Combinatorial Biosynthetic Pathway Engineering

生物活性天然产物具有重要的生物技术、生物医学、环境和经济意义。因此，许多公司和学术团体都对利用微生物的生物合成途径来制造天然产品感兴趣。然而，这类产品的生产和提纯成本可能很高，有时高得令人望而却步，因此最大限度地提高途径的生产率是一个主要的优先事项。该项目的总体目标是开发一种计算和实验相结合的策略，以优化微生物生物合成途径的生产率，重点放在通常被认为是安全的(GRAS)有机体S啤酒酵母，即面包酵母。制造有用的生物活性分子的一系列微生物途径可以在基因水平上输入酵母。然而，这些途径的产量往往很低，对于希望通过经济上可行的工艺生产有价值产品的公司来说，这是一个问题。因此，我们希望建立一套规则，如何优化酵母中的生物合成途径，从而使更广泛的科学和商业界能够以有效和可预测的方式最大化这些途径的生产力。我们特别感兴趣的是创造易于使用的实验和计算工具的“包”，这些工具可供中小型商业实体使用，因为它们通常受到用于实施新技术的资源量的限制。与此同时，我们相信，我们的整体战略将潜在地使任何对高效生产生物活性分子感兴趣的工业或学术团队受益。为了开发和应用我们的方法，我们将重点放在一个模型途径上，在这种情况下，一个使聚酮分子bikverin的途径。我们将使用合成的DNA片段组装非常大量的基因簇变体，该基因簇编码Bikavin途径酶。一旦基因簇变体被引入酵母，将在机器人平台上对其性能进行分析。这种自动化将大大加快比较不同变种的性能的过程。通过上述实验程序获得的分析数据将被输入到先进的新计算模型中，从而增强我们对如何最好地最大化Bikavin途径的整体活性的理解。该模型将相应地作为这一战略的结果而发展，最终成为如何设计其他生物合成途径以最大化未来性能的洞察力来源。

Bioactive natural products are of great biotechnological, biomedical, environmental and economic importance. For this reason, a large number of companies and academic groups are interested in using biosynthetic pathways derived from microorganisms to manufacture natural products. However, the production and purification of such products can be expensive, sometimes prohibitively so, and therefore maximising the productivity of the pathways is a major priority.The general aim of this project is to develop a combined computational and experimental strategy for optimising the productivity of microbial biosynthetic pathways, focusing on the Generally Regarded As Safe (GRAS) organism S cerevisiae, i.e. baker's yeast. A wide range of microbial pathways that make useful bioactive molecules can be imported at a genetic level into yeast. However, these pathways often have poor yields, which is a problem for a company that wants to manufacture a valuable product via an economically viable process. We therefore want to establish a set of rules how to optimise biosynthetic pathways in yeast, thus enabling the wider scientific and commercial community to maximise the productivity of these pathways in an efficient and predictable way. We are particularly interested in creating easy-to-use 'packages' of experimental and computational tools that can be utilised by small- to medium- sized commercial entities, since these are often constrained in terms of the amount of resources that they can dedicate to implementing new technologies. At the same time, we believe that our overall strategy will potentially benefit any industrial or academic team interested in producing bioactive molecules efficiently. In order to develop and apply our methods, we will focus on a model pathway, in this case a pathway that makes the polyketide molecule bikaverin. We will assemble a very large number of variants of the gene cluster that codes for the bikaverin pathway enzymes using synthetic DNA fragments. The performance of the resulting gene cluster variants will be analysed on a robotic platform once the variants have been introduced into yeast. This automation will greatly accelerate the process of comparing the performance of the different variants. The analytical data obtained through the above experimental procedures will be fed into an advanced new computational model, thus enhancing our understanding of how best to maximise the overall activity of the bikaverin pathway. The model will accordingly evolve as a result of this strategy, ultimately acting as a source of insight into how to design other biosynthetic pathways in order to maximise performance in the future.