Mechanochemical feedback during developmental patterning and morphogenesis
Project ID: 2531ad1548
(You will need this ID for your application)
Research Theme: Physical Sciences
UCL Lead department: London Centre for Nanotechnology (LCN)
Lead Supervisor: Zena Hadjivasiliou
Project Summary:
During morphogenesis cell size, shape and spatial organization undergo dramatic changes, culminating in the reorganization of form at the tissue level. Recent studies suggest that tissue and cell architecture play a key role in the spread and accumulation of patterning molecules in tissues that undergo morphogenesis. However, we understand little about the physical mechanisms via which cellular and tissue architecture impact ligand transport and kinetics, and emergent properties due to feedback between ligand transport and tissue geometry. The focus of this project is to develop theoretical frameworks, grounded in and coupled to experimental observations, to advance our understanding of how tissue organization interacts with biochemical patterning.
The student will build on theoretical tools developed in the Hadjivasiliou lab to make predictions about how cell size and packing impacts the shape and range of morphogen gradients in 2D epithelia and in tissues that undergo 3D morphogenetics movements. The student will learn how to perform reaction-diffusion simulations coupled with algorithms that follow individual cell shape and size. Theoretical findings will be compared to ongoing experiments in the group our collaborator Alberto Elosegui-Artola.
Depending on the interest and expertise of the student there will be the option to perform experiments using cells that are synthetically engineered to secrete GFP to test and challenge the predictions of the theoretical model. We therefore welcome applications from students with a biology background that are keen to work on the interface of physics and biology.
The host lab will provide training in techniques necessary for the project such as best coding practices, High Performance Computing, analytical and numerical techniques to solve ODEs/PDEs, and Machine Learning for data analysis. More broadly, the student will develop the skills to independently apply physics and mathematics to biological questions and work collaborative at the interface of these disciplines.