5Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict 6 ways. Here, we demonstrate that these interactions can be accounted for by "translation bottlenecks": 7 points in the translation cycle where antibiotics block ribosomal progression. To elucidate the under-8 lying mechanisms of drug interactions between translation inhibitors, we generated translation bot-9 tlenecks genetically using inducible control of translation factors that regulate well-defined translation 10 cycle steps. These perturbations accurately mimicked antibiotic action and their interactions, support-11 ing that the interplay of different translation bottlenecks causes these interactions. We further showed 12 that the kinetics of drug uptake and binding together with growth laws allows direct prediction of a 13 large fraction of observed interactions, yet fails for suppression. Simultaneously varying two trans-14 lation bottlenecks in the same cell revealed how the dense traffic of ribosomes and competition for 15 translation factors results in previously unexplained suppression. This result highlights the importance 16 of "continuous epistasis" in bacterial physiology. 17 22 23Inhibiting translation is one of the most common antibiotic modes of action, crucial for restraining 24 pathogenic bacteria [Walsh, 2003]. Antibiotics targeting translation interfere with either the assem-25 bly or the processing of the ribosome, or with the proper utilization of charged tRNAs and trans-26 lation factors ( Fig. 1A,B; Table 1) [Wilson, 2014]. Still, the exact modes of action and physiolog-27 ical responses to many such translation inhibitors are less clear, and responses to drug combina-28 tions are even harder to understand, even though they offer effective ways of fighting antibiotic re-29 sistance [Yeh et al., 2009]. Recently, mechanism-independent mathematical approaches to predict the 30 responses to multi-drug combinations were proposed [Zimmer et al., 2016; Wood et al., 2012], yet 31 these approaches rely on prior knowledge of pairwise drug interactions, which are diverse and have 32 notoriously resisted prediction. They include synergism (inhibition is stronger than predicted), antag-33 onism (inhibition is weaker), and suppression (one of the drugs loses potency) [Bollenbach, 2015; 34 Mitosch and Bollenbach, 2014] (Fig. 1C). To design optimized treatments, the ability to predict or alter 35 drug interactions is crucial -a challenge that would be facilitated by understanding their underlying 36 mechanisms [Chevereau and Bollenbach, 2015]. 37 Apart from their clinical relevance, antibiotic combinations provide powerful, quantitative and con-38 trolled means of studying perturbations of cell physiology [Falconer et al., 2011] -conceptually similar 39 to studies of epistasis between double gene knockouts [Yeh et al., 2006; Segre et al., 2005]. Trans-40 lation inhibitors are particularly suited for this purpose since translation is a fundamental, yet complex 41 multi-step process that still la...