Felix Höfling, Carsten Hartmann
Arthur Straube (FU, from 07/19 to 12/19), Upanshu Sharma (FU, from 10/19)
01.01.2019 – 31.12.2021
Diffusion in cellular environments, consisting of a variety of interacting entities, is a multiscale process. The project’s objective is to infer effective stochastic models and to quantify memory, using novel stochastic modelling and data assimilation techniques, based on data from experiments and simulations.
Macromolecular crowding is characterised by a dense and heterogeneous packing of differently sized, mobile and immobile components. From the interaction between these entitites, complex behaviour can emerge on large scales, including long time scales and persistent memory. Experiments on labelled molecules frequently show non-Markovian or non-Gaussian behaviour or both, depending on the window of time scales probed .
A description by an idealised Brownian motion is, at best, approximate, and may lead to significant modelling errors with respect to, e.g., spatial particle distributions and their reaction kinetics. Accurate and efficient modelling demands linking complex spatio-temporal data to suitable stochastic models, which should be able to accomodate the feature-rich experimental observations, yet allow for the robust estimation of parameters.
Non-Gaussian transport, yet with a linearly growing mean-square displacement (MSD) has attracted recent interest, whereas previous research almost exclusively focussed on the striking observation of (transient) subdiffusion . A stochastic model that partially captures the former treats the particle’s effective diffusivity as a stochastic mean-reversion process, justified by the constantly rearranging environment . In the most recent version of this “diffusing diffusivity (DD)” model, the molecule’s displacement 𝑋 obeys a non-degenerate diffusion that bears some resemblance with the well-known Cox-Ingersoll-Ross (CIR) process in mathematical finance. The full DD model is Markovian. However, the dynamics of 𝑋 alone may display significant memory effects or non-Markovianity, unless the system exhibits a clear time scale separation.
We will derive a hierarchy of the effective models for the diffusing particle in various scaling regimes and study properties of the parameter estimator for the effective diffusivities. When multiple scales are present, statistical estimators, such as the maximum likelihood estimator, are known to suffer from a systematic bias unless clever subsampling strategies are employed, and one part of the project consists in studying the properties of non-parameteric and semi-parametric estimators for the diffusivities. For finite time scale separation we expect, however, an effective model to exhibit significant sub-exponential correlations, which suggests to systematically seek non-Gaussian approximations, akin to the (non-Markovian) Kac-Zwanzig heat bath model.
One possibility to describe non-Markovian effects is by means of the generalised Langevin equation (GLE) that, in its most frequently used Gaussian form, is specified by the autocorrelation function (ACF) of the noise increments or, equivalently, its memory kernel. A model-free alternative studied in this project uses higher-order memory functions that allow for the consistent interpolation from short to long time scales. The approach is based on a Fourier representation of the ACF of a stationary process in terms of a spectral density (e.g. ). The key quantity here, which is experimentally accessible, is the characteristic function of the process (i.e. the Fourier transform of its spectral density) which after systematic short-time approximations captures non-Markovian processes with persistent memory (long-time anomalies) as well as non-Gaussian transport with spatiotemporal memory or non-Gaussian noise, beyond the standard GLE.
A scientific novelty is that a completely data-driven framework is developed to study memory and crowding for diffusing particles that incorporates many available standard approaches, such as the GLE framework, and that rests on very few generic assumptions. The project explores the potential and limitations of the class of DD models, based on results of complex analysis and on actual data. The analysis of ACF including the frequency dependence is still in its infancies. Estimating spatiotemporal memory from trajectory data is new and goes beyond standard GLE approaches.
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