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Denis Horvath

Publications and source records attributed to Denis Horvath.

14 recordsLinked to original sources
arXiv2023

The bouncing dynamics of inertial self-propelled particles reveals directional asymmetry

This study aims to examine experimental conditions in which active particles are forced by their surroundings to move forward and backward in a continuous oscillatory manner. The experimental design is based on using a vibrating self-propelled toy-robot called hexbug, which is placed inside a narrow channel closed on one end by a rigid moving wall. Using the end-wall velocity as a controlling factor, the main forward mode of the hexbug movement can be turned to mostly rearward mode. We investigate the bouncing hexbug motion on both experimental and theoretical grounds. The Brownian model of active particles with inertia is employed in the theoretical framework. The model itself uses a pulsed Langevin equation in order to simulate abrupt changes in velocity that mimic hexbug propulsion in the moments when its legs make contact with the base plate. Significant directional asymmetry is caused by the legs bending backward. We demonstrate that the simulation successfully reproduces the experimental characteristics of hexbug motion after regressing the spatial and temporal statistical characteristics, especially when directional asymmetry is under consideration.

cond-mat.soft↗
arXiv2019

In Silico Implementation of Evolutionary Paradigm in Therapy Design: Towards Anti-Cancer Therapy as Darwinian Process

In here presented in silico study we suggest a way how to implement the evolutionary principles into anti-cancer therapy design. We hypothesize that instead of its ongoing supervised adaptation, the therapy may be constructed as a self-sustaining evolutionary process in a dynamic fitness landscape established implicitly by evolving cancer cells, microenvironment and the therapy itself. For these purposes, we replace a unified therapy with the `therapy species', which is a population of heterogeneous elementary therapies, and propose a way how to turn the toxicity of the elementary therapy into its fitness in a way conforming to evolutionary causation. As a result, not only the therapies govern the evolution of different cell phenotypes, but the cells' resistances govern the evolution of the therapies as well. We illustrate the approach by the minimalistic ad hoc evolutionary model. Its results indicate that the resistant cells could bias the evolution towards more toxic elementary therapies by inhibiting the less toxic ones. As the evolutionary causation of cancer drug resistance has been intensively studied for a few decades, we refer to cancer as a special case to illustrate purely theoretical analysis.

q-bio.PE↗
arXiv2018

Toward understanding of the role of reversibility of phenotypic switching in the evolution of resistance to therapy

Reversibility of state transitions is intensively studied topic in many scientific disciplines over many years. In cell biology, it plays an important role in epigenetic variation of phenotypes, known as phenotypic plasticity. More interestingly, the cell state reversibility is probably crucial in the adaptation of population phenotypic heterogeneity to environmental fluctuations by evolving bet-hedging strategy, which might confer to cancer cells resistance to therapy. In this article, we propose a formalization of the evolution of highly reversible states in the environments of periodic variability. Two interrelated models of heterogeneous cell populations are proposed and their behavior is studied. The first model captures selection dynamics of the cell clones for the respective levels of phenotypic reversibility. The second model focuses on the interplay between reversibility and drug resistance in the particular case of cancer. Overall, our results show that the threshold dependencies are emergent features of the investigated model with eventual therapeutic relevance. Presented examples demonstrate importance of taking into account cell to cell heterogeneity within a system of clones with different reversibility quantified by appropriately chosen genetic and epigenetic entropy measures.

q-bio.PE↗
arXiv2015

Towards Inverse Modeling of Intratumoral Heterogeneity

Development of resistance limits efficiency of present anticancer therapies and preventing it remains big challenge in cancer research. It is accepted, at intuitive level, that the resistance emerges as a consequence of cancer cells heterogeneity at molecular, genetic and cellular levels. Produced by many sources, tumor heterogeneity is extremely complex time dependent statistical characteristics which may be quantified by the measures defined in many different ways, most of them coming from statistical mechanics. In the paper we apply Markovian framework to relate population heterogeneity with the statistics of environment. As, from the evolutionary viewpoint, therapy corresponds to a purposeful modification of the cells fitness landscape, we assume that understanding general relation between spatiotemporal statistics of tumor microenvironment and intratumor heterogeneity enables to conceive the therapy as the inverse problem and solve it by optimization techniques. To account for the inherent stochasticity of biological processes at cellular scale, the generalized distance-based concept was applied to express distances between probabilistically described cell states and environmental conditions, respectively.

q-bio.QM↗
arXiv2014

Self-organized manifold learning and heuristic charting via adaptive metrics

Classical metric and non-metric multidimensional scaling (MDS) variants are widely known manifold learning (ML) methods which enable construction of low dimensional representation (projections) of high dimensional data inputs. However, their use is crucially limited to the cases when data are inherently reducible to low dimensionality. In general, drawbacks and limitations of these, as well as pure, MDS variants become more apparent when the exploration (learning) is exposed to the structured data of high intrinsic dimension. As we demonstrate on artificial and real-world datasets, the over-determination problem can be solved by means of the hybrid and multi-component discrete-continuous multi-modal optimization heuristics. Its remarkable feature is, that projections onto 2D are constructed simultaneously with the data categorization (classification) compensating in part for the loss of original input information. We observed, that the optimization module integrated with ML modeling, metric learning and categorization leads to a nontrivial mechanism resulting in generation of patterns of categorical variables which can be interpreted as a heuristic charting. The method provides visual information in the form of non-convex clusters or separated regions. Furthermore, the ability to categorize the surfaces into back and front parts of the analyzed 3D data objects have been attained through self-organized structuring without supervising.

physics.data-an↗
arXiv2007

Statistical properties of agent-based market area model

One dimensional stylized model taking into account spatial activity of firms with uniformly distributed customers is proposed. The spatial selling area of each firm is defined by a short interval cut out from selling space (large interval). In this representation, the firm size is directly associated with the size of its selling interval. The recursive synchronous dynamics of economic evolution is discussed where the growth rate is proportional to the firm size incremented by the term including the overlap of the selling area with areas of competing firms. Other words, the overlap of selling areas inherently generate a negative feedback originated from the pattern of demand. Numerical simulations focused on the obtaining of the firm size distributions uncovered that the range of free parameters where the Pareto's law holds corresponds to the range for which the pair correlation between the nearest neighbor firms attains its minimum.

q-fin.GN↗
arXiv2007

The dynamical response to the node defect in thermally activated remagnetization of magnetic dot array

The influence of nonmagnetic central node defect on dynamical properties of regular square-shaped 5 x 5 segment of magnetic dot array under the thermal activation is investigated via computer simulations. Using stochastic Landau-Lifshitz-Gilbert equation we simulate hysteresis and relaxation processes. The remarkable quantitative and qualitative differences between magnetic dot arrays with nonmagnetic central node defect and magnetic dot arrays without defects have been found.

cond-mat.other↗
arXiv2006

Simulation of Multicellular Tumor Spheroids Growth Dynamics

The inverse geometric approach to the modeling of the growth of circular objects revealing required features, such as the velocity of the growth and fractal behavior of their contours, is presented. It enables to reproduce some of the recent findings in morphometry of tumors with the possible implications for cancer research. The technique is based on cellular automata paradigm with the transition rules selected by optimization procedure performed by the genetic algorithms.

physics.med-ph↗
arXiv2005

A self-adjusted Monte Carlo simulation as model of financial markets with central regulation

Properties of the self-adjusted Monte Carlo algorithm applied to 2d Ising ferromagnet are studied numerically. The endogenous feedback form expressed in terms of the instant running averages is suggested in order to generate a biased random walk of the temperature that converges to criticality without an external tuning. The robustness of a stationary regime with respect to partial accessibility of the information is demonstrated. Several statistical and scaling aspects have been identified which allow to establish an alternative spin lattice model of the financial market. It turns out that our model alike model suggested by S. Bornholdt, Int. J. Mod. Phys. C {\bf 12} (2001) 667, may be described by Lévy-type stationary distribution of feedback variations with unique exponent $α_1 \sim 3.3$. However, the differences reflected by Hurst exponents suggest that resemblances between the studied models seem to be nontrivial.

cond-mat.stat-mech↗
arXiv2004

The self-organized multi-lattice Monte Carlo simulation

The self-organized Monte Carlo simulations of 2D Ising ferromagnet on the square lattice are performed. The essence of devised simulation method is the artificial dynamics consisting of the single-spin-flip algorithm of Metropolis supplemented by the random walk in the temperature space. The walk is biased to the critical region through the feedback equation utilizing the memory-based filtering recursion instantly estimating the energy cumulants. The simulations establish that the peak of the temperature probability density function is located nearly the pseudocritical temperature pertaining to canonical equilibrium. In order to eliminate the finite-size effects, the self-organized approach is extended to multi-lattice systems, where feedback is constructed from the pairs of the instantaneous running fourth-order cumulants of the magnetization. The replica-based simulations indicate that several properly chosen steady statistical distributions of the self-organized Monte Carlo systems resemble characteristics of the standard self-organized critical systems.

physics.comp-ph↗
arXiv2003

The magnetic reversal in dot arrays recognized by the self-organized adaptive neural network

The remagnetization dynamics of monolayer dot array superlattice XY 2-D spin model with dipole-dipole interactions is simulated. Within the proposed model of array, the square dots are described by the spatially modulated exchange-couplings. The dipole-dipole interactions are approximated by the hierarchical sums and spin dynamics is considered in regime of the Landau-Lifshitz equation. The simulation of reversal for $40 000$ spins exhibits formation of nonuniform intra-dot configurations with nonlinear wave/anti-wave pairs developed at intra-dot and inter-dot scales. Several geometric and parametric dependences are calculated and compared with oversimplified four-spin model of reversal. The role of initial conditions and the occurrence of coherent rotation mode is also investigated. The emphasis is on the classification of intra-dot or inter-dot (interfacial) magnetic configurations done by adaptive neural network with varying number of neurons.

cond-mat.dis-nn↗
arXiv2002

Neural network analysis of the magnetization reversal in magnetic dot arrays

We simulated the remagnetization dynamics of the ultra-dense and ultra-thin magnetic dot array system with dipole-dipole and exchange coupling interactions. Within the proposed 2D XY superlattice model, the square dots are modeled by the spatially modulated exchange-couplings. The dipole-dipole interactions were approximated by the hierarchical sums and dynamics was reduced to damping term of the Landau-Lifshitz-Gilbert equation. The simulation of 40 000 spin system leads to nonequilibrium nonuniform configurations with soliton-antisoliton pairs detected at intra-dot and inter-dot scales. The classification of intra-dot magnetic configurations was performed using the self-adaptive neural networks with varying number of neurons.

cond-mat.dis-nn↗
arXiv2002

Magnetic dot arrays modeling via the system of the radial basis function networks

Two dimensional square lattice general model of the magnetic dot array is introduced. In this model the intradot self-energy is predicted via the neural network and interdot magnetostatic coupling is approximated by the collection of several dipolar terms. The model has been applied to disk-shaped cluster involving 193 ultrathin dots and 772 interaction centers. In this case among the intradot magnetic structures retrieved by neural networks the important role play single-vortex magnetization modes. Several aspects of the model have been understood numerically by means of the simulated annealing method.

cond-mat.dis-nn↗