25,570 research outputs found
Anomalous dynamics of cell migration
Cell movement, for example during embryogenesis or tumor metastasis, is a
complex dynamical process resulting from an intricate interplay of multiple
components of the cellular migration machinery. At first sight, the paths of
migrating cells resemble those of thermally driven Brownian particles. However,
cell migration is an active biological process putting a characterization in
terms of normal Brownian motion into question. By analyzing the trajectories of
wildtype and mutated epithelial (MDCK-F) cells we show experimentally that
anomalous dynamics characterizes cell migration. A superdiffusive increase of
the mean squared displacement, non-Gaussian spatial probability distributions,
and power-law decays of the velocity autocorrelations are the basis for this
interpretation. Almost all results can be explained with a fractional Klein-
Kramers equation allowing the quantitative classification of cell migration by
a few parameters. Thereby it discloses the influence and relative importance of
individual components of the cellular migration apparatus to the behavior of
the cell as a whole.Comment: 20 pages, 3 figures, 1 tabl
The impact of temporal sampling resolution on parameter inference for biological transport models
Imaging data has become widely available to study biological systems at
various scales, for example the motile behaviour of bacteria or the transport
of mRNA, and it has the potential to transform our understanding of key
transport mechanisms. Often these imaging studies require us to compare
biological species or mutants, and to do this we need to quantitatively
characterise their behaviour. Mathematical models offer a quantitative
description of a system that enables us to perform this comparison, but to
relate these mechanistic mathematical models to imaging data, we need to
estimate the parameters of the models. In this work, we study the impact of
collecting data at different temporal resolutions on parameter inference for
biological transport models by performing exact inference for simple velocity
jump process models in a Bayesian framework. This issue is prominent in a host
of studies because the majority of imaging technologies place constraints on
the frequency with which images can be collected, and the discrete nature of
observations can introduce errors into parameter estimates. In this work, we
avoid such errors by formulating the velocity jump process model within a
hidden states framework. This allows us to obtain estimates of the
reorientation rate and noise amplitude for noisy observations of a simple
velocity jump process. We demonstrate the sensitivity of these estimates to
temporal variations in the sampling resolution and extent of measurement noise.
We use our methodology to provide experimental guidelines for researchers
aiming to characterise motile behaviour that can be described by a velocity
jump process. In particular, we consider how experimental constraints resulting
in a trade-off between temporal sampling resolution and observation noise may
affect parameter estimates.Comment: Published in PLOS Computational Biolog
Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures
Here, using an integrative experimental and computational approach, Imle et al. show how cell motility and density affect HIV cell-associated transmission in a three-dimensional tissue-like culture system of CD4+ T cells and collagen, and how different collagen matrices restrict infection by cell-free virions
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