"Exploiting the syntegron technology platform for assembly and optimisation of complex genetic ensembles"

之前，我们开发了一种创新的，通用的技术，用于酶组装和动态重排DNA模块。这种“Syntegron”平台能够将多个标准化DNA模块快速组装成大型组件，例如代谢途径，并交换组件的单个部分（例如调节元件），以允许变化和优化。我们建议以这些核心技术为基础，开发一个全面的实验和计算工具包，使重要生物分子的快速生成成为可能。目标1：开发Syntegron“部件”，使多种有价值的生物分子的发现和生产成为可能。药物先导物的一个重要来源是植物天然产物，但其中许多药物在其原生宿主体内的产量极低，这使得这些药物价格昂贵，而且对环境造成负担。有机化学方法已广泛用于药物合成，但许多天然药物相关的支架不能通过这些方法获得。另一种选择是使用酶，使候选药物的生产从廉价的，绿色的起始材料。本研究的目标是利用在第一阶段资助中构建的Syntegron技术平台合成多种天然产物变体，例如萜烯，并制造这些天然产物变体用于药物发现应用。我们将1)利用最近描述的植物代谢基因簇现象，通过开发新的生物信息学和数据挖掘工具来识别参与植物次生代谢途径的酶编码基因。2)利用Syntegron平台组装天然产物的合成代谢途径，如三萜和酶，用官能团修饰它们，合成各种天然产物变体。3)为代谢工程开发新的Syntegron宿主菌株4)为天然产物及其变体设计体内生物传感器，并将这些传感器用于高通量筛选，从而实现代谢途径的动态优化。目标2：开发分析工具、方法和概念见解，使用Syntegron技术优化多种多基因功能。无细胞技术有助于将细胞内生化转化从混杂的实验因素（包括细胞毒性和传质限制）中解耦。事实证明，这对于定量表征基本合成生物学“部分”是有用的。无细胞部件表征与体内性能之间的关系尚不清楚，这仍然是一个重要的科学问题。我们建议将无细胞方法扩展到使用Syntegron平台构建的多基因组装的高通量表征，并将其与体内性能进行比较。要将Syntegron平台发展成为合成生物学领域的真正技术，需要开发新的计算建模框架和定量工具，以分析和执行基于Syntegron的定向进化。因此，我们将1)开发一个定量计算模型框架，描述基于syntegron的多样化和选择。2)利用与实验相结合的计算机模拟优化基于syntegron的定向进化。实验和计算相结合的Syntegron平台的最终测试将是执行、分析并最终指导基于Syntegron的模型代谢途径的定向进化。我们将首先将计算机模拟与计算优化配对，以探索可调实验参数对以下预测机会的影响：(a)产生足够的遗传多样性以采样许多潜在的功能性合成子配置，以及(b)成功选择表现出优化生物合成的变体。

Previously we developed an innovative, versatile technology for enzymatically assembling and dynamically rearranging DNA modules. This "Syntegron" platform enables rapid assembly of multiple standardized DNA modules into large assemblies such as metabolic pathways and to exchange individual parts of assemblies (e.g., regulatory elements) to allow variation and optimization. We propose to build on these core technologies to develop a comprehensive experimental and computational toolkit enabling the rapid generation of important biomolecules:Objective 1: Develop Syntegron "parts" enabling the discovery and production of diverse, valuable biomolecules. An important source for drug leads has been plant natural products but many of these drugs are produced in miniscule amounts in their native hosts, making the drugs expensive and environmentally taxing to harvest. Organic chemistry methodologies have been widely used to synthesize pharmaceuticals but many natural pharmaceutically-relevant scaffolds cannot be achieved by these methods. An alternative is the use of enzymes to enable the production of drug candidates from inexpensive, green starting materials. The goal of this research is to synthesize a variety of natural product variants e.g. terpenes using the Syntegron technology platform constructed in the first phase of funding, and to manufacture these natural product variants for drug discovery applications. We will 1) Take advantage of the recently described phenomena of plant metabolic gene clusters to identify genes encoding enzymes involved in plant secondary metabolite pathways via the development of novel bioinformatic and data mining tools. 2) Use the Syntegron platform to assemble the synthetic metabolic pathways for natural products e.g. the triterpenes and enzymes that will decorate them with functional groups to synthesize a variety of natural product variants. 3) Develop new Syntegron host strains for metabolic engineering 4) Engineer in vivo biosensors for natural products and their variants and use these sensors in high throughput screens allowing dynamic optimization of metabolic pathways.Objective 2: Develop analytical tools, methods, and conceptual insights enabling the optimisation of diverse multigenic functions using Syntegron technology. Cell-free technology is useful for decoupling intracellular biochemical transformations from confounding experimental factors including cellular toxicity and mass transfer limitations. This has proven useful for quantitatively characterizing fundamental synthetic biology "parts". The relationship between cell-free parts characterization and performance in vivo remains unclear, and remains a fundamentally important scientific question. We propose to extend cell-free approaches to the high-throughput characterization of multi-genic assemblies constructed using the Syntegron platform, and to compare them with performance in vivo. The development of the Syntegron platform into a true technology for the synthetic biology community requires the development of novel computational modelling frameworks and quantitative tools for analysing and performing Syntegron-based directed evolution. We will therefore 1) develop a quantitative computational modelling framework describing Syntegron-based diversification and selection. 2) Optimise Syntegron-based directed evolution using in silico simulations paired with experiments. The ultimate test of the combined experimental and computational Syntegron platform will be to perform, analyse, and ultimately guide Syntegron-based directed evolution of a model metabolic pathway. We will initially pair in silico simulation with computational optimization to explore the influence of tuneable experimental parameters on the predicted chances of (a) generating sufficient genetic diversity to sample many potentially functional syntegron configurations, and (b) successfully selecting for variants that exhibit optimised biosynthesis.