Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany.
Nat Commun. 2020 Oct 16;11(1):5260. doi: 10.1038/s41467-020-18948-x.
Protein synthesis is the most expensive process in fast-growing bacteria. Experimentally observed growth rate dependencies of the translation machinery form the basis of powerful phenomenological growth laws; however, a quantitative theory on the basis of biochemical and biophysical constraints is lacking. Here, we show that the growth rate-dependence of the concentrations of ribosomes, tRNAs, mRNA, and elongation factors observed in Escherichia coli can be predicted accurately from a minimization of cellular costs in a mechanistic model of protein translation. The model is constrained only by the physicochemical properties of the molecules and has no adjustable parameters. The costs of individual components (made of protein and RNA parts) can be approximated through molecular masses, which correlate strongly with alternative cost measures such as the molecules' carbon content or the requirement of energy or enzymes for their biosynthesis. Analogous cost minimization approaches may facilitate similar quantitative insights also for other cellular subsystems.
蛋白质合成是快速生长细菌中最昂贵的过程。实验观察到的翻译机制的生长速率依赖性为强大的现象学生长规律奠定了基础;然而,缺乏基于生化和生物物理约束的定量理论。在这里,我们表明,从蛋白质翻译的机械模型中细胞成本最小化,可以准确预测大肠杆菌中核糖体、tRNA、mRNA 和延伸因子浓度的生长速率依赖性。该模型仅受分子的物理化学性质约束,没有可调参数。单个组件(由蛋白质和 RNA 部分组成)的成本可以通过分子质量来近似,分子质量与替代成本度量(如分子的碳含量或生物合成所需的能量或酶)强烈相关。类似的成本最小化方法也可能为其他细胞子系统提供类似的定量见解。