4 research outputs found
A stochastic model for protrusion activity
In this work we approach cell migration under a large-scale assumption, so
that the system reduces to a particle in motion. Unlike classical particle
models, the cell displacement results from its internal activity: the cell
velocity is a function of the (discrete) protrusive forces exerted by filopodia
on the substrate. Cell polarisation ability is modeled in the feedback that the
cell motion exerts on the protrusion rates: faster cells form preferentially
protrusions in the direction of motion. By using the mathematical framework of
structured population processes previously developed to study population
dynamics [Fournier and M{\'e}l{\'e}ard, 2004], we introduce rigorously the
mathematical model and we derive some of its fundamental properties. We perform
numerical simulations on this model showing that different types of
trajectories may be obtained: Brownian-like, persistent, or intermittent when
the cell switches between both previous regimes. We find back the trajectories
usually described in the literature for cell migration
A Predictive Model for Yeast Cell Polarization in Pheromone Gradients
Budding yeast cells exist in two mating types, a and α, which use peptide pheromones to communicate with each other during mating. Mating depends on the ability of cells to polarize up pheromone gradients, but cells also respond to spatially uniform fields of pheromone by polarizing along a single axis. We used quantitative measurements of the response of a cells to α-factor to produce a predictive model of yeast polarization towards a pheromone gradient. We found that cells make a sharp transition between budding cycles and mating induced polarization and that they detect pheromone gradients accurately only over a narrow range of pheromone concentrations corresponding to this transition. We fit all the parameters of the mathematical model by using quantitative data on spontaneous polarization in uniform pheromone concentration. Once these parameters have been computed, and without any further fit, our model quantitatively predicts the yeast cell response to pheromone gradient providing an important step toward understanding how cells communicate with each other