Modeling work-speed-accuracy trade-offs in a stochastic rotary machine

Area of Science:

• Biophysics
• Molecular Biology
• Biochemistry

Background:

• Living cells rely on molecular machines, which are stochastic systems driving essential energetic processes.
• Examples like F1-ATP synthase and bacterial flagellum inspire the study of these biological nanomachines.
• Understanding the principles governing their function is crucial for cell viability and structure.

Purpose of the Study:

• To present a minimal model of an externally driven stochastic rotary machine.
• To investigate the trade-offs between work output, driving speed, and accuracy.
• To analyze how system dynamics and driving parameters influence machine performance.

Main Methods:

• Development of a minimal theoretical model for externally driven stochastic rotary machines.
• Systematic exploration of parameter space including driving strength, speed, and system dynamics.
• Analysis of work, accuracy, and cycle rate under varying conditions.

Main Results:

• An upper bound for accuracy and work was identified at a specific driving speed.
• Slow driving was found to be optimal for minimizing the work-accuracy ratio and maximizing successful cycle rates.
• A significant decay in driving accuracy was observed at physiological rotation rates for parameters mimicking F1-ATP synthase.

Conclusions:

• The study highlights the critical role of driving speed in the performance of molecular rotary machines.
• Observed accuracy decay at physiological speeds raises questions about the in vivo efficiency mechanisms of ATP synthase.
• Further research is needed to elucidate how biological molecular machines maintain efficiency under cellular conditions.