Accelerating organism engineering through the application of AI

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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.