AI for quantitative modelling and prediction in cellular biology

Our progress in understanding and engineering living systems, and developing therapies, is severely limited by inability to build predictive, data driven models of cellular processes. Much of current cellular biology research, including work with human stem cells, microbes, and cell lines, proceeds by optically labelling cellular components such as proteins and by measuring and manipulating physiological signals optically. Microscope imaging is then used to track and quantify the interactions of these signals and components in living cells, including cells that have been genetically engineered or exposed to pharmacological agents. Quantities of interest, such as where proteins aggregate, or how rapidly cells grow are then extracted from images or movies and then quantified. This is challenging, slow and error prone because the experiments are often done piecemeal, often by hand, and focus on a handful of types of molecules or cellular interactions that are inferred from a condensed snapshot of the data, such as an average protein density.  

This project leverages recent advances in AI to analyse image data gathered from microbial populations (E coli). Our goal is to build predictive models of processes such as cell division and virus infection using high throughput microscope data. We approach this using a fusion of simulated and real data, with model-based predictions tested in automated, high throughput experiments. We wish to scale this up to cover other types of cells, including human stem cells and microbiota through collaboration with suitable groups at NIH.  This project would suit trainees with strong quantitative skills, a first degree in a STEM discipline and proficiency in coding in more than one language.