Fig. 2: Mathematical model partially predicts drug interactions.

a Schematic of antibiotic binding and transport into the cell. Antibiotics (circles) bind to the unbound ribosomes (gray) in the first binding step (above dashed line); bound ribosomes can be bound by a second antibiotic (second binding step; below dashed line). b Schematic of antibiotics binding independently (top) or competing for the same binding site (bottom). c Growth laws link intracellular ribosome concentration to the growth rate. Solid line: ribosome concentration when the growth rate is varied by varying nutrient quality; dashed lines: ribosome concentration when the growth rate is lowered by perturbation of translation. Circles show data from ref. ¹⁴. d Data points are dose–response curves for ERM and KSG; lines show the best-fits of the mathematical model. The best-fit values of the response parameter α that encapsulates kinetic and physiological parameters (Supplementary Information) are shown. Both shallow (top panel, ERM) and steep (bottom panel, KSG) dose–response curves are observed. e Examples of predicted dose–response surfaces. The scatter plot depicts the correlation between predicted and measured growth rates. Means and error bars (standard deviation) of predicted growth rates are estimated from n = 100 bootstrap repetitions. The binding scheme assumed is indicated on the bottom right and Pearson’s ρ on the top left. Predicted and measured dose–response surfaces are shown below the scatter plot. Color of 20% isobole (bottom) denotes the type of predicted interaction. The model correctly predicts response surfaces for KSG-ERM, ERM-TET, and ERM-CHL, yet it fails to predict the interaction between STR and KSG.