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Traditional assays for counting bacteriophages and their lysogens are labor-intensive and perturbative to the host cells. Here, we present a high-throughput infection method in a microplate reader, where the growth dynamics of the infected culture is measured using the optical density (OD). We find that the OD at which the culture lyses scales linearly with the logarithm of the initial phage concentration, providing a way of measuring phage numbers over nine orders of magnitude and down to single-phage sensitivity. Interpreting the measured dynamics using a mathematical model allows us to infer the phage growth rate, which is a function of the phage-cell encounter rate, latent period, and burst size. Adding antibiotic selection provides the ability to measure the rate of host lysogenization. Using this method, we found that when E. coli growth slows down, the lytic growth rate of lambda phages decreases, and the propensity for lysogeny increases, demonstrating how host physiology influences the viral developmental program. 

In Escherichia coli , translocation of RNA polymerase (RNAP) during transcription introduces supercoiling to DNA, which influences the initiation and elongation behaviors of RNAP. To quantify the role of supercoiling in transcription regulation, we developed a spatially resolved supercoiling model of transcription. The integrated model describes how RNAP activity feeds back with the local DNA supercoiling and how this mechanochemical feedback controls transcription, subject to topoisomerase activities and stochastic topological domain formation. This model establishes that transcription-induced supercoiling mediates the cooperation of co-transcribing RNAP molecules in highly expressed genes, and this cooperation is achieved under moderate supercoiling diffusion and high topoisomerase unbinding rates. It predicts that a topological domain could serve as a transcription regulator, generating substantial transcriptional noise. It also shows the relative orientation of two closely arranged genes plays an important role in regulating their transcription. The model provides a quantitative platform for investigating how genome organization impacts transcription.