Jacob Kimmel

And so at the level of individual cells, you can actually measure every gene that they're using at a given time and get this really complete picture of a cell state, everything it's doing, lots of mutual information to other features.

And from that profile, you can train something like a model that discriminates young and aged cells with really high performance.

It turns out there's no one gene that actually has that same characteristic.

So unlike in Yamanaka's case where a single gene on or off is like an amazing binary classifier, you don't have that same feature of easy detection of success in aging.

The second feature is as you highlighted, we can't just turn these into cancer cells, success doesn't amplify.

And so in some ways, the bar for a medicine is higher than what Yamanaka achieved in his laboratory discovery.

You can't just have 0.001% success and then wait for the cells to grow a whole bunch in order to treat a patient's disease or, you know, make their liver younger, make their immune system younger, make their endothelium younger.

You need to actually have it be fairly efficient across many cells at a time.

And so because of this, we don't have the same luxury Yamanaka did of taking a relatively small number of factors and finding a success case within there that was pretty low efficiency.

We actually need to search a much broader portion of TF space in order to be successful.

And when you start playing that game and you think, okay, how many TFs are there?

Somewhere between 1,000 and 2,000, depends on exactly where you draw the line and developmental biologists love to argue about this over beer, but let's call it 2,000 for now.

And you wanna choose some combination, let's say you guess it's like somewhere between one and six factors might be required.

The number of possible combinations is about 10 to 16.

So if you do any like math on the back of a napkin, in order to just screen through all of those, you would need to do many orders of magnitude more single cell sequencing than the entire world has done to date cumulatively across all experiments.

And so it's just not tractable to do exhaustively.

And so that's where actually having models that can predict the effect of these interventions comes in.

If I can do a sparse sampling, I can test a large number of these combinations.

And I can start to learn the relationship of what a given transcription factor is going to do to an age cell.

Is it going to make it look younger?