Silvana Konermann

We're not actually running a billion little individual kind of reactions.

We're able to use kind of different barcoding technologies to kind of run these experiments in bigger pools and then back out what we did to the cells.

Yeah, so just to give a sense of why I feel that we can do the billion experiments is we've done about 60 million experiments so far.

Yeah, so we feel pretty good.

We can keep going.

But yeah, the whole point of this is that we want to learn not just, okay,

if I have this cell and I make this change, what happens to this cell?

Really, my motivation for generating this model is ultimately for human health.

And so for that, we can now have a disease state.

And importantly, this could be, for example, a certain cell, an Alzheimer's disease, let's say it's an immune cell in the brain, microglia.

And we can measure what that looks like, not just for one patient, but across many patients.

And this data is out there, so we don't even have to generate it.

And so we can see, OK, all these diseased cells, and we can have all the healthy cells, but again, across people.

And then we can ask the model, OK, the model knows how to change cells, right?

So what intervention, what genetic change, what chemical change do I need to make to convert all the diseased cells across all the patients with the same disease back to the healthy cells?

It could be that it's a complex combination of things, or it's really just a question even of picking the correct one.

There's 20,000 possibilities.

It could be up or down to 40,000 possibilities.

And normally, the way this target identification in biomedicine works today is really this kind of guess-and-check approach.

So you have a hypothesis, one gene,