Pavel Kraikivski

The paper develops a stochastic Boolean model of apoptosis signaling in MCF7:5C cells to predict cell death inducers and inhibitors, identifying genetic and drug-target combinations that affect apoptosis pathways.

This study shows that alternating WEE1 inhibition with CDK4/6 inhibitor and antiestrogen treatment delays resistance in ER+ breast cancer cells.

This paper introduces a perturbation method to refine Boolean models of cell cycle regulation in budding yeast and mammalian cells by optimizing network dynamics.

The paper develops a stochastic Boolean model of the budding yeast cell cycle that accurately replicates wild-type and mutant phenotypes, providing a validated framework for studying cell cycle regulation.

The paper was withdrawn due to authorship disputes and is not intended for citation.

The paper builds a computational model of the nutrient signaling network in budding yeast to reconcile experimental data with a proposed molecular mechanism and test predictions in mutant strains.

The paper quantitatively estimates genetic interactions between 36 yeast cell-cycle genes using high-throughput phenotyping of 7,350 mutants and refines a mathematical cell cycle model.