OMG, he’s using the “but they’re still just X” argument!
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Yes Gilbert, it is just “an intuition” – your intuition that it is an “absurd outcome”.
Ha … … … … ha – very funny. </sarcasm>
“Are you not aware that” I’m not alone in holding your godawfully-bad analogies in low regard?
Or have I simply missed the comments of people saying “Gil, we love your vacuous analogies – my day wouldn’t be complete without them”?
You aren’t “engaging” with me now Gil – so there’s nothing to cease!
You carefully avoided my main point:
Rather than substantively engaging with me, your post was simply a parade of smug, self-satisfied, self-serving and most importantly irrelevant point-scoring.
So I won’t have @Giltil inflicting his vacuous analogies and point-scoring on me any more. Oh woe is me! Whatever shall I do?
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That’s not obvious at all. There are viruses so distantly related to SARS-Cov2 that their presumed spike homologues have diverged so much they are, well, subject to some doubt about whether they actually are related. And you can find various viruses with spike proteins that appear to “bridge” that sequence and structural gap.
How different must it become before we no longer say it’s recognizable as a spike protein?
This is an incorrect use of terminology. Microevolution is not a term that applies to the degree of protein sequence divergence (it’s a term that describes evolution below the species level, but protein functional, sequence and structural divergence can be little or a lot regardless of whether it occurs between or across periods of speciation), so your response here just doesn’t make any sense.
What position am I strawmanning? I am providing additional detail to Ron’s response to Bill’s stupidity regarding his fatuous denial that mice and humans proteins could diverge by a few dozen mutations without the proteins being rendered nonfunctional. Bill is an ID proponent, and he’s using arguments he was inspired to use by Michael Behe’s misleading nonsense.
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John’
We both do not understand these inferences as we have no detailed answers to support their validity. This is not because the subjects are not diligently studied it is because they are not formed from bottom up research but from the inference of common descent.
For example if we infer common descent is true then we only need gene gain and gene loss as high level concepts to explain the differences. If we do not use the inference of common descent the problem explaining these differences gets much more challenging as we need to show how genes are randomly lost and survival is maintained and we need to show how genes are gained through the reproductive process.
If we see genes not following an inheritance pattern we can infer convergent evolution. Without common descent we need to explain how two genes evolved almost identical sequences. A claim that has almost no empirical support.
The possibility that some of these like proteins started from different sequences is something I think is potentially false at this point. What I have noted is the deflection and lack of interest that has been shown in understanding the origin of these sequences. Finally @RonSewell posted a challenge with supporting evidence.
Perhaps another angle on the math might help with questions of (im)plausibility.
Each flask has roughly 500 million cells. E. coli have a DNA replication error rate that works out to roughly 1 mutation in every 1,000 replications. So in every generation, there are roughly 500,000 cells with a mutation.
If the probability that a mutation is beneficial is 1 in a million, then we expect \frac{1}{2} of a beneficial mutation in every generation. Or in other words, the probability that there are no beneficial mutations in a given generation is (1 - 10^{-6})^{5e5} \approx 0.61, meaning the probability of at least one beneficial mutation in a given generation is \approx 0.39.
I don’t know exactly at what point the 25-40 beneficial mutations were accumulated, but there have been roughly 80,000 generations to date. That gives an expectation of 40,000 beneficial mutations occurring. That gives us an estimate of 0.000625–0.001 for the probability of fixation for these beneficial mutations. Under a basic model of fixation, that implies a fairly modest selective advantage.
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What exactly are you talking about here? Is there a real, concrete example?
I would say that it’s almost certainly false. Do you have a case in mind?
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Yeah that’s a much better way of doing that, than what I was trying to do. I realize now also I made a pretty large but basic error in my calculation.
When I calculated the probability of any given specific beneficial mutation (assuming they’re all substitutions for simplicity), I took 1 in all possible substitutions in the E coli genome (1 in 3×4.5×106). But that would obviously be the probability of any given specific substitution mutation, not the probability of any specific beneficial mutation.
Since only a minority of mutations are beneficial, that implies that there’s only rather few possible beneficial substition mutations in the E coli genome at any given moment, and that other mutations have to occur first to enable new beneficial ones (thought to be one of the reasons why the Cit+ trait only evolved in the Ara-3 lineage, as the mutations that enable the cells to fully take advantage of the tandem duplication that puts the citT transporter under control of another promoter doesn’t seem to have fixed in the other lineages).
Afaik this is also consistent with what Lenski and others have written in their published work regarding the experiment, that many of the beneficial mutations in the experiment are quite strongly dependent on the genetic background.
The number of possible beneficial mutations are of course incredibly muich larger than this because there’s other mutations than just substitutions. The number of possible insertion mutations are theoretically infinite, for example. And many of the beneficial mutations have been both deletions, recombinations, and insertions too.
This makes it basically impossible to make a fully realistic calculation of these probabilities. We will always have to make some sort of simplifying assumption.
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I would add that not only are @Giltil ‘s analogies poor, but they are also self-defeating.
Ignoring real-world counter examples while relying on fictional scenarios is never a good way to make an argument.
Though I do find this a remarkably common pattern among creationists and ID proponents.
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Yes, absolutely, there are lot of details of the molecular biology which are elided by my simple calculations. I was aiming for ballpark estimates rather than a rigorous and comprehensive model.
Yes, in addition to grossly misrepresenting cherry-picked real-world cases.
I think he defeated himself when the caveman failed to recognize the coconut and the kangaroo as designed.
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Then you understand neither the concept of consensus sequences in databases nor the concept of fixation.
No.
No one who understands how this works labors under the misconception that sequences in databases are random samples, as you apparently do.
Biology and biochemistry explain those differences (or lack thereof) easily. You aren’t interested.
If someone wanted to calculate the probability of an event and, through a reasoning you don’t really understand, would come to the conclusion that said event is extraordinary rare but that in the real world you observe that said event occurs very frequently, wouldn’t you be entitled not only to suspect but to conclude that the reasoning is flawed ?
You already asked this, and multiple users already answered in some detail. Then you asked again, and got the same reply again. If memory serves, you even tried a third time already. This is nothing short of sealioning at this point, re-stating the question instead of addressing the perfectly thorough and complete replies to it. Still, out of charity, I’ll reiterate:
- If you cannot demonstrate one or more flaws, then no, it’s not a conclusion.
- Regarding the actual topic at hand, there is no such discrepancy between the theoretical implications and the experimental data in the first place.
So, in summary, both your premise (there being something to raise an eyebrow about), and your conclusion (therefore unwarranted leaps in logic may be valid) is false.
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That seems to create a bit of a problem for the idea that you can a priori calculate the FI of some biological entity like a protein sequence. You seem to be saying you need to observe the frequency with which it “occurs” in some setting/experiment, before you can really say how rare it is.
If you calculate the FI of some function and say “this amount of FI can’t evolve”, and we can show that under these conditions that much FI can evolve, and your response is “then it doesn’t have that FI”, what the hell use is that FI calculation in the first place? Have you then really shown that high FI can’t evolve if you’re just going to say that if it evolves it isn’t high FI? Can’t you see the lunacy of what you’re doing?
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That is, in fact, exactly how one would go about testing the validity of the calculation.
Can you then explain why, in the 25 years since Dembski first devised his concept of “functional information”, not a single ID scientist has attempted such an experiment?
If someone calculates that the probability of occurrence of an event is 1/10^150, assuming his calculation is correct, how would you empirically test it?
No, I am not saying this, not at all.
Except that my response wouldn’t be the one you say I would give. In your scenario, if I was convinced by your demonstration, I would simply acknowledge that the assertion that high FI can’t evolve is wrong.
No, for the reason that, as I said above, I am not doing what you claim I am doing.
To answer that we need to know what is meant by the phrase “occurrence of an event”, because probabilities strongly depend on the assumptions going into the calculation about the behavior of the system, and the conditions under which we’re doing the test.
Some claims about probability are untestable in practice. They require time or resources on a scale we simply can’t provide. In such situations the best we can hope for is to apply reason to the question.
If someone calculates that the probability of finding a particular level of function in one lucky random guess is 10-77, if we can consistently guess it in significantly fewer than 1077 attempts, the calculation is of course obviously incorrect. That would be a test by falsification. We have proven the calculation wrong.
But suppose we test for decades, and we reach perhapts 1030 attempts and have still not found that level of function in random guesses, can we really say we have tested the calculation? Of course not, we’re still off by an incredible 47 orders of magnitude.
But what if we then show that we can evolve that level of function incrementally, because mutation can find partial answers, and because selection can retain them for some degree of function, and we gradually build up towards that level of function. Have we disproven the calculation that that degree of function has a rarity of 10-77 for one lucky guess? Of course not. We’ve just used a different method than one lucky random guess to get to that level of function. In other words, it is entirely possible that something could have a “rate of occurrence” of 10-77, and yet it could be routinely evolved in experiments, because it can be broken down into much more accessible probabilities by evolution (cumulatively find improvements that are as rare as 10-6 and build one upon the other).
Makes sense, yes? Yes!
But that was the essence of your response to my FI calculation for a specific series of beneficial mutations. That because we could routinely evolve that number of beneficial mutations, such a series could simply not have a probability that low.
I think your mistake was to confuse the general (what is the probability of evolving any series of 25 beneficial mutations in X amount of generations) for the specific (what is the probability of this specific series of beneficial mutations in X amount of generations).