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So, coming from the science side of things where we 'program' DNA in order to design genes not found in nature, it's clear evolution works at many scales simult

So, coming from the science side of things where we 'program' DNA in order to design genes not found in nature, it's clear evolution works at many scales simultaneously. And those scales each need to be accounted for in order to traverse the large search-space. And in regards to this particular problem, evolution did not grant the complexity of multi-cellular life by doing any kind of random walk across a sequence. Life had a few billion years to come up with 'useful components' as well as 'good' heuristics and tools that commonly arrive at useful components. And then it reused that code, and started running different genetic algorithms on the same underlying code, but at another abstraction level upwards. It allowed those components that were useful to be duplicated, redone, and rearranged to form higher-order tools. Where the components remained relatively stable, but were remixed, redone, reordered, and otherwise reused - with only slight modification to lock them into their new tool. We see this with the results of evolutionary modifications to DNA. One of the proteins responsible for multi-cellularity is itself a mashup of 4 or 5 proteins found in single-celled organisms, but where those smaller proteins are repeated, smashed together, and otherwise remixed to form a protein with a new function that was synergistic with the original components. And that synergy allowed for the higher-order complexity associated with multi-cellularity [1]. And once that took off and proteins started to get large, the proteins would themselves be split up into pathways - and then those pathways would get duplicated. Etc. And this is just one mechanism real life has used evolution to scan that underlying space more rapidly than random walks. This is actually what we do at Serotiny [2] - we don't design novel proteins by sequence (in the 20^300 landscape) but rather by functional domain (in the 20000^5 landscape). And I think there are mechanisms to incorporate that kind of scaling layer into a good genetic algorithm that allow you to traverse short paths by going upwards into the different space, and then coming back down when you need granularity, And so, I think when going back to the algorithms, if you want to make them pragmatic rather than theoretical, many scales should be addressed simultaneously. > “DNA is extremely important, but it makes no sense if [it is] not in a cell, in an organism, in an ecosystem.” Other interactions are at play, and this application of algorithmic information cannot capture the extent of that complexity. Yep. [1] https://www.ncbi.nlm.nih.gov/pubmed/11466531 https://www.ncbi.nlm.nih.gov/pubmed/11466531 [2] https://serotiny.bio https://serotiny.bio

e_gal 8y ago
Notice that the claim: "DNA is extremely important, but it makes no sense if [it is] not in a cell, in an organism, in an ecosystem. Other interactions are at play, and this application of algorithmic information cannot capture the extent of that complexity." Completely mischaracterizes the research (Zenil et al.). They are looking at networks evolving over time of interacting elements. Such elements are genes but networks can also be metabolic or signaling and everything they say still holds. The subsection about chasing dynamic evolving targets even addresses the question of ever-changing environments that have some raised here. So the approach is way beyond treating life as 'DNA' but even so, I doubt anyone would object about the power of CRISPR-CAS9 only because it targets DNA... DNA is still the most fundamental element of life and other tend to embrace mysticism and are quite cartoonish about life. Anyway, my recommendation is, do read the paper.
- jfarlow 8y ago
  You're right! The paper itself is strongly agnostic to its analysis scale, while the Quanta article focuses entirely on DNA sequence. > Our embedded computational network architecture can be interpreted either at the levels of cells (internal network of molecules such as genes, proteins, metabolites) or alternatively as group or module of cells effective performing input-output transformation of external signals relative to the module. Naturally, this does not exclude a nested architecture ranging from cells encapsulating molecular networks to interacting modules composed of cells And in fact the study actually meshes quite nicely with the paper cited before showing the evolution of signaling networks in multi-cellular systems.

e_gal 8y ago

Notice that the claim: "DNA is extremely important, but it makes no sense if [it is] not in a cell, in an organism, in an ecosystem. Other interactions are at play, and this application of algorithmic information cannot capture the extent of that complexity." Completely mischaracterizes the research (Zenil et al.). They are looking at networks evolving over time of interacting elements. Such elements are genes but networks can also be metabolic or signaling and everything they say still holds. The subsection about chasing dynamic evolving targets even addresses the question of ever-changing environments that have some raised here. So the approach is way beyond treating life as 'DNA' but even so, I doubt anyone would object about the power of CRISPR-CAS9 only because it targets DNA... DNA is still the most fundamental element of life and other tend to embrace mysticism and are quite cartoonish about life. Anyway, my recommendation is, do read the paper.

jfarlow 8y ago
You're right! The paper itself is strongly agnostic to its analysis scale, while the Quanta article focuses entirely on DNA sequence. > Our embedded computational network architecture can be interpreted either at the levels of cells (internal network of molecules such as genes, proteins, metabolites) or alternatively as group or module of cells effective performing input-output transformation of external signals relative to the module. Naturally, this does not exclude a nested architecture ranging from cells encapsulating molecular networks to interacting modules composed of cells And in fact the study actually meshes quite nicely with the paper cited before showing the evolution of signaling networks in multi-cellular systems.