Measuring differential fitness costs and interactions between genetic cassettes using fluorescent spectrophotometry.

In this article, we present a method for designing, executing, and analyzing data from a microbial competition experiment. We use fluorescent reporters to label different competing strains and resolve individual growth curves using a fluorescent spectrophotometer. Our comprehensive data analysis pipeline integrates multiple experiments to simultaneously infer sources of variation, extract selection coefficients, and estimate the genetic contributions to fitness for various synthetic genetic cassettes (SGCs). To demonstrate the method, we employ a synthetic biological system based on Escherichia coli. Strains carry 1 of 10 different plasmids and one of three genomically integrated fluorescent markers. All strains are co-cultured to obtain real-time measurements of optical density (total population density) and fluorescence (sub-population densities). We identify challenges in calibrating between fluorescence and density and of fluorescent proteins maturing at different rates. To resolve these issues, we compare two methods of fluorescence calibration and correct for maturation by measuring in vivo maturation times. We provide evidence of genetic interactions occurring between our SGCs and further show how to use our statistical model to test some hypotheses about microbial growth and the costs of protein expression.IMPORTANCEFluorescently labeled co-cultures are becoming increasingly popular. The approach proposed here offers a high standard for experimental design and data analysis to measure selection coefficients and growth rates in competition. Measuring competitive differences is useful in many laboratory studies, allowing for fitness cost-correction of growth rates and ecological interactions and testing hypotheses in synthetic biology. Using time-resolved growth curves, rather than endpoint measurements, for competition assays allows us to construct a detailed scientific model that can be used to ask questions about fine-grained phenomena, such as bacterial growth dynamics, as well as higher-level phenomena, such as the interactions between synthetic cassette expression.