Many organisms such as plants, viruses and microbes swap genes. This can this can give them important advantages (e.g. antibiotic resistance in microbes or pest resistance for plants). However, this also creates challenges when trying to understand how these organisms have evolved, since the swap of genetic information does not directly map onto Darwin’s Tree of Life.
In this project, we will develop new discrete mathematical and algorithmic approaches to build evolutionary networks. These graphs, whose study forms an exciting part of the interdisciplinary area of phylogenetics, generalise evolutionary trees and permit the representation of evolutionary processes such as gene transfer. The main goal of the project will be to develop a rooted version of the NeighborNet algorithm for building evolutionary networks. NeighborNet networks have been used in numerous evolutionary studies, and NeighborNet is one of the core algorithms within the SplitsTree program which has been cited over 7000 times. However, with the advent of huge genomic datasets, biologists now need directed networks that provide more explicit information on past evolutionary events such as gene transfer. The new network approaches will have important applications such as understanding how viruses evolve and the mechanisms by which microbes cause disease.
The student will work with a leading international team of researchers who have worked together with biologists and statisticians for several years on developing theoretical underpinnings and novel algorithms for building evolutionary networks. The student should have a background in discrete mathematics and/or computer science. Due to the interdisciplinary nature of the work, the project can be tailored to suit students with either a mathematical or a computational interest (or both). No background in biology is required, but a strong interest in working within an interdisciplinary environment is highly desirable.