I don't think it's as easy as "simulating cellular processes". I feel like sometimes HN tends to severely underestimate the difficulty of problems in other fields. Just off the top of my head I can see several hard problems in this situation
- How precise do you want the simulation? Do you want physical processes fully simulated? You'd need that to get an accurate simulation of molecular interactions, but full physical simulation is computationally expensive even for single, simple molecules.
- Even simplificated simulations are hard. Simulating just the shape of DNA strands is computationally very expensive (usually done with Monte Carlo simulations).
- How many cells do you simulate? The body has ~37 trillion cells. Even if it only took one processor cycle to simulate a cell, you'd need 9250 4GHz processors. That should give an idea of how hard it would be to simulate just an organ.
- How do you take into account interactions? There's a lot of difficulty in understanding how drugs affect the whole human system. Lots of trials show promising results against in-vitro cells and then fail spectacularly in animal models. There's a lot influencing how drugs work and single cellular processes are just a small part of it. The body is incredibly complex.
- How do you validate the models? It's not like we can go into a cell and see where the molecules are. We don't have enough visibility into actual cellular processes to build such complex models to a sufficient degree of accuracy.
Compared to even a single human cell, a monstruous language model is a trivial thing.
All of those things you say are correct, but scientists are doing it anyway[^1]. In that paper, you will see there are ways to "compress" the amount of computation needed.
Five years ago, I would have said that we were further away from having a computer correctly interpret a joke (i.e., I would have agreed with [2]) than from simulating E.Coli. The thing is, people went and did (both) anyway. But they did one more than the other. Research effort and capital flowed, and now we have LLMs. IMO, a big reason for this paradox is that there is no stigma in wanting to make a computer smarter, and everybody started playing with computer code and data and due to the huge amount of effort, there have been results. But when it comes to the very things that keep us alive, we are not so eager to play with computer code. That, I think, has less to do with the complexity of the subject and more with a certain moral disposition...which is the thing I find perplexing.
But when it comes to the very things that keep us alive, we are not so eager to play with computer code. That, I think, has less to do with the complexity of the subject and more with a certain moral disposition...which is the thing I find perplexing.
Honestly it's the first time I hear about moral stigma having anything to do with research. Again, I think you underestimate how much harder it is to simulate biological processes than LLMs. Even from the paper you've linked, you'll see the massive amount of simplifications they had to do: they're using a minimal cell, not all metabolites are included, multimeric proteins are left out/replaced, spatial distributions are simplified, the simulation timescale is below 10μs, they do not simulate reactive processes, they do not talk about how much time did they need to perform the simulation... Don't get me wrong, it's a massive achievement. But the amount of computation that needs to be done just to simulate a single cell is absolutely massive, let alone simulating multiple cells in a system. Considering how much it would cost it's no wonder other avenues are explored first.
Comments
I don't think it's as easy as "simulating cellular processes". I feel like sometimes HN tends to severely underestimate the difficulty of problems in other fields. Just off the top of my head I can see several hard problems in this situation
- How precise do you want the simulation? Do you want physical processes fully simulated? You'd need that to get an accurate simulation of molecular interactions, but full physical simulation is computationally expensive even for single, simple molecules.
- Even simplificated simulations are hard. Simulating just the shape of DNA strands is computationally very expensive (usually done with Monte Carlo simulations).
- How many cells do you simulate? The body has ~37 trillion cells. Even if it only took one processor cycle to simulate a cell, you'd need 9250 4GHz processors. That should give an idea of how hard it would be to simulate just an organ.
- How do you take into account interactions? There's a lot of difficulty in understanding how drugs affect the whole human system. Lots of trials show promising results against in-vitro cells and then fail spectacularly in animal models. There's a lot influencing how drugs work and single cellular processes are just a small part of it. The body is incredibly complex.
- How do you validate the models? It's not like we can go into a cell and see where the molecules are. We don't have enough visibility into actual cellular processes to build such complex models to a sufficient degree of accuracy.
Compared to even a single human cell, a monstruous language model is a trivial thing.
All of those things you say are correct, but scientists are doing it anyway[^1]. In that paper, you will see there are ways to "compress" the amount of computation needed.
Five years ago, I would have said that we were further away from having a computer correctly interpret a joke (i.e., I would have agreed with [2]) than from simulating E.Coli. The thing is, people went and did (both) anyway. But they did one more than the other. Research effort and capital flowed, and now we have LLMs. IMO, a big reason for this paradox is that there is no stigma in wanting to make a computer smarter, and everybody started playing with computer code and data and due to the huge amount of effort, there have been results. But when it comes to the very things that keep us alive, we are not so eager to play with computer code. That, I think, has less to do with the complexity of the subject and more with a certain moral disposition...which is the thing I find perplexing.
[^1]: https://www.frontiersin.org/articles/10.3389/fchem.2023.1106...
[^2]: https://karpathy.github.io/2012/10/22/state-of-computer-visi...
Honestly it's the first time I hear about moral stigma having anything to do with research. Again, I think you underestimate how much harder it is to simulate biological processes than LLMs. Even from the paper you've linked, you'll see the massive amount of simplifications they had to do: they're using a minimal cell, not all metabolites are included, multimeric proteins are left out/replaced, spatial distributions are simplified, the simulation timescale is below 10μs, they do not simulate reactive processes, they do not talk about how much time did they need to perform the simulation... Don't get me wrong, it's a massive achievement. But the amount of computation that needs to be done just to simulate a single cell is absolutely massive, let alone simulating multiple cells in a system. Considering how much it would cost it's no wonder other avenues are explored first.