Quantifying the roles of random motility and directed motility using advection-diffusion theory for a 3T3 fibroblast cell migration assay stimulated with an electric field

• Published in: BMC systems biology Vol. 11; no. 1; p. 39
• Main Authors: Simpson, Matthew J., Lo, Kai-Yin, Sun, Yung-Shin
• Format: Journal Article
• Language: English
• Published: London BioMed Central 17.03.2017
• Subjects: 3T3 Cells; Advection; Algorithms; Animals; Anisotropy; Bioinformatics; Biomedical and Life Sciences; Cell adhesion & migration; Cell culture; Cell growth; Cell migration; Cell Migration Assays; Cell Movement; Cellular and Medical Topics; Chemotaxis; Computational Biology/Bioinformatics; Continuum modeling; Diffusion; Diffusion theory; Drift; Drift estimation; Electric fields; Electric potential; Electricity; External stimuli; Fibroblasts; Life Sciences; Mathematical models; Methods; Mice; Models, Biological; Motility; Parameter estimation; Partial differential equations; Physiological; Physiological effects; Physiology; Receptors; Research Article; Saturation; Simulation and Modeling; software and technology; Spatial data; Systems Biology; Temperature effects; Thermotaxis; Velocity; United States > US; Random motility; Keller-Segal model; Directed motility; Cell migration; Partial differential equation; Electrotaxis
• ISSN: 1752-0509; 1752-0509
• DOI: 10.1186/s12918-017-0413-5

Background Directed cell migration can be driven by a range of external stimuli, such as spatial gradients of: chemical signals (chemotaxis); adhesion sites (haptotaxis); or temperature (thermotaxis). Continuum models of cell migration typically include a diffusion term to capture the undirected component of cell motility and an advection term to capture the directed component of cell motility. However, there is no consensus in the literature about the form that the advection term takes. Some theoretical studies suggest that the advection term ought to include receptor saturation effects. However, others adopt a much simpler constant coefficient. One of the limitations of including receptor saturation effects is that it introduces several additional unknown parameters into the model. Therefore, a relevant research question is to investigate whether directed cell migration is best described by a simple constant tactic coefficient or a more complicated model incorporating saturation effects. Results We study directed cell migration using an experimental device in which the directed component of the cell motility is driven by a spatial gradient of electric potential, which is known as electrotaxis. The electric field ( EF ) is proportional to the spatial gradient of the electric potential. The spatial variation of electric potential across the experimental device varies in such a way that there are several subregions on the device in which the EF takes on different values that are approximately constant within those subregions. We use cell trajectory data to quantify the motion of 3T3 fibroblast cells at different locations on the device to examine how different values of the EF influences cell motility. The undirected (random) motility of the cells is quantified in terms of the cell diffusivity, D , and the directed motility is quantified in terms of a cell drift velocity, v . Estimates D and v are obtained under a range of four different EF conditions, which correspond to normal physiological conditions. Our results suggest that there is no anisotropy in D , and that D appears to be approximately independent of the EF and the electric potential. The drift velocity increases approximately linearly with the EF , suggesting that the simplest linear advection term, with no additional saturation parameters, provides a good explanation of these physiologically relevant data. Conclusions We find that the simplest linear advection term in a continuum model of directed cell motility is sufficient to describe a range of different electrotaxis experiments for 3T3 fibroblast cells subject to normal physiological values of the electric field. This is useful information because alternative models that include saturation effects involve additional parameters that need to be estimated before a partial differential equation model can be applied to interpret or predict a cell migration experiment.