Quantifying phage infectivity from characteristics of bacterial population dynamics

A frequent goal of phage biology is to quantify how well a phage kills a population of host bacteria. Unfortunately, traditional methods to quantify phage success can be time-consuming, limiting the throughput of experiments. Here, we use theory to show how the effects of phages on their hosts can be quantified using bacterial population dynamics measured in a high-throughput microplate reader (automated spectrophotometer). We use mathematical models to simulate bacterial population dynamics where specific phage and bacterial traits are known a priori . We then test common metrics of those dynamics (e.g. growth rate, time and height of peak bacterial density, death rate, extinction time, area under the curve) to determine which best predict: 1) infectivity over the short-term, and 2) phage suppression over the long-term. We find that many metrics predict infectivity and are strongly correlated with one another. We also find that metrics can predict phage growth rate, providing an effective way to quantify the combined effects of multiple phage traits. Finally, we show that peak density, time of peak density, and extinction time are the best metrics when comparing across different bacterial hosts or over longer timescales where plasticity or evolution may play a role. In all, we establish a foundation for using bacterial population dynamics to quantify the effects of phages on their bacterial hosts, supporting the design of in vitro empirical experiments using microplate readers. Significance Bacteriophages are viruses that infect bacteria, with relevance from basic science to medical application. Frequently we seek to quantify how these viruses negatively impact bacterial growth. Typical methods are labor-intense, limiting the number of experiments that can be done. Here, we show how easily-collectable data (called ‘bacterial population dynamics’ or ‘growth curves’) can be used to quantify virus killing of bacteria across a wide range of conditions. In all, our work suggests that these dynamics provide an effective and high-throughput method to quantify phage effects on their hosts.