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dc.contributor.authord’Avigneau, Alix Marieen
dc.contributor.authorSingh, Sumeetpalen
dc.contributor.authorOber, Raimund Jen
dc.date.accessioned2021-09-16T23:31:08Z
dc.date.available2021-09-16T23:31:08Z
dc.identifier.issn1936-4954
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/328156
dc.description.abstractAssessing the quality of parameter estimates for models describing the motion of single molecules in cellular environments is an important problem in fluorescence microscopy. In this work, we consider the fundamental data model, where molecules emit photons at random time instances and these photons arrive at random locations on the detector ac- cording to complex point spread functions (PSFs). The randomness and non-Gaussian PSF of the detection process, and the random trajectory of the molecule, makes inference challenging. Moreover, the presence of other closely spaced molecules causes further un- certainty in the origin of the measurements, which impacts the statistical precision of the estimates. We quantify the limits of accuracy of model parameter estimates and separation distance between closely spaced molecules (known as the resolution problem) by computing the Cram ́er-Rao lower bound (CRLB), or equivalently the inverse of the Fisher informa- tion matrix (FIM), for the variance of estimates. Results on the CRLB obtained from the fundamental model are crucial, in that they provide a lower bound for more practical scenar- ios. While analytic expressions for the FIM can be derived for static and deterministically moving molecules, the analytical tools to evaluate the FIM for molecules whose trajecto- ries follow stochastic differential equations (SDEs) are still for the most part missing. We address this by presenting a general sequential Monte Carlo (SMC) based methodology for both parameter inference and computing the desired accuracy limits for non-static molecules and a non-Gaussian fundamental detection model. For the first time, we are able to estimate the FIM for stochastically moving molecules observed through the Airy and Born and Wolf detection models. This is achieved by estimating the score and observed information matrix via SMC. We summarise the outcome of our numerical work by delineating the qualitative behaviours for the accuracy limits as functions of various experimental settings like collected photon count, molecule diffusion, etc. We also verify that we can recover known results from the static molecule case.
dc.publisherSociety for Industrial and Applied Mathematics
dc.rightsAll rights reserved
dc.titleLimits of accuracy for parameter estimation and localisation in Single-Molecule Microscopy via sequential Monte Carlo methodsen
dc.typeArticle
prism.publicationNameSIAM Journal on Imaging Sciencesen
dc.identifier.doi10.17863/CAM.75611
dcterms.dateAccepted2021-09-15en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2021-09-15en
rioxxterms.typeJournal Article/Reviewen
cam.orpheus.counter10*
rioxxterms.freetoread.startdate2024-09-16


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