Please feel free to contact supervisors if you would like more information on a project.
Single-molecule profiling of DNA damage with magnetic tweezers
Supervisor: Nick Bell | eligible for Brian Duff studentship (Yr 2 BSc or MSci/Yr 3 MSci registered undergraduate studying at UCL in the department of Physics and Astronomy or UCL Natural Sciences undergraduate with Physics as the mainstream)
Magnetic tweezers is a single-molecule technique for applying force and torque to individual biological molecules. Our lab uses magnetic tweezers to study the molecular mechanisms of DNA-protein interactions such as the repair of DNA damage by proteins in the cell nucleus. In this project, the student will learn how to use magnetic tweezers and apply them to study the rates of folding and unfolding of different damaged DNA structures under force. The goal of the project is to learn how the dynamics of these structures differs from undamaged DNA and to establish a platform for measuring rates of protein binding at such damaged DNA structures.
Mathematical modelling of Wnt ligand binding and interaction dynamics
Supervisor: Luke Davis (Pearce Lab) | eligible for IPLS studentship
Wingless-related integration site (Wnt) ligands are highly-conserved proteins that trigger multiple signaling cascades that help to govern cell migration, proliferation, and fate. Indeed, when Wnt signalling goes wrong many pathologies result. These pathologies include developmental disorders, such as defective stem cell homeostasis and bone density defects, and cancers, such as colorectal, pancreatic and breast cancers. Although numerous intracellular signalling pathways activated by Wnts have been described, how Wnts interact with cell membrane proteins remains unresolved. This project involves building a theoretical model of Wnt ligand binding and interaction dynamics with membrane receptors, with detailed consultation with experimental data from the Ashmore lab in Neuroscience (UCL Ear Institute). This project will reward the student with demonstrable experience in building and executing theoretical modelling of a biomedically relevant system, and you will gain skills in applied mathematics, biophysics, computer programming, and data analysis (with scope for machine learning approaches).
Statistical physics of the adaptive immune system
Supervisor: Andreas Mayer | eligible for IPLS studentship
To defend against rapidly evolving pathogens, organisms across the tree of life use adaptive immune mechanisms, which specifically target pathogens through a learning-and-memory approach. In recent years, approaches from statistical physics have emerged as key to the study of the complex co-evolutionary dynamics between highly diverse defense machinery and equally diverse pathogens. This project can involve either mathematical modeling to uncover design principles of the bacterial CRISPR-Cas adaptive immune system, or data-driven work on the biophysics underlying the specificity of T cell receptors. Depending on the chosen topic the work will involve a combination of numerical simulations, analytical derivations, data analysis, or machine learning.
How do animal species ensure robust patterning between individuals?
Supervisor: Lewis Mosby (Hadjivasiliou Lab) | eligible for BioP studentship
The size, shape and patterning of animals are thought to be governed by the distributions of evolutionarily conserved molecules called morphogens, which activate different genes in a concentration-dependent manner. Following localised secretion at one end of a tissue, morphogens form decaying distributions through the processes of diffusion and degradation. In order for patterns to adapt as animals grow, it is vital that the characteristic length scales associated with morphogen gradients scale with the size of the animal, and also that these gradients are robust to fluctuations in morphogen production. It has been proposed that both morphogen gradient scaling and robustness can be achieved by introducing bidirectional feedback between morphogens and other diffusing molecules, but strong coupling between diffusible species can result in concentration fluctuations in space and time that could inhibit the morphogen’s ability to define cell fate boundaries. In this project we will develop a mathematical model to investigate morphogen concentration fluctuations, with the aim of understanding how biological systems can minimise the time it takes for feedback to travel across a tissue and reach steady state. We will use numerical simulations to study how time-dependent perturbations in the production of morphogen molecules influence the morphogen distribution, before testing whether these perturbations could drive instabilities in systems containing multiple species of interacting, diffusing molecules. This project can also be placed in an evolutionary context by investigating whether the system properties that result in morphogen distributions reaching steady state quickly and without fluctuating are the same as those that ensure morphogen distribution scaling, or whether the properties compete when trying to optimise a fitness function.