No it isn’t. You have no observation that suggests function is rare in sequence space. All you can refer to is studies of particular sequence-structure-function relationships.
All attempts to probe sequence space for large numbers of possible functions contradicts the assumption you make.
The total size of the space is not relevant, it’s how often you find something functional in that space, how functional things are connected, and how you move around in it.
By function demonstrably not being all that rare, and then evolving more complex function from simpler functions.
Perfect example of what I just wrote. The bacterial flagellum is of course a more complex assemblage of simpler sub-components. There’s an ATP synthase, a protein export apperatus, various homologues of adhesins and secretins and so on.
Also a perfect example of the ever-fleeting goalposts. New genes. New sequences. Optimized sequences. Complex systems. Eukaryotes from prokaryotes. Multicellularity from single-cellularity.
And now we must explain the evolution of flagella, cast in the context of “randomly mutating through sequence space” as if the flagellum is supposed to have sprung fully formed into existence through the random accumulation of mutations until suddenly one day we went from junk-DNA to an entire flagellar operon with one final mutation. ALLAKAZAM! poof … and there it was.
The evidence does not nullify my claim. The issue with the model I presented you evaded as it supports ID; a theoretical method you simply want to destroy. If you cannot look at both sides of an argument objectively you cannot be informed on a subject.
The claim is about the paper being a complete model for de novo gene origin. It is not which the authors admit.
Function easily evolving is not a substantive statement. To explain the observation you need to explain the origin of the functions you are observing. There are a lot of them to explain and many have different requirements.
OK, let us specifically apply this. Mutation and natural selection models are not adequate to explain the rise of the Covid variants from the wild strain, and the better alternative explanation is that they were designed. That the functional peaks they exhibit are highly optimized is clear demonstration.
Similarly, such models are inadequate to explain optimization induced by environmental changes in higher order eukaryotes, particularly pesticide resistance by insects. The observed genetic functional sequence changes are by design.
How would you distinguish and generalize the design model of sequence optimization?
Nobody knows what you mean by a “complete model” and it’s not clear why that is even needed, other than to provide you with yet another vacuous (and hypocritical) excuse to avoid admitting you have no good reason for thinking new functions can’t evolve.
Why should we even care? Seems like a fruitless argument, as you can always just insist on some variant of the definition and claim DNA applies. It’s like that whole “the flagellum is a motor” argument. Okay, then it’s a motor that evolved. It doesn’t matter what category of things DNA sequences might qualify for here with respect to the question of how new protein coding genes evolve.
…are using a metaphor. Codes entail abstractions, which don’t exist anywhere in the system.
I have to agree, Bill. You have never presented a model and never presented any math to support any of your grand assertions. Why is that?
Functions are easy to find in random sequences. For the IDcreationists’ favorite case, beta-lactamase, it is present at more than 10^-8, because we can easily find it in antibody libraries.
Gil doesn’t realize that when Crick wrote that statement, there were many things he didn’t know that we know now. For example, see how Crick wrongly equates DNA with genes as if that’s all DNA is.
Gil has to define what a code means so that we can figure out whether DNA is indeed one.
Hi Ron
In the case of covid how would tell if the sequence was optimized or just changed? Since covid studies start from functional sequences I don’t see any reason standard population genetic models won’t work.
The interesting discussion is if the original covid 19 was designed to bind to the human ace receptor from a wild type corona virus that was previously found in Bats or possibly other animal populations.
I realize this. But I also realize that the general notion that « DNA is a code » (see the meaning of that term in my previous post) hasn’t changed one iota since Crick times, and will never change.
the genetic mechanisms underlying the emergence of these ‘ de novo ’ protein coding genes (‘ de novo emergence’) are now quite well understood
Thus biologists are focusing their attention on the roles and interactions of the emergent de novo proteins. The 5-stage Pittsburgh model of de novo protein evolution is discussed.
There is of course plenty of room for exploration and discovery on the topic of de novo proteins, but this summary article should put to rest the erroneous notion that biologists do not have a working model that can be tested. Computational tools (such as the maximum likelihood and MCMC models of the Mani and Tlusty paper) are particularly important in resolving wait time issues.
False, Gil. It is substantiated by everything we know about replication, transcription, and translation. There are no abstractions in any of those processes.
But I’m glad we agree that codes require abstractions.
You don’t. If you did, you would know DNA is not a code. You are yet to provide a definition of code and tell us how it applies to DNA. Don’t think of using what Crick said because that is wrong as he equates genes with DNA which is terribly false (I don’t blame him though, he probably didn’t know).
Again, define code and tell us how it applies to DNA?
Biologists have moved past that simplification. We know more about DNA now that disqualifies it from a being a code in commonly used senses. Define code and clearly tell me how it applies to DNA?
Well if the spike protein was changed in such a way that it has greater affinity for the ACE2 receptor, that would be more optimized. As certain of the same mutations ( D614G, E484K, N501Y), have arisen and persisted independently, and feature in ascendant strains, they are likely adaptive and thus relatively optimized.
While I agree that is an important discussion, and is yet another contradiction to Sanford and Carter’s genetic entropy brainchild, the public health driven surveillance of covid-19 variants offer an unparalleled tracking of disease evolution in real time. What came before the wild strain Wuhan sequence may be debated and inferred, typically from phylogenic inferences, but from that point forward there is little in the way of gaps to infer. The massive and global scope of scrutiny has no precedent. To a large extent, the resulting data is the model.