Translation of mRNA into protein is a fundamental yet complex biological process with multiple factors that can potentially affect its efficiency. Here, we study a stochastic model describing the traffic flow of ribosomes along the mRNA and identify the key parameters that govern the overall rate of protein synthesis, sensitivity to initiation rate changes, and efficiency of ribosome usage. By analyzing a continuum limit of the model, we obtain closed-form expressions for stationary currents and ribosomal densities, which agree well with Monte Carlo simulations. Furthermore, we completely characterize the phase transitions in the system, and by applying our theoretical results, we formulate design principles that detail how to tune the key parameters we identified to optimize translation efficiency. Using ribosome profiling data from S. cerevisiae, we show that its translation system is generally consistent with these principles. Our theoretical results have implications for evolutionary biology, as well as for synthetic biology.

从mRNA到蛋白质的翻译是一个基本而复杂的生物学过程，具有多种可能影响其效率的因素。在这里，我们研究了一个随机模型，该模型描述了核糖体沿mRNA的流量，并确定了控制蛋白质合成总体速率，对起始速率变化的敏感性以及核糖体使用效率的关键参数。通过分析模型的连续极限，我们获得了稳定电流和核糖体密度的闭式表达式，这与蒙特卡洛模拟非常吻合。此外，我们完全表征了系统中的相变，并通过应用理论结果，制定了设计原则，详细说明了如何调整我们确定的关键参数以优化翻译效率。使用来自的核糖体分析数据酿酒酵母（S. cerevisiae），我们证明其翻译系统通常与这些原则一致。我们的理论结果对进化生物学以及合成生物学都有影响。