Memory efficient RNA energy landscape exploration

Energy landscapes provide a valuable means for studying the fold-ing 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 per-form a detailed investigation of gradient basins in RNA energy land-scapes. Source code is part of the C++ Energy Landscape Library available a