Simulation Studies of a Particle-Based Molecular Motor Toy Model

Molecular motors are at the heart of life. These diminutive engines drive muscle contraction, molecular transport, cellular motion, and other important life processes by transducing energy stored in chemical fuel into mechanical work. I will introduce a particle-based toy model for an "information ratchet", a motor that rectifies thermal noise into directed motion. I will discuss simulation methodology to sample that model's steady-state behavior and show how those simulations reveal the tradeoffs between motor accuracy, velocity, and efficiency and demonstrate the motor's response to a range of operating conditions. I will further show how modest design changes can reverse the motor's direction. Finally, I will present how this approach creates a bridge between experimental studies and theoretical frameworks like stochastic thermodynamics and thermodynamic uncertainty relationships. Going forward, these computational methods should prove to be valuable tools for creating better artificial molecular motors.