Stochastic noise induced transition in a molecular motor system

The stochastic dynamics of molecular motors play a pivotal role in comprehending and simulating critical processes in biological systems. This understanding is essential for elucidating how these motors achieve precise navigation and functionality amidst the intricacies of cellular environments. Firstly, we employed the stochastic energy landscape function to analyze the stability of the stochastic molecular motor systems under various conditions. In this paper, we concentrate on the stochastic dynamics of a molecular motor system, particularly examining the mean trajectories and the most probable trajectories under varying levels of noise. In terms of the mean trajectories, we observe that molecular motors, regardless of their initial concentrations, can transition to the same position. Moreover, we discover that an increase in noise intensity correlates with a reduction in the time needed to transition to the mean position. In terms of the most probable trajectories, an escalation in noise intensity results in a decrease in concentration at the most probable position for the molecular motor x, in contrast to an increase in concentration at the most probable position for the external system y. Our findings, which are dedicated to understanding the behavior of motors under different stochastic noise, contribute significantly to a more profound comprehension of molecular motors.