Stochastic models of cell motility

Cristian Gradinaru

    Research output: Book/ReportPh.D. thesisResearch

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    Abstract

    Cell motility and migration are central to the development and maintenance of
    multicellular organisms, and errors during this process can lead to major diseases. Consequently,
    the mechanisms and phenomenology of cell motility are currently under intense study. In recent
    years, a new interdisciplinary field focusing on the study of biological processes at the nanoscale
    level, with a range of technological applications in medicine and biological research, has
    emerged.
    The work presented in this thesis is at the interface of cell biology, image
    processing, and stochastic modeling. The stochastic models introduced here are based on
    persistent random motion, which I apply to real-life studies of cell motility on flat and
    nanostructured surfaces. These models aim to predict the time-dependent position of cell
    centroids in a stochastic manner, and conversely determine directly from experimental recordings
    of cell motility the various motility parameters. This can aid the experimentalist to draw
    biologically relevant conclusions about cell-substrate interactions.
    The need to track cells in a large number of movies has raised the question of
    automation of cell tracking and that of reproducibility and robustness of cell centroid
    measurement. To address this, I wrote the PACT cell tracking program, which is optimized for
    uniform as well as non-uniform backgrounds such as nanostructured surfaces. Rapid progress in
    the field of the automation of cell tracking steered us into a comparative study of PACT’s
    performance against other cell tracking programs. We find that different programs yield
    somewhat different results when applied to the same movie of migrating cells but that the
    differences are not statistically significant.
    To introduce persistent random motion, I first present a study of idealized random
    motion in two dimensions. This finds direct application to experimental studies of cell membrane
    fluidity and membrane protein dynamics, and I improve on the methodology currently used in that
    field by showing how to assess the randomness of the motility and how to optimally determine the
    diffusion coefficient. By adding a persistence component to simple random motion I introduce the
    standard Ornstein-Uhlenbeck process. I build on this commonly used cell motility model to
    address the challenges of working with real-life data: positional (centroid coordinate measuring)
    error and time discretization (due to finite frame rate in a movie of motile cells). This includes
    optimally measuring the motility parameters and balancing precision of measurement against the
    mathematical complexity of real-life models of cell motility. Finally, I expanded our
    understanding of cell response to surface topography by generalizing the Orstein-Uhlenbeck
    process to study cell motility on anisotropic substrates. I apply the general model to analyze cell
    motility on a series of anisotropic substrates and discuss the implications of our observations.
    This work is potentially useful to cell biologists by addressing their need for precise
    yet simple tools for studies of cell motility. The advances in the theoretical understanding of
    motility presented here bear the experimentalists’ needs in mind, and can find direct technological
    applications such as cell guidance and growth using nanotopography.
    Original languageEnglish
    Place of PublicationKgs. Lyngby
    PublisherTechnical University of Denmark
    Number of pages142
    Publication statusPublished - 2012

    Bibliographical note

    Ph.D. thesis

    Projects

    Modelling and Understanding Couplex Motion

    Gradinaru, C., Mølhave, K., Flyvbjerg, H., Bohr, J., Lagerholm, B. C. & Gadegaard, N.

    Technical University of Denmark

    01/10/200822/03/2012

    Project: PhD

    Cite this

    Gradinaru, C. (2012). Stochastic models of cell motility. Technical University of Denmark.