Accelerating organism engineering through the application of AI

通过人工智能的应用加速生物工程

• 批准号: 2898854
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2898854
• 关键词: Accelerating; organism; engineering; through; application

The engineering of biological systems requires the integration of experimental inputs and outputdata across different levels of biodesign and implementation. These can readily be classed intoeither intrinsic factors, which relate to the biology, or external factors that relate to physicalconditions such as media and temperature. When considering the design and implementation of amicrobial biosynthetic pathway, they do not follow a set of prior-known rules, but we can breakthem down into 3 classes: firstly, the genetic design of the pathway itself can be modulated byenzyme identity, and genetic elements used for its instantiation, including promoter, 5'UTR, RBS,gene order, copy number. Secondly, the host metabolic network, which usually provides the inputprecursor and energy for the biosynthetic process and the metabolic load that is exerted on thesystem by the new pathway. Finally, the external factors such as cell strain, media composition, timeand temperature.This project will focus on extending the Robot Synthetic Biologist to more ambitious biosyntheticpathways for the production of high value chemicals in E. coli. The first approach will be to developCRISPR based tools that can modulate the host metabolic network, thus providing the system withinteractive nodes that can thus learn how to optimise the host for the specific bioproduction task.Secondly, it will integrate Artificial Metabolic Networks (AMN), which are a white box neural netimplementation of the E. coli Flux Balance Analysis (FBA) model of metabolic reactions. Thirdly,these will be implemented in biosynthetic pathways: initially for Violacein, a model 5-genebiosynthetic pathway with a pigment output and subsequently for butyl acetate, a volatile flavourcompound that naturally accumulates external to the cell and can easily be captured using a solventoverlay.

生物系统的工程需要在不同层次的生物设计和实施中整合实验输入和输出数据。这些因素可以很容易地分为与生物学相关的内在因素，或与物理条件（如介质和温度）相关的外部因素。在考虑微生物生物合成途径的设计和实施时，它们并不遵循一套先前已知的规则，但我们可以将它们分为三类：第一，途径本身的遗传设计可以通过酶的身份来调节，并且用于其实例化的遗传元件包括启动子、5 'UTR、RBS、基因顺序、拷贝数。第二，宿主代谢网络，它通常为生物合成过程提供输入前体和能量，以及新途径施加在宿主上的代谢负荷。最后，外部因素，如细胞株，培养基组成，时间和温度。本项目将重点扩展机器人合成生物学家更雄心勃勃的生物合成途径，生产高价值的化学品在E。杆菌第一种方法是开发基于CRISPR的工具，可以调节宿主代谢网络，从而为系统提供交互式节点，从而学习如何优化宿主以完成特定的生物生产任务。其次，它将整合人工代谢网络（AMN），这是E.大肠杆菌通量平衡分析（FBA）模型的代谢反应。第三，这些将在生物合成途径中实施：首先是紫色素，一种具有色素输出的模型5-基因生物合成途径，随后是乙酸丁酯，一种挥发性风味化合物，其自然积累在细胞外部，并且可以很容易地使用溶剂覆盖物捕获。