jfarlow

Network effects + energetic fficiencies. On an energy landscape that includes integration over very short and very long lifetimes, the thalweg of utility/energy rests right about where the current codon optimizations are. And any schemes that deviate don't get to share in the others' bounty. Reusing your foods' effort saves a lot, metabolically.

This is a challenge, even for someone who has professionally used the breadth of proteins. I really like the test. I'm actually kind of surprised at my own pulling on knowledge to make a guess - it's an orthogonal way to think about the question than is usually posed.

I wonder if there's a way to ease the difficulty by filling in 'correct' features of the guesses: if your guess is a 'transmembrane' then it reveals that as a property. On the other hand, I don't think the annotations are clean enough - and are often designed for 'at all' rather than 'primary' features. For one of the examples, once I noticed it was an adhesion protein, it would have been interesting to sift through classes or cell types as opposed to just continuing to shoot in the dark based on the structure alone.

I presume you're showing even the 'low confidence' portions of the predicted structure? Please do.

You could also show the primary amino acid sequence too - there's a weird familiarity with those given how often the structures themselves have historically not been so accessible. BLASTING each of the guesses would be another interesting thing to see.

And the atoms in the proteins and DNA that are exactly replicated to the atom each have a feature sizes resolved at fractions of a nanometer in 3 dimensions (and likely in time/dynamics too).

This is an AI-generated response, and is inaccurate.

That was one of the first cases of _germline_ gene editing using CRISPR - NOT "the first instance of gene editing." There have been quite a few other genetic editing tools that predate CRISPR, and there have been other edits using CRISPR that were not of the entire human's genome.

"Custom" in that this therapy was designed AFTER a specific patient showed a need, and then given to _that_ patient. In most every other context a particular class of disease is known, a drug designed, and then patients sought that have that disease that matches the purpose of the drug.

What's intriguing is not the 'custom' part, but the speed part (which permits it to be custom). Part of what makes CRISPR so powerful is that it can easily be 'adjusted' to work on different sequences based on a quick (DNA) string change - a day or two. Prior custom protein engineering would take minimum of months at full speed to 'adjust'.

That ease of manipulating DNA strings to enable rapid turnaround is similar to the difference between old-school protein based vaccines and the mRNA based vaccines. When you're manipulating 'source code' nucleic acid sequences you can move very quickly compared to manipulating the 'compiled' protein.

>to build something but to not know how it actually works and yet it works.

Welcome to Biology!

Clearly RAM-stingy Apple has found a use for RAM - almost certainly in loading local LLMs.

Llama 8G loads and runs pretty well on the new M-series Macs with a reasonable amount of RAM.

There are a number of companies working on 'in vivo' deliveries for CARs. Oftentimes using the same tools as proven out by the Moderna vaccine.

There are two kinds of personalization in a [CAR] [T] therapy:

1) using the patient's own cells [personalization of T Cells]

2) customized therapeutic genetic payload, per patient [personalization of the CAR]

There are current competing factions for #1 - where cells are from just the patient ["Autologous"] (safer, slower, more expensive), and where the cells are from a universal donor ["Allogeneic"] (possible immune response, but can be manufactured at scale).

The therapeutic payload is a DNA sequence encoding a synthetic chimeric receptor ["CAR"]. This sequence is customized based on the details of the patient's particular cancer, but are common across many people. If the cancer has an excess of "Protein X" on it, then the CAR sequence is designed to target Protein X. All patients with a similar cancer profile receive the same CAR sequence as a payload to the T cells. This too could be personalized, to not just profile the _class_ of cancer, but particular to that _specific patent's cancer's profile_ - but this is not yet feasible given the turnaround time to build, test and evaluate a new genetic payload in the context of a person's specific tumor cells.

This particular therapy has the cells be from the patient (personalized), but the CAR sequence provided to the cells is common for all people that have the same cancer profile (semi-personalized). In this case, the cancer profile includes those that have an abundance of the protein called Claudin-6.

Using the above chemistry, you can attach DNA oligos to lipids, DNA oligos to proteins, fluorophores to DNA, fluorophores to proteins at particular locations or other complex drugs to DNA, protein or lipids.

Once you get DNA oligos in there you can do computation, as X binds X', and Y to Y'. So you can have all sorts of complex synthetic & designed interactions using chemistry that is both seamless and doesn't interfere with normal molecular biolgy.

Once you have proteins, you can localize particular chemistries.