time distribution then follows by associating the rate constant to a stochastic variable log normally distributed. The incubation time distribution is then also shown to be log normal and fits the observed BSE (bovine spongiform encephalopathy) data very well. Computer simulation results also yield the correct BSE incubation time distribution at low PrP(C) densities.