Resonant Forcing of Chaotic Dynamics

Gintautas, Vadas; Foster, Glenn; Hübler, Alfred

doi:10.1007/s10955-007-9444-4

Cited by 7 publications

(7 citation statements)

References 18 publications

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“…For the periodic driving, the optimal fluctuation frequency (

ν

=1/ θ ) corresponds well to the folding transition rate of the unperturbed system ( k f ). This is in agreement with earlier theoretical calculations for resonances found in nonlinear systems of differential equations, which apply to a wide class of problems including equations of motion . An additional interesting observation is the decrease of the optimal θ value with increasing noise amplitude in the case of the harmonic fluctuations.…”

Section: Resultsmentioning

confidence: 99%

Stochastic Resonance in Protein Folding Dynamics

et al. 2016

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Although protein folding reactions are usually studied under static external conditions, it is likely that proteins fold in a locally fluctuating cellular environment in vivo. To mimic such behavior in in vitro experiments, the local temperature of the solvent can be modulated either harmonically or using correlated noise. In this study, coarse-grained molecular simulations are used to investigate these possibilities, and it is found that both periodic and correlated random fluctuations of the environment can indeed accelerate folding kinetics if the characteristic frequencies of the applied fluctuations are commensurate with the internal timescale of the folding reaction; this is consistent with the phenomenon of stochastic resonance observed in many other condensed-matter processes. To test this theoretical prediction, the folding dynamics of phosphoglycerate kinase under harmonic temperature fluctuations are experimentally probed using Förster resonance energy transfer fluorescence measurements. To analyze these experiments, a combination of theoretical approaches is developed, including stochastic simulations of folding kinetics and an analytical mean-field kinetic theory. The experimental observations are consistent with the theoretical predictions of stochastic resonance in phosphoglycerate kinase folding. When combined with an alternative experiment on the protein VlsE using a power spectrum analysis, elaborated in Dave et al., ChemPhysChem 2016, 10.1002/cphc.201501041, the overall data overwhelmingly point to the experimental confirmation of stochastic resonance in protein folding dynamics.

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“…For the periodic driving, the optimal fluctuation frequency (

ν

Section: Resultsmentioning

confidence: 99%

Stochastic Resonance in Protein Folding Dynamics

et al. 2016

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“…Each sub space is further divided into H 2 sub squares. This way, the possible samples (solutions) (4,4), (2,6), (6,5), (1,2), (3,1), (5,7), (8,8), (6,10), (7,3), (9,2), (10,9) are produced by the LHS method. The number of sub-squares for every single hypercube will be 100 which means that the search space is splited into 100 equal sub-squares.…”

Section: U I and L Lmentioning

confidence: 99%

“…Dynamic system models have been applied to a broad scope of areas, including physics [1], biology [2], chemistry [3], engineering, economics, and medicine [4]. Inferring models of dynamic systems has always been a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Inferring structure and parameters of dynamic system models simultaneously using swarm intelligence approaches

2020

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Inferring dynamic system models from observed time course data is very challenging compared to static system identification tasks. Dynamic system models are complicated to infer due to the underlying large search space and high computational cost for simulation and verification. In this research we aim to infer both the structure and parameters of a dynamic system simultaneously by particle swarm optimization (PSO) improved by efficient stratified sampling approaches. More specifically, we enhance PSO with two modern stratified sampling techniques, i.e., Latin hyper cube sampling (LHS) and Latin hyper cube multi dimensional uniformity (LHSMDU). We propose and evaluate two novel swarm-inspired algorithms, LHS-PSO and LHSMDU-PSO, which can be used particularly to learn the model structure and parameters of complex dynamic systems efficiently. The performance of LHS-PSO and LHSMDU-PSO is further compared with the original PSO and genetic algorithm (GA). We chose real-world cancer biological model called Kinetochores to asses the learning performance of LHSMDU-PSO and LHS-PSO in comparison with GA and the original PSO. The experimental results show that LHSMDU-PSO can find promising models with reasonable parameters and structure, and it outperforms LHS-PSO, PSO, and GA in our experiments.

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“…Dynamical systems form the basis of the nonlinear methods of signal analysis [ 15 – 17 ]. The study of the dynamical systems has found applications in a number of fields like physics [ 15 – 17 ], engineering [ 15 ], biology, and medicine [ 16 ]. A dynamical system can be defined as a system, whose state can be described by a set of time-varying (continuous or discrete) variables governed by the mathematical laws [ 17 ].…”

Section: Dynamical Systemmentioning

confidence: 99%

A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal

Nayak

Bit

Dey

et al. 2018

Journal of Healthcare Engineering

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Electrocardiogram (ECG) signal analysis has received special attention of the researchers in the recent past because of its ability to divulge crucial information about the electrophysiology of the heart and the autonomic nervous system activity in a noninvasive manner. Analysis of the ECG signals has been explored using both linear and nonlinear methods. However, the nonlinear methods of ECG signal analysis are gaining popularity because of their robustness in feature extraction and classification. The current study presents a review of the nonlinear signal analysis methods, namely, reconstructed phase space analysis, Lyapunov exponents, correlation dimension, detrended fluctuation analysis (DFA), recurrence plot, Poincaré plot, approximate entropy, and sample entropy along with their recent applications in the ECG signal analysis.

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Resonant Forcing of Chaotic Dynamics

Cited by 7 publications

References 18 publications

Stochastic Resonance in Protein Folding Dynamics

Stochastic Resonance in Protein Folding Dynamics

Inferring structure and parameters of dynamic system models simultaneously using swarm intelligence approaches

A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal

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