You’re both right, sort of. Multiple changes are possible, but they all lead to the same function. It’s Texas sharpshooter because he’s looking for a particular new function in a particular gene, rather than any new function in any gene. That there are a few alternative substitutions that could result in this particular function is not very important.
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Bill they are constrained to number sites required (2 or more simultaneously), location in the gene (2 particular locations in the gene), and which gene (a particular set of a gene and it’s duplicate) out of all genes present in the genome.
Behe & Snoke 2004:
The basic “task” that the model asks a duplicate gene to perform is to accumulate λ mutations at the correct nucleotide positions to code for a new selectable feature before suffering a null mutation.
A particular MR feature in a particular set of genes, instead of any feature in any set of genes. Behe agrees this makes a difference. After all, he says:
On the other hand, because the simulation looks for the production of a particular MR feature in a particular gene, the values will be overestimates of the time necessary to produce some MR feature in some duplicated gene.
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Oh and, none of it has anything to do with chromosomal fusions in deer. Another gigantic red herring.
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The claims are vague as the label is not clearly defined.
This is a fair point. How rare, is about 1 in 1000 births. How deleterious is not yet quantified but I cited a paper to show that it can interfere with reproduction in males.
What is dependency graph GitHub?
The dependency graph is a summary of the manifest and lock files stored in a repository and any dependencies that are submitted for the repository using the Dependency submission API (beta). For each repository, it shows: Dependencies, the ecosystems and packages it depends on.
How does it not meet this definition?
The mechanism of reproduction is the process by which two species form new species.
???
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This is accurate but he is also not counting the waiting time for duplication that creates an underestimate. Since your are able to make your argument from his disclosures you must trust his disclosures.
I agree that factor in their simulation makes it their numbers an underestimate. The other factors make them an overestimate. Depending on what you include or exclude from the model, the numbers can go up and down, and different factors would push the outcome of the simulation in different directions.
This is not in any way a help to your case, because you too must trust his “disclosures”, so you must admit his numbers are an overestimate as they focus on specifics. Which you denied. And yet Behe says so.
And none of this is relevant to Howe et al. or chromosomal fusions.
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Of course it is relevant Rum. It is a model that tests time to fixation exactly what were are observing with the different gene patterns. A discussion about the level of accuracy is ok but categorical denial is not.
Math is not always about precise answers it is often about estimates to access feasibility.
What claims? What label? I assure you that Texas sharpshooter and nested hierarchy are both clearly defined, if that’s what you mean.
Not true. That’s the rate of one particular fusion in humans. That rate would of course be larger if there were more acrocentric chromosomes in the population.
That’s one particular fusion in humans. There are many examples in the literature of fusions in other species that are only slightly deleterious and of a few that are advantageous. You need the specifics for the case at hand.
There is no manifest, no lock files, no repository, no dependencies submitted, no Dependency submission API (beta). No Dependencies, no ecosystems, no packages. Aside from that, no problem.
No, speciation is the process. (True, reproduction is involved, but what you said is meaningless.) You seem to know nothing about speciation.
Not quite. A proper model would not test time to fixation but number of fixations per unit time. This may be another distinction you don’t understand. And I do believe your Texas sharpshooter problem is showing again.
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Of nothing of relevance under conditions not relevant.
Ehh no, no gene in Howe et al is known to have anything that would have to evolve in the scenario concocted by Behe. And they’re not chromosomal fusions.
Find me a novel gene gain in Howe et al that has a novel function that requires 2 or more individually deleterious mutations to evolve in a specific gene.
That’s what I’m saying to you:
Behe & Snoke 2004: On the other hand, because the simulation looks for the production of a particular MR feature in a particular gene, the values will be overestimates of the time necessary to produce some MR feature in some duplicated gene.
The only one engaging in categorical denial is you.
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I completely reject your line of reasoning here. You’re in denial because it is an inconvenient truth for current evolutionary theory. This is the same denial you have with the sequence problem.
You’re observing different functional genes that can be different by hundreds of amino acids.
Then do what I told you: Find me a novel gene gain in Howe et al that has a novel function that requires a specific set of 2 or more individually deleterious mutations to evolve in a specific set of duplicate genes.
I don’t agree. Now what?
So what?
That is an expected outcome of evolution from a common ancestor. The more time passes since the common ancestor in two different lineages, the more mutations will accumulate in each lineage. Their genes are expected to become more and more different with time. The rate at which different genes will accumulate mutations will depend on the rate at which mutations in those genes are deleterious, neutral, and beneficial. Which is impossible to know a priori.
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They may be clearly defined that does not mean they are relevant to the observation.
Texas sharp shooter is about someone producing a pattern and deceptively measuring the pattern. There is nothing relevant here to Behe’s work.
The nested hierarch is a pattern where we can show a hierarchal relationship to the data. It can point us toward possible causes but cannot isolate cause.
Ok please support.
There are joint dependencies. Genes in different animal types that are not closely related share genes.
No speciation is a different process. Let’s try a different cut. The mechanism of reproduction is the process where parents mate and generate offspring.
https://www.nature.com/articles/s41421-022-00511-1
Engineered chromosomal fusion with no discernable fitness effect.
Abstract
Chromosomes occupy discrete spaces in the interphase cell nucleus, called chromosome territory. The structural and functional relevance of chromosome territory remains elusive. We fused chromosome 15 and 17 in mouse haploid embryonic stem cells (haESCs), resulting in distinct changes of territories in the cognate chromosomes, but with little effect on gene expression, pluripotency and gamete functions of haESCs. The karyotype-engineered haESCs were successfully implemented in generating heterozygous (2n = 39) and homozygous (2n = 38) mouse models. Mice containing the fusion chromosome are fertile, and their representative tissues and organs display no phenotypic abnormalities, suggesting unscathed development. These results indicate that the mammalian chromosome architectures are highly resilient, and reorganization of chromosome territories can be readily tolerated during cell differentiation and mouse development.
The chromosome number in naturally evolved house mice Mus musculus domesticus , which have populated in Western Europe and North Africa, ranges from 2n = 40 to 2n = 22[16]
So Bill, does this single species of mouse share a common ancestor? Were individuals of this species magically poofed into existence?
(Chromosome territory reorganization through artificial chromosome fusion is compatible with cell fate determination and mouse development | Cell Discovery),17,18. Some of their chromosomes are metacentric, i.e., the centromere is at the middle of each chromosome due to fusions of two telocentric chromosomes, which are commonly found in the laboratory mouse (e.g., C57BL/6)19. In addition, Muntjac deer (Muntiacus, Muntiacinae, Cervidae) have evolved quite diverse karyotypes (e.g., 2n = 46 of M. reevesi and 2n = 6/7 of M. muntjak vaginalis ) through chromosome translocation, tandem fusion, and pericentric inversion20,21,22. Recently, deliberate artificial chromosome engineering has succeeded in generating single-chromosomal Saccharomyces cerevisiae and Schizosaccharomyces pombe strains, which show drastic changes in global chromosome structures, but grow as robustly as the naturally evolved strains23,24,25. These lines of evidence suggest that chromosome architecture in eukaryotes is highly resilient, and chromosome territories could be self-organizing representations of the genome, or simply be a manifestation of random chromatin collisions driven by intrinsic interactions between chromatin loci and/or geometric constraints within the nucleus.
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But that’s not your initial objection. This is you changing the subject again when you have no defense of your previous claim. Of course they’re relevant, though the reason isn’t just because they’re defined.
Ah, so you don’t understand the Texas sharpshooter analogy. Makes sense.
Word salad, and useless. Clearly you don’t understand nested hierarchy either.
Easy enough for you to google; just don’t look only at things you like. Try an unbiased search of the literature.
How does that make a joint dependency? What is a dependency, and how do you recognize one?
Again you abandon your prior claim and run off in a new direction. Where are you even trying to go?
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You’re back to your impossible to know arguments. Behe gave us a model that shows functional changes take a long time. Up to 100 million years.
The Howe Venn shows functional changes that are much larger with longer generation times and smaller populations.
It is not logical to deny this shows a problem with explaining the Howe or another Venn I will post later with gene gain from reproductive mechanisms. There are real problems with modeling the origin of new genes in an existing population.
How could you know a priori what the ratio of deleterious:neutral:beneficial mutations are?
Under a set of assumptions he also admits exaggerates that time. The Texas sharpshooter.
No it just shows gains and losses. There are just numbers on the Venn diagram for shared and not shared genes. It says nothing about what any of these gained genes do, how any mutations in them affect their functions (if at all) or anything of the sort.
It is perfectly logical for me to give the explanations for why these two things are not related:
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You have no reason to think any of the genes gained in the venn diagram would have to evolve according to Behe’s scenario where each mutation would be individually deleterious until combined in sets of 2 or more, where they would replace the duplicate gene’s original function.
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Chromosomal fusion events are not a specific set of duplicate genes where one has a set of 2 or more individually deleterious mutations that has given it a new function that replaced the old one, where no other set of mutations at other locations in the gene could have produced a new function.
Another in a long pile of assertions you can’t back up with math. You’re just throwing random stuff together of no relation.
I guess this explains why you misuse the analogy. It’s about shooting a gun at a wall and drawing targets around the bullet holes to deceive people that you are a sharp shooter.
Another “you don’t understand non argument.”
Like the Texas sharp shooter you misuse the Nested Hierarchy as you always end with a bald assertion. “Common descent is the only explanation for the nested hierarchy.”
Have you read a paper that supports this claim?
Both animals are dependent on the gene families they share. As in the software example different software programs are dependent on different modules.
I am trying to get you to see that reproduction is an evolutionary mechanism.
The model that is needed is to show the gene duplication mechanism is feasible. There is no such thing as an assumption free mathematical proof that something is impossible.
The problem is Behe showed that a model based on gene duplication and divergence is very difficult to model when we are dealing with thousands of cases of as many as hundreds of changes.
