The Genomic Code: The genome instantiates a generative model of the organism

It’s an intriguingly framed paper, and others have written about how gene regulatory networks store weights, etc, but this seems to me like it is putting too much emphasis on the direct mapping of the developmental process to a generative process just because it is popular at the moment.

The encoder being evolution is an idea that has been developed by Sui Huang and numerous others.

The genetic decoder being creative/generative is an idea that has been put forth by Richard Watson and others.

What I’m more interested in at this point is how do the layers interact? Rather than waving at energy landscapes, stable attractors, and catastrophe theory, why can’t we put this to use producing alife simulations that deliver open-ended evolution?

I have only read the abstract so far but this seems to align pretty well with the idea of how the relationship between genes and tissues/organs is framed in Michael Levin's group's research: Genes mostly encode the molecular hardware and this helps to set up the initial-state of the 'software' during morphogenesis, and the cells primarily follow the software, but within the bounds of what is supported by the hardware.

The 'software' of biology in this framework is described as like pattern-memories stored in "vMem" voltage-gradient patterns between cells in the tissue, analagously to how neurons store information. I think the analogy breaks down slightly here because the memory is more like a remebered-target than something that 'can be executed' like software can.

The vMem 'memory' of what 'shape' to grow-into can be altered (by adding chemicals that open or close specific gap junctions) such that any regrowth or development can target a different location in morphospace (ie grow an eye instead of epithelial tissue as in the tadpole example from Levin's research).

Fascinating and I hope to have a read of the whole paper soon!

DNA "generates" the body, which generates behaviour, which affects gene survival, closing the loop.

<rant> It's a syntactic process with the ability to update syntax based on outcomes in the environment. I think this proves that syntax is sufficient for semantics, given the environment.

Wondering why Searle affirmed the opposite. Didn't he know about compilers, functional programming, lambda calculus, homoiconicity - syntax can operate on syntax, can modify or update it. Rules can create rules because they have a dual status - of behaviour and data. They can be both "verbs" and "objects". Gödel's incompleteness theorems use Arithmetization to encode math statements as data, making math available to itself as object of study.

So syntax not fixed, it has unappreciated depth and adaptive capability. In neural nets both the fw and bw passes are purely syntactic, yet they affect the behaviour/rules/syntax of the model. Can we say AlphaZero and AlphaProof don't really understand even if they are better than most of us in non-parroting situations? </>

Very nice perspective. I used to joke about the missing comment lines in the genetic code: if "God" had been a good, conscientious programmer, he would have left a reasonable amount of comments in the code to make it maintainable for all the next developers - but it seems the task went over his head, and now we have to reverse-engineer all that chaos...

If you're holding a hammer, everything looks like a nail ...

> “Malcolm sat back in his seat. ‘And fractals…fractal patterns are everywhere in nature. Trees, clouds, shells, lightning. Everything in nature is fractal, in the sense that nothing can be broken down into simple shapes. In fact, I now think that animals, and perhaps especially large animals like the dinosaurs, are more efficient than we ever imagined. Their bulk allows them to utilize fractal designs in their biological systems, which means that larger animals have greater efficiency of scale than smaller ones.’”

I had a similar idea in university, when I was fascinated by fractal designs and graphics, that allows to generate complex and different structures with just an algorithm and a seed.

Jurrasic Park and the quote above helped too, because it played with the idea that the bigger a system is, the more efficient it is in nature, instead of less efficient, like an organization.

This paper kind of supports my idea that DNA is just a seed for our biological system to produce a specific output.

While this paper is perhaps is a bit of an overfitting of the concept of generative weights, another fun take is Evo-Devo[0], an acapella overview of evolutionary developmental biology.

[0]: https://www.youtube.com/watch?v=ydqReeTV_vk

its a very poetic interpretation

i think i would take it a step further, most organisms alive today operate at the level of a generative model for a generative model (continue umpteen times) until you arrive at the level of physiology that assembles nerves and organs to work at the scale they do

and i would also comment on the impeccability of the feedback mechanisms across each layer, that every message eventually gets into a cell, binds to a protein, which probably cascades into a binding of a 100 different proteins at some point, that eventually sends a message to the tiny nucleus to wrap or unwrap a specific segment of DNA, is quite a beautiful way of thinking about it

Maybe… but if you take the view that it instantiates automata that create the morphology then i think it’s like, “well.. duh!”

ChatGPT's response to "can you summarize this in lay terms":

In studies of the "RNA world," a theoretical early stage in the origin of life where RNA molecules played a crucial role, researchers have observed that parasitism is a common phenomenon. This means that some molecules can exploit others for their own benefit, which could lead to the extinction of those being exploited unless certain protective measures are in place, such as separating the molecules into compartments or arranging them in specific patterns.

By thinking of RNA replication as a kind of active process, similar to a computer running a program, researchers can explore various strategies that RNA might use to adapt to challenges in its environment. The study uses computer models to investigate how parasitism emerges and how complexity develops in response.

Initially, the system starts with a designed RNA molecule that can copy itself and occasionally makes small mistakes (mutations) during this process. Very quickly, shorter RNA molecules that act as parasites appear. These parasites are copied more rapidly because of their shorter length, giving them an advantage. In response, the original replicating molecules also become shorter to speed up their own replication. They develop ways to slow down the copying process, which helps reduce the advantage parasites have.

Over time, the replicating molecules also evolve more complex methods to distinguish between their own copies and the parasites. This complexity grows as new parasite species keep arising, not from evolving existing parasites, but from mutations in the replicating molecules themselves.

The process of evolution changes as well, with increases in mutation rates and the emergence of new mutation processes. As a result, parasitism not only drives the evolution of more complex replicators but also leads to the development of complex ecosystems. In summary, the study shows how parasitism can be a powerful force that promotes complexity and diversity in evolving systems.

While I am sure some people will roll their eyes at the idea, I thought it was pretty interesting. I wish they were able to make some predictions using their model though.