EPSRC-SFI: Supercoiling-driven gene control in synthetic DNA circuits

合成生物学的中心目标之一是利用我们对基因表达的日益增长的理解来“重新连接”细菌细胞，使它们在特定条件下表现出设计的行为。超螺旋是DNA中的一种结构转变，是基因表达的关键过程，尚未被纳入合成生物学的计算设计工具中。我们已经确定了一系列由超螺旋控制的样本细菌开关，这些开关将用于指导和验证物理和计算模型。我们的总体愿景是开发一个合成生物学工具包，我们称之为TORC，它包括DNA超螺旋的信息处理能力及其程序性调制。TORC工具包将基于一个物理模型和一个抽象计算模型，其中物理模型捕捉通过DNA超螺旋处理信息的机制，抽象计算模型将为高级合成生物学应用提供工程开发方法，并为模拟生物遗传过程提供科学语言。这个跨学科的项目将物理学、生物学和计算机科学结合在一起，以实施迈向这一愿景的第一步：TORC1.0，一种通过物理模拟和湿法实验室实验开发的新的计算语言。我们的计划分为三个阶段：(I)我们将在单个可控制的质粒中放置特征良好的细菌开关，在那里它们将在细菌中表达。(Ii)我们将使用成熟的DNA转录统计物理模型来构建每个开关的模型，预测输出将如何随着生物条件的变化而变化，并根据湿实验室的结果进行验证。(Iii)我们将使用经过验证的物理模型和湿实验室的结果来推导出一个新的超螺旋计算模型，支持一种新的计算语言来编程合成生物学设计和应用。

One of the central aims of synthetic biology is to use our growing understanding of gene expression to `rewire' bacterial cells so that they exhibit designed behaviour under specific conditions. Supercoiling, a structural transition in the DNA, is a key process in gene expression that has yet to be incorporated into synthetic biology's computational design tools. We have identified a series of exemplar bacterial switches that are controlled by supercoiling, which will be used to guide and validate physical and computational models.Our overarching vision is to develop a synthetic biology toolkit, which we call TORC, that includes the information processing capabilities of DNA supercoiling, and its programmatic modulation. The TORC toolkit will be based on a physical model that captures the mechanism of information processing through DNA supercoiling, and an abstract computational model that will provide both an engineering development approach for advanced synthetic biology applications, and a scientific language for modelling biological genetic processes. This cross-disciplinary project brings together Physics, Biology, and Computer Science to implement the initial steps towards this vision: TORC1.0, a new computational language developed through physical simulations and wet-lab experiments.Our programme has three stages:(i) We will place well-characterised bacterial switches within a single, controllable plasmid, where they will be expressed in bacteria. (ii) We will use well-established statistical physics models of DNA transcription to construct a model of each switch, predict how the output will change with varying biological conditions, and validate it against the wet-lab results.(iii) We will use the validated physical model and wet-lab results to derive a novel computational model of supercoiling, supporting a new computational language for programming synthetic biology designs and applications.