AF: Medium: Algorithmic Foundations for Phylogenetic Networks

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Phylogenies, or evolutionary histories, play a central role in biology as a framework within which to understand all of Life's diversity. In Charles Darwin's Origin of Species, the depiction of an evolutionary history of species took the shape of a tree. Ever since, trees have been the most commonly used structure to model evolutionary histories. However, while trees capture how, for example, one species splits into two that subsequently diversify and how genetic material is transmitted from ancestor to descendants, they fail to capture other evolutionary events. For example, some plant species arise due to hybridization between pairs of other species. In the microbial world, bacteria transmit genetic material horizontally by various means. In these cases, a tree gives an incomplete picture of the evolutionary history at best, and a very misleading one in the worst case. Indeed, a more appropriate model of evolutionary histories in these cases is a phylogenetic network, which extends the tree model by incorporating evolutionary events such as hybridization and horizontal gene transfer. Despite an increased research activity in the area of phylogenetic networks in recent years, their reconstruction and evaluation remain largely ad hoc processes and limited in their applicability to specific datasets. To enable the development of methodologies for systematic reconstructing and evaluating phylogenetic networks, this project is aimed at developing (1) algorithms for evaluating the quality of phylogenetic networks using genomic data, and (2) algorithms for searching the phylogenetic network space to enable automatic inference of evolutionary histories. The outcome of the proposed work will help significantly extend the applicability of phylogeny to groups of organisms for which trees are inappropriate, as well as to understand the phylogenetic network model, thus allowing for a more systematic development of methodologies for its accurate reconstruction. Further, the results will enable a more accurate reconstruction of the Tree of Life, help unravel microbial genome diversification, facilitate the reconstruction of hybridization scenarios in plants and other groups of organisms, and help understand the mechanisms by which microbes develop resistance to antibiotics. The project provides outstanding opportunities for training graduate and undergraduate students in an interdisciplinary research area.