Using approximate Bayesian computation to quantify cell-cell adhesion parameters in a cell migratory process.

Area of Science:

• Computational Biology
• Cell Biology
• Biophysics

Background:

• Cell-cell interactions like adhesion and repulsion influence cell migration.
• Wound-healing assays are commonly used to study cell migration.
• Accurate quantification of cell motility parameters is crucial for understanding biological processes.

Purpose of the Study:

• To improve the design of wound-healing assays for quantifying cell-cell interactions.
• To investigate the utility of approximate Bayesian computation (ABC) methods in assay design.
• To identify optimal experimental parameters for accurate cell motility model identification.

Main Methods:

• Implementation of approximate Bayesian computational (ABC) methods.
• Modeling of both unrealistic and practically realizable wound-healing experiments.
• Simulation-based comparison of different experimental design strategies (domain size vs. replicates).

Main Results:

• ABC methods can identify model parameters in idealized experiments with appropriate summary statistics.
• Current, commonly used experimental setups hinder accurate parameter identification via ABC.
• Expanding the experimental domain size, rather than increasing replicates, significantly improves parameter identification accuracy.

Conclusions:

• The study proposes time- and cost-saving modifications for cell motility assays.
• Expanded experimental domains enhance the identification of parameters governing cell migration.
• Findings are relevant for researchers studying cell migration, particularly with significant cell-cell adhesion or repulsion.