Biology AI needs experiments to generate missing data

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 4:51

   and you want to build a frontier ai lab but you need to couple that with a frontier biology effort that can do the work of of basically being able to understand and get the data that you need to actually be able to build these models. Because unlike language models, there's just like a lot of data out there on the internet. That's not really the case with biology. I mean, there are obviously a bunch of different data sets that exist that academia and scientists have generated over the decades.

2. 5:20

   But a lot of the stuff that I think we want to put into this, it doesn't exist, right? It's like you want to be able to visualize things that people haven't been able to see before, which is why we were doing the imaging work. You want to be able to record things that are going on inside the body, which is why we're doing the kind of cellular engineering work. You want to be able to measure things like inflammation in ways that haven't been possible, which is why the Chicago Biohub is focused on building those kind of devices and being able to do that. And that will fundamentally create new types of data sets that will allow new types of models. And I think it's just a very exciting thing that, going back to what you're saying, if the scientific field primarily needs kind of tool development that now is going to empower

3. 6:02

   scientists across the field to be able to do their work faster, that's what we think we can provide through this kind of long-term focus on tool development. But I think there's a fun through line on where we started and, you know, bringing us to our work that Alex is driving now is that our very first request for application RFA here was around single cell sequencing. And we wanted to look at sort of like the RNA that is transcribed in individual cells.

4. 6:33

   And that was possible, but it was still pretty early on in understanding how different cells were expressing their DNA to the point where at the beginning we were just funding methods, like getting people to describe how to do it so that others could share that methodology. And then that became us funding the Human Cell Atlas, which is now one of the largest databases of single-cell transcriptomes.

5. 6:58

   It was getting hard for scientists to annotate the data. So we built Cell by Gene, which was like a very simple annotation tool that scientists could use to make use of that data. Then a community came around Cell by Gene, built around Cell by Gene, and started contributing more and more data that we had nothing to do with sort of creating or funding or making happen in the world. And now Cell by Gene is a corpus of knowledge that a lot of the transcriptomic-based models are based off of.

6. 7:30

   used regularly by the scientific community. But still, there are always critiques, like, this is just stamp collecting. Like, you're just gathering bits of knowledge, sorry, bits of data, and we're not going to be able to pull scientific knowledge and wisdom and insights out of. And we're like, well, we didn't have an answer for a while. And then imagine our delight when large language models became a huge topic of conversation that could make sense of large amounts of data. And I just, for me, it was like, what if we could actually understand how biology worked? Move it from a discovery-based science to an engineering-based science, where we could systematically understand how living beings, living cells worked, and be able to understand why things go wrong. And so when we saw that moment, we were like, this is it. Something really big