The biggest problem here is that they claim this experiment says something important and novel about evolution: that having development reduces selection pressure on genes. I think that's generally accepted, actually, but it's not well supported by this experiment.

In a sense, what they really showed here is that bubble sort is a more efficient sorting algorithm than mutation and crossover, which is obvious. Their simulated organisms did not evolve to do bubble sort. They were either granted a certain number of sorting steps every generation, or they evolved to increase the number of sorting steps allotted to them. More sorting produced more sorted lists faster.

Then there's "genotypic fitness." The very idea undermines the point of this study, which is that genes don't determine organisms or fitness. So what does it mean for genes to be fit? Why are they measuring deviation between genotype and phenotype when the whole point is that they don't correspond in nature?

(3/3)

#science #alife

This experiment lets two forces compete for shaping a population: development and evolution. They simulate organisms with different balances between the two, and compare how well they adapt. More precisely, they simulated evolving lists of numbers, to see whether it was easier to evolve a sorted list or to sort a list via local interactions (ie, bubble sort) during a simulated "lifetime."

They successfully demonstrate that evolving a program to do some task can in some cases be easier than evolving a solution to that task. They also explore some interesting evolutionary dynamics in that scenario. Evolution creates competition an interaction between these strategies, which can be shaped by factors like the cost of computation "during a lifetime."

There's a clear analogy to developmental biology, it's just a tenuous one. I think this is suggestive of what we might see in some species. It encourages a way of thinking about development and evolution that may be useful.

(2/3)

#science #alife

I have such mixed feelings about this paper!

It's asking a really interesting question: how does the ability of single cells to self-organize affect evolution? They observe that genes don't determine what an organism will look like or how it behaves. Some species, like planaria, have messy, corrupted genomes, yet they manage to produce the same body shape reliably, every time, and can even regenerate it after an injury. Seemingly, it's the cells that know how to do this, not the DNA! That's extraordinary, and it seems likely this same thing is happening, to different extents, in all multicellular organisms.

This is a profound observation that suggests we need a better and more nuanced story of evolution. It's also a fun thing to study with simulation experiments, like this one!

I do really like the experiment, but it should have huge caveats, and they're way too generous in interpreting these results, and in generalizing them to biology.

(1/3)

#science #alife

Cellular Competency during Development Alters Evolutionary Dynamics in an Artificial Embryogeny Model

Biological genotypes do not code directly for phenotypes; developmental physiology is the control layer that separates genomes from capacities ascertained by selection. A key aspect is cellular competency, since cells are not passive materials but descendants of unicellular organisms with complex context-sensitive behavioral capabilities. To probe the effects of different degrees of cellular competency on evolutionary dynamics, we used an evolutionary simulation in the context of minimal artificial embryogeny. Virtual embryos consisted of a single axis of positional information values provided by cells’ ‘structural genes’, operated upon by an evolutionary cycle in which embryos’ fitness was proportional to monotonicity of the axial gradient. Evolutionary dynamics were evaluated in two modes: hardwired development (genotype directly encodes phenotype), and a more realistic mode in which cells interact prior to evaluation by the fitness function (“regulative” development). We find that even minimal ability of cells with to improve their position in the embryo results in better performance of the evolutionary search. Crucially, we observed that increasing the behavioral competency masks the raw fitness encoded by structural genes, with selection favoring improvements to its developmental problem-solving capacities over improvements to its structural genome. This suggests the existence of a powerful ratchet mechanism: evolution progressively becomes locked in to improvements in the intelligence of its agential substrate, with reduced pressure on the structural genome. This kind of feedback loop in which evolution increasingly puts more effort into the developmental software than perfecting the hardware explains the very puzzling divergence of genome from anatomy in species like planaria. In addition, it identifies a possible driver for scaling intelligence over evolutionary time, and suggests strategies for engineering novel systems in silico and in bioengineering.

MDPI
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