Overcoming stochastic variations in culture variables to quantify and compare growth curve data

Abstract

The comparison of growth, whether it is between different strains or under different growth conditions, is a classic microbiological technique that can provide genetic, epigenetic, cell biological, and chemical biological information depending on how the assay is used. When employing solid growth media, this technique is limited by being largely qualitative and low throughput. Collecting data in the form of growth curves, especially automated data collection in multi-well plates, circumvents these issues. However, the growth curves themselves are subject to stochastic variation in several variables, most notably the length of the lag phase, the doubling rate, and the maximum expansion of the culture. Thus, growth curves are indicative of trends but cannot always be conveniently averaged and statistically compared. Here, we summarize a simple method to compile growth curve data into a quantitative format that is amenable to statistical comparisons and easy to graph and display. K E Y W O R D Sgrowth curve, microbe, Saccharomyces cerevisiae (e.g., the calculation of mutant fitness in synthetic genetic array [SGA]) analyses [8] ), but it is cumbersome, time consuming, and difficult to perform with spot dilution assays relative to SGA analyses.