Silvana Konermann

R&D language or biological language has evolved.

It was not generated by humans.

So it's basically impenetrable for us, right?

But AI doesn't care.

Yeah, absolutely.

So, I mean, really what we learned, again, for large language models is just they're very hungry.

They're very data hungry.

And really, we've, you know...

been generating data for these language models for thousands of years, right?

They're using all human language that's been generated over generations in civilizations.

In biology, we don't have anything similar to that, especially when you're thinking about, OK, we need these precise measurements, kind of one cell at a time, and we also need to know what actually happened to that cell, because we're trying to build a predictive model, a dynamic model that can predict how a cell will change when something happens to it.

And so we need to generate that data set, and that's kind of really core to being able to build any useful model here.

Essentially, this is really combining those first two elements I was talking about, which is making a targeted change.

In this case, we're using CRISPR technology to turn a gene off or to turn it on, and we're doing that one gene at a time for one cell at a time, and then we're measuring the outcome using single-cell RNA sequencing, so we're capturing what happened to the cell.

Yeah, so our plan is to do at least a billion of these experiments.

It's a lot of experiments over the next four years.

I mean, I'm a

biologists and experimental biologists.

We're working with a lot of experiments in the lab.

And yeah, the way that we can do this is kind of using some tricks that makes this much more scalable.