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

此前，我们开发了一种创新的、多功能的技术，用于酶法组装和动态重新排列DNA模块。利用这一“同步整合”平台，可以将多个标准化DNA模块快速组装成代谢途径等大型组件，并交换组件的各个部分(例如，调节元件)，从而实现变异和优化。我们建议在这些核心技术的基础上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：开发能够发现和生产各种有价值的生物分子的同步集成“部件”。药物铅的一个重要来源是植物天然产物，但这些药物中的许多都是在其本土宿主中生产的微量药物，使得药物价格昂贵，对收获的环境造成负担。有机化学方法已被广泛应用于药物的合成，但许多天然的药物相关支架无法通过这些方法实现。另一种选择是使用酶来从廉价的绿色起始材料中生产候选药物。这项研究的目标是利用第一阶段资助中构建的SynIntegron技术平台合成各种天然产品变体，如萜类化合物，并生产这些天然产品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物途径的酶的编码基因。2)使用SynIntegron平台组装天然产品的合成代谢途径，例如三萜和酶，这些酶将用官能团装饰它们，以合成各种天然产品变体。3)开发新的代谢工程宿主菌株4)为天然产物及其变体设计体内生物传感器，并在高通量筛选中使用这些传感器，从而实现代谢途径的动态优化。目标2：开发分析工具、方法和概念性见解，使之能够使用Syntigon技术优化不同的多基因功能。无细胞技术有助于将细胞内生化转化与混杂的实验因素(包括细胞毒性和传质限制)分离开来。事实证明，这对定量描述基本合成生物学的“部分”很有用。无细胞部件的特性和体内性能之间的关系仍然不清楚，仍然是一个根本性的重要科学问题。我们建议将无细胞方法扩展到使用Syntigon平台构建的多基因组件的高通量表征，并将其与体内性能进行比较。要将合成生物学平台发展成为合成生物界的真正技术，需要开发新的计算模型框架和量化工具，用于分析和执行基于合成生物学的定向进化。因此，我们将1)开发一个定量的计算模型框架，描述基于新整合子的多元化和选择。2)在计算机模拟和实验配对的情况下，优化基于同步整合子的定向进化。综合实验和计算平台的最终测试将是执行、分析并最终指导基于同步整合子的模型代谢途径的定向进化。我们首先将计算机模拟与计算优化配对，以探索可调整的实验参数对预测的机会的影响，这些机会(A)产生足够的遗传多样性来采样许多潜在的功能整合子配置，以及(B)成功地选择表现出优化的生物合成的变体。

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.