Models aren’t used by real scientists to “support claims.”
Real scientists use data to support or falsify hypotheses. Got any of either? No hearsay, please.
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That’s known as “squid ink”, though in your case I’m not sure it’s on purpose.
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Models are used by real scientists to help support claims.
No, they are used to help test hypotheses, which are not synonymous with claims. But evidence is the most important element.
From your response, I infer that you aren’t interested in evidence. Is my inference correct?
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I agree. Both Lynch and Behe used evidence to support their models. Problematic evidence for the gene duplication hypothesis is the evidence of deleterious mutations when AA substitutions occurred prior to gene duplication.
Then cite the evidence instead of describing it.
I’ll do as I wish, thanks. If you want to moderate, volunteer to do so.
I know.
Speaking of arguing, did the proteins you were claiming to be representative of ALL proteins WRT a mutational bias favoring decreased happen to be small, globular, easy-to-purify-and-express ones? I’m happy to check for you if you provide a working link to the paper you claimed had universal validity.
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The waiting time problem doesn’t exist until you produce some calculation showing that there actually is a problem. Which you will never do, so the waiting problem will never exist.
The only ‘waiting time problem’ is determining how long to wait for you to admit you can’t show there is a ‘waiting time problem’ before giving up and going back to ignoring you.
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Is there a plausible model where everything stays static for four hundred million years of reproduction?
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There are models based on empirical evidence that show it is a problem. If you do not want to accept those models that is up to you. For me the evidence is overwhelming that the problem is a killer for multicellular evolution based on gene duplication.
There is evidence that certain proteins can remain highly static over hundreds of millions of years.
Sure. So why not all of them?
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Some amino acid substitutions are neutral depending on the protein. What you need to keep in mind is that non neutral mutations potentially limit variation. We also do not know the starting points of certain proteins in different animals.
Do you doubt there is evidence for deleterious mutations? Studies are cited in the papers.
The sharp shooter claim is not based on evidence so I am surprised you would use it.
??? If it is non neutral, that variation is subject to selection. That does not limit variation, but acts to eliminate or entrench it.
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Since the majority of non neutral AA substituting mutations are deleterious then they tend to limit variation.
The variation permitted by neutral mutations is not at all affected by the deleteriousness of deleterious mutations. Therefore, deleterious mutations do not limit that base variability. Meanwhile, if there exist non-deleterious non-neutral mutations, they too are added to the variation that is permitted to occur. Deleterious non-neutral mutations add less to the overall variation, depending on how strongly they end up selected against. Neither deleterious nor non-deleterious non-neutral mutations actually decrease how much variation is permitted to occur or to spread. What you mean by limiting variation is either completely opaque, or complete nonsense, or both.
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Until you produce calculations based on those models there is nothing to accept, hence no problem that needs a solution.
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I acknowledge that you don’t accept the data from the published models. That’s up to you. I accept the data from the published models as problematic for the current hypothesis of gene duplication and divergence as a viable evolutionary driver.
There is no such thing as a “the data from the published models”. Data does not come from models, published or otherwise. It is either collected in experiment or in the field.
If there is data that conflicts with the theoretical prediction, let’s actually see both. Let’s review how the prediction was arrived at, and see if the problem is with the theory, the input, or both. Your insistence that there is a problem will keep meeting well-deserved dismissal, at least as long as you keep completely refusing to actually point at one.
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You haven’t presented any data from published models. Or any calculations based on those models. So there is nothing to accept, and there is still no problem that needs a solution[1].
Other than the problem of how to get you to stop making empty claims about things you can’t demonstrate and don’t understand. ↩︎
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