ABSTRACT
Chapter 1 The rate at which unicellular micro-organisms progress through the cell cycle is a major component of their evolutionary fitness. Measuring fitness phenotypes in a given environment or genetic background forms the basis of most quantitative assays of drug sensitivity or genetic interaction, including genome-wide assays. Growth rate is typically measured in bulk cell populations, inoculated with anything from hundreds to millions of cells sampled from purified, isogenic colonies. High-throughput microscopy reveals that striking levels of growth rate heterogeneity arise between isogenic cell lineages (Levy et al., 2012). Using published Saccharomyces cerevisiae data, I examine the implications for interpreting bulk, population scale growth rate observations, given observed levels of growth rate heterogeneity at the lineage level. I demonstrate that selection between cell lineages with a range of growth rates can give rise to an apparent lag phase at the population level, even in the absence of evidence for a lag phase at the lineage level. My simulations further predict that, given observed levels of heterogeneity, final populations should be dominated by one or a few lineages.
Chapter 2 In order to validate and further explore the conclusions from Chapter 1, I re-analyzed high-throughput microscopy experiments carried out on Quantitative Fitness Analysis (QFA) S. cerevisiae cultures (Addinall et al., 2011), an approach referred to as μQFA. To allow for precise observation of purely clonal lineages including very fast-growing lineages and non-dividing cells, I re-designed an existing image analysis tool for μQFA, now available as an open source Python package. Fast-growing outliers in particular influence the extent of the lag phase apparent at the population level, making the precision of growth rate estimation a key ingredient for successfully simulating population observations. μ QFA data include population observations which I used to validate the population simulations generated from individual lineage data. I explored various options for modeling lineage growth curves and for carrying out growth rate parameter inference, and included the full workflow in an open source R package.
Contact helena.herrmann{at}postgrad.manchester.ac.uk
