I think there are multiple strands to prediction. There's prediction in a healthcare sense - which patient is going to respond best to which drug – often termed precision medicine. The Sanger Institute is already playing a big role there and I see this continuing. We can take some of the nascent technologies that we can apply at scale, and show whether they're useful or not, for particular, clinical problems. Those technologies might be in genetics, somatic mutations, or single-cell atlasing. I think there is a question of what can these deep molecular measurements do for the clinical world.
Also, there is the question of what can these technologies do for public health surveillance of pathogens. How can we predict what's going to happen, and intervene sooner?
And then there's the prediction in terms of engineering biology. Can we design a gene that could be used to treat a specific disease, for example? Or one that might clear up pollution? Can we fundamentally understand biology well enough that we can predict which genetic variants will cause disease and which won't? Can we design genomes that test our predictive understanding?
So I think there's a combination of new technologies, generating data at scale, the complex computational analyses of those data, and then working to see whether those are useful for society.
We can do proof of concept projects that enable society to say, this is useful, this isn't useful. It's about empowering society, not telling people what to do.
Before joining the Sanger Institute, Matt was researching prehistoric migrations in the Pacific, working at Cambridge University alongside archaeologists and linguists, using genetics as an independent record of the past. We spoke about his early career and training.