Jacob Kimmel

Is it going to preserve the same type?

I can learn that across combinations.

I can start to learn their interaction terms.

Now I can use those models to actually predict in silico for all the combinations I haven't seen, which are most likely to give me the state I want.

And you can actually treat that as a generative problem and start sampling and asking which of these combinations is most likely to take my cell to some target destination in state space.

In our case, I want to take an old cell to a young state.

Yeah.

But you could imagine some arbitrary mappings as well.

And so I think as you get to these more complex problems that don't have the same features that Shinya benefited from, which were the ability, again, to measure success really easily.

You can see it with your bare eyes.

You don't even need a microscope.

And two, amplification.

As you get into these more challenging problems, you're going to need to be able to search a larger fraction of the space to hit that higher bar.

That would be very much my contention.

And one piece of evidence for this is that's the way the development works.

You know, it's kind of a crazy thing to think about, but you and I were both just like a single cell.

And then we were a bag of undifferentiated cells that were all exactly alike.

And then somehow we became humans with hundreds of different cell types all doing very different things.

And when you look at how development specifies those unique fates of cells, it is through groups of these transcription factors that each identify a unique type.

And in many cases, actually, the groups of transcription factors, the sets that specify very different fates, are actually pretty similar to one another.