[FRIAM] (no subject)

[jon zingale]

"""
I like what Leslie Valiant has to say in PAC, as a way of framing theissues,
even though I know he is a punching bag for the geneticistsbecause of
various things they understand as important that he isn’ttrying to
understand deeply or deal with.  I don’t care that he hadlimitations; the
question for me is whether there is a good insightthat could be developed
further.
What I intend to suggest above is that the lifecycle/hypergraphabstraction
is a more expressive class of formal models within which onecan pose such
problems, and we should be able to generate moreinteresting answers by using
it.
"""
There are many references above to investigate, and so far I have onlybegun
to engage with some. I am preparing to read  yourpreprint
<https://www.biorxiv.org/content/10.1101/2021.02.09.430402v1.abstract>  ,
and I have managed to track down a copy of Valiant's PAC.Two notable ideas
Valiant raises are:
0. Membership to a complexity class as /real/: A machine is identified with
the nature of its computation and not necessarily the fine details of its
instantiation. For instance, fine details inthe case of biology could be
/every/ aspect of its being in the world.I am reminded of the philosophical
problems associated with /identitytracing/, as well as a certain empiricist
perspective that /days liketoday/ are in some sense /more real/ than
/today/. Valiantmentions that the /universality/ of Turing's machine /is/
the stablefeature that ultimately matters, that a machine ought to be
considered"the same" under perturbation of its parts so long as what it
does/computationally/ remains invariant.
The slipperiness of notions like "remaining computationally invariant"and
"perturbation of its parts" seem to be hotly debatable locales.In the spirit
of Ackley's robust algorithms,perturbations of a quick-sort rapidly lead to
nonviable algorithms, whilebubble-sort can remain identifiable under
significantperturbation. Additionally, as with genetics, there is the
possibility ofidentifying perturbations (mutations) as an indispensable
/part/of the organism. This kind of analysis does leave some questions
open.Should we (by the thesis) consider a BPP-classed algorithm to be the
same under perturbationwhen it becomes both /determined/ and its /expected
time complexity/remains invariant?
1. Scaffolding in protein expression networks: Here, Valiant suggests a 
protein level analogy to Nick's white smokers.  Chaperone proteins
<https://en.wikipedia.org/wiki/Chaperone_(protein)>  , atthe very least, are
known to participate structurally in the process oferror correction, namely
correcting errors in folding. I amreminded of recent dives into other
aspects of protein dynamics suchas  allosteric signaling
<https://en.wikipedia.org/wiki/Allosteric_regulation>  . I can only imagine
the computational libertiespresent for scaffolding when considering
variation in PH (as narrow asit allows) or temperature. In these musings, I
am reminded of the inhibitory  (epiphenomenal?) role of the dictionary in
the functioning of LZW datacompression.
That Glen found your paper "positively pornographic" is high praise.I hope
to find the time to take the dive myself. In the meantime, I would love to
hear more about your ideas concerning graphical models, as it is a place I
have thought a bit about.



--
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