Engineered genetic systems are prone to failure when their genetic parts contain repetitive sequences. Designing many nonrepetitive genetic parts with desired functionalities remains a difficult challenge with high computational complexity. To overcome this challenge, we developed the Nonrepetitive Parts Calculator to rapidly generate thousands of highly nonrepetitive genetic parts from specified design constraints, including promoters, ribosome-binding sites and terminators. As a demonstration, we designed and experimentally characterized 4,350 nonrepetitive bacterial promoters with transcription rates that varied across a 820,000-fold range, and 1,722 highly nonrepetitive yeast promoters with transcription rates that varied across a 25,000-fold range. We applied machine learning to explain how specific interactions controlled the promoters’ transcription rates. We also show that using nonrepetitive genetic parts substantially reduces homologous recombination, resulting in greater genetic stability.

当基因工程系统的遗传部分包含重复序列时，它们很容易发生故障。设计具有所需功能的许多非重复性遗传部分仍然是具有高计算复杂性的困难挑战。为克服这一挑战，我们开发了非重复部分计算器，可根据指定的设计约束条件快速生成数千个高度非重复的遗传部分，包括启动子，核糖体结合位点和终止子。作为演示，我们设计并实验表征了4,350个非重复细菌启动子，其转录率在820,000倍的范围内变化，以及1,722个高度非重复性酵母启动子，其转录率在25,000倍的范围内变化。我们应用机器学习来解释特定的相互作用如何控制启动子的转录速率。我们还表明，使用非重复性遗传部分会大大减少同源重组，从而导致更大的遗传稳定性。