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We report new statistical time-series analysis tools providing significantimprovements in the rapid, precision extraction of discrete state dynamics fromlarge databases of experimental observations of molecular machines. By buildingphysical knowledge and statistical innovations into analysis tools, wedemonstrate new techniques for recovering discrete state transitions buried inhighly correlated molecular noise. We demonstrate the effectiveness of ourapproach on simulated and real examples of step-like rotation of the bacterialflagellar motor and the F1-ATPase enzyme. We show that our method can clearlyidentify molecular steps, symmetries and cascaded processes that are too weakfor existing algorithms to detect, and can do so much faster than existingalgorithms. Our techniques represent a major advance in the drive towardsautomated, precision, highthroughput studies of molecular machine dynamics.Modular, open-source software that implements these techniques is provided athttp://www.eng.ox.ac.uk/samp/members/max/software/

Steps and bumps: precision extraction of discrete states of molecular machines using physically-based, high-throughput time series analysis