Machine Learning and Artificial Intelligence Future Science Platform, CSIRO, Canberra, ACT 2601, Australia.
Department of Computer Science, Australian National University, Canberra, ACT 2601, Australia.
ACS Synth Biol. 2022 Jul 15;11(7):2314-2326. doi: 10.1021/acssynbio.2c00015. Epub 2022 Jun 15.
Optimization of gene expression levels is an essential part of the organism design process. Fine control of this process can be achieved by engineering transcription and translation control elements, including the ribosome binding site (RBS). Unfortunately, the design of specific genetic parts remains challenging because of the lack of reliable design methods. To address this problem, we have created a machine learning guided Design-Build-Test-Learn (DBTL) cycle for the experimental design of bacterial RBSs to demonstrate how small genetic parts can be reliably designed using relatively small, high-quality data sets. We used Gaussian Process Regression for the Learn phase of the cycle and the Upper Confidence Bound multiarmed bandit algorithm for the Design of genetic variants to be tested in vivo. We have integrated these machine learning algorithms with laboratory automation and high-throughput processes for reliable data generation. Notably, by Testing a total of 450 RBS variants in four DBTL cycles, we have experimentally validated RBSs with high translation initiation rates equaling or exceeding our benchmark RBS by up to 34%. Overall, our results show that machine learning is a powerful tool for designing RBSs, and they pave the way toward more complicated genetic devices.
优化基因表达水平是生物体设计过程的重要组成部分。通过工程转录和翻译控制元件，包括核糖体结合位点（RBS），可以实现对此过程的精细控制。然而，由于缺乏可靠的设计方法，特定遗传部件的设计仍然具有挑战性。为了解决这个问题，我们创建了一个机器学习指导的设计-构建-测试-学习（DBTL）循环，用于细菌 RBS 的实验设计，以展示如何使用相对较小的高质量数据集可靠地设计小遗传部件。我们在循环的学习阶段使用了高斯过程回归，在体内测试的遗传变体的设计阶段使用了上置信界多臂赌博机算法。我们已经将这些机器学习算法与实验室自动化和高通量过程集成在一起，以生成可靠的数据。值得注意的是，通过在四个 DBTL 循环中总共测试 450 个 RBS 变体，我们通过实验验证了具有高翻译起始率的 RBS，其高达 34%的起始率与我们的基准 RBS 相当或超过。总的来说，我们的结果表明，机器学习是设计 RBS 的强大工具，并为更复杂的遗传器件铺平了道路。
