how close we already are to this vision via AI-led full sequence design of a synthetic yeast chromosome

在设计、构建和测试酵母染色体的人工智能写入区域时，我将了解目前缺少哪些基因组知识和人工智能设计能力，以便在未来十年进行定制的染色体设计。我将为这些目前的差距开发解决方案，以便在项目结束时，我们可以使用人工智能来生成合成染色体臂的完整序列，为人工智能提供仅需包括的基因列表、这些基因的肽序列、它们的目标表达和任何所需的调控。该项目将建立在Imperial构建和测试酵母合成染色体区域(Ellis)的工作基础上，并设计人工智能驱动的工程生物学工具(Stan)。重要的是，它将建立在已发表的关键人工智能工具的现有成功的基础上，这些工具使用Gans和CNN来编写合成酵母启动子[1]、5‘UTRs[2]和3’UTRs[3]。这三个部分占酵母染色体中非编码DNA的85%。作为一项初步研究，我的目标是整合已发表的用于酿酒酵母遗传部分设计的人工智能工具，创建一个程序，该程序建议编码一组代谢基因的完整序列，以便它们在与野生型酵母中典型的水平相匹配的水平上表达。这将使用一种酵母菌株来完成，该菌株已经被修改，将所有编码色氨酸和组氨酸合成的基因重新定位到“试验床”簇--在那里，途径从构成启动子(Ellis Lab)表达。为了识别设计失败，合成的DNA将被购买并交换以取代当前的簇DNA，当全部或部分DNA被人工智能设计时，将测试细胞的活性、生长速度和编码路径的功能。为了改进调控DNA的设计，我将扩展由Zrimec和合作者开发的最先进的启动子编写的生成性对抗网络(GAN)的工作[4]，在57%的情况下，生成的高表达合成序列超过高表达的自然对照的表达水平。这将使我能够使用自然调控序列数据来训练一个深度生成模型，它将学习可行的和生物上一致的候选序列。生成器的能力将由一个预测模型来指导，该模型将生成的序列微调为目标基因的表达。针对每个受调控启动子的多个设计将被合成，并在其集群中的酵母中进行功能测试，以迭代地改进基于AI的受调控启动子设计，并了解如何将其纳入AI染色体设计。

In designing, building, and testing AI-written regions of yeast chromosomes, I will learn what genomic knowledge and AI design capabilities are currently missing that would enable bespoke chromosome design in the next decade. I will develop solutions to these current gaps, so that by the end of the project we can use AI to generate the full sequence of a synthetic chromosome arm, providing the AI with only a list of genes to be included, the peptide sequences of these, their target expression and any regulation required. The project will build on work at Imperial for building and testing synthetic chromosome regions in yeast (Ellis), and in designing AI-driven tools for engineering biology (Stan). Importantly, it will build on the existing success of key published AI tools that use GANs and CNNs to write synthetic yeast promoters [1], 5'UTRs [2] and 3'UTRs [3] to specification. These 3 parts account for >85% of non-coding DNA in a yeast chromosome.As a pilot study, I aim to integrate published AI tools for genetic part design in S. cerevisiae yeast to create a program that suggests the full sequence encoding a cluster of metabolic genes so that they are expressed at levels that match what is typical in wildtype yeast. This will be done using a yeast strain which has been modified by relocating all the genes encoding the synthesis of tryptophan and histidine to 'testbed' clusters - where the pathways are expressed from constitutive promoters (Ellis lab). To identify design failures, the synthetic DNA will be purchased and swapped-in to replace the current cluster DNA, and cells will be tested for viability, growth rate and functionality of the encoding pathways when all or some of the DNA is AI-designed.To improve the design of regulatory DNA, I will extend the work on a state-of-the-art promoter-writing Generative Adversarial Network (GAN) developed by Zrimec and collaborators [4], which, in 57% of the cases, generated highly-expressed synthetic sequences surpassing the expression levels of highly-expressed natural controls. This will allow me to use natural regulatory sequence data to train a deep generative model, which will learn feasible and biologically consistent candidatesequences. The generator's ability will be guided by a predictor model that fine-tunes generated sequences toward target gene expression. Multiple designs for each regulated promoter will be synthesised and tested in the yeast in their cluster for functionality, to iteratively improve the AI-based design for regulated promoters and understand how to incorporate this into AI chromosome design.