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SUMMARY:Recovering a stochastic process from super-resolution noisy ensemb
 les of single particle trajectories - Nathanael Hoze (ETH Zürich)
DTSTART:20160620T100000Z
DTEND:20160620T104500Z
UID:TALK66507@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:<span> <span>Co-author: David Holcman (ENS Paris)  <br></span>
 </span> <span> <span> <br>Recovering a stochastic process from noisy ensem
 bles of single particle  trajectories is resolved here using the Langevin 
 equation as a model. The  massive redundancy contained in single particle 
 trajectories allows recovering  local parameters of the underlying physica
 l model. However\, point localization  is perturbed by instrumental noise\
 , which\, although of the order of ~10  nanometers\, affects the estimatio
 n of biophysical parameters such as the drift  and diffusion of the motion
 . Moreover\, even if the acquisition frequency of  modern tracking algorit
 hm is very high\, it is not instantaneous\, and this biases  parameter est
 imation. Here\, we use several parametric and non-parametric  estimators t
 o compute the first and second moment of the process and to recover  the l
 ocal drift\, its derivative and the diffusion tensor\, in diffusion proces
 ses  whose observation is perturbed by instrumental noise and non-instanta
 neous  sampling rate. Using a local asymptotic expansion of the estimators
  and  computing the empirical transition probability function\, we develop
  here a  method to deconvolve the instrumental from the physical noise. We
  use numerical  simulations to explore the range of validity for the estim
 ators. The present  analysis allows characterizing what can exactly be rec
 overed from the statistics  of super-resolution microscopy trajectories us
 ed in molecular tracking and  underlying cellular function.</span></span>
LOCATION:Seminar Room 1\, Newton Institute
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