Towards full sequence design of functional chromosomes by AI

Current progress in synthetic genomics means that the scale at which we design and build DNA is
rapidly increasing. In parallel, AI-enabled tools are accelerating our capabilities to design
genetically-encoded parts to achieve desired specifications. It is logical, therefore, that we should
expect the 'biodesigner of the future' to be using AI to help write custom DNA sequences that can
be assembled to build whole chromosomes and genomes that drive cells to perform desired tasks.
In this project, we plan to see how close we already are to AI-led full sequence design of a
synthetic yeast chromosome. In designing, building and testing AI-written regions of yeast
chromosomes, we will learn what genomic knowledge and AI design capabilities are currently
missing that would enable bespoke chromosome design in the next decade. We plan to develop
solutions to these current gaps, so that by the end of the project we can use AI to generate the full
sequence of a synthetic chromosome arm, providing the AI with only a list of genes to be included,
the peptide sequences of these, their target expression levels and any regulation they require.
The project will leverage existing work in our two research groups on building and testing
synthetic chromosome regions in yeast (Ellis), and in designing AI-driven tools for engineering
biology (Stan). Importantly, it will build on the existing success of key published AI tools that use
GANs and CNNs to write synthetic yeast promoters [1], 5'UTRs [2] and 3'UTRs [3] to specification.
These 3 genetic parts together account for >85% of non-coding DNA in a yeast chromosome