Microtubule Drug Repositioning Predictions using high-throughput live-cell microscopy

Together with the Sastry group, we are building SpinX, a spindle detection tool using
Machine Learning approaches and have developed Microtubule-end tracker, an automated
tracking tool using a mathematical linear assignment framework (Tamura et al., 2016). Building on
this progress, this project will achieve the following:
1. Build a high-throughput image dataset of microtubule function variations in cells treated with
drugs that alter microtubule-associated protein expression based on transcriptomic
analysis.
2. Use SpinX and Microtubule-end tracker to quantify how spindle movements, microtubule
growth and shrinkage rates vary in the dataset above using novel joint embedding models
(e.g., see https://arxiv.org/abs/1703.03862
3. To identify relationships between subcellular phenotypes quantified through imaging, and
network relationships at the transcriptome level, and thereby reveal drug-drug interactions,
predict and confirm new microtubule targeting drugs.
In summary, the work will allow us to develop transcriptomic network analysis tools suitable