|M.Sc Student||Elyahu Noa|
|Subject||Population Structure and its Role in Adapting to an|
|Department||Department of Chemical Engineering||Supervisors||Professor Erez Braun|
|Professor Naama Brenner|
|Full Thesis text|
Populations of microorganisms are phenotypically variable, even when genetically homogeneous and when grown in uniform well-controlled conditions. Recent developments in single-cell measurement techniques, such as flow cytometry and time-lapse microscopy have shed light on mechanisms of phenotypic variation. These techniques provide measurements of phenotypic properties (fluorescent signal, morphology, granularity) in individual cells, as opposed to measurements averaged over large populations. Still, little is known regarding the role of phenotypic variability in population fitness and survival.
In this work, we focused on strains of yeast with a fluorescent protein (GFP) placed under a regulatory network responsible for utilizing galactose (GAL system), so that the GFP expression is correlated to the GAL system's activity. Flow cytometry is then used to analyze the variability in the quantities of GFP in a population of cells from these strains of yeast, and the distribution of quantities is used to characterize the population structure. Using synthetic gene recruitment to place an essential gene (HIS3) under the GAL system (which is foreign to it), we put the yeast cells under stressful conditions imitating an evolutionary-like event and examine the correlation between the levels of GFP expression and the cells' potential to adapt and survive the stress.
Our study shows an extremely robust steady state distribution of GFP in the cell population, insensitive to cell metabolism and growth conditions. Subpopulations separated according to fluorescent levels (low fluorescent only or high fluorescent only), are shown to rebuild the complete steady state distribution in less than 20 generations. The steady state and dynamic behavior of this distribution was examined in view of the correlation between cell growth rates and protein production using a mathematical model. The suggested model successfully simulated a variety of interesting dynamic behaviors of the GFP distribution in both shape and timescales.
Finally, we find significant correlations between the level of protein expression, as well as other physiological parameters, and the ability of individual cells to adapt to the stressful conditions. We present and analyze this correlation.