Please feel free to contact supervisors if you would like more information on a project.
Simulation of protein nanoparticles aggregation and liquid-liquid phase separation in cells
Supervisors: Maxim Molodtsov and Lee-Ya Chu | 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)
Multivalent interactions are the interactions between large molecules or nanoparticles that have multiple binding sites distributed across their surface. Interaction via multiple binding sites can lead to reversible and irreversible aggregation. Understanding how aggregation of molecules and particles is controlled is important because this process lies at the heart of neurodegeneration diseases, but also can be harnessed to interrogate cells mechanically by applying localized intracellular mechanical forces. It also underlines liquid-liquid phase separation (LLPS), which is a crucial mechanism facilitating various biological processes, including the promotion of biochemical reactions, sequestration of specific proteins, and cellular transportation. To understand how we can control molecular aggregation and LLPS, we developed a method based on Brownian dynamics using LAMMPS simulator and Python to study multivalent protein-protein interactions in silico. This project aims to extend these simulations to the particles with various geometries, number of binding sites and the strength of their interaction. We will also determine experimentally how aggregation and phase separation can be externally controlled, by designing multivalent protein scaffolds whose affinities we can control by optogenetic tools in vitro, and in live cells. Our goal is to generate simulations to complement and rationalize these experiments done in the lab to understand protein aggregation and LLPS in cells.
Understanding the interplay of 3D cell shape and mechanical properties in epithelial developmental patterning
Supervisor: Giulia Paci (Mao Lab) | eligible for IPLS studentship
In this project we will investigate the role of 3D cell shapes and mechanical properties in developmental patterning using the wing disc of 3rd instar Drosophila larvae as a model tissue and an interdisciplinary approach combining fly genetics, microscopy, biomechanics and computational methods. Using a mechanical stretcher to apply forces to dissected wing discs, we have previously observed that cells fated to become sensory bristles in the adult fly (SOPs) stretch less compared to neighbouring cells, which indicates that they are stiffer. We have recently been able to use 2 photon microscopy to image the entire volume of wing discs and reconstruct 3D shapes of SOPs and their neighbours using machine learning. This has revealed that SOPs make multiple contacts with difference cells along the z axis, which are likely to play an important role in their specification through Notch-Delta signalling (which occurs at the site of membrane contacts). In this project we will investigate the interplay of 3D cell shapes and mechanical properties in achieving robust patterning of SOPs (two evenly spaced rows of cells).
We will use genetic perturbations to alter, in turn, the cell shapes (making them shorter/taller) and cell mechanical properties (making them softer/stiffer) and quantify the effect on patterning precision, which will involve the development of an automated image analysis pipeline. By using different drivers we will be able to cause these perturbations in the whole wing disc, affect just one half of the SOP pattern, or even just the SOPs themselves. Building on the current imaging pipeline, we will further improve our imaging and analysis capabilities to track cell-cell contacts changes along the whole volume in the different conditions. This will allow us to disentangle the mutual contribution of 3D cell shapes and cell mechanical properties in patterning precision. Finally, using technologies developed in the lab to mechanically perturb wing discs both ex-vivo and in vivo, we will also be able to analyze pattern robustness in the presence of different external forces. This will allow us to understand not only how robust patterns are achieved but also how they are maintained in the presence of external mechanical stimuli. Building on previous modelling efforts in the Mao lab, the experimental findings could also be incorporated into computational simulations that would allow us to better understand robustness of developmental patterning under different regimes of mechanical stress.
Agent-based modelling of bacterial growth in complex environments
Supervisor: Freya Bull | eligible for IPLS studentship
In nature, bacteria frequently grow in complex environmental conditions. In applications including pharmaceutical production, food/drink manufacturing, marine ecology, and bacterial infections within the gut/bladder; bacteria encounter low concentrations of many diverse nutrients. This project will investigate how bacteria uptake multiple substrates (nutrients required for growth) simultaneously: an important missing piece in modelling bacterial growth outside of the laboratory. The student will develop and computationally implement an agent-based stochastic model to compare some potential bacterial growth rules. This project will involve both analytic mathematical modelling and computer programming, allowing the student to develop skills in statistical physics, biophysics, and computational physics.
Mechano-chemical patterning in epithelial tissues
Supervisor: Zena Hadjivasiliou | 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)
Morphogenesis requires precise spatio-temporal coordination of mechanical forces, morphogen signalling gradients, and tissue geometry. How heterogeneities in cell and tissue architecture affect the spread of morphogens in developing tissues is not understood. This computational project will use a generalised vertex model  to investigate a link between local tissue geometry and reaction-diffusion of morphogen molecules. We want to study how spatial patterns in the morphogen concentration can emerge from a coupling of cell-mechanical parameters of the vertex model to local morphogen levels.
This is an excellent opportunity to gain experience working at the interface of physics and biology. The student will be based in the highly dynamic and interdisciplinary environment of the Francis Crick Institute and gain experience in mathematical modelling of a complex biological system (reaction-diffusion equations, active mechanics of cells and tissues) and computer simulations (Matlab, HPC).
 Alt, Ganguli, Salbreux, 2017.