Using AI based modelling to drive the engineering of biology

使用基于人工智能的建模来推动生物学工程

• 批准号: BB/Y514056/1
• 负责人: Geoffrey Baldwin
• 金额: $ 32.91万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY514056%2F1
• 关键词: Using; AI; based; modelling; drive

This proposal seeks to apply AI methodology to drive the engineering of biology. The ultimate vision is to create a self-driving lab, where AI can select the next experiment, interpret data and iterate towards a given design objective. This will enable rapid and rational engineering of microbes for the production of chemicals, compounds and food (e.g. protein) in a sustainable manner that addresses the needs of net zero.We consider that a true harnessing of the power of AI will facilitate the learning (by the AI) of the biological system, so that future design and optimisation tasks will build on the existing knowledge base. This will represent a significant advance compared to treating biodesign problems as isolated optimisation tasks where no learning can be carried forward from one task to the next. Here we will implement the Artificial Metabolic Network (AMN) methodology developed by our international collaborators (1). This encodes the mechanistic metabolic model of E. coli in an innovative Machine Learning (ML) framework that enables learning in way that is not possible with the classical approach to Flux Balance Analysis (FBA), which has been the mainstay of metabolic models for the past 3 decades.We seek to integrate our experimental platform for automated building of biological systems (2) with an AI guided approach to modelling engineered E. coli cell metabolism. A key limitation of classic FBA modelling is that it has poor quantitative predictors of engineered systems, such as gene knockouts (KOs) and thus has only limited utility in the rational forward engineering of biological systems. It has value in identifying likely genes to consider but falls short in quantitative predictions of gene edits. It is also not compatible with current deep learning technologies. Our collaborators have developed AMN (1), a hybrid method that embeds FBA as a white-box mechanistic layer in a form that enables training of a black-box Neutral Net (NN) input layer. The trainability has been demonstrated to provide an excellent approach to fit data derived from genetic KOs that could not be adequately modelled with classical FBA, thus demonstrating that the hybrid AMN approach could be a powerful tool in predicting the behaviour of engineered systems.

该提案旨在应用人工智能方法来推动生物工程。最终的愿景是创建一个自动驾驶实验室，人工智能可以选择下一个实验，解释数据并朝着给定的设计目标进行优化。这将使微生物的快速和合理的工程化，以可持续的方式生产化学品，化合物和食品（如蛋白质），满足净零的需求。我们认为，真正利用人工智能的力量将促进生物系统的学习（通过人工智能），以便未来的设计和优化任务将建立在现有的知识基础上。与将生物设计问题视为孤立的优化任务相比，这将是一个重大的进步，其中没有学习可以从一个任务转移到下一个任务。在这里，我们将实施由我们的国际合作者开发的人工代谢网络（AMN）方法（1）。这编码了E.大肠杆菌在一个创新的机器学习（ML）框架，使学习的方式是不可能的与经典的方法通量平衡分析（FBA），这一直是代谢模型的支柱在过去的30年。大肠杆菌细胞代谢。经典FBA建模的一个关键限制是，它具有工程系统的差的定量预测因子，例如基因敲除（科斯），因此在生物系统的理性正向工程中仅具有有限的效用。它在识别可能考虑的基因方面具有价值，但在基因编辑的定量预测方面福尔斯不足。它也与当前的深度学习技术不兼容。我们的合作者开发了AMN（1），这是一种混合方法，它将FBA嵌入为白盒机制层，其形式可以训练黑盒神经网络（NN）输入层。可训练性已被证明提供了一个很好的方法，以适应数据来自遗传科斯，不能充分模拟与经典的FBA，从而表明，混合AMN的方法可以是一个强大的工具，在预测工程系统的行为。