Prof. Hirohisa Kishino, PhD
Laboratory of Biometry and Bioinformatics,
University of Tokyo, JAPAN.
Plenary Talk Title: Statistical modeling of molecular evolution and its potential
Abstract: Statistical modeling of molecular evolution provides the clues to identify the adaptive evolution and biodiversity. In a micro scale, proteins adapt to novel environments, and the strength of selection pressure vary among the regions of the protein. By introducing Potts model as a prior on the spatial distribution of diversifying selection, the model of protein sequence evolution identified a diversifying region of influenza HA1 protein, which overlapped the antigenic sites. In a global scale, species comprise a biological community. The distribution of divergence times between member species of a community reflects the pattern of species composition. A newly defined effective species sampling proportion explains the amount of the difference between the divergence time distributions of the community and that of the meta-community, assuming random species sampling. The ratio of its maximum-likelihood estimate to the observed sampling proportion becomes an index of phylogenetic skew (PS), which can be used to detect candidate communities with unique species compositions from a large number of communities. Finally, we note that the rate of molecular evolution is the product of the mutation rate and the proportion of neutral mutations. The former factor depends on generation length and exposure to mutagens, while the latter depends on the strength of functional constraints and selection. A multiplicative gene-by-branch ANOVA model provides reliable estimates of divergence times and mutation rates. Regression on the gene-specific rates of molecular evolution allowed us to predict ancestral states of traits related to life history, social/reproductive behavior and food preference.