|M.Sc Student||Ventura Irit|
|Subject||Morphological Analysis of Cell Cycle in Yeast Populations|
|Department||Department of Chemical Engineering||Supervisor||Professor Naama Brenner|
|Full Thesis text|
The budding yeast S. cerevisiae is a model eukaryotic organism used to study many fundamental aspects of biology. In this study we focus on the analysis of cell cycle and growth characteristics of yeast cells in a continuous culture by using microscope imaging of the population. We developed indirect, simple and tractable computational methods to extract cell cycle parameters from morphological analysis of cell populations. A deterministic model, based on Hartwell's cell cycle model, allows estimation of the average timing and size parameters of the cell cycle. Moreover, a simplified stochastic model was used to provide more information about the population variability in the duration of the different cell cycle times, and corresponding variability in cell sizes. In addition, we developed a method to calculate the growth curves of a single cell based on the experimental measured cell size distributions. This is particularly relevant given that the shape of the growth curve of an individual yeast cell can provide extra information that is not available from studies of cell populations.
Our experimental method is an indirect measurement but maintains the following advantages: steady state conditions and well-controlled environment, large populations, distinction between sub-populations and non-invasive measurements. The results provide a simple method for estimating cell cycle phase durations. They demonstrate how the cells adjust to the specified growth rate with different sugars by changing the duration of the cell-cycle intervals. This indicates the flexibility of the duration of the cell cycle intervals under different growth conditions. We showed that the growth patterns of a single cell have a concave shape which is consistent with a time-lapse measurement previously found in our research group but is in contrast to other studies that usually point to exponential or bi-linear growth.
In order to account for the population variability, we developed a simple stochastic extension of the deterministic cell cycle model. We developed balanced equations for two subpopulations and created a Monte Carlo simulation. This model describes the main features of the experimentally measured cell-size distributions, with a relatively small number of parameters. The fit between the simulation results and the experimental cell-size distributions is satisfactory. This enables to characterize the main properties of the population cell cycle and the dynamics of growth and division.