That article listing long-lived proteins is a handy one. I’m highly suspicious of a lot of those; radioisotope methods are gold-standard but a large chunk of the listed results are based on racemization and other chemical characteristics, which I don’t trust nearly as much. That lack of trust is based more on priors than deep knowledge at this point, though; I’ll have to dig more into it in the future.
I don’t think “controllers of the aging rate” are quite the right place to focus; there’s too many of them. The things I’ve been calling “root causes” should be less numerous, and “controllers of the aging rate” would be exactly the things which are upstream of those root causes—i.e. things which cause the root causes to accumulate faster/slower over the course of life. (Side note: I think using the phrase “root cause” has been throwing a lot of people off; I’m considering switching to “mediator of history”, i.e. things which mediate the effect of the aged organism’s history on its current state.)
If you had the perfect bioinformatics database + genomically-obsessed autist, it would be easier to deal with larger quantities of genes. Like, the human genome has 20k genes, and let’s say 1% are super-relevant for aging or brain preservation—that would be 2k genes, and that would be super-easy for an autistically-obsessed person to manage
I mean, sure, if we had a really fast car we could drive from New York to Orlando by going through Seattle. But (a) we don’t have that amazing database, and (b) it’s probably easier to be more efficient than to build the perfect bioinformatics database. With a focus on very-slow-turnover factors, the problem is unlikely to involve even 200 genes, let alone 2k.
You personally might very well be able to identify the full list of root causes, to a reasonably-high degree of certainty, without any tools beyond what you have now, by being more strategic—focusing effort on exactly the questions which matter.
That article listing long-lived proteins is a handy one. I’m highly suspicious of a lot of those; radioisotope methods are gold-standard but a large chunk of the listed results are based on racemization and other chemical characteristics, which I don’t trust nearly as much. That lack of trust is based more on priors than deep knowledge at this point, though; I’ll have to dig more into it in the future.
I don’t think “controllers of the aging rate” are quite the right place to focus; there’s too many of them. The things I’ve been calling “root causes” should be less numerous, and “controllers of the aging rate” would be exactly the things which are upstream of those root causes—i.e. things which cause the root causes to accumulate faster/slower over the course of life. (Side note: I think using the phrase “root cause” has been throwing a lot of people off; I’m considering switching to “mediator of history”, i.e. things which mediate the effect of the aged organism’s history on its current state.)
If you had the perfect bioinformatics database + genomically-obsessed autist, it would be easier to deal with larger quantities of genes. Like, the human genome has 20k genes, and let’s say 1% are super-relevant for aging or brain preservation—that would be 2k genes, and that would be super-easy for an autistically-obsessed person to manage
Alternatively, aging (like most non-discrete phenotypes) may be omnigenic.
I mean, sure, if we had a really fast car we could drive from New York to Orlando by going through Seattle. But (a) we don’t have that amazing database, and (b) it’s probably easier to be more efficient than to build the perfect bioinformatics database. With a focus on very-slow-turnover factors, the problem is unlikely to involve even 200 genes, let alone 2k.
You personally might very well be able to identify the full list of root causes, to a reasonably-high degree of certainty, without any tools beyond what you have now, by being more strategic—focusing effort on exactly the questions which matter.
https://www.pnas.org/content/116/44/22173