  We report new statistical time-series analysis tools providing significant
improvements in the rapid, precision extraction of discrete state dynamics from
large databases of experimental observations of molecular machines. By building
physical knowledge and statistical innovations into analysis tools, we
demonstrate new techniques for recovering discrete state transitions buried in
highly correlated molecular noise. We demonstrate the effectiveness of our
approach on simulated and real examples of step-like rotation of the bacterial
flagellar motor and the F1-ATPase enzyme. We show that our method can clearly
identify molecular steps, symmetries and cascaded processes that are too weak
for existing algorithms to detect, and can do so much faster than existing
algorithms. Our techniques represent a major advance in the drive towards
automated, precision, highthroughput studies of molecular machine dynamics.
Modular, open-source software that implements these techniques is provided at
http://www.eng.ox.ac.uk/samp/members/max/software/
