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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Recovering a stochastic process from super-resolut
ion noisy ensembles of single particle trajectorie
s - Nathanael Hoze (ETH Zürich)
DTSTART;TZID=Europe/London:20160620T110000
DTEND;TZID=Europe/London:20160620T114500
UID:TALK66507AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/66507
DESCRIPTION: Co-author: David Holcman (ENS Paris)
Recovering a
stochastic process from noisy ensembles of single
particle trajectories is resolved here using the
Langevin equation as a model. The massive redund
ancy contained in single particle trajectories all
ows recovering local parameters of the underlying
physical model. However\, point localization is
perturbed by instrumental noise\, which\, although
of the order of ~10 nanometers\, affects the est
imation of biophysical parameters such as the drif
t and diffusion of the motion. Moreover\, even if
the acquisition frequency of modern tracking alg
orithm is very high\, it is not instantaneous\, an
d this biases parameter estimation. Here\, we use
several parametric and non-parametric estimators
to compute the first and second moment of the pro
cess and to recover the local drift\, its derivat
ive and the diffusion tensor\, in diffusion proces
ses whose observation is perturbed by instrumenta
l noise and non-instantaneous sampling rate. Usin
g a local asymptotic expansion of the estimators a
nd computing the empirical transition probability
function\, we develop here a method to deconvolv
e the instrumental from the physical noise. We use
numerical simulations to explore the range of va
lidity for the estimators. The present analysis a
llows characterizing what can exactly be recovered
from the statistics of super-resolution microsco
py trajectories used in molecular tracking and un
derlying cellular function.
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:INI IT
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