Comment by RivieraKid
1 day ago
I wish there's some breakthrough in cell simulation that would allow us to create simulations that are similarly useful to molecular dynamics but feasible on modern supercomputers. Not being able to see what's happening inside cells seems like the main blocker to biological research.
Molecular dynamics describes very short, very small dynamics, like on the scale of nanoseconds and angstroms (.1nm)
What you’re describing is more like whole cell simulation. Whole cells are thousands of times larger than a protein and cellular processes can take days to finish. Cells contain millions of individual proteins.
So that means that we just can’t simulate all the individual proteins, it’s way too costly and might permanently remain that way.
The problem is that biology is insanely tightly coupled across scales. Cancer is the prototypical example. A single mutated letter in DNA in a single cell can cause a tumor that kills a blue whale. And it works the other way too. Big changes like changing your diet gets funneled down to epigenetic molecular changes to your DNA.
Basically, we have to at least consider molecular detail when simulating things as large as a whole cell. With machine learning tools and enough data we can learn some common patterns, but I think both physical and machine learned models are always going to smooth over interesting emergent behavior.
Also you’re absolutely correct about not being able to “see” inside cells. But, the models can only really see as far as the data lets them. So better microscopes and sequencing methods are going to drive better models as much as (or more than) better algorithms or more GPUs.
> A single mutated letter in DNA in a single cell can cause a tumor that kills a blue whale.
Side note: whales rarely get cancer.
https://en.wikipedia.org/wiki/Peto's_paradox
https://www.youtube.com/watch?v=1AElONvi9WQ
Simulating the real world at increasingly accurate scales is not that useful, because in biology - more than any other field - our assumptions are incorrect/flawed most of the time. The most useful thing simulations allow us to do is directly test those assumptions and in these cases, the simpler the model the better. Jeremy Gunawardena wrote a great piece on this: https://bmcbiol.biomedcentral.com/articles/10.1186/1741-7007...
And the extremely difficult, expensive, and often resultless process of confirming/denying these assumptions is one of the greatest uses of tax dollars and university degrees I can think of, yet, the current admin has taken the perspective that it's all Miasma but also cut the EPA, which by their logic, would stop the Miasma
The folks at Arc are trying to build this! https://arcinstitute.org/news/virtual-cell-model-state
STATE is not a simulation. It's a trained graphical model that does property prediction as a result of a perturbation. There is no physical model of a cell.
Personally, I think arc's approach is more likely to produce usable scientific results in a reasonable amount of time. You would have to make a very coarse model of the cell to get any reasonable amount of sampling and you would probably spend huge amounts of time computing things which are not relevant to the properties you care amount. An embedding and graphical model seems well-suited to problems like this, as long as the underlying data is representative and comprehensive.
How can you simulate what is not yet reliably known? Ugh it's so frustrating to hear AI 'thought leaders' going on and on about this being a pancea, especially when a majority of funding for the research even needed to train models has been substantially cut so Elon could have more rocket dollars
You may enjoy this, from a top-down experimental perspective (https://www.nikonsmallworld.com/galleries/small-world-in-mot...). Only a few entries so far show intracellular dynamics (like this one: https://www.nikonsmallworld.com/galleries/2024-small-world-i...), but I always enjoy the wide variety of dynamics some groups have been able to capture, like nervous system development (https://www.nikonsmallworld.com/galleries/2018-small-world-i...); absolutely incredible.
Very interesting, thanks.
It's a main aim at DeepMind. I hope they succeed as it could be very useful.
Do they specifically state that it's their main aim anywhere?
Edit: Never mind, I've googled the answer.
It seems that this would be a very coarse-grained simulation of a cell, nowhere close to the usefulness to a proper molecular dynamics simulation, if I understand correctly.
'Seeing' inside cells/tissues/organs/organisms is pretty much most modern biological research.
What's missing feels like the equivalent of a "fast-forward" button for cell-scale dynamics
Why simulate? We can already do it experimentally
In my field, we're always wanting to see what will happen when DNA is changed in a human pancreatic beta cell. We kind of have a protocol for producing things that look like human pancreatic beta cells from human stem cells, but we're not really sure that they are really going to behave like real human pancreatic beta cells for any particular DNA change, and we have examples of cases where they definitely do not behave the same.
You can't see what's going on in most cases.
I believe this is where quantum computing comes in but could be a decade out, but AI acceleration is hard to predict
I wish there were more interest in general in building true deterministic simulations than black boxes that hallucinate and can't show their work.