Comment by mchinen
11 hours ago
Cool to see this from Brian Hie, who was doing interesting computational bio research at Meta's FAIR before they axed it. Interesting that this is work on the more physical/testing/manufacturing level than the computational, but it seems very useful.
It's hard to quantify the impact of new foundational tools like this at launch. Most of the time it falls flat, but even the successes are difficult. For example, CRISPR has led to interesting experiments and treatments on the way, but the effect does feel muted compared to the initial predictions. But there are many other related techniques that can be pulled out of this original research (e.g. dCas9 which lets you operate without cutting).
Similar story with cellular reprogramming.
Eventually one of these things will surface that will be GPU/transistor type innovations.
> but even the successes are difficult.
Yeah, it feels like we need a phase transition in the speed and practicality of the process. But I don't believe we need a single concrete lab tech.
Years ago when I did research, my impression was that there was complexity galore. A researcher on Drosophila developmental signaling would have a very disjoint knowledge domain than that of a researcher in horizontal gene transfer and antibiotic resistance. Both would exist in a different planet altogether than a clinician prescribing a cancer treatment. And the three of them would generally lack the tooling that somebody doing systems biology was used to.
So, to me, the key thing we need is some sort of "domain cement", or a good way to pull operative knowledge and usable skills from everywhere.
> the key thing we need is some sort of "domain cement", or a good way to pull operative knowledge and usable skills from everywhere.
Isn't that what LLMs are shaping up to be? Once we manage to divorce the knowledge from the weights in some way we could have in effect a frontier model whose awareness was limited to the sum total of the scientific literature.
I attended a talk by Brian at Stanford, and I asked why not just use Gibson assembly to stitch together 5 kb synthesized strands from a company like Twist Biosciences.
The answer I got was along the lines that they were simply going to get around to do the actual lab work at some point.
DNA synthesis technology hasn't really been a blocker for generative bio projects except at the full chromosome level.
And I think simply generating a full chromosome and booting it up without doing due diligence is probably a recipe for disaster.
We honestly aren't that far away from AI slop enzymes, AI slop ligases, and eventually AI slop bio weapons...
> Eventually one of these things will surface that will be GPU/transistor type innovations
Why do you think that?
I just meant a big innovation that reshapes everything. I should have used 'level' instead of 'type' here.
But there are a lot of analogies to computation in bio as a physical, atomic forces-driven, massively parallel computer, so it's possible there will be something related to electronics and computers that falls out. For example, there's also applications directly related to other fields including DNA storage of data and neuron-based computation.
> I just meant a big innovation that reshapes everything.
Biology has had many of these over the centuries.
> But there are a lot of analogies to computation
People have been saying this since pretty much the start of computation and I don't think anything's ever come of it.