PhD Research Project: Modelling genomic and phenotypic evolution in a natural population

Location
United Kingdom
Posted
Nov 28, 2016
Closes
Jan 23, 2017
Organization Type
University and College
Hours
Full Time
Details

We will not achieve an adequate understanding of adaptation in natural populations until we can model the interaction among the genome, the environment and individual organisms, and so predict the phenotypes and genomic composition of subsequent generations. This project will exploit the Flexible Large Scale Agent Modelling Environment for the Graphics Processing Unit (FLAMEGPU) agent-based modelling (ABM) environment, developed by Paul Richmond, to model the evolution of individual reproductive success in the Lundy population of house sparrows. FLAMEGPU is open source and provides an abstraction level that allows modellers to focus on specifying agent behaviour, whilst providing automated code targeted to GPU architectures that provide accelerated levels of computational performance. The Lundy Sparrow Project, led by Terry Burke, has collected detailed phenotypes and complete individual life-histories of individual sparrows for more than 16 years, comprising many generations. The study will combine an open-source genetic modelling package that allows recombination (based on, e.g., FIGG or GPOPSIM) with the ABM to simulate the evolution of whole genomes in the population through time. The model will be tested by comparing the genomes and phenotypes of the current population with those in the starting population. The project will be unique in combining and ABM with whole-genome data, and in combining population dynamics, population genetics and real-world fitness data.

Supervisors
Terry Burke (APS) will provide the data to be used in the project and expertise in population genomics and avian life history.
Paul Richmond (Computer Science) will provide expertise in state of the art agent-based modelling and computer programming, especially using Graphics Processing Units (GPUs).
Mark Rees (APS) will provide expertise in demographic and life-history modelling, including the statistical verification of model performance.

How to apply: Go to http://www.sheffield.ac.uk/postgraduate/research/apply/applying after reading the information contained on that page click the link to the Postgraduate online application form

Funding Notes

This project will be funded by the Leverhulme Trust Centre for Advanced Biological Modelling