Engineering robustness in collective migration and cell state transitions
Project ID: 2228cd1332 (You will need this ID for your application)
Research Theme: Physical Sciences
UCL Lead department: Division of Biosciences
Lead Supervisor: Mie Wong
Project Summary:
A fundamental question in tissue engineering is how cells, tissues and organisms are robustly shaped under fluctuations. It remains a major challenge to design engineered tissues with robust functionality, owing to complex internal and external interactions that hinder clear predictions of behavioural outcomes. To overcome this, our project will reveal universal principles underlying how multicellular systems reliably make collective decisions, allowing the development of bio-inspired tissues with robust functionalities. We will bring together in vivo quantitative biology, tissue engineering, and mathematical modelling to reveal universal principles underlying how multicellular systems reliably make collective decisions. This project falls within the EPSRC remits of “Physical Sciences” and “Healthcare Technologies”.
We will use as model system the developing zebrafish lateral line sensory organ, a group of ~100 collectively migrating cells that self-generates a gradient in its surrounding environment to robustly reach its destination. While directionally migrating, this developing tissue also undergoes dynamic cell state transitions, namely mesenchymal-to-epithelial transition (MET). The primary lab has unique expertise in engineering microenvironmental signal fluctuations within the embryo on demand to investigate robustness mechanisms and employing machine learning-based image analysis to analyse multiplexed imaging data. We will iterate between wet lab and mathematical simulations, developed by the Pearce group (secondary), based on their previous work on dynamics and robustness in complex biological systems. The PhD student will be trained in the relevant wet lab (Tissue engineering and Quantitative microscopy) and dry lab techniques (Python and Machine learning).
This project combines state-of-the-art methods in timelapse microscopy, tissue engineering, machine learning and mathematical modelling. Our interdisciplinary approach will reveal how processes inside cells, between cells, and at the level of the entire tissue combine to enable robust collective decision-making. These will provide valuable insights into designing engineered tissues with tuneable robustness, currently a major challenge in medicine.