Stochastic Methods for Inferring States of Cell Migration

Area of Science:

• Cell biology
• Computational biology
• Biophysics

Background:

• Cell migration is crucial for biological processes and requires precise spatiotemporal regulation of the actin cytoskeleton.
• Understanding the regulatory mechanisms driving directed cell movement necessitates robust computational analysis of cell track data.

Purpose of the Study:

• To develop and apply computational tools for analyzing cell migration patterns.
• To identify and quantify parameters characterizing cell movement from time-series position data.
• To investigate the migratory behaviors of Mouse Embryonic Fibroblasts (MEFs) and HeLa cells.

Main Methods:

• Stochastic modeling was employed to create computational tools for analyzing cell track data.
• The developed methods were used to estimate parameters characterizing cell movement.
• The approach was applied to time-series data of randomly migrating MEFs and HeLa cells.

Main Results:

• MEFs exhibit two distinct migration states (speed and persistence), while HeLa cells display a single state.
• The Rho-family GTPase RhoG was identified as a factor influencing the two migratory states in MEFs.
• Computational methods predicted individual cell migration states from time-series data.
• HeLa cells expressing a Rac1 biosensor at high concentrations showed two migratory states correlated with Rac1 spatial distribution.

Conclusions:

• The developed computational approach efficiently estimates cell movement parameters and predicts migration states.
• RhoG plays a significant role in regulating MEF migratory behavior.
• Rac1 activity spatial distribution is linked to migratory state plasticity in HeLa cells.