Lex Fridman: uncertainty

Transcript context

…So what I’ve tried to do throughout my career is I have these really grand dreams and then I try to, as you’ve noticed, but I try to break them down. It’s easy to have a kind of crazily ambitious dream, but the trick is how do you break it down into manageable, achievable, interim steps that are meaningful and useful in their own right? And so Virtual Cell, which is what I call the project of modeling a cell, I’ve had this idea of wanting to do that for maybe more like 25 years. And I used to talk with Paul Nurse, who is a bit of a mentor of mine in biology. He runs the founded the Crick Institute and won the Nobel Prize in 2001. We’ve been talking about it since the nineties, and I used to come back to it every five years. It’s like, what would you need to model the full internals of a cell so that you could do experiments on the virtual cell and what those experiment in silico and those predictions would be useful for you to save you a lot of time in the wet lab. That would be the dream. Maybe you could a hundred x speed up experiments by doing most of it in silico the search in silico, and then you do the validation step in the wet lab. That’s the dream. But maybe now, finally, so I was trying to build these components, AlphaFold being one, that would allow you eventually to model the full interaction, a full simulation of a cell, and I’d probably start with a yeast cell. And partly that’s what Paul Nurse studied because the yeast cell is like a full organism, that’s a single cell. So it’s the kind of simplest single cell organism. And so it’s not just a cell, it’s a full organism. And yeast is very well understood. And so that would be a good candidate for a kind of full simulated model. Now AlphaFold is the solution to the kind of static picture of what does a 3D structure protein look like? A static picture of it. But we know that biology, all the interesting things happen with the dynamics, the interactions, and that’s what AlphaFold 3 is, the first step towards is modeling those interactions. So first of all, pair wise proteins with proteins, proteins with RNA and DNA. But then the next step after that would be modeling maybe a whole pathway, maybe like the tour pathway that’s involved in cancer or something like this. And then eventually you might be able to model a whole cell. Also, there’s another complexity here that stuff in a cell happens at different time scales. Is that tricky? Protein folding is super fast. I don’t know all the biological mechanisms, but some of them take a long time. And so the levels of interaction has a different temporal scale that you have to be able to model. So that would be hard. So you’d probably need several simulated systems that can interact at these different temporal dynamics, or at least maybe it’s like a hierarchical system so you can jump up or down the different temporal stages.…

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