Thermal fluctuation statistics in a molecular motor described by a multidimensional master equation

Area of Science:

• Theoretical physics
• Chemical kinetics
• Biophysics

Background:

• Molecular motors are crucial biological machines that convert chemical energy into mechanical work.
• Understanding their energy transfer mechanisms and fluctuation statistics is key to deciphering their function.
• Existing models often simplify the complex interplay of different degrees of freedom.

Purpose of the Study:

• To theoretically investigate thermal fluctuation statistics in a molecular motor.
• To explore the impact of energy transfer on statistical correlations between fluctuations.
• To develop methods for distinguishing different operating regimes of molecular motors.

Main Methods:

• Utilizing a multidimensional discrete master equation with nearest-neighbor hopping to model energy transfer.
• Describing energy transfer as a multivariate diffusion process in the long-time limit.
• Deriving formal expressions for the probability distribution.
• Simulating single system trajectories in near- and far-from-equilibrium conditions for both strong and weak coupling.

Main Results:

• Energy transfer induces statistical correlations between thermal fluctuations across different degrees of freedom.
• In the strong-coupling limit, fluctuations and drift align, allowing for a simplified one-dimensional description.
• Simulations in near- and far-from-equilibrium regimes reveal distinct behaviors under strong and weak coupling.
• Hopping statistics demonstrate potential for differentiating molecular motor operating regimes.

Conclusions:

• The theoretical framework successfully captures the complex dynamics of thermal fluctuations in molecular motors.
• The identified correlations and diffusion processes offer insights into energy transduction mechanisms.
• Hopping statistics emerge as a valuable tool for characterizing and distinguishing the operational states of molecular motors.