The cell-cycle is a fundamental molecular process, and disruptions in this process are a hallmark of cancer. When studying eukaryotic cell cycle, the most common approach involves synchronizing a cell culture. In this method, the cells, grown in a medium, are prevented to progress through the cell cycle past a certain phase, usually through use of a chemical agent. The block is then released, and the cells are allowed to progress through the cell cycle.
We can assign each cell a phase angle α, representing its progress in the cell cycle. Then, we can describe the cell-cycle phase distribution of a culture. Moreover, for cell-cycle related genes, we may represent their expression as a function of α. An unsynchronized culture will then attain an equilibrium distribution, whereas a synchronized culture starts with a narrow phase distribution, which then evolves in time upon release of the block, converging to the said equilibrium distribution.
In this project, the student should create a model that captures the evolution of cell-cycle phase distribution in time, upon synchronizing cells of different types, using different synchronization methods. Specifically, certain synchronization methods cause initially slower, or vice versa faster, cell cycle. Moreover, the cells may not behave completely alike to one another, but may rather feature several distinct populations. A model for this stratification might take as a starting point data from single cell sequencing. Besides the advancements for fundamental research, such a model should prove useful in deconvolving and interpolating expression profiles from different experiments, thereby making the data comparable.
The current state-of-the-art models are rather unsophisticated, and a student might very well come up with a state-of-the-art model, applying known methods of function approximation, machine learning, or even generalizing certain models from physics. The model would be tested and verified on synthetic, as well as experimental data on phase distribution in experiments (fluorescence-activated cell sorting data), and in cooperation with the student applied to the existing analyses in our group.
For more info, contact Antonin Klima (firstname.lastname@example.org), or Pål Sætrom (email@example.com).