Large Separation Of Time Scales Research Articles

Parameterization of stochastic multiscale triads

Abstract. We discuss applications of a recently developed method for model reduction based on linear response theory of weakly coupled dynamical systems. We apply the weak coupling method to simple stochastic differential equations with slow and fast degrees of freedom. The weak coupling model reduction method results in general in a non-Markovian system; we therefore discuss the Markovianization of the system to allow for straightforward numerical integration. We compare the applied method to the equations obtained through homogenization in the limit of large timescale separation between slow and fast degrees of freedom. We numerically compare the ensemble spread from a fixed initial condition, correlation functions and exit times from a domain. The weak coupling method gives more accurate results in all test cases, albeit with a higher numerical cost.

Dynamics of actin monomers assembled into long filaments

We investigate the dynamics of actin monomers that are assembled into long filaments via the particle-based Brownian dynamics simulations. In order to study the dynamics of long filaments containing up to several hundred protomers, a coarse-grained model for actin polymerization involving several simplifications is used. In order to overcome the large separation of time scales between the diffusive motion of the free monomers and the relatively slow polymerized and depolymerized processes at the two ends of the filaments, all polymerized and depolymerized rates are rescaled by a dimensionless parameter. Actin protomers within a filament generally possess three nucleotide states corresponding to a bound adenosine triphosphate (ATP), adenosine diphosphate with inorganic phosphate (ADP. Pi), and ADP molecules in the presence of ATP hydrolysis. Here in this paper, single nucleotide state and two nucleotide states of actin protomers are described by the simplified theoretical model, giving the dependence of the growth rate on actin concentration. The simplest case where all protomers are identical, is provided by the assembly of ADP-actins. In the simulations, the growth rate is found to increase linearly with free monomer concentration, which agrees quantitatively with in vitro experimental result. These surprised phenomena observed in the experiments, such as treadmilling processes and length diffusion of actin filaments at the steady state, are presented in detail by Brownian dynamics simulations. For free actin concentrations close to the critical concentration, cT ccr, T, the filaments undergo treadmilling, that is, they grow at the barbed end and shrink at the pointed end, leading to the directed translational motion of the filament. In the absence of ATP hydrolysis, the functional dependence of a length diffusion constant on ADP-actin monomer concentration implies that a length diffusion constant is found to increase linearly with ADP-actin monomer concentration. With the coupling of ATP hydrolysis, a peak of the filament length diffusion as a function of ATP-actin monomer concentration is observed i. e. , the length diffusion coefficient is peaked near to 35 mon2/s below the critical concentration and recovers to the expected estimate of 1 mon2/s above the critical concentration. These obtained results are well consistent with the experimental results and stochastic theoretical analysis. Furthermore, several other quantities and relations that are difficult to study experimentally but provide nontrivial crosschecks on the consistency of our simulations, are investigated in the particle-based simulations. The particle-based simulations developed in our studies would easily extend to study a variety of more complex systems, such as the assembly process of other dynamic cytoskeletons

A Three-Scale Model of Spatio-Temporal Bursting

We study spatio-temporal bursting in a three-scale reaction diffusion equation organized by the winged cusp singularity. For large time-scale separation the model exhibits traveling bursts, whereas for large space-scale separation the model exhibits standing bursts. Both behaviors exhibit a common singular skeleton, whose geometry is fully determined by persistent bifurcation diagrams of the winged cusp. The modulation of spatio-temporal bursting in such a model naturally translates into paths in the universal unfolding of the winged cusp.

Communication: Experimentally determined profile of local glass transition temperature across a glassy-rubbery polymer interface with a T(g) difference of 80 K.

Studying the local glass transition temperature T(g) across a boundary, we investigate the characteristic length scales of cooperative dynamics. High molecular weight polymers have a large separation in time scales between cooperative segmental motion (α-relaxation) and chain diffusion allowing us to measure the local T(g)(z) profile across a glassy-rubbery interface of polystyrene/poly(n-butyl methacrylate) using fluorescence. We find this profile in cooperative dynamics does not correlate with the 7-nm wide symmetric composition profile of the interface, but instead is very broad, spanning 350-400 nm from one bulk T(g) value to another, and highly asymmetric, extending further into the glassy side.

Stochastic thermodynamics of hidden pumps

We show that a reversible pumping mechanism operating between two states of a kinetic network can give rise to Poisson transitions between these two states. An external observer, for whom the pumping mechanism is not accessible, will observe a Markov chain satisfying local detailed balance with an emerging effective force induced by the hidden pump. Due to the reversibility of the pump, the actual entropy production turns out to be lower than the coarse-grained entropy production estimated from the flows and affinities of the resulting Markov chain. Moreover, in presence of a large time scale separation between the fast-pumping dynamics and the slow-network dynamics, a finite current with zero dissipation may be produced. We make use of these general results to build a synthetase-like kinetic scheme able to reversibly produce high free-energy molecules at a finite rate and a rotatory motor achieving 100% efficiency at finite speed.

Stochastic thermodynamics of rapidly driven systems

We present the stochastic thermodynamics analysis of an open quantum system weakly coupled to multiple reservoirs and driven by a rapidly oscillating external field. The analysis is built on a modified stochastic master equation in the Floquet basis. Transition rates are shown to satisfy the local detailed balance involving the entropy flowing out of the reservoirs. The first and second law of thermodynamics are also identified at the trajectory level. Mechanical work is identified by means of initial and final projections on energy eigenstates of the system. We explicitly show that this two step measurement becomes unnecessary in the long time limit. A steady-state fluctuation theorem for the currents and rate of mechanical work is also established. This relation does not require the introduction of a time reversed external driving which is usually needed when considering systems subjected to time asymmetric external fields. This is understood as a consequence of the secular approximation applied in consistency with the large time scale separation between the fast driving oscillations and the slower relaxation dynamics induced by the environment. Our results are finally illustrated on a model describing a thermodynamic engine.

Approximation of Random Slow Manifolds and Settling of Inertial Particles Under Uncertainty

A method is provided for approximating random slow manifolds of a class of slow–fast stochastic dynamical systems. Thus approximate, low dimensional, reduced slow systems are obtained analytically in the case of sufficiently large time scale separation. To illustrate this dimension reduction procedure, the impact of random environmental fluctuations on the settling motion of inertial particles in a cellular flow field is examined. It is found that noise delays settling for some particles but enhances settling for others. A deterministic stable manifold is an agent to facilitate this phenomenon. Overall, noise appears to delay the settling in an averaged sense.

Stationary solutions for metapopulation Moran models with mutation and selection.

We construct an individual-based metapopulation model of population genetics featuring migration, mutation, selection, and genetic drift. In the case of a single "island," the model reduces to the Moran model. Using the diffusion approximation and time-scale separation arguments, an effective one-variable description of the model is developed. The effective description bears similarities to the well-mixed Moran model with effective parameters that depend on the network structure and island sizes, and it is amenable to analysis. Predictions from the reduced theory match the results from stochastic simulations across a range of parameters. The nature of the fast-variable elimination technique we adopt is further studied by applying it to a linear system, where it provides a precise description of the slow dynamics in the limit of large time-scale separation.

Understanding the distinctively skewed and heavy tailed character of atmospheric and oceanic probability distributions.

The probability distributions of large-scale atmospheric and oceanic variables are generally skewed and heavy-tailed. We argue that their distinctive departures from Gaussianity arise fundamentally from the fact that in a quadratically nonlinear system with a quadratic invariant, the coupling coefficients between system components are not constant but depend linearly on the system state in a distinctive way. In particular, the skewness arises from a tendency of the system trajectory to linger near states of weak coupling. We show that the salient features of the observed non-Gaussianity can be captured in the simplest such nonlinear 2-component system. If the system is stochastically forced and linearly damped, with one component damped much more strongly than the other, then the strongly damped fast component becomes effectively decoupled from the weakly damped slow component, and its impact on the slow component can be approximated as a stochastic noise forcing plus an augmented nonlinear damping. In the limit of large time-scale separation, the nonlinear augmentation of the damping becomes small, and the noise forcing can be approximated as an additive noise plus a correlated additive and multiplicative noise (CAM noise) forcing. Much of the diversity of observed large-scale atmospheric and oceanic probability distributions can be interpreted in this minimal framework.

Heterogeneous anomalous diffusion in view of superstatistics

It is experimentally known that virus exhibits stochastic motion in cytoplasm of a living cell in the free form as well as the form being contained in the endosome and the exponent of anomalous diffusion of the virus fluctuates depending on localized areas of the cytoplasm. Here, a theory is developed for establishing a generalized fractional kinetics for the infection pathway of the virus in the cytoplasm in view of superstatistics, which offers a general framework for describing nonequilibrium complex systems with two largely separated time scales. In the present theory, the existence of a large time-scale separation in the infection pathway is explicitly taken into account. A comment is also made on scaling nature of the motion of the virus that is suggested by the theory.

Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model

Excitability is observed in a variety of natural systems, such as neuronal dynamics, cardiovascular tissues, or climate dynamics. The stochastic FitzHugh-Nagumo model is a prominent example representing an excitable system. To validate the practical use of a model, the first step is to estimate model parameters from experimental data. This is not an easy task because of the inherent nonlinearity necessary to produce the excitable dynamics, and because the two coordinates of the model are moving on different time scales. Here we propose a Bayesian framework for parameter estimation, which can handle multidimensional nonlinear diffusions with large time scale separation. The estimation method is illustrated on simulated data.

Velocity of pulses in discrete excitable systems

The pulse solution of the spatially discrete excitable FitzHugh–Nagumo (FHN) system is approximately constructed using matched asymptotic expansions in the limit of large time scale separation (as measured by a small dimensionless parameter ϵ). The pulse profile typically consists of slowly varying regions of the excitatory variable separated by sharp wave fronts. In the FHN system, the velocity of a pulse is decided by the interaction between its leading and trailing fronts, but the leading order approximation gives only a fair result when compared with direct numerical solutions. A higher order approximation to the wave fronts comprising the FHN pulse is found. Our approximation provides an ϵ-dependent pulse velocity that approximates much better the velocity obtained from numerical solutions. As a result, the reconstruction of the FHN pulse using the improved wave fronts is much closer to the numerically obtained pulse.

The barrier method: A technique for calculating very long transition times

In many dynamical systems, there is a large separation of time scales between typical events and "rare" events which can be the cases of interest. Rare-event rates are quite difficult to compute numerically, but they are of considerable practical importance in many fields, for example, transition times in chemical physics and extinction times in epidemiology can be very long, but are quite important. We present a very fast numerical technique that can be used to find long transition times (very small rates) in low-dimensional systems, even if they lack detailed balance. We illustrate the method for a bistable nonequilibrium system introduced by Maier and Stein and a two-dimensional (in parameter space) epidemiology model.

Intrinsic noise and division cycle effects on an abstract biological oscillator

Oscillatory dynamics are common in biological pathways, emerging from the coupling of positive and negative feedback loops. Due to the small numbers of molecules typically contained in cellular volumes, stochastic effects may play an important role in system behavior. Thus, for moderate noise strengths, stochasticity has been shown to enhance signal-to-noise ratios or even induce oscillations in a class of phenomena referred to as "stochastic resonance" and "coherence resonance," respectively. Furthermore, the biological oscillators are subject to influences from the division cycle of the cell. In this paper we consider a biologically relevant oscillator and investigate the effect of intrinsic noise as well as division cycle which encompasses the processes of growth, DNA duplication, and cell division. We first construct a minimal reaction network which can oscillate in the presence of large or negligible timescale separation. We then derive corresponding deterministic and stochastic models and compare their dynamical behaviors with respect to (i) the extent of the parameter space where each model can exhibit oscillatory behavior and (ii) the oscillation characteristics, namely, the amplitude and the period. We further incorporate division cycle effects on both models and investigate the effect of growth rate on system behavior. Our results show that in the presence but not in the absence of large timescale separation, coherence resonance effects result in extending the oscillatory region and lowering the period for the stochastic model. When the division cycle is taken into account, the oscillatory region of the deterministic model is shown to extend or shrink for moderate or high growth rates, respectively. Further, under the influence of the division cycle, the stochastic model can oscillate for parameter sets for which the deterministic model does not. The division cycle is also found to be able to resonate with the oscillator, thereby enhancing oscillation robustness. The results of this study can give valuable insight into the complex interplay between oscillatory intracellular dynamics and various noise sources, stemming from gene expression, cell growth, and division.

Self-assembly of actin monomers into long filaments: Brownian dynamics simulations

Brownian dynamics simulations are used to study the dynamical process of self-assembly of actin monomers into long filaments containing up to 1000 actin protomers. In order to overcome the large separation of time scales between the diffusive motion of the free monomers and the relatively slow attachment and detachment processes at the two ends of the filaments, we introduce a novel rescaling procedure by which we speed all dynamical processes related to actin polymerization and depolymerization up by the same factor. In general, the actin protomers within a filament can attain three different states corresponding to a bound adenosine triphosphate (ATP), adenosine diphosphate with inorganic phosphate (ADP/P), and ADP molecule. The simplest situation that has been studied experimentally is provided by the polymerization of ADP-actin, for which all protomers are identical. This case is used to unravel certain relations between the filament's physical properties and the model parameters such as the attachment rate constant and the size of the capture zone, the detachment rate and the probability of the detached event, as well as the growth rate and waiting times between two successive attachment/detachment events. When a single filament is allowed to grow in a bath of constant concentration of free ADP-actin monomers, its growth rate increases linearly with the free monomer concentration in quantitative agreement with in vitro experiments. The results also show that the waiting time is governed by exponential distributions and that the two ends of a filament undergo biased random walks. The filament length fluctuations are described by a length diffusion constant that is found to attain a constant value at low ADP-actin concentration and to increase linearly with this concentration. It is straightforward to apply our simulation code to more complex processes such as polymerization of ATP-actin coupled to ATP hydrolysis, force generation by filaments, formation of filament bundles, and filament-membrane interactions.

Efficient simulation of quantum evolution using dynamical coarse graining

A scheme to simulate the evolution of a restricted set of observables of a quantum system is proposed. The set comprises the spectrum-generating algebra of the Hamiltonian. Focusing on the simulation of the restricted set allows to drastically reduce the cost of the simulation. This reduction is the result of replacing the original unitary dynamics by a special open-system evolution. This open-system evolution can be interpreted as a process of weak measurement of the distinguished observables performed on the evolving system of interest. Under the condition that the observables are ``classical'' and the Hamiltonian is moderately nonlinear, the open-system dynamics displays a large time-scale separation between the relaxation of the observables and the decoherence of a generic state. This time-scale separation allows the unitary dynamics of the observables to be efficiently simulated by the open-system dynamics on the intermediate time scale. The simulation employs unraveling of the corresponding master equations into pure-state evolutions, governed by the stochastic nonlinear Schr\odinger equation. The stochastic pure-state evolution can be simulated efficiently using a representation of the state in the time-dependent basis of the generalized coherent states, associated with the spectrum-generating algebra.

Tests of mode coupling theory in a simple model for two-component miscible polymerblends

We present molecular dynamics simulations of the structural relaxation of a simplebead–spring model for polymer blends. The introduction of a different monomer sizeinduces a large timescale separation for the dynamics of the two components. Simulationresults for a large set of observables probing density correlations, Rouse modes, andorientations of bond and chain end-to-end vectors are analysed within the framework of themode coupling theory (MCT). An unusually large value of the exponent parameter isobtained. This feature suggests the possibility of an underlying higher-order MCT scenariofor dynamic arrest.

Supersymmetric Langevin equation to explore free-energy landscapes

The recently discovered supersymmetric generalizations of the Langevin dynamics and Kramers equation can be utilized for the exploration of free-energy landscapes of systems whose large time-scale separation hampers the usefulness of standard molecular dynamics techniques. The first realistic application is here presented. The system chosen is a minimalist model for a short alanine peptide exhibiting a helix-coil transition.

Multiscale spatial Monte Carlo simulations: Multigriding, computational singular perturbation, and hierarchical stochastic closures

Monte Carlo (MC) simulation of most spatially distributed systems is plagued by several problems, namely, execution of one process at a time, large separation of time scales of various processes, and large length scales. Recently, a coarse-grained Monte Carlo (CGMC) method was introduced that can capture large length scales at reasonable computational times. An inherent assumption in this CGMC method revolves around a mean-field closure invoked in each coarse cell that is inaccurate for short-ranged interactions. Two new approaches are explored to improve upon this closure. The first employs the local quasichemical approximation, which is applicable to first nearest-neighbor interactions. The second, termed multiscale CGMC method, employs singular perturbation ideas on multiple grids to capture the entire cluster probability distribution function via short microscopic MC simulations on small, fine-grid lattices by taking advantage of the time scale separation of multiple processes. Computational strategies for coupling the fast process at small length scales (fine grid) with the slow processes at large length scales (coarse grid) are discussed. Finally, the binomial tau-leap method is combined with the multiscale CGMC method to execute multiple processes over the entire lattice and provide additional computational acceleration. Numerical simulations demonstrate that in the presence of fast diffusion and slow adsorption and desorption processes the two new approaches provide more accurate solutions in comparison to the previously introduced CGMC method.

Two distinct mechanisms of coherence in randomly perturbed dynamical systems

We carefully examine two mechanisms--coherence resonance and self-induced stochastic resonance--by which small random perturbations of excitable systems with large time scale separation may lead to the emergence of new coherent behaviors in the form of limit cycles. We analyze what controls the degree of coherence in these two mechanisms and classify their very different properties. In particular we show that coherence resonance arises only at the onset of bifurcation and is rather insensitive against variations in the noise amplitude and the time scale separation ratio. In contrast, self-induced stochastic resonance may arise away from bifurcations and the properties of the limit cycle it induces are controlled by both the noise amplitude and the time scale separation ratio.