Single-particle tracking allows to infer the motion of single molecules in living cells. When we observe a long trajectory (more than 100 points), it is possible that the particle switches mode of motion over time. Then, fitting a single model to the trajectory can be misleading. In this paper, we propose a method to detect the temporal change points: the times at which a change of dynamics occurs. More specifically, we consider that the particle switches between three main modes of motion: Brownian motion, subdiffusion and superdiffusion. We use an algorithm based on a statistic (Briane et al. 2016) computed on local windows along the trajectory. The method is non parametric as the statistic is not related to any particular model. This algorithm controls the number of false change point detections in the case where the trajectory is fully Brownian. A Monte Carlo study is proposed to demonstrate the performances of the method and also to compare the procedure to two competitive algorithms. At the end, we illustrate the utility of the method on real data depicting the motion of mRNA complexes -called mRNP-in neuronal dendrites.

It is of primary interest for biologists to be able to visualize the dynamics of proteins within the cell. In this paper, we propose a new mapping method to robustly estimate dynamics in the entire cell from particle tracks. To obtain satisfying diffusion and drift maps, we use a spatiotemporal kernel estimator. Trajectory classification data is used as input and allows to automatically label particle movements into three classes: confined motion (or subdiffusion), Brownian motion, and directed motion (or superdiffusion). We then use this information to calculate diffusion coefficient and drift maps separately on each class of motion.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Charles Kervrann. A computational approach for detecting microdomains and confinement domains in cells: a simulation study. Physical Biology, Institute of Physics: Hybrid Open Access, In press, pp.Abstract. In this paper, we aim at detecting trapping areas (equivalently microdomains or confinement areas) within cells, corresponding to regions where molecules are trapped and thereby undergo subdiffusion. We propose an original computational approach that takes as input a set of molecule trajectories estimated by appropriate tracking methods. The core of the algorithm is based on a combination of clustering algorithms with trajectory classification procedures able to distinguish subdiffusion, superdiffusion and Brownian motion. The idea is to automatically identify trapping areas where we observe a high concentration of subdiffusive particles. We evaluate our proof of concept on artificial sequences obtained with a biophysicsbased simulator (Fluosim), and we illustrate its potential on real TIRF microscopy data.