Nucleotide sequence data from DNA and RNA have become the most exciting basis on which to infer evolutionary relationships. However, biologists currently are better able to generate these data than to analyze them, owing to the lack of appropriate and well-tested statistical methods. Dr. James Cavender is a mathematician interested in the use of these data for constructing evolutionary trees, and he proposes an algebraic analysis of a new technique authored recently by Dr. James Lake. This new approach ("evolutionary parsimony") takes into account the different probabilities associated with different types of nucleotide change. The proposed research would explore the limits of this new method, establish the asumptions on which it is based, and the degree to which these statistical assumptions are true. The proposed research has the potential for openning a new approach to the use of nucleotide sequence data. Developing and testing this method would have wide impact in systematic and evolutionary biology, and in genetics, molecular biology, and biochemistry as well.
