Ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. An RNA molecule is a linear polymer which folds back on itself to form a three dimensional (3D) functional structure. While experimental determination of precise 3D RNA structures is a time consuming and costly process, useful insight into the molecule can be gained from knowing its secondary structure. Structural elements in RNA secondary structures can be separated into two large categories: stem-loops and pseudoknots. The development of mathematical models and computational prediction algorithms for simple stem-loop structures started early in the 1980's. However, building systems that provide the tremendous computer time and memory needed for RNA analysis of both stem-loops and pseudoknots remains a challenge even today. The recently developed grid computing technology can offer a possible solution to this challenge.
In this paper we briefly address mathematical problems associated with the grid computing approach to RNA structure prediction. In particular, we introduce models to partition a large RNA molecule into smaller segments to be assigned to different computers on the grid. Based on these models, we formulate a sampling strategy to select RNA segments for computational prediction to maximize prediction consistency. This strategy is under construction as part of RNAVLab, our unified environment for computational RNA structure analysis, i.e., prediction, alignment, comparison, and classification. A first prototype of RNAVLab is presented and used to investigate the possible association of secondary structure types with RNA functions by analyzing secondary structures for a family of nodavirus genomes.