Posted by i_love_limes 6/26/2025
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.
Side note: whales rarely get cancer.
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.
Edit: Never mind, I've googled the answer.
On a millions or billions of year time frame, the organisms with the flexibility of ncRNA would have an advantage, but this is extremely hard to figure out with a "single point in time" view point.
Anyway, that was the basic lesson I took from studying non-coding RNA 10 years ago. Projects like ENCODE definitely helped, but they really just exposed transcription of elements that are noisy, without providing the evidence that any of it is actually "functional". Therefore, I'm skeptical that more of the same approach will be helpful, but I'd be pleasantly surprised if wrong.
For example, we don't keep transposons in general because they're useful, which are almost half of our genomes, and are a major source of disruptive variation. They persist because we're just not very good at preventing them from spreading, we have some suppressive mechanisms but they don't work all the time, and there's a bit of an arms race between transposons and host. Nonetheless, they can occasionally provide variation that is beneficial.
Can't emphasize enough about how DNA requires human data curation to make things work, even from day one alignments models were driven based on biological observations. Glad to see UBERON, which represents a massive amount of human insight and data curation of what is for all intents and purposes a semantic-web product (OWL based RDF at the heart) playing a significant role.
I’d pitch this paper as a very solid demonstration of the approach, and im sure it will lead to some pretty rapid developments (similar to what Rosettafold/alphafold did)
For instance, Evo2 by the Arc Institute is a DNA Foundation Model that can do some really remarkable things to understand/interpret/design DNA sequences, and there are now multiple open weight models for working with biomolecules at a structural level that are equivalent to AlphaFold 3.
Some pharmas like Genentech or GSK also have excellent AI groups.
To a man with a hammer…
There are technologies applicable broadly, across all business segments. Heat engines. Electricity. Liquid fuels. Gears. Glass. Plastics. Digital computers. And yes, transformers.