Supervisors
Project Description
Do you want to advance interpretable machine learning tools for biomedical science, develop a framework to guide biomedical engineering of synthetic tissues, and help the production of specific cell types for regenerative medicine? This project will develop an interpretable machine learning framework to infer cell fate patterning mechanisms from sequences of microscopy images, and to use this framework to identify evolutionary design principles underlying robust cell fate patterning during tissue development.A hallmark of living systems is their ability to self-organise in a manner that is both robust and evolvable. Robustness provides resistance to genetic and environmental variability, while evolvability maximises phenotypic innovations and ensures adaptability. Robustness and evolvability appear to be opposing characteristics, and how biological systems combine these two properties is not understood. One possibility is that there exists a set of motifs within molecular and cellular interaction networks that explain how developmental systems achieve both robustness and evolvability at the same time. Identifying and gaining a quantitative understanding of these motifs would enable a breakthrough to rationalise our strategies to control, manipulate, and repair biological systems.On this project you will use an innovative machine learning framework, Neural Cellular Automata (NCA) [Richardson et al. 2024], to (1) develop its interpretability as mechanistic mathematical models, (2) analyse in-house experimental data from synthetic embryology [Robles Garcia et al. 2023], (3) quantify evolvability of molecular and cellular interaction networks through in-silico evolution of trained models and information-theoretic quantification of patterning and self-organisation. Your responsibilities will include developing code and designing workflows, understanding the underlying mathematics, and collaborating with experimental biologists to analyse existing data as well as designing new experiments based on model predictions. This project would suit a student with quantitative and computational skills interested in developing interpretable AI methods as well as solving biological problems.
