While structurally very different, protein and RNA molecules share an important attribute. The motions they undergo are highly related to the function they perform. For example, many diseases such as Mad Cow disease or Alzheimer's disease are associated with protein misfolding and aggregation. Similarly, RNA folding velocity may regulate the plasmid copy number, and RNA folding kinetics can regulate gene expression at the translational level. Knowledge of the stability, folding, kinetics and detailed mechanics of the folding process may help provide insight into how proteins and RNAs fold. In this paper, we describe a novel computational method for studying molecular motions that we have proposed and validated against experimental data. We demonstrate that this method can capture biological results such as stochastic folding pathways, compute the population kinetics of various conformations, and calculate relative folding rates. Thus, our method provides both a detailed view (e.g., individual pathways) and a global view (e.g., population kinetics, relative folding rates, and reaction coordinates) of energy landscapes of both proteins and RNAs. We validated these techniques by showing that we observe the same relative folding rates as shown in experiments for structurally similar protein molecules that exhibit different folding behaviors. Our analysis is also able to predict the same relative gene expression rate for wild-type MS2 phage RNA and three of its mutants.