Using AI based modelling to drive the engineering of biology

这项提议寻求应用人工智能方法论来推动生物工程。最终的愿景是创建一个自动驾驶实验室，在那里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迭代。这将使微生物能够以可持续的方式生产化学品、化合物和食品(例如蛋白质)，以满足净零的需求。我们认为，真正利用人工智能的力量将促进(人工智能)生物系统的学习，从而使未来的设计和优化任务将建立在现有知识库的基础上。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，在这种任务中，无法将学习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)。这在创新的机器学习(ML)框架中编码了大肠杆菌的机械代谢模型，使学习能够以通量平衡分析(FBA)的经典方法所不可能的方式进行，FBA在过去30年一直是代谢模型的主流。我们寻求将我们用于自动构建生物系统的实验平台(2)与人工智能指导的建模工程大肠杆菌细胞新陈代谢的方法相结合。经典FBA模型的一个关键局限性是它对工程系统(如基因敲除(KO))的定量预测很差，因此在生物系统的理性正向工程中的应用有限。它在识别可能要考虑的基因方面有价值，但在基因编辑的定量预测方面做得不够。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嵌入为白盒机械层，其形式允许训练黑盒神经网络(NN)输入层。这种可训练性已被证明提供了一种很好的方法来拟合来自遗传KO的数据，这些数据无法用经典的FBA进行充分建模，从而表明混合AMN方法可以成为预测工程系统行为的强大工具。

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.