Stochastic models of cell motility

Publication: ResearchPh.D. thesis – Annual report year: 2012

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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
Publication date2012
Number of pages142
StatePublished

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Ph.D. thesis

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