
Life as Thermodynamic Evidence of Algorithmic Structure in Nature (2012) - jkhdigital
https://www.mdpi.com/1099-4300/14/11/2173
======
michaelhoffman
Many regard MDPI as a predatory publisher. That's not a universal view and
there is clearly a substantial amount of valid research that appears in MDPI
journals. But they seem to have a bias towards accepting papers with cursory
review and collection of article processing fees. For example, they ask that
peer-reviewers submit reviews in 7 days or less, which is something few
experts are going to have time for.

Then there is the matter of MDPI creating deceptive journal titles like
_Cells_ which surely is often confused with the incredibly prestigious journal
_Cell_.

Anyway, caveat lector.

------
meroes
Isn't _everything_ [1] thermodynamic evidence of algorithmic structure of
nature?

When you have intial conditions (big bang, fundamental constants) and
dynamical laws (physics), everything is an algorithmic construction to some
degree, at least according to physics as we know it (i.e. who knows what's
truly fundamental).

[1] Not eveything is governmed by thermodynamics like initial conditions of
the big bang, fundamental constants being the values they are.

~~~
LolWolf
Well, every closed, Markovian system is governed by thermodynamics in a rather
specific mathematical sense [0]; but yes I agree that initial conditions and
parameters (such as constants) are not, but the dynamics themselves are.

Unless our assumption about Markovian dynamics is wrong, in which case it’s
not even clear we can make useful predictions from such a theory.

———

[0] see, e.g., [https://guille.site/second-law-
markov.html](https://guille.site/second-law-markov.html) or Cover’s own
textbook.

------
whatshisface
This really screams "BS" to me, but I should temper that perception by the
fact that I only read the abstract (and so this comment itself would,
ironically, qualify for at least one BS flag.)

The most suspicious bit is the claim that organisms can't survive in
completely unpredictable environments. That statement is both obviously true
or obviously false depending on the variance of the unpredictable random
variables... A totally unpredictable temperature between 25 and 26C would not
be much of an issue, a totally unpredictable temperature between 0K and 1,000K
would be. In fact it would be so much of an issue that its unpredictability
wouldn't contribute very much to its difficulty!

~~~
canjobear
If there are no regularities whatsoever in the environment, how could an
organism possibly form and survive?

~~~
eveningcoffee
This was also my question.

Obviously there should be some regularity for an organism to form.

But lets assume that the environment for an organism is indistinguishable from
the random noise.

Then the only strategy the organism can form is to randomly sample the
environment for food.

If there is enough food in the environment then the organism survives, if not
then it does not survive.

Therefor it is disproven that an organisms can survive only in predictable
environments.

It could be probably displayed that any more complex strategy could be not
developed though.

~~~
jkhdigital
Except that organisms are generally not ergodic systems, so they are subject
to gambler’s ruin. Even with “enough” food the probability of non-survival is
non-zero and asymptotically increasing over time.

------
TheOtherHobbes
This ignores epigenetics and proteomics.

There's a naively fashionable idea that DNA is basically the same as a Turing
tape.

It isn't. It may be true that biological systems can be understood in terms of
information theory, but the information _is in the entire ecosystem_ \-
including the sum total of all individuals and species and their previous and
current state.

E.g. on Earth, the entire planetary ecosystem eventually evolved a species
with the ability to understand quantum theory - which happened to be a useful
adaption, at least for a while.

But you're not going to find an explicit formalism for Schrodinger's Equation
in human DNA no matter how hard you look for it.

~~~
jkhdigital
Pretty sure you’ve attacked a straw man here. The article does not make the
claim that DNA is the one and only source of information used in biological
computation. It does, perhaps, imply that DNA is the most important source of
biological memory.

~~~
downerending
Yeah. Epigenetics is more like a small twist in the Fundamental Dogma of
Biology. And "proteomics" doesn't mean much of anything--all proteins derive
from DNA.

~~~
joycian
I don't know, are go-to statements and code reuse a small twist in the dogma
of software engineering?

Is your Python-level code directly interacting with CPU operations and memory
bit flipping a small twist?

Hi-C 3D DNA structure experiments as well as small RNA fragments interfering
with transcriptions and protein function are not a small twist in my opinion -
it's like finding out there's an entire extra hierarchy of layers to the
system.

~~~
downerending
Those are great and non-obvious discoveries, but they don't particularly
change the fact that (virtually?) all genetic information is carried via DNA.
(...and possibly similar mechanisms that don't particular change the overall
story)

~~~
joycian
Well, no, it doesn't change the fact that DNA contains (most of) the
information. What it means is that even if I give you all of the bases on all
of the chromosomes, if you don't know how these strings of beads will fold in
a real cell (in the presence of all kinds of other things, like proteins and
metal ions, which in a fertilized egg will come from the mother - a
bootstrapping problem), it does not get you very far. It might not be unlike
getting all the lines of code in a codebase, but not getting the ordering,
only a bag of lines of code.

------
chrischen
Isn’t “life” only significant because we ascribe significance to it. It’s like
saying if a a random number generator spit out 7777777 that it must be rigged.

~~~
notduncansmith
It’s hard to find a definition of “significant” (or rather “meaningful”) that
is not “whatever humans ascribe meaning to” and is also disprovable.

This blog explores the topic in detail, and offers a remarkably balanced
perspective: [https://meaningness.com/preview-eternalism-and-
nihilism](https://meaningness.com/preview-eternalism-and-nihilism)

------
carapace
ORT (Only Read Title) and abstract so far but it reminds me:

Cf. "Life as Evolving Software, Greg Chaitin at PPGC UFRGS"
[https://www.youtube.com/watch?v=RlYS_GiAnK8](https://www.youtube.com/watch?v=RlYS_GiAnK8)

> Few people remember Turing's work on pattern formation in biology
> (morphogenesis), but Turing's famous 1936 paper On Computable Numbers
> exerted an immense influence on the birth of molecular biology indirectly,
> through the work of John von Neumann on self-reproducing automata, which
> influenced Sydney Brenner who in turn influenced Francis Crick, the Crick of
> Watson and Crick, the discoverers of the molecular structure of DNA.
> Furthermore, von Neumann's application of Turing's ideas to biology is
> beautifully supported by recent work on evo-devo (evolutionary developmental
> biology). The crucial idea: DNA is multi-billion year old software, but we
> could not recognize it as such before Turing's 1936 paper, which according
> to von Neumann creates the idea of computer hardware and software.

\- - - -

Also "Algorithmically probable mutations reproduce aspects of evolution such
as convergence rate, genetic memory, and modularity"

[https://arxiv.org/abs/1709.00268v8](https://arxiv.org/abs/1709.00268v8)

> Natural selection explains how life has evolved over millions of years from
> more primitive forms. The speed at which this happens, however, has
> sometimes defied formal explanations when based on random (uniformly
> distributed) mutations. Here we investigate the application of a simplicity
> bias based on a natural but algorithmic distribution of mutations (no
> recombination) in various examples, particularly binary matrices in order to
> compare evolutionary convergence rates. Results both on synthetic and on
> small biological examples indicate an accelerated rate when mutations are
> not statistical uniform but \textit{algorithmic uniform}. We show that
> algorithmic distributions can evolve modularity and genetic memory by
> preservation of structures when they first occur sometimes leading to an
> accelerated production of diversity but also population extinctions,
> possibly explaining naturally occurring phenomena such as diversity
> explosions (e.g. the Cambrian) and massive extinctions (e.g. the End
> Triassic) whose causes are currently a cause for debate. The natural
> approach introduced here appears to be a better approximation to biological
> evolution than models based exclusively upon random uniform mutations, and
> it also approaches a formal version of open-ended evolution based on
> previous formal results. These results validate some suggestions in the
> direction that computation may be an equally important driver of evolution.
> We also show that inducing the method on problems of optimization, such as
> genetic algorithms, has the potential to accelerate convergence of
> artificial evolutionary algorithms.

(quoting myself from ~2 years ago,
[https://news.ycombinator.com/item?id=18571878](https://news.ycombinator.com/item?id=18571878)
hope nobody minds.)

------
Koshkin
I think a similar observation can be made by watching crystals grow.

