Grant: Discovery Grants Program - Individual

Objectives We aim to devise tractable computational methods for implementing rich probabilistic phylogenetic models of molecular evolution. Our modeling objectives operate along two main axes: 1) the incorporation of greater realism within a statistically sound population-genetics-based formulation, so as to define a relevant null model; 2) the development of alternative models, that test for departures from our progressively more realistic null. Along the first axis, we have previously established a basic null model of nearly-neutral evolution for protein-coding DNA sequences. We will seek to improve the structure of our null model, by pursuing our efforts at capturing context-dependent mutational features, across-site dependence due to protein structure constraints, and variable population sizes across the branches of the phylogeny. Along the second axis of our modeling objectives, we will focus on measuring departures from the nearly-neutral null model that result in site-specific signatures of adaptive evolution. Scientific approach Although context-dependent mutation models, or structurally constrained models, are computationally intractable with conventional methods, we will pursue two different systems for implementing the models: Approximate Bayesian Computation (ABC) and data-augmentation-based Markov chain Monte Carlo methods, with which we have had previous success. We will use simulations to study the properties of the models and computational techniques. We will also use posterior predictive methods to assess the performance of alternative models in light of real data. Finally, we will apply models on large sets of data, to gain empirical insights on the measurements they provide. Novelty and expected significance Our past work on rich evolutionary models and their implementations has had a major impact on our understanding of the evolutionary relationships between organisms, such as contributing to establishing the modern tree of animals. Much more work remains to devise models that recognize our basic understanding of molecular evolutionary biology. In spite of the underlying theoretical developments having a history dating back to the early 20th century, mechanistic models of molecular evolution that integrate this theory have only recently gained traction. Our research efforts in this area are most distinctive in that they are focused on an improved null model. A few applications of our models suggest that they can further improve phylogenetic inference per se, but, more fundamentally, that they can help reveal underlying features of the evolutionary process. Our work contributes to a broad modeling project, aimed at integrating sequence data, protein structure data, and other forms of data, into a well-grounded probability framework, as a central engine to inference in biology.