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?

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

> why can’t we put this to use producing alife simulations that deliver open-ended evolution?

you might like this: https://www.gregegan.net/DIASPORA/01/Orphanogenesis.html

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!

> “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.