Collaborative Research: Mathematical Models for RNA

University Of Texas At El Paso, El Paso TX

Investigators

Abstract

Ribonucleic acid (RNA) molecules play important roles in many biological processes, including gene expression and regulation. An RNA molecule is a linear polymer that folds back on itself to form a three-dimensional functional structure. While experimental determination of precise RNA structures is a time-consuming and costly process, useful information about the molecule can be gained from knowing its secondary structure, a collection of hydrogen-bonded base pairs. Structural elements in RNA secondary structures can be separated into two large categories: stem-loops and pseudoknots. Development of mathematical models and prediction algorithms for simple stem-loop structures has started in the 1980?s. However, the tremendous demand on computer memory and time by pseudoknot prediction remains a computing challenge even today. The recently developed grid computing technology can offer a possible solution to this challenge. In this project, the investigators shall address some mathematical problems associated with the grid computing approach to RNA secondary structure prediction. To partition a large RNA molecule to smaller segments assigned to different computers on the grid, a good cutting strategy is necessary. The investigators propose to develop probabilistic models to study the inversion distribution in RNA sequences and to combine the results with the general theory of excursions to maximize the prediction accuracy using an optimal RNA segment length. The mathematical results will be integrated into a toolset for computational RNA structure analysis to test their applicability. The toolset will be used to investigate the possible association of pseudoknot types with functions using data in public domains and applied to the prediction of secondary structures adopted by nodavirus genomes. The prediction results will be compared with the secondary structures experimentally determined by mutational studies. Studies on RNA secondary structures and functions contribute significantly to the understanding and control of RNA viruses in plant and animal diseases. Computational methods recently used in these RNA studies have been shown to greatly reduce time and cost. Solving computationally intensive problems is now feasible using grid computing technology that simulates high-performance computing on a superstructure of networked computers. By combining rigorous mathematical methods, current computing technology, and careful experimental verification, this project develops an interactive investigative approach in an interdisciplinary research endeavor. The validation of computational prediction results by wet-lab experiments will encourage experimental scientists to make use of computing technology with confidence to assist their scientific pursuits. Such a collaborative effort will have significant impacts on education and training of new scientists by promoting the concept of interdisciplinary research designs, by enhancing diversity in the student population from different geographical and economic areas, and by encouraging the development of long-term collaborative research and educational partnerships between minority-serving institutions and research universities. The work of these investigators will result in a research product for viral RNA genome analyses with mathematical concepts useful for studying many other patterns in genetic sequences. This project represents a direct contribution of mathematics through the advancement of computing technologies to the elucidation of diverse biological functions of RNA.