Understanding stochastic metabolic oscillators in single cells

Biological rhythms are a fundamental property of life, and even single cells can generate both cell cycles and circadian oscillations. Less well known but potentially just as ancient, cells also undergo metabolic oscillations, with metabolic activity cycling once but occasionally multiple times during a cell cycle.
We have measured the intensities of fluorescent protein markers for metabolic rhythms and the cell-cycle stage simultaneously in hundreds of single cells of budding yeast over time, in constant and changing environments.
The student will use a combination of machine learning, time-series analysis, and stochastic modelling to quantify the dynamics of the metabolic oscillator and to determine whether it drives the cell cycle or vice versa. There will be opportunities to generate new data, particularly data gathered in response to the student's modelling.
The project will give a solid foundation in the use of neural networks and stochastic modelling to better understand subcellular dynamics, as well as on microfluidic technology and time-lapse microscopy.