Reinforcement learning of biomimetic navigation: a model problem for sperm chemotaxis

Area of Science:

• Biophysics
• Computational Biology
• Robotics

Background:

• Motile biological cells navigate using environmental cues and tunable biophysical parameters.
• Cellular navigation strategies involve modulating trajectories in response to detected signals.

Purpose of the Study:

• To introduce a reinforcement learning (RL) approach for modulating biophysical parameters and achieving biological cell-like navigation strategies.
• To demonstrate the RL approach using sperm chemotaxis as a model system.

Main Methods:

• Developed an RL framework to control key biophysical parameters of a cell model.
• Applied the RL approach to simulate sperm chemotaxis towards an egg.
• Focused on modulating trajectory curvature for navigation.

Main Results:

• The RL-informed navigation strategies successfully mimicked experimental sperm chemotaxis.
• The model demonstrated the ability to modulate trajectory curvature for directed movement.
• Identified parameter modulations necessary for specific navigation behaviors.

Conclusions:

• Reinforcement learning offers an effective method for capturing and replicating biological navigation strategies.
• This approach can inform the design of biomimetic micro-robotics with advanced navigation capabilities.
• The study highlights the potential of RL in understanding and engineering cellular motility.