Neural crest migration with continuous cell states.

Area of Science:

• Developmental Biology
• Computational Biology
• Cell Migration Dynamics

Background:

• Cranial neural crest cell migration models often assume discrete leader and follower states.
• Previous models utilized experimental perturbations and gene expression analysis.
• The necessity of discrete states versus a continuum of behaviors has been debated.

Purpose of the Study:

• To investigate whether a continuum of cell behaviors can equivalently describe cranial neural crest cell migration.
• To implement and compare continuous-state models against discrete-state models.
• To align computational models with experimental data and biological plausibility.

Main Methods:

• Implementation of two continuous-state models: signal choice and signal combination.
• Comparison of model predictions with discrete-state models and experimental observations.
• Parameter adjustment, specifically chemoattractant consumption, to match experimental regimes.

Main Results:

• Continuous-state models predicted greater cell population migration than discrete-state models and experimental data.
• The signal combination model, unlike the signal choice model, could be adjusted to achieve experimentally plausible migration.
• Reducing chemoattractant consumption in the signal combination model improved its fit to experimental data.

Conclusions:

• A continuum of cell behaviors offers a plausible and experimentally motivated description of cranial neural crest cell migration.
• The signal combination model provides a more robust framework for understanding migration dynamics.
• Computational modeling, guided by experimental data, is crucial for refining our understanding of developmental processes.