Stochastic Methods for Inferring States of Cell Migration

Abstract

Cell migration refers to the ability of cells to translocate across a substrate or through a matrix. To achieve net movement requires spatiotemporal regulation of the actin cytoskeleton. Computational approaches are necessary to identify and quantify the regulatory mechanisms that generate directed cell movement. To address this need, we developed computational tools, based on stochastic modeling, to analyze time series data for the position of randomly migrating cells. Our approach allows parameters that characterize cell movement to be efficiently estimated from cell track data. We applied our methods to analyze the random migration of Mouse Embryonic Fibroblasts (MEFS) and HeLa cells. Our analysis revealed that MEFs exist in two distinct states of migration characterized by differences in cell speed and persistence, whereas HeLa cells only exhibit a single state. Further analysis revealed that the Rho-family GTPase RhoG plays a role in determining the properties of the two migratory states of MEFs. An important feature of our computational approach is that it provides a method for predicting the current migration state of an individual cell from time series data. Finally, we applied our computational methods to HeLa cells expressing a Rac1 biosensor. The Rac1 biosensor is known to perturb movement when expressed at overly high concentrations; at these expression levels the HeLa cells showed two migratory states, which correlated with differences in the spatial distribution of active Rac1.

COMMBINI: an experimentally-informed COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse

“…( 40 ); Allen et al. ( 41 )]. However, COMMBINI also includes deterministic rules to investigate the processes that drive healing progression, such as chemotaxis.…”

“…By employing agent-based modeling, each biological cell is represented as a single discrete entity and not as an element of a dynamic continuous concentration, providing a novel perspective on the investigation of the early phase of the bone healing process. The agent-based model is designed with stochastic algorithms to faithfully reproduce the biological behavior of cells [Andrews et al (59); Wehrens et al (40); Allen et al (41)]. However, COMMBINI also includes deterministic rules to investigate the processes that drive healing progression, such as chemotaxis.…”

“…( 40 ); Allen et al. ( 41 )]. The simulation was performed within a geometrical domain that represents the shape of a murine tibia fracture over a virtual period of 3 days, encompassing the early inflammatory stage.…”

“…In COMMBINI, cellular migration is stochastically simulated as a sequence of “jumps” in random directions to create a movement pathway in the 2D space [Allen et al. ( 41 )]. The span of each jump is defined by the migration speed ( k v ) associated with each cell phenotype.…”

“…To simulate this cellular level, an agent-based model has been developed. With this approach, each cell was simulated independently and not as a passive component of a cell population, thereby providing stochasticity to the investigation and guaranteeing the spatio-temporal variability that characterizes biological systems [Wehrens et al (40); Allen et al (41)]. The simulation was performed within a geometrical domain that represents the shape of a murine tibia fracture over a virtual period of 3 days, encompassing the early inflammatory stage.…”