Lehr François-Xavier, Hanst Maleen, Vogel Marc, Kremer Jennifer, Göringer H Ulrich, Suess Beatrix, Koeppl Heinz
Department of Biology , Technische Universität Darmstadt , 64287 Darmstadt , Germany.
Department of Electrical Engineering , Technische Universität Darmstadt , 64283 Darmstadt , Germany.
ACS Synth Biol. 2019 Sep 20;8(9):2163-2173. doi: 10.1021/acssynbio.9b00238. Epub 2019 Aug 27.
RNA-based devices controlling gene expression bear great promise for synthetic biology, as they offer many advantages such as short response times and light metabolic burden compared to protein-circuits. However, little work has been done regarding their integration to multilevel regulated circuits. In this work, we combined a variety of small transcriptional activator RNAs (STARs) and toehold switches to build highly effective AND-gates. To characterize the components and their dynamic range, we used an () cell-free transcription-translation (TX-TL) system dispensed via nanoliter droplets. We analyzed a prototype gate as well as , employing parametrized ordinary differential equations (ODEs), for which parameters were inferred via parallel tempering, a Markov chain Monte Carlo (MCMC) method. On the basis of this analysis, we created nine additional AND-gates and tested them . The functionality of the gates was found to be highly dependent on the concentration of the activating RNA for either the STAR or the toehold switch. All gates were successfully implemented , offering a dynamic range comparable to the level of protein circuits. This study shows the potential of a rapid prototyping approach for RNA circuit design, using cell-free systems in combination with a model prediction.
基于RNA的基因表达控制装置在合成生物学领域极具潜力,因为与蛋白质电路相比,它们具有许多优势,如响应时间短和代谢负担轻。然而,关于将它们整合到多级调控电路方面的研究还很少。在这项工作中,我们将多种小转录激活RNA(STARs)和toehold开关相结合,构建了高效的与门。为了表征这些组件及其动态范围,我们使用了通过纳升级液滴分配的无细胞转录-翻译(TX-TL)系统。我们分析了一个原型门以及,采用参数化常微分方程(ODEs),通过马尔可夫链蒙特卡罗(MCMC)方法平行回火推断其参数。基于这一分析,我们又创建了九个与门并对其进行测试。发现这些门的功能高度依赖于STAR或toehold开关的激活RNA浓度。所有门都成功实现,其动态范围与蛋白质电路水平相当。这项研究展示了一种快速原型设计方法在RNA电路设计中的潜力,该方法使用无细胞系统并结合模型预测。