Evolution is responsible for the immense biological diversity of our planet; however, despite its central role as the most fundamental property of life, the process of evolution remains poorly understood, and current models have typically been unable to span the diversity of scales at which evolution can act.
The goal of this project is to develop computational models, techniques, and software tools to help biologists infer most likely evolutionary scenarios and thus gain insight into evolution. These methods can be used to understand the relationships between genes and species, between individuals within and across populations, and between pairs of species that coevolved.
This project is co-advised by Prof Wu and Prof Libeskind-Hadas.
Essay prompt: What interests you about this project, and what do you hope to gain from the research experience?
Our group works at the intersection of a variety of fields, including algorithms, mathematical modeling, and evolutionary biology. Students gain experience in (1) designing, implementing, and analyzing algorithms, (2) analyzing real genetic data, (3) interacting with biologists who may use these methods, and (4) addressing problems in evolution and disease.
Learn more about the PIs' work:
- (software) "eMPRess: a systematic cophylogeny reconciliation tool." Bioinformatics (in press). [link]
- (algorithms) "An Integer Linear Programming Solution for the Most Parsimonious Reconciliation Problem under the Duplication-Loss-Coalescence Model." ACM BCB '20, Virtual Event due to COVID-19, Sept 2020. [link]