About the Project
Evolutionary biologists have long been interested in the question how populations adapt to changing environments. Until recently researchers were only able to sequence a few individuals at a single time-point. Yet, with the growing affordability of genome sequencing, it is now possible to consider multiple individuals at multiple points in time. We have developed an approach called Polymorphism-aware phylogenetic models (PoMos, e.g., Braichenko, Borges & Kosiol, 2024; Borges et al., 2022) that…
allows the use of inter and intra-species data. The multiple time-points could include samples from Natural History Museums; they could be multiple generations of a population housed in controlled laboratory environments or diverse samples collected from the wild.
The PhD student will develop deep learning models to analyse of allelic frequency trajectories under selection, which incorporate prior information about shifts in the environment and allow tracking of the course of gene frequency changes during adaptation. Our group has also worked on temporal genomic data in particular data especially from experimental evolution studies in flies (Topa, Jónás Á, Kofler R, Kosiol C, Honkela A, 2015; Barata, Snook, Ritchie & Kosiol, 2023). In particular, we have developed frequency-based PoMo-like method for time-series data to detect targets of selection (Bait‐ER; Barata, Borges, Kosiol, 2023). We plan to adapt this method such that it can be used for time-series data. We will achieve this by embedding the approach in a Deep Learning framework that can then be combined with our work on natural selection.
The result will be a Deep Learning (DL) approach that is based on a simulation process that allows to incorporate complex and spatial and demographic scenarios for the different species. In a next step this simulated data is then used to train a convolutional neural network (CNN). Then the CNN is used on real data not only to classify evolutionary process put also estimate parameters of the PoMo approach. The advantage of DL methods is that they cannot only infer invents in the past but also predict future biodiversity.
HOW TO APPLY
Application instructions can be found on the EASTBIO website - How to Apply | EastBio Doctoral Training Partnership | Biology
- Download and complete the Equality, Diversity and Inclusion survey.
- Download and complete the EASTBIO Application Form.
- Please complete an application on our online portal: How to apply - Study at St Andrews - University of St Andrews
- Select the course 'PhD with internship'
- Your online application must include the following documents:
- Completed EASTBIO application form
- Academic Qualifications
- English Language Qualification (if applicable)
- 2 References: this must be completed on the EASTBIO Reference Form, also found on the EASTBIO website.
Please download the EASTBIO reference form and send it to your referees. They can either upload it directly to the portal using the automated email they will receive, or they can email it to Rachel at pgrecbiology@st-andrews.ac.uk
CONTACT
Queries on the project can be directed to the project supervisor.
Queries on the application process can be directed to Rachel Horn at pgrecbiology@st-andrews.ac.uk
UKRI eligibility guidance: Terms and Conditions: View Website International/EU: View Website
Funding Notes
This 4 year PhD project is part of a competition funded by EASTBIO BBSRC Doctoral Training Partnership.
This opportunity is open to UK and International students and provides funding to cover stipend at UKRI standard rate and UK level tuition fees. The University of St Andrews will cover the Home-International fee difference.