Why? The sequence is designed and it mutates neutrally over time. The neutral mutations is what you are observing in the pattern.
The sequence space is too large. Many more ways to fail than succeed and as Behe showed empirically you may find a pony occasionally, however the trend is toward non function unless stopped by purifying selection, repair or apoptosis. If this is the case then there is not enough variation for evolution to occur beyond the species genus level.
What is stopping this same process from producing beneficial mutations?
Selection removes deleterious mutations.
Thatâs a bare assertion.
If it is doing this evolution is grinding to a halt with mutationally sensitive proteins.
The problem is not beneficial mutations it is complex sequences that create new features.
Yet another bare assertion.
HOW IS THIS A PROBLEM???
Itâs only a problem for Billâs religiously motivated scientific ignorance. Not a problem at all for evolutionary biology. Everyone is pretty clear on that except Bill.
Itâs not the case. There is nothing preventing variations for accumulating and producing change beyond species or genus level.
Beheâs idiotic âdegrading genomeâ scenario fails because he assumes a constant environment. In the real world environments are constantly changing and neutral mutations can become beneficial under the new selection pressures.
No, it is a direct result of the process. Without variation evolution grinds to a halt.
The large sequence space that is too large to randomly search. If a flagellum needs 100k nucleotides to find an advantage (mobility) there is 4^100k possible ways to arrange this sequence. How many will build a flagellum or any other mobility solution. Substantially less then 4^100k.
This is why mind is a much more likely mechanism.
Evolution doesnât have to randomly search the entire sequence space. It only searches for variations in the immediate vicinity of an already viable morphology.
Thatâs another point youâre had explained to you several dozen times in the past few years but still you come back with the same bogus claim. You make Dory look like a memory expert.
Tim you think you have explained it but you donât understand it. A search space has to be searched until advantage is found or drift fixes the change in the population.
But the whole enormous search space doesnât have to be searched Bill. Just the local space. That simple fact kills your argument deader than dead, and you know it.
Random mutation occurs in every generation, so that isnât a problem.
Yet another bare assertion.
And how many of those sequences can produce a functional motility system in bacteria?
I suggest you think about your claim here.
I am going to post a random search simulator for you to play with. You will see how difficult to just get 5 letters to find a target.
Orders of magnitude less than 4^100K.
Here is a simulator where you can run a small character random search and other algorithmic searches. With three characters I failed to find the target with 5 trials of 1000 generations. This will show you how challenging even a small random search to find an advantage is.
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=2ahUKEwiJmr29j_bhAhWG4J4KHa2ICxEQFjACegQIABAB&url=https%3A%2F%2Fwww.evoinfo.org%2Fweasel.html&usg=AOvVaw3IN05KphXd9ILIZ98tZfW-
I suggest the same for you. Each human is born with about 50 mutations. There are 3 billion bases in the haploid human genome, so there are 9 billion possible substitution mutations across the entire genome. To get every possible substitution mutation you need just 180 million births. How is this a problem?
You are assuming that there is a set target. There isnât.
How many orders? Whereâs the math?
What are all of the possible combinations of amino acids that can produce a motility system? Without this number, you are just making assertions.
We know of 10^50 100k genome segments that are not mobile bacteria producers. The number is likely orders of magnitude higher than this. There is no reasonable argument for the blind watchmaker here.
You need to learn the difference between and estimation and an assertion.
Itâs worse without a set target.
Yes, there is a problem it is called fixation.
Whereâs the math?
Umm, no itâs not. The lottery doesnât have a set target which is why it doesnât take 150 million drawings before there is a winner.
What problem? This is yet another assertion with no evidence to back it.
How large is it, Bill?
Behe hasnât done anything empirical WRT evolutionary biology.
You seem to have definitions for the terms âevidenceâ and âempiricalâ that are very different from the generally accepted ones.
Why was that flagged? Itâs absolutely true.
When you try to explain this with the lottery fallacy it is clear you donât understand the problem. Yes, this is an assertion but if you want I will take you through the reasons. If you want to dismiss my objection out of hand letâs not go through the process.
Thereâs no such thing as a lottery fallacy.
You are dismissing my reasoning.
Letâs look at some macroscopic examples. Birds have many flight adapted features. According to your logic, all flying species have to produce those same exact features and the same exact DNA sequences. This is wrong. Bats, for example, have very different flight adapted features, and very different DNA sequences. This means there is more than one target. When you pretend that the solution evolution finds is the only possible target you are ignoring all of the other possible solutions that werenât taken. This is the Sharpshooter fallacy.
More evidence you donât understand the problem.
I am not assuming one target is the only possible solution. More targets (successful solutions) can improve the odds but those are calculable. In the case of 1 million tickets in 150 million long combinations you need 150 trials so you can simulate this with 1 target or winning ticket out of 150 tickets. So the target simulator works here.
The lottery fallacy is that the example is made with a short sequence where ratio of success to tries is well contained. This is not what we see in biology as we are observing long sequences.