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Roman Belousov

Publications and source records attributed to Roman Belousov.

At least 19 recordsLinked to original sources

Repulsive particle interactions enable selective information processing at cellular interfaces

Living systems relay information across membrane interfaces to coordinate compartment functions. We identify a physical mechanism for selective information transmission that arises from the sigmoidal response of surface-bound particle densities to spatial features in adjacent external structures through a non-uniform binding energy. This mechanism implements a form of spatial thresholding, enabling the binary classification of external cues. Expansion microscopy measurements of nuclear pore complex distributions in S. arctica show signatures of such physical thresholding.

cond-mat.stat-mech

Microcanonical ensemble out of equilibrium

Introduced by Boltzmann under the name "monode," the microcanonical ensemble serves as the fundamental representation of equilibrium thermodynamics in statistical mechanics by counting all possible realizations of a system's states. Ensemble theory connects this idea with probability and information theory, leading to the notion of Shannon-Gibbs entropy and, ultimately, to the principle of maximum caliber describing trajectories of systems--in and out of equilibrium. While the latter phenomenological generalization reproduces many results of nonequilibrium thermodynamics, given a proper choice of observables, its physical justification remains an open area of research. What is the microscopic origin and physical interpretation of this variational approach? What guides the choice of relevant observables? We address these questions by extending Boltzmann's method to a microcanonical caliber principle and counting realizations of a system's trajectories--all assumed equally probable. Maximizing the microcanonical caliber under the imposed constraints, we systematically develop generalized detailed-balance relations, clarify the statistical origins of inhomogeneous transport, and provide an independent derivation of key equations from stochastic thermodynamics. This approach introduces a dynamical ensemble theory for nonequilibrium steady states in spatially extended and active systems. While verifying the equivalence of ensembles, e.g. those of Norton and Thevenin, our framework contests other common assumptions about nonequilibrium regimes, with supporting evidence provided by stochastic simulations. Our theory suggests further connections to the first principles of microscopic dynamics in classical statistical mechanics, which are essential for investigating systems where the necessary conditions for thermodynamic behavior are not satisfied.

cond-mat.stat-mech

Active energy harvesting and work transduction by hair-cell bundles in bullfrog's inner ear

Hair cells actively drive oscillations of their mechanosensitive organelles--the hair bundles that enable hearing and balance sensing in vertebrates. Why and how some hair cells expend energy by sustaining this oscillatory motion in order to fulfill their function as signal sensors and others--as amplifiers, remains unknown. We develop a stochastic thermodynamic theory to describe energy flows in a periodically-driven hair bundle. Our analysis of thermodynamic fluxes associated with hair bundles' motion and external sinusoidal stimulus reveals that these organelles operate as thermodynamic work-to-work machines under different operational modes. One operational mode transduces the signal's power into the cell, whereas another allows the external stimulus to harvest the energy supplied by the cell. These two regimes might represent thermodynamic signatures of signal sensing and amplification respectively. In addition to work transduction and energy harvesting, our model also substantiates the capability of hair-cell bundles to operate as heaters and, at the expense of external driving, as active feedback refrigerators. We quantify the performance and robustness of the work-to-work conversion by hair bundles, whose efficiency in some conditions exceeds 80 % of the applied power.

cond-mat.stat-mech

Machine learning for cerebral blood vessels' malformations

Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. While surgical intervention is often essential to prevent fatal outcomes, it carries significant risks both during the procedure and in the postoperative period, making the management of these conditions highly challenging. Parameters of cerebral blood flow, routinely monitored during medical interventions or with modern noninvasive high-resolution imaging methods, could potentially be utilized in machine learning-assisted protocols for risk assessment and therapeutic prognosis. To this end, we developed a linear oscillatory model of blood velocity and pressure for clinical data acquired from neurosurgical operations. Using the method of Sparse Identification of Nonlinear Dynamics (SINDy), the parameters of our model can be reconstructed online within milliseconds from a short time series of the hemodynamic variables. The identified parameter values enable automated classification of the blood-flow pathologies by means of logistic regression, achieving an accuracy of 73 \%}. Our results demonstrate the potential of this model for both diagnostic and prognostic applications, providing a robust and interpretable framework for assessing cerebral blood vessel conditions.

cs.LG

Feedback between microscopic activity and macroscopic dynamics drives excitability and oscillations in mechanochemical matter

The macroscopic behaviour of active matter arises from nonequilibrium microscopic processes. In soft materials, active stresses typically drive macroscopic shape changes, which in turn alter the geometry constraining the microscopic dynamics, leading to complex feedback effects. Although such mechanochemical coupling is common in living matter and associated with biological functions such as cell migration, division, and differentiation, the underlying principles are not well understood due to a lack of minimal models that bridge the scales from the microscopic biochemical processes to the macroscopic shape dynamics. To address this gap, we derive tractable coarse-grained equations from microscopic dynamics for a class of mechanochemical systems, in which biochemical signal processing is coupled to shape dynamics. Specifically, we consider molecular interactions at the surface of biological cells that commonly drive cell-cell signaling and adhesion, and obtain a macroscopic description of cells as signal-processing droplets that adaptively change their interfacial tensions. We find a rich phenomenology, including multistability, symmetry-breaking, excitability, and self-sustained shape oscillations, with the underlying critical points revealing universal characteristics of such systems. Our tractable framework provides a paradigm for how soft active materials respond to shape-dependent signals, and suggests novel modes of self-organisation at the collective scale. These are explored further in our companion paper [arxiv 2402.08664v3].

cond-mat.soft

Poissonian cellular Potts models reveal nonequilibrium kinetics of cell sorting

Cellular Potts models are broadly applied across developmental biology and cancer research. We overcome limitations of the traditional approach, which reinterprets a modified Metropolis sampling as ad hoc dynamics, by introducing a physical timescale through Poissonian kinetics and by applying principles of stochastic thermodynamics to separate thermal and relaxation effects from athermal noise and nonconservative forces. Our method accurately describes cell-sorting dynamics in mouse-embryo development and identifies the distinct contributions of nonequilibrium processes, e.g. cell growth and active fluctuations.

cond-mat.stat-mech

Shape Switching and Tunable Oscillations of Adaptive Droplets

Living materials adapt their shape to signals from the environment, yet the impact of shape changes on signal processing and associated feedback dynamics remain unclear. We find that droplets with signal-responsive interfacial tensions exhibit shape bistability, excitable dynamics, and oscillations. The underlying critical points reveal novel mechanisms for physical signal processing through shape adaptation in soft active materials. We recover signatures of one such critical point in experimental data from zebrafish embryos, where it supports boundary formation.

cond-mat.soft

Statistical physics of inhomogeneous transport: Unification of diffusion laws and inference from first-passage statistics

Characterization of composite materials, whose properties vary in space over microscopic scales, has become a problem of broad interdisciplinary interest. In particular, estimation of the inhomogeneous transport coefficients, e.g. the diffusion coefficient or the heat conductivity which shape important processes in biology and engineering, is a challenging task. The analysis of such systems is further complicated, because two alternative formulations of the inhomogeneous transport equations exist in the literature -- the Smoluchowski and Fokker-Planck equations, which are also related to the so-called Ito-Stratonovich dilemma. Using the theory of statistical physics, we show that the two formulations, usually regarded as distinct models, are physically equivalent. From this result we develop efficient estimates for the transverse space-dependent diffusion coefficient in fluids near a phase boundary. Our method requires only measurements of escape probabilities and mean exit times of molecules leaving a narrow spatial region. We test our estimates in three case studies: (i) a Langevin model of a Buettikker-Landauer ratchet; atomistic molecular-dynamics simulations of liquid-water molecules in contact with (ii) vapor and (iii) soap (surfactant) film which has promising applications in physical chemistry. Our analysis reveals that near the surfactant monolayer the mobility of water molecules is slowed down almost twice with respect to the bulk liquid. Moreover, the diffusion coefficient of water correlates with the transition from hydrophilic to hydrophobic parts of the film.

cond-mat.stat-mech

Modelling Active Non-Markovian Oscillations

Modelling noisy oscillations of active systems is one of the current challenges in physics and biology. Because the physical mechanisms of such processes are often difficult to identify, we propose a linear stochastic model driven by a non-Markovian bistable noise that is capable of generating self-sustained periodic oscillation. We derive analytical predictions for most relevant dynamical and thermodynamic properties of the model. This minimal model turns out to describe accurately bistable-like oscillatory motion of hair bundles in bullfrog sacculus, extracted from experimental data. Based on and in agreement with these data, we estimate the power required to sustain such active oscillations to be of the order of one hundred $k_B T$ per oscillation cycle.

cond-mat.stat-mech

Volterra-series approach to stochastic nonlinear dynamics: linear response of the Van der Pol oscillator driven by white noise

The Van der Pol equation is a paradigmatic model of relaxation oscillations. This remarkable nonlinear phenomenon of self-sustained oscillatory motion underlies important rhythmic processes in nature and electrical engineering. Relaxation oscillations in a real system are usually coupled to environmental noise, which further enriches their dynamics, but makes theoretical analysis of such systems and determination of the equation's parameter values a difficult task. In a companion paper we have proposed an analytic approach to a similar problem for another classical nonlinear model, the bistable Duffing oscillator. Here we extend our techniques to the case of the Van der Pol equation driven by white noise. We analyze the statistics of solutions and propose a method to estimate parameter values from the oscillator's time series. We use experimental data of active oscillations in a biological system to demonstrate how our method applies to real observations and how it can be generalized for more complex models.

cond-mat.stat-mech

First-passage Fingerprints of Water Diffusion near Glutamine Surfaces

The extent to which biological interfaces affect the dynamics of water plays a key role in the exchange of matter and chemical interactions that are essential for life. The density and the mobility of water molecules depend on their proximity to biological interfaces and can play an important role in processes such as protein folding and aggregation. In this work, we study the dynamics of water near glutamine surfaces---a system of interest in studies of neurodegenerative diseases. Combining molecular-dynamics simulations and stochastic modelling, we study how the mean first-passage time and related statistics of water molecules escaping subnanometer-sized regions vary from the interface to the bulk. Our analysis reveals a dynamical complexity that reflects underlying chemical and geometrical properties of the glutamine surfaces. From the first-passage time statistics of water molecules, we infer their space-dependent diffusion coefficient in directions normal to the surfaces. Interestingly, our results suggest that the mobility of water varies over a longer length scale than the chemical potential associated with the water-protein interactions. The synergy of molecular dynamics and first-passage techniques opens the possibility for extracting space-dependent diffusion coefficients in more complex, inhomogeneous environments that are commonplace in living matter.

cond-mat.stat-mech

A Volterra-series approach to stochastic nonlinear dynamics: The Duffing oscillator driven by white noise

The Duffing oscillator is a paradigm of bistable oscillatory motion in physics, engineering, and biology. Time series of such oscillations are often observed experimentally in a nonlinear system excited by a spontaneously fluctuating force. One is then interested in estimating effective parameter values of the stochastic Duffing model from these observations--a task that has not yielded to simple means of analysis. To this end we derive theoretical formulas for the statistics of the Duffing oscillator's time series. Expanding on our analytical results, we introduce methods of statistical inference for the parameter values of the stochastic Duffing model. By applying our method to time series from stochastic simulations, we accurately reconstruct the underlying Duffing oscillator. This approach is quite straightforward--similar techniques are used with linear Langevin models--and can be applied to time series of bistable oscillations that are frequently observed in experiments.

cond-mat.stat-mech

Fluctuation theory in space and time: white noise in reaction-diffusion models of morphogenesis

The precision of reaction-diffusion models for mesoscopic physical systems is limited by fluctuations. To account for this uncertainty, Van Kampen derived a stochastic Langevin-like reaction-diffusion equation that incorporates spatio-temporal white noise. The resulting solutions, however, have infinite standard deviation. Ad hoc modifications that address this issue by introducing microscopic correlations are inconvenient in many physical contexts of wide interest. We instead estimate the magnitude of fluctuations by coarse-graining solutions of the Van Kampen equation at a relevant mesoscopic scale. The ensuing theory yields fluctuations of finite magnitude. Our approach is demonstrated for a specific biophysical model--the encoding of positional information. We discuss the properties of the fluctuations and the role played by the macroscopic parameters of the underlying reaction-diffusion model. The analysis and numerical methods developed here can be applied in physical problems to predict the magnitude of fluctuations. This general approach can also be extended to other classes of dynamical systems that are described by partial differential equations.

cond-mat.stat-mech

Nonequilibrium Langevin dynamics: a demonstration study of shear flow fluctuations in a simple fluid

The present study is based on a recent success of the second-order stochastic fluctuation theory in describing time autocorrelations of equilibrium and nonequilibrium physical systems. In particular, it was shown to yield values of the related deterministic parameters of the Langevin equation for a Couette flow in a microscopic Molecular Dynamics model of a simple fluid. In this paper we find all the remaining constants of the stochastic dynamics, which is then numerically simulated and directly compared with the original physical system. By using these data, we study in detail the accuracy and precision of a second-order Langevin model for nonequilibrium physical systems, theoretically and computationally. In addition, an intriguing relation is found between an applied external force and cumulants of the resulting flow fluctuations. This is characterized by a linear dependence of athermal cumulant ratio, a new quantity introduced here.

cond-mat.stat-mech

Second-order fluctuation theory and time autocorrelation function for currents

By using recent developments for the Langevin dynamics of spatially asymmetric systems, we routinely generalize the Onsager-Machlup fluctuation theory of the second order in time. In this form, it becomes applicable to fluctuating variables, including hydrodynamic currents, in equilibrium as well as nonequilibrium steady states. From the solution of the obtained stochastic equations we derive an analytical expression for the time autocorrelation function of a general fluctuating quantity. This theoretical result is then tested in a study of a shear flow by molecular dynamics simulations. The proposed form of the time autocorrelation function yields an excellent fit to our computational data for both equilibrium and nonequilibrium steady states. Unlike the analogous result of the first-order Onsager-Machlup theory, our expression correctly describes the short-time correlations. Its utility is demonstrated in an application of the Green-Kubo formula for the transport coefficient. Curiously, the normalized time autocorrelation function for the shear flow, which only depends on the deterministic part of the fluctuation dynamics, appears independent of the external shear force in the linear nonequilibrium regime.

cond-mat.stat-mech

The Langevin equation for systems with a preferred spatial direction

In this paper, we generalize the theory of Brownian motion and the Onsager-Machlup theory of fluctuations for spatially symmetric systems to equilibrium and nonequilibrium steady-state systems with a preferred spatial direction, due to an external force. To do this, we extend the Langevin equation to include a bias, which is introduced by the external force and alters the Gaussian structure of the system's fluctuations. By solving this extended equation, we demonstrate that the statistical properties of the fluctuations in these systems can be predicted from physical observables, such as the temperature and the hydrodynamic gradients.

cond-mat.stat-mech

Asymmetry of steady state current fluctuations in nonequilibrium systems

For systems in nonequilibrium steady states, a novel modulated Gaussian probability distribution is derived to incorporate a new phenomenon of biased current fluctuations, discovered by recent laboratory experiments and confirmed by molecular dynamics simulations. Our results consistently extend Onsager-Machlup fluctuation theory for systems in thermal equilibrium. Connections with the principles of Statistical Mechanics due to Boltzmann and Gibbs are discussed. At last, the modulated Gaussian distribution is of potential interest for other statistical disciplines, which make use of the Large Deviation theory.

cond-mat.stat-mech

Equations of state of magnesium perovskite and postperovskite: diagnostics from ab initio simulations

The isothermal compression of magnesium perovskite and postperovskite is examined through the F-f plot and the diagnostic plot of Vinet universal model theoretically from the ab initio quantum-mechanical calculations at the hybrid Hartree-Fock / Density Functional Theory level. A purely numerical approach, first time applied in this paper, shows that the discrepancies largely observed between studies on the perovskite and criticized in geophysical applications are due to the inadequate choice of the Birch-Murnaghan equation of state; meanwhile the Vinet model is found utterly appropriate for the mineral and infers consistent estimations of the bulk modulus and its pressure derivative. The diagnostics of the postperovskite suggest similar conclusions.

cond-mat.mtrl-sci