Persistent pseudopod splitting is an effective chemotaxis strategy in shallow gradients

Area of Science:

• Cell Biology
• Biophysics
• Computational Biology

Background:

• Cells utilize diverse motility strategies to follow chemical gradients.
• Optimal strategies for different gradient types remain poorly understood.
• Chemotaxis is crucial for processes like development and immune response.

Purpose of the Study:

• To model directional decision-making in chemotactic amoeboid cells.
• To understand how actin dynamics influence cell movement strategies.
• To investigate the role of pseudopod suppression in chemotaxis.

Main Methods:

• Developed a minimal model of stimulus-dependent actin recruitment as a contest.
• Simulated pseudopod competition for a finite actin pool.
• Employed reinforcement learning to optimize pseudopod suppression strategies.

Main Results:

• The model quantitatively explains how cells achieve accurate chemotaxis without explicit gradient sensing or memory.
• Pseudopod suppression emerges as an effective algorithm for enhancing chemotaxis.
• Different environments elicit distinct pseudopod-based strategies, balancing speed and accuracy.

Conclusions:

• Mechanical intelligence, driven by actin dynamics and pseudopod suppression, underlies efficient chemotaxis.
• Cells adapt their movement strategies based on environmental conditions (static vs. dynamic gradients).
• Minimal cellular regulation can lead to high chemotactic performance.