Long run average sequential testing brownian motion

#Long run average sequential testing brownian motion series

The biological interpretation of subdiffusion is that the particle is confined in a domain or evolves in an open but crowded area ( Berry and ChatГ©, 2014 Bressloff and Newby, 2013, Section 3). If the MSD is sublinear (respectively, superlinear) the trajectory is a subdiffusion (respectively, a superdiffusion) (see Bressloff, 2014, Chapter 7 Metzler and Klafter, 2000). If the mean square displacement (MSD) function is linear, the trajectory is Brownian ( Qian et al., 1991). The definition of these dynamics is related to the criterion of the mean square displacement, a function of time widely used in biophysics and in cellular imaging to quantify motion. In biophysics, the dynamics of these trajectories are usually classified into three groups: subdiffusion, superdiffusion and Brownian motion. In this context, change-point detection (see Truong et al., 2018 for a review) is an importance task since change-points are often indicative of a new interaction of the particle with another component of the cell.

#Long run average sequential testing brownian motion series

This trajectory is a time series describing the behaviour of the particle which can change over time. Tracking algorithms ( Chenouard et al., 2014 Maroulas et al., 2015 Roudot et al., 2017) allow to reconstruct frame by frame the trajectory of a particle over a fixed time period. Indeed living cells are dynamic structures and their constituent particles are constantly moving within and between cellular compartments, domains and microdomains.

The study of these new data with state-of-the-art statistical and image analysis techniques will allow to decipher the dynamic coordination and organization of interacting molecules, which determine the different functions of the cell, see Kervrann et al. High-resolution fluorescence imaging have made possible the observation of the dynamic behaviour of single molecules.