[memexy]

What's missing from current mathematics to make predictive models for biology?

I did a search for "neural network cell simulation" and got a few hits, e.g. https://ieeexplore.ieee.org/document/8805421.

So it seems that people are working on the problem of predictability (or at least augmenting the researcher's/experimenter's ability to do some analysis ahead of time based on simplified models).

[cgiles]

> What's missing from current mathematics to make predictive models for biology?

Well, I think that, no joke, there is a Nobel Prize waiting for anyone who knows the answer to that. I think this is the next big paradigm shift needed in biology, not to mention several other fields.

Who is to say that the problem is strictly mathematical, though? It could be that the math exists, but no one knows how to fit existing data into it, or it could be that there is not enough data, or the right kind of data, to make such a model yet. It could be that both the data and algorithm exists, but we need to turn the Earth into computronium to run it. Who knows?

> So it seems that people are working on the problem of predictability

I'm sure they are. They have been for decades. The last time I did a systematic review of this area was before the resurgence of neural networks, so I can't really say what is the latest progress, or whether the progress in ANNs can inform this problem. I suspect it's very possible.

The situation right now, as far as I know is that: A) most biologists don't even know this is a problem, and B) those who do, don't have any idea what the solution is, or if one even exists (note the author of the linked article was pessimistic on that point).

[Balgair]

Flops.

Cells balance right on the edge of Maxwell's Demon. Even a few thousand ions can change behavior radically. So, you are forced to track all the ions, proteins, lipids, etc. Which means you have to do a lot of atom-by-atom tracking. There are a few tricks here, but since the cell is not crystalline, you can't do a lot of fun physicsy math to get the problem to be easier.

Also, most of the time, since this is 'research' to begin with, you don't know what's in the cell. That's the point of looking. We've nearly no idea what all the proteins are in any given cell. DNA gives some guide, but a stochastic switch from coding to non-coding happens, constantly. So you don't know what all the proteins in a cell are, where they are, what they do, what they don't do, what the extracellular space is like, etc.

Cells are just really complicated. So you need a lot of flops.

[memexy]

How is "edge of Maxwell's Demon" related to "edge of chaos"?

Re: flops. I understand brute force is a good way to simulate dynamics but we constantly solve hard problems by approximation and have gotten pretty far with that approach. So what approximations have been tried and why have they been considered failures?

Also https://mobile.twitter.com/SteveStuWill/status/1268111230020...: > "Scientists created fully functional mini-livers out of human skin cells, then successfully transplanted them into rats. The research is a proof-of-concept for potentially revolutionary technology and provides a glimpse of an organ donor-free future." Wow!

That's unrelated to the original points but I see plenty of innovative approaches to problems in biology. Simulating cells is just one way to figure them out and we don't need to figure them out completely through computational means to put them to good uses. Biology is already computronium and if we can understand how to "program" then we don't need to simulate everything.