Vojislav Gligorovski

This paper uses prime editing to classify LDLR variants by measuring their impact on LDL-C uptake, reclassifying many previously unclassified variants.

This paper introduces optovolution, a directed evolution method using light and cell cycle oscillations to evolve dynamic protein functionalities, and demonstrates its application in yeast.

The paper introduces neural networks for tracking and lineage tracing in budding yeast, achieving robust performance on new and existing datasets.

The paper uses a single-cell optogenetic method to test models of how DNA damage checkpoints count DNA breaks in yeast, finding that proteins at ss/dsDNA boundaries are essential for checkpoint maintenance.

The paper shows that Cdc13 nuclear export occurs before mitosis in S. pombe and is linked to spindle pole body integration, but not sufficient to trigger mitotic entry.

The paper introduces PIPOline, a computational tool for designing plasmids for marker-free genome editing in budding yeast, and demonstrates its application for gene tagging and deletion.

The paper introduces YeaZ, a CNN for accurate yeast cell segmentation without fluorescent markers, outperforming existing methods and revealing new biology in cell morphology.

The paper benchmarks inducible promoters in budding yeast to evaluate their dynamic control and leakiness, identifying key limitations and introducing new tools for precise gene regulation.

The paper develops a theory of optimal checkpoint strategies to predict DNA damage checkpoint override times in yeast, validated with single-cell experiments.