  Energy landscapes provide a valuable means for studying the folding dynamics
of short RNA molecules in detail by modeling all possible structures and their
transitions. Higher abstraction levels based on a macro-state decomposition of
the landscape enable the study of larger systems, however they are still
restricted by huge memory requirements of exact approaches.
  We present a highly parallelizable local enumeration scheme that enables the
computation of exact macro-state transition models with highly reduced memory
requirements. The approach is evaluated on RNA secondary structure landscapes
using a gradient basin definition for macro-states. Furthermore, we demonstrate
the need for exact transition models by comparing two barrier-based appoaches
and perform a detailed investigation of gradient basins in RNA energy
landscapes.
  Source code is part of the C++ Energy Landscape Library available at
http://www.bioinf.uni-freiburg.de/Software/.
