Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.

诸如欧姆定律或傅立叶定律之类的现象学关系在物理学中有着悠久的历史，但在生物学上仍然很少。这种情况限制了预测理论。在这里，我们建立在细菌“生长定律”的基础上，该定律捕获了翻译和细胞生长之间的生理反馈，从而构建了针对核糖体靶向抗生素的联合作用的最小生物物理模型。我们的模型仅通过对单个药物的反应来预测药物的相互作用，例如拮抗作用或协同作用。我们提供了有限情况下的分析结果，这些结果与数值结果非常吻合。我们通过包括核糖体上不同抗生素的直接物理相互作用来系统地完善模型。在有限的情况下，我们的模型为使用熵最大化推导的高阶相互作用的最新预测提供了机械基础。我们进一步完善该模型，使其包括模拟饥饿和产生抗性基因的抗生素的作用。我们通过分析描述了模拟饥饿的抗生素对药物相互作用的影响，并通过实验进行了验证。我们的扩展模型表明，药物相互作用类型的变化取决于耐药性的强度，这将挑战已建立的重新定型范式。我们通过实验表明，未调控的抗性基因的存在可导致药物相互作用的改变，这与模型的预测相符。虽然极少，