Cell-cycle tracking using Machine Learning approaches

Cell-cycle is a process that enables cells to divide into two daughter cells. Live-cell confocal imaging of the FUCCI transfected mammalian cells permits simultaneous tracking of the progress of cell-cycle and cell-division in an ensemble of cells. Understanding the cell-cycle duration variabilities requires tracking the cell-cycle at single-cell level across different frames and reconstruction of its transients. We have recently developed a preliminary machine learning based model, trained using in-house generated confocal images, for a coarse-grained reconstruction of cell-cycle transients. The objective of this project is to develop a robust machine learning approaches based model for predicting the distribution of cell-cycle trajectories across different cell lines and conditions.