How to connect time-lapse recorded trajectories of motile microorganisms with dynamical models in continuous time

Nikolay Kutuzov, Henrik Flyvbjerg, Martin Lauritzen

    Research output: Contribution to conferenceConference abstract for conferenceResearchpeer-review

    Abstract

    We provide a tool for data-driven modeling of motility, data being time-lapse recorded trajectories. Several mathematical properties of a model to be found can be gleaned from appropriate model-independent experimental statistics if one understands how such statistics are distorted by the finite sampling frequency of time-lapse recording, by experimental errors on recorded positions, and by conditional averaging. We give exact analytical expressions for these effects in the simplest possible model for persistent random motion, the Ornstein-Uhlenbeck process. Then we describe those aspects of these effects that are valid for any reasonable model for persistent random motion. Our findings are illustrated with experimental data and Monte Carlo simulations.
    Original languageEnglish
    Publication date2018
    Publication statusPublished - 2018
    EventGeneration and Control of Forces in Cells - Nordita, Stockholm, Sweden
    Duration: 11 Jun 201829 Jun 2018

    Workshop

    WorkshopGeneration and Control of Forces in Cells
    LocationNordita
    Country/TerritorySweden
    CityStockholm
    Period11/06/201829/06/2018

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