  Linear bio-molecular motors move unidirectionally along a track by
coordinating several different processes, such as fuel (ATP) capture,
hydrolysis, conformational changes, binding and unbinding from a track, and
center-of-mass diffusion. A better understanding of the interdependencies
between these processes, which take place over a wide range of different time
scales, would help elucidate the general operational principles of molecular
motors. Artificial molecular motors present a unique opportunity for such a
study because motor structure and function are a priori known. Here we describe
use of a Master equation approach, integrated with input from Langevin and
molecular dynamics modeling, to stochastically model a molecular motor across
many time scales. We apply this approach to a specific concept for an
artificial protein motor, the Tumbleweed.
