C3BI Seminars – Methodological – Biomolecules Random Walks, Heterogeneities and Model Selection: What Information is accessible from experimental Biomolecules Random Walks?

Biomolecules Random Walks, Heterogeneities and Model Selection: What Information is accessible from experimental Biomolecules Random Walk?

Upcoming Events : C3BI Seminars – Methodological – 09/03/2015 at 02:00 pm in Retrovirus room – LWOFF (22)

Date : 09/03/2015 at 02:00 pm Location : Retrovirus room – LWOFF (22) Speakers/Trainers : Jean-Baptiste Masson, Visiting Scientist from Janelia Farm Research Campus, Ashburn, VA, USA For any questions, suggestions (or to volunteer) for future talks/trainings or general feedback please contact us at c3bi-ask@pasteur.fr

Biomolecules Random Walks, Heterogeneities and Model Selection: What Information is accessible from experimental Biomolecules Random Walks?

The development during the last 20 years of single biomolecule tagging allows unprecedented access to single biomolecule dynamics. Trajectories are now space filling in 2D and densities in 3D are rapidly rising. Thus, large amount of random walks are now accessible. These random walks bear information on both the biomolecule dynamics and on the environment properties. One of the key questions is how to exploit these random walks to gain quantitative information on the biological processes taking place and the nature of these random walks. Growing number of data being accessible multiple statistical hypothesis can be tested. Bayesian Inference [1] is a natural tool to handle multiple environment models, large amounts of data and multiple statistical hypothesis. We will discuss the use of Bayesian Inference to analyse single biomolecule trajectories[2–4], show various local models to describe biomolecule dynamics, methods to analyse multi-scale dynamics and describe transitions to anomalous dynamics [3]. We will also comment on out-of-equilibrium dynamics and stochastic integrals dilemma. Furthermore, numerous estimators lack robustness regarding linking mistakes of the tracking algorithm. We will show how to remove tracking by efficiently sampling the biomolecules assignment graph between images. We will comment the various methods to perform these sampling and discuss the inference of multi-scale fields. We will show time evolving maps at the full cell scale using high density tagging. We will discuss toxins receptor interactions with lipid platforms and Glycine Receptors dynamics at the full cell scale in both neurons and transfected Hela Cells. Finally, we will quickly introduce InferenceMAP a user-friendly software to analyse random walks trajectories. [1] U. Von Toussaint. 2011. Bayesian inference in physics. Review of Modern Physics 83:943-999. [2] El Beheiry, M., Dahan, M. and J.-B. Masson. 2015. InferenceMAP: Mapping of Single-Molecule Dynamics with Bayesian Inference. Nature Methods (In Press). [3] Masson, J.-B, Dionne, P., Salvatico, C., Renner, M., Specht, C. G. , Triller, A. and M. Dahan. 2014. Mapping the Energy and Diffusion Landscapes of Membrane Proteins at the Cell Surface Using High-Density Single-Molecule Imaging and Bayesian Inference: Application to the Multiscale Dynamics of Glycine Receptors in the Neuronal Membrane. Biophysical Journal 106 (1), 74–83 . [4] Masson,J.-B., Casanova, D. , Tu ̈rkcan, S., Voisinne, G., Popoff, M. R., Vergassola, M. and A. Alexandrou. 2009. Inferring Maps of Forces Inside Cell Membrane Microdomains. Physical Review Letters 102 (4), 048103.

 

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