The classic example of metastability (infrequent jumps between deterministically-stable states)  arises in noisy systems when the thermal energy is small relative to the energy barrier separating two energy-minimizing states.  My work seeks to extend this idea to infinite dimensional systems and systems with non-gradient forces, extending the usefulness of the underlying energy landscape in the classic metastability analysis.  I will discuss stochastic coarse-graining techniques as well as methods to asymptotically approximate transition times between metastable states in different limits with different types of noise.  Example applications are a spatially-extended magnetic system with spatially-correlated noise designed to sample the Gibbs distribution relative to a defined energy functional, and a polymer bead-spring model of chromosome dynamics with additional stochastically-binding proteins that push the system out of equilibrium.