Using AI based modelling to drive the engineering of biology

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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.