  Modern methods for sampling rugged landscapes in state space mainly rely on
knowledge of the relative probabilities of microstates, which is given by the
Boltzmann factor for equilibrium systems. In principle, trajectory reweighting
provides an elegant way to extend these algorithms to non-equilibrium systems,
by numerically calculating the relative weights that can be directly
substituted for the Boltzmann factor. We show that trajectory reweighting has
many commonalities with Rosenbluth sampling for chain macromolecules, including
practical problems which stem from the fact that both are iterated importance
sampling schemes: for long trajectories the distribution of trajectory weights
becomes very broad and trajectories carrying high weights are infrequently
sampled, yet long trajectories are unavoidable in rugged landscapes. For
probing the probability landscapes of genetic switches and similar systems,
these issues preclude the straightforward use of trajectory reweighting. The
analogy to Rosenbluth sampling suggests though that path ensemble methods such
as PERM (pruned-enriched Rosenbluth method) could provide a way forward.
