Chemotaxis: a feedback-based computational model robustly predicts multiple aspects of real cell behaviour

Area of Science:

• Cell Biology
• Biophysics
• Computational Biology

Background:

• Eukaryotic chemotaxis mechanisms are not fully understood.
• A common model involves attractants triggering actin polymerization for cell movement.
• An alternative hypothesis suggests a self-generated pseudopod cycle modulated by attractants.

Purpose of the Study:

• To test the explanatory and predictive power of pseudopod-based models for chemotaxis.
• To investigate an alternative mechanism where chemoattractants modulate, rather than directly cause, pseudopod formation.
• To computationally model cell movement and chemotaxis based on pseudopod autocatalysis.

Main Methods:

• Developed a computational model of cell movement and chemotaxis.
• Simulated cell polarization, persistence, and pseudopod selection under chemoattractant gradients.
• Measured pseudopod angles in chemotaxing Dictyostelium cells to test model predictions.

Main Results:

• The model successfully reproduced existing data on cell movement and chemotaxis.
• It simulated cell polarization and accurate pseudopod selection in gradients.
• The model predicted and experimental data confirmed an increase in pseudopod angles with gradient differences.

Conclusions:

• The study supports an intrinsic pseudopod cycle as central to rapid cell motility in Dictyostelium and neutrophils.
• Chemotaxis likely involves modulation of this intrinsic cycle rather than direct actin polymerization by attractants.
• The findings offer a new perspective on the fundamental mechanisms driving directed cell movement.