TY - JOUR A1 - Wang, Wei A1 - Cherstvy, Andrey G. A1 - Chechkin, Aleksei V. A1 - Thapa, Samudrajit A1 - Seno, Flavio A1 - Liu, Xianbin A1 - Metzler, Ralf T1 - Fractional Brownian motion with random diffusivity BT - emerging residual nonergodicity below the correlation time JF - Journal of physics : A, Mathematical and theoretical N2 - Numerous examples for a priori unexpected non-Gaussian behaviour for normal and anomalous diffusion have recently been reported in single-particle tracking experiments. Here, we address the case of non-Gaussian anomalous diffusion in terms of a random-diffusivity mechanism in the presence of power-law correlated fractional Gaussian noise. We study the ergodic properties of this model via examining the ensemble- and time-averaged mean-squared displacements as well as the ergodicity breaking parameter EB quantifying the trajectory-to-trajectory fluctuations of the latter. For long measurement times, interesting crossover behaviour is found as function of the correlation time tau characterising the diffusivity dynamics. We unveil that at short lag times the EB parameter reaches a universal plateau. The corresponding residual value of EB is shown to depend only on tau and the trajectory length. The EB parameter at long lag times, however, follows the same power-law scaling as for fractional Brownian motion. We also determine a corresponding plateau at short lag times for the discrete representation of fractional Brownian motion, absent in the continuous-time formulation. These analytical predictions are in excellent agreement with results of computer simulations of the underlying stochastic processes. Our findings can help distinguishing and categorising certain nonergodic and non-Gaussian features of particle displacements, as observed in recent single-particle tracking experiments. KW - stochastic processes KW - anomalous diffusion KW - fractional Brownian motion KW - diffusing diffusivity KW - weak ergodicity breaking Y1 - 2020 U6 - https://doi.org/10.1088/1751-8121/aba467 SN - 1751-8113 SN - 1751-8121 VL - 53 IS - 47 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Thapa, Samudrajit A1 - Wyłomańska, Agnieszka A1 - Sikora, Grzegorz A1 - Wagner, Caroline E. A1 - Krapf, Diego A1 - Kantz, Holger A1 - Chechkin, Aleksei V. A1 - Metzler, Ralf T1 - Leveraging large-deviation statistics to decipher the stochastic properties of measured trajectories JF - New Journal of Physics N2 - Extensive time-series encoding the position of particles such as viruses, vesicles, or individualproteins are routinely garnered insingle-particle tracking experiments or supercomputing studies.They contain vital clues on how viruses spread or drugs may be delivered in biological cells.Similar time-series are being recorded of stock values in financial markets and of climate data.Such time-series are most typically evaluated in terms of time-averaged mean-squareddisplacements (TAMSDs), which remain random variables for finite measurement times. Theirstatistical properties are different for differentphysical stochastic processes, thus allowing us toextract valuable information on the stochastic process itself. To exploit the full potential of thestatistical information encoded in measured time-series we here propose an easy-to-implementand computationally inexpensive new methodology, based on deviations of the TAMSD from itsensemble average counterpart. Specifically, we use the upper bound of these deviations forBrownian motion (BM) to check the applicability of this approach to simulated and real data sets.By comparing the probability of deviations fordifferent data sets, we demonstrate how thetheoretical bound for BM reveals additional information about observed stochastic processes. Weapply the large-deviation method to data sets of tracer beads tracked in aqueous solution, tracerbeads measured in mucin hydrogels, and of geographic surface temperature anomalies. Ouranalysis shows how the large-deviation properties can be efficiently used as a simple yet effectiveroutine test to reject the BM hypothesis and unveil relevant information on statistical propertiessuch as ergodicity breaking and short-time correlations. KW - diffusion KW - anomalous diffusion KW - large-deviation statistic KW - time-averaged mean squared displacement KW - Chebyshev inequality Y1 - 2020 U6 - https://doi.org/10.1088/1367-2630/abd50e SN - 1367-2630 VL - 23 PB - Dt. Physikalische Ges. ; IOP CY - Bad Honnef ; London ER - TY - GEN A1 - Thapa, Samudrajit A1 - Wyłomańska, Agnieszka A1 - Sikora, Grzegorz A1 - Wagner, Caroline E. A1 - Krapf, Diego A1 - Kantz, Holger A1 - Chechkin, Aleksei V. A1 - Metzler, Ralf T1 - Leveraging large-deviation statistics to decipher the stochastic properties of measured trajectories T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Extensive time-series encoding the position of particles such as viruses, vesicles, or individualproteins are routinely garnered insingle-particle tracking experiments or supercomputing studies.They contain vital clues on how viruses spread or drugs may be delivered in biological cells.Similar time-series are being recorded of stock values in financial markets and of climate data.Such time-series are most typically evaluated in terms of time-averaged mean-squareddisplacements (TAMSDs), which remain random variables for finite measurement times. Theirstatistical properties are different for differentphysical stochastic processes, thus allowing us toextract valuable information on the stochastic process itself. To exploit the full potential of thestatistical information encoded in measured time-series we here propose an easy-to-implementand computationally inexpensive new methodology, based on deviations of the TAMSD from itsensemble average counterpart. Specifically, we use the upper bound of these deviations forBrownian motion (BM) to check the applicability of this approach to simulated and real data sets.By comparing the probability of deviations fordifferent data sets, we demonstrate how thetheoretical bound for BM reveals additional information about observed stochastic processes. Weapply the large-deviation method to data sets of tracer beads tracked in aqueous solution, tracerbeads measured in mucin hydrogels, and of geographic surface temperature anomalies. Ouranalysis shows how the large-deviation properties can be efficiently used as a simple yet effectiveroutine test to reject the BM hypothesis and unveil relevant information on statistical propertiessuch as ergodicity breaking and short-time correlations. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1118 KW - diffusion KW - anomalous diffusion KW - large-deviation statistic KW - time-averaged mean squared displacement KW - Chebyshev inequality Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-493494 SN - 1866-8372 IS - 1118 ER - TY - JOUR A1 - Thapa, Samudrajit A1 - Park, Seongyu A1 - Kim, Yeongjin A1 - Jeon, Jae-Hyung A1 - Metzler, Ralf A1 - Lomholt, Michael A. T1 - Bayesian inference of scaled versus fractional Brownian motion JF - Journal of physics : A, mathematical and theoretical N2 - We present a Bayesian inference scheme for scaled Brownian motion, and investigate its performance on synthetic data for parameter estimation and model selection in a combined inference with fractional Brownian motion. We include the possibility of measurement noise in both models. We find that for trajectories of a few hundred time points the procedure is able to resolve well the true model and parameters. Using the prior of the synthetic data generation process also for the inference, the approach is optimal based on decision theory. We include a comparison with inference using a prior different from the data generating one. KW - Bayesian inference KW - scaled Brownian motion KW - single particle tracking Y1 - 2022 U6 - https://doi.org/10.1088/1751-8121/ac60e7 SN - 1751-8113 SN - 1751-8121 VL - 55 IS - 19 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Thapa, Samudrajit A1 - Lukat, Nils A1 - Selhuber-Unkel, Christine A1 - Cherstvy, Andrey G. A1 - Metzler, Ralf T1 - Transient superdiffusion of polydisperse vacuoles in highly motile amoeboid cells JF - The journal of chemical physics : bridges a gap between journals of physics and journals of chemistr N2 - We perform a detailed statistical analysis of diffusive trajectories of membrane-enclosed vesicles (vacuoles) in the supercrowded cytoplasm of living Acanthamoeba castellanii cells. From the vacuole traces recorded in the center-of-area frame of moving amoebae, we examine the statistics of the time-averaged mean-squared displacements of vacuoles, their generalized diffusion coefficients and anomalous scaling exponents, the ergodicity breaking parameter, the non-Gaussian features of displacement distributions of vacuoles, the displacement autocorrelation function, as well as the distributions of speeds and positions of vacuoles inside the amoeba cells. Our findings deliver novel insights into the internal dynamics of cellular structures in these infectious pathogens. Published under license by AIP Publishing. Y1 - 2019 U6 - https://doi.org/10.1063/1.5086269 SN - 0021-9606 SN - 1089-7690 VL - 150 IS - 14 PB - American Institute of Physics CY - Melville ER - TY - JOUR A1 - Thapa, Samudrajit A1 - Lomholt, Michael Andersen A1 - Krog, Jens A1 - Cherstvy, Andrey G. A1 - Metzler, Ralf T1 - Bayesian analysis of single-particle tracking data using the nested-sampling algorithm: maximum-likelihood model selection applied to stochastic-diffusivity data JF - Physical chemistry, chemical physics : PCCP ; a journal of European Chemical Societies N2 - We employ Bayesian statistics using the nested-sampling algorithm to compare and rank multiple models of ergodic diffusion (including anomalous diffusion) as well as to assess their optimal parameters for in silico-generated and real time-series. We focus on the recently-introduced model of Brownian motion with "diffusing diffusivity'-giving rise to widely-observed non-Gaussian displacement statistics-and its comparison to Brownian and fractional Brownian motion, also for the time-series with some measurement noise. We conduct this model-assessment analysis using Bayesian statistics and the nested-sampling algorithm on the level of individual particle trajectories. We evaluate relative model probabilities and compute best-parameter sets for each diffusion model, comparing the estimated parameters to the true ones. We test the performance of the nested-sampling algorithm and its predictive power both for computer-generated (idealised) trajectories as well as for real single-particle-tracking trajectories. Our approach delivers new important insight into the objective selection of the most suitable stochastic model for a given time-series. We also present first model-ranking results in application to experimental data of tracer diffusion in polymer-based hydrogels. Y1 - 2018 U6 - https://doi.org/10.1039/c8cp04043e SN - 1463-9076 SN - 1463-9084 VL - 20 IS - 46 SP - 29018 EP - 29037 PB - Royal Society of Chemistry CY - Cambridge ER - TY - THES A1 - Thapa, Samudrajit T1 - Deciphering anomalous diffusion in complex systems using Bayesian inference and large deviation theory N2 - The development of methods such as super-resolution microscopy (Nobel prize in Chemistry, 2014) and multi-scale computer modelling (Nobel prize in Chemistry, 2013) have provided scientists with powerful tools to study microscopic systems. Sub-micron particles or even fluorescently labelled single molecules can now be tracked for long times in a variety of systems such as living cells, biological membranes, colloidal solutions etc. at spatial and temporal resolutions previously inaccessible. Parallel to such single-particle tracking experiments, super-computing techniques enable simulations of large atomistic or coarse-grained systems such as biologically relevant membranes or proteins from picoseconds to seconds, generating large volume of data. These have led to an unprecedented rise in the number of reported cases of anomalous diffusion wherein the characteristic features of Brownian motion—namely linear growth of the mean squared displacement with time and the Gaussian form of the probability density function (PDF) to find a particle at a given position at some fixed time—are routinely violated. This presents a big challenge in identifying the underlying stochastic process and also estimating the corresponding parameters of the process to completely describe the observed behaviour. Finding the correct physical mechanism which leads to the observed dynamics is of paramount importance, for example, to understand the first-arrival time of transcription factors which govern gene regulation, or the survival probability of a pathogen in a biological cell post drug administration. Statistical Physics provides useful methods that can be applied to extract such vital information. This cumulative dissertation, based on five publications, focuses on the development, implementation and application of such tools with special emphasis on Bayesian inference and large deviation theory. Together with the implementation of Bayesian model comparison and parameter estimation methods for models of diffusion, complementary tools are developed based on different observables and large deviation theory to classify stochastic processes and gather pivotal information. Bayesian analysis of the data of micron-sized particles traced in mucin hydrogels at different pH conditions unveiled several interesting features and we gained insights into, for example, how in going from basic to acidic pH, the hydrogel becomes more heterogeneous and phase separation can set in, leading to observed non-ergodicity (non-equivalence of time and ensemble averages) and non-Gaussian PDF. With large deviation theory based analysis we could detect, for instance, non-Gaussianity in seeming Brownian diffusion of beads in aqueous solution, anisotropic motion of the beads in mucin at neutral pH conditions, and short-time correlations in climate data. Thus through the application of the developed methods to biological and meteorological datasets crucial information is garnered about the underlying stochastic processes and significant insights are obtained in understanding the physical nature of these systems. N2 - Die Entwicklung von Methoden wie der superauflösenden Mikroskopie (Nobelpreis für Chemie, 2014) und der Multiskalen-Computermodellierung (Nobelpreis für Chemie, 2013) hat Wis- senschaftlern mächtige Werkzeuge zur Untersuchung mikroskopischer Systeme zur Verfügung gestellt. Submikrometer Partikel und sogar einzelne fluoreszent markierte Moleküle können heute über lange Beobachtungszeiten in einer Vielzahl von Systemen, wie z.B. lebenden Zellen, biologischen Membranen und kolloidalen Suspensionen, mit bisher unerreichter räumlicher und zeitlicher Auflösung verfolgt werden. Neben solchen Einzelpartikelverfolgungsexperi- menten ermöglichen Supercomputer die Simulation großer atomistischer oder coarse-grained Systeme, wie z..B. biologisch relevante Membranen oder Proteine, über wenige Picosekunden bis hin zu einigen Sekunden, wobei große Datenmengen produziert werden. Diese haben zu einem beispiellosen Anstieg in der Zahl berichteter Fälle von anomaler Diffusion geführt, bei welcher die charakteristischen Eigenschaften der Brownschen Diffusion—nämlich das lineare Wachstum der mittleren quadratischen Verschiebung mit der Zeit und die Gaußsche Form der Wahrscheinlichkeitsdichtefunktion ein Partikel an einem gegebenen Ort und zu gegebener Zeit zu finden—verletzt sind. Dies stellt eine große Herausforderung bei der Identifizierung des zugrundeliegenden stochastischen Prozesses und der Schätzung der zugehörigen Prozess- parameter dar, was zur vollständigen Beschreibung des beobachteten Verhaltens nötig ist. Das Auffinden des korrekten physikalischen Mechanismus, welcher zum beobachteten Verhal- ten führt, ist von überragender Bedeutung, z.B. beim Verständnis der, die Genregulation steuernden, first-arrival time von Transkriptionsfaktoren oder der Überlebensfunktion eines Pathogens in einer biologischen Zelle nach Medikamentengabe. Die statistische Physik stellt nützliche Methoden bereit, die angewendet werden können, um solch wichtige Informationen zu erhalten. Der Schwerpunkt der vorliegenden, auf fünf Publikationen basierenden, kumulativen Dissertation liegt auf der Entwicklung, Implementierung und Anwendung solcher Methoden, mit einem besonderen Schwerpunkt auf der Bayesschen Inferenz und der Theorie der großen Abweichungen. Zusammen mit der Implementierung eines Bayesschen Modellvergleichs und Methoden zur Parameterschätzung für Diffusionsmodelle werden ergänzende Methoden en- twickelt, welche auf unterschiedliche Observablen und der Theorie der großen Abweichungen basieren, um stochastische Prozesse zu klassifizieren und wichtige Informationen zu erhalten. Die Bayessche Analyse der Bewegungsdaten von Mikrometerpartikeln, welche in Mucin- hydrogelen mit verschiedenen pH-Werten verfolgt wurden, enthüllte mehrere interessante Eigenschaften und wir haben z.B. Einsichten darüber gewonnen, wie der Übergang von basischen zu sauren pH-Werten die Heterogenität des Hydrogels erhöht und Phasentrennung einsetzen kann, was zur beobachteten Nicht-Ergodizität (Inäquivalenz von Zeit- und En- semblemittelwert) und nicht-Gaußscher Wahrscheinlichkeitsdichte führt. Mit einer auf der Theorie der großen Abweichungen basierenden Analyse konnten wir, z.B., nicht-Gaußsches Verhalten bei der scheinbaren Brownschen Diffusion von Partikeln in wässriger Lösung, anisotrope Bewegung von Partikeln in Mucin bei neutralem pH-Wert und Kurzzeitkorrela- tionen in Klimadaten detektieren. Folglich werden durch die Anwendung der entwickelten Methoden auf biologische und meteorologische Daten entscheidende Informationen über die zugrundeliegenden stochastischen Prozesse gesammelt und bedeutende Erkenntnisse für das Verständnis der Eigenschaften dieser Systeme erhalten. KW - anomalous diffusion KW - Bayesian inference KW - large deviation theory KW - statistical physics Y1 - 2020 ER - TY - JOUR A1 - Cherstvy, Andrey G. A1 - Thapa, Samudrajit A1 - Wagner, Caroline E. A1 - Metzler, Ralf T1 - Non-Gaussian, non-ergodic, and non-Fickian diffusion of tracers in mucin hydrogels JF - Soft matter N2 - Native mucus is polymer-based soft-matter material of paramount biological importance. How non-Gaussian and non-ergodic is the diffusive spreading of pathogens in mucus? We study the passive, thermally driven motion of micron-sized tracers in hydrogels of mucins, the main polymeric component of mucus. We report the results of the Bayesian analysis for ranking several diffusion models for a set of tracer trajectories [C. E. Wagner et al., Biomacromolecules, 2017, 18, 3654]. The models with "diffusing diffusivity', fractional and standard Brownian motion are used. The likelihood functions and evidences of each model are computed, ranking the significance of each model for individual traces. We find that viscoelastic anomalous diffusion is often most probable, followed by Brownian motion, while the model with a diffusing diffusion coefficient is only realised rarely. Our analysis also clarifies the distribution of time-averaged displacements, correlations of scaling exponents and diffusion coefficients, and the degree of non-Gaussianity of displacements at varying pH levels. Weak ergodicity breaking is also quantified. We conclude that-consistent with the original study-diffusion of tracers in the mucin gels is most non-Gaussian and non-ergodic at low pH that corresponds to the most heterogeneous networks. Using the Bayesian approach with the nested-sampling algorithm, together with the quantitative analysis of multiple statistical measures, we report new insights into possible physical mechanisms of diffusion in mucin gels. Y1 - 2019 U6 - https://doi.org/10.1039/c8sm02096e SN - 1744-683X SN - 1744-6848 VL - 15 IS - 12 SP - 2526 EP - 2551 PB - Royal Society of Chemistry CY - Cambridge ER - TY - JOUR A1 - Cherstvy, Andrey G. A1 - Thapa, Samudrajit A1 - Mardoukhi, Yousof A1 - Chechkin, Aleksei V. A1 - Metzler, Ralf T1 - Time averages and their statistical variation for the Ornstein-Uhlenbeck process BT - Role of initial particle distributions and relaxation to stationarity JF - Physical review : E, Statistical, nonlinear and soft matter physics N2 - How ergodic is diffusion under harmonic confinements? How strongly do ensemble- and time-averaged displacements differ for a thermally-agitated particle performing confined motion for different initial conditions? We here study these questions for the generic Ornstein-Uhlenbeck (OU) process and derive the analytical expressions for the second and fourth moment. These quantifiers are particularly relevant for the increasing number of single-particle tracking experiments using optical traps. For a fixed starting position, we discuss the definitions underlying the ensemble averages. We also quantify effects of equilibrium and nonequilibrium initial particle distributions onto the relaxation properties and emerging nonequivalence of the ensemble- and time-averaged displacements (even in the limit of long trajectories). We derive analytical expressions for the ergodicity breaking parameter quantifying the amplitude scatter of individual time-averaged trajectories, both for equilibrium and outof-equilibrium initial particle positions, in the entire range of lag times. Our analytical predictions are in excellent agreement with results of computer simulations of the Langevin equation in a parabolic potential. We also examine the validity of the Einstein relation for the ensemble- and time-averaged moments of the OU-particle. Some physical systems, in which the relaxation and nonergodic features we unveiled may be observable, are discussed. Y1 - 2018 U6 - https://doi.org/10.1103/PhysRevE.98.022134 SN - 2470-0045 SN - 2470-0053 VL - 98 IS - 2 PB - American Physical Society CY - College Park ER -