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Seminars


Seminar
STATISTICAL METHODS FOR MOLECULAR DYNAMICS AND INTERACTION ANALYSIS IN FLUORESCENCE MICROSCOPY – Charles KERVRANN
Thu 27 Feb 2020 02:00 pm Institut Pasteur Auditorium Francois Jacob – BIME (26)

Institut Curie  CNRS  UPMC  PSL Research University SERPICO Project Team Date : 27-02-2020 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) , Institut Pasteur, Paris



Seminar
STATISTICAL METHODS FOR MOLECULAR DYNAMICS AND INTERACTION ANALYSIS IN FLUORESCENCE MICROSCOPY – Charles KERVRANN
Thu 27 Feb 2020 02:00 pm Institut Pasteur Auditorium Francois Jacob – BIME (26)

The characterization of biomolecule dynamics and interactions in living cells is essential to decipher biological mechanisms and processes. This topic is usually addressed in fluorescent video-microscopy from particle trajectories computed by object tracking algorithms. However, classifying individual trajectories into predefined diffusion classes (e.g. sub-diffusion, free diffusion (or Brownian motion), super-diffusion), estimating diffusion model parameters, or detecting diffusion mode switches, is a difficult task in most cases. Meanwhile, colocalization is generally applied to detect interactions between two biomolecules observed in an image pair. Colocalization aims at characterizing spatial associations between two fluorescently-tagged biomolecules by quantifying the co- occurrence and correlation between the two channels acquired in fluorescence microscopy. This problem remains an open issue in diffraction-limited microscopy and raises new challenges with the emergence of super-resolution imaging. To address these challenging issues, we propose a computational framework based on statistical tests to both classify biomolecule trajectories and to detect spatially-varying colocalization in single molecule imaging (PALM, STORM). The methodological approach is well-grounded in statistics and is more robust than previous techniques. In this talk, I will present the underlying concepts and methods. The resulting algorithms are flexible in most cases, with a minimal number of control parameters to be tuned (p-values). They can be applied to a large range of problems in cell imaging and can be integrated in generic image- based workflows, including for high content screening applications.