  We extend the non-parametric framework of reaction coordinate optimization to
non-equilibrium ensembles of (short) trajectories. For example, we show how,
starting from such an ensemble, one can obtain an equilibrium free energy
profile along the committor, which can be used to determine important
properties of the dynamics exactly. New adaptive sampling approach, the
transition state ensemble enrichment, is suggested, which samples the
configuration space by "growing" committor segments towards each other starting
from the boundary states. This framework is suggested as a general tool,
alternative to the Markov state models, for a rigorous and accurate analysis of
simulations of large biomolecular systems, as it has the following attractive
properties. It is immune to the curse of dimensionality, it does not require
system specific information, it can approximate arbitrary reaction coordinates
with high accuracy and it has sensitive and rigorous criteria to test
optimality and convergence. The approaches are illustrated on a 50-dimensional
model system and a realistic protein folding trajectory.
