Statistical Physics Approaches to RNA Editing

Ohio State University Research Foundation -Do Not Use, Columbus OH

Investigators

Abstract

TECHNICAL SUMMARY: This award supports theoretical research and education at the interface of biology with statistical physics. The PI seeks to apply the methods of statistical physics to the problem of RNA editing which bears on how biological systems synthesize proteins from the instructions encoded in DNA. RNA is a biomolecule that is deeply involved in all aspects of molecular biology, such as protein production, gene regulation, and viral replication. In spite of RNA's importance for all living organisms, there are many significant aspects of RNA biology that we know surprisingly little about. One such significant biological mystery surrounding RNA is the mechanism of RNA editing. RNA editing is the process in which individual or short stretches of nucleotides in a messenger or functional RNA are inserted, deleted, or substituted. Different forms of RNA editing occur in many different organisms. In this project, the combined information in the mitochondrial genomes of two related organisms with insertional RNA editing, Physarum polycephalum and Didymium iridis, and in the millions of protein sequences from other organisms is exploited to computationally identify genes in the two organisms and to provide experimentally verifiable predictions about the location of editing sites within these genes. A complete annotation of the mitochondrial genome and characterization of the editing sites in these genes addresses two questions about the so far elusive RNA editing mechanism: (i) Sequence features identified from the editing sites in the new genes indicate how the RNA editing machinery knows which sites to edit. (ii) The new genes themselves reveal what some of the constituents of the RNA editing machinery itself are. The new approaches developed to meet this end are based on interpreting prediction quality as a complex energy landscape over the space of all possible predictions. This allows computational methods from statistical physics to be used to find optimal predictions. This project will yield new algorithms for the identification of genes and editing sites in the presence of RNA editing. These algorithms as well as the editing site data will be freely accessible to non commercial users. All projects will involve undergraduate and graduate students who will receive an interdisciplinary education and training in computational techniques. They will help address the high demand in the workforce for individuals with a strong quantitative and biological background. NON-TECHNICAL SUMMARY: This award supports theoretical research and education at the interface of biology and statistical physics. This area has been experiencing explosive growth owing to new experimental techniques for biology and high intellectual opportunity. The PI will apply methods of statistical physics to understand aspects of how the instructions contained in DNA are used to create proteins and their role in other cellular functions. The information contained in DNA is used to create RNA which is instrumental in how cells make proteins. In this way, information from DNA is reflected in the created proteins. In some organisms the RNA is modified after it is created by a process known as RNA editing, leading to changes in the resulting proteins. The PI will use the methods of statistical physics to better understand the process of RNA editing. The theoretical and computational techniques that are developed will in turn contribute to the methods of statistical physics and be used to attack other problems. The synergy helps to advance both fields. The concepts of statistical mechanics are applicable to a wide range of systems and phenomena in our world and have the potential to illuminate our understanding to advance fundamental knowledge as well as for practical benefit. This project will yield new computational methods for the identification of genes and editing sites in the presence of RNA editing. These methods will be freely accessible to non commercial users. This project will involve undergraduate and graduate students who will receive an interdisciplinary education and training in computational techniques. They will help address the high demand in the workforce for individuals with a strong quantitative and biological background.