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 systematically refine the model by including direct physical interactions of different drugs on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions derived using entropy maximization. It further makes parameter-free predictions for combined drug effects on cells carrying resistance genes and for drugs that mimic poor nutrient environments. We show experimentally that resistance genes can drastically alter drug interactions in notable agreement with our theoretical predictions. 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.

中文翻译：

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结合抗生素作用的最小生物物理模型

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，该定律捕获了翻译与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。我们的模型仅根据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。我们通过包括核糖体上不同药物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导出的高阶交互作用的最新预测提供了机械基础。它进一步针对结合药物对携带抗性基因的细胞的作用以及模拟营养不良环境的药物做出了无参数预测。我们实验表明抗性基因可以大大改变药物相互作用，与我们的理论预测显着一致。虽然最小，但是该模型很容易适应，并为预测广泛的生物系统中第二阶和更高阶的相互作用打开了大门。