Bacteria evolve resistance to antibiotics by a multitude of mechanisms. A central, yet unsolved question is how resistance evolution affects cell growth at different drug levels. Here, we develop a fitness model that predicts growth rates of common resistance mutants from their effects on cell metabolism. The model maps metabolic effects of resistance mutations in drug-free environments and under drug challenge; the resulting fitness trade-off defines a Pareto surface of resistance evolution. We predict evolutionary trajectories of growth rates and resistance levels, which characterize Pareto resistance mutations emerging at different drug dosages. We also predict the prevalent resistance mechanism depending on drug and nutrient levels: low-dosage drug defence is mounted by regulation, evolution of distinct metabolic sectors sets in at successive threshold dosages. Evolutionary resistance mechanisms include membrane permeability changes and drug target mutations. These predictions are confirmed by empirical growth inhibition curves and genomic data of Escherichia coli populations. Our results show that resistance evolution, by coupling major metabolic pathways, is strongly intertwined with systems biology and ecology of microbial populations.

细菌通过多种机制进化出对抗生素的抗性。一个核心但尚未解决的问题是耐药性进化如何影响不同药物水平的细胞生长。在这里，我们开发了一个适应度模型，根据它们对细胞代谢的影响来预测常见抗性突变体的生长速率。该模型绘制了无药物环境和药物挑战下耐药性突变的代谢效应；由此产生的适应度权衡定义了阻力演化的帕累托面。我们预测了增长率和耐药水平的进化轨迹，这些轨迹表征了不同药物剂量下出现的帕累托耐药突变。我们还预测了取决于药物和营养水平的普遍耐药机制：低剂量药物防御是通过调节建立的，不同代谢部门的演变以连续的阈值剂量开始。进化耐药机制包括膜通透性变化和药物靶点突变。这些预测由经验生长抑制曲线和基因组数据证实大肠杆菌种群。我们的研究结果表明，通过耦合主要代谢途径的抗性进化与微生物种群的系统生物学和生态学密切相关。