I described how science is done. If you don’t want to go through the process, that’s completely fine with me. I would only suggest that you not claim that your argument is based on science and math if you don’t want to do the work.
Of course, I also note that you made an effort at a somewhat more rigorous analysis after you made this statement. Tip o’ the hat to you, Marty. I respond to that below.
Wait no more. Then check out this new tune.
I agree 100%.
These discussions require spiritual and emotional work, along with the scientific and logical work. That does not mean we should get frustrated. And I am sure that some of us on the forum occasionally tempt you to say, “I’m outta here!” So let’s just agree to march on as best we can, enabled by God’s grace and mercy toward us all.
Yes, he serves his entree of evidence and conclusions with a side of invective. I agree with you that, given the invective, we should be careful in evaluating the evidence and logical inference. I also think you agree with me that we should not ignore the evidence and logic, either.
I will accept in arguendo that the probability of a chloroquine resistance mutation is 10-20 per Falciparum cell, rather than 10-10 as Coyne and Levin seem to object. However, this does not begin to address the fundamental problem that Levin, Coyne, et al. point out: Behe’s model does not include survival analysis.
Because of this serious omission, Behe’s model ends up assuming that any event of chloroquine resistance (CR) is I.I.D with respect to its co-occurrence in the genome with other CR mutations. It is only by this unwarranted assumption that we can derive the probability of multiple mutations appearing in one genome by multiplication. In turn this unwarranted assumption leads to the further erroneous conclusion that the the accumulation of such mutations is beyond “the edge of evolution.”
However, when survival analysis is incorporated into the model, the probability that a second CR mutation will appear in the same genome as an earlier CR mutation approaches 1. Consequently, the only probability that needs to be considered is that of one CR mutation, which is 10-20. In a world where 1020 new Falciparum cells come into existence every year, this is easily within the “edge of evolution.”
While this argument is inherently complex, I have tried to simplify it as much as possible. I hope I have succeeded. Please offer any feedback about anything that needs to be clarified. Thanks!
I think you misread Levin here. His claim is that binding can begin with just two amino acids, then be refined into a stronger binding of say 5 amino acids by further mutations.
Speaking of invective…
This is the linchpin of your back-of-the-napkin analysis, Marty. I appreciate the effort that you went through to calculate probabilities and explain them. However, your analysis does not take into account two important considerations;
(1) Survival analysis - The genetic instructions for complex proteins can evolve over time from genetic instructions for simpler proteins. Each step/mutation along the way is well within the edge of evolution, and the probabilities of individual mutations should not be multiplied.
(2) Every day, something even less probable than the formation of that protein happens 360,000 times: A baby is born.
Consider that every cute, lovable baby has about 25,000 genes. Conservatively assume that every gene has just 2 different alleles. That means that the probability of a baby being born with the allele set of Marty is 2-25000, or 10-7526.(*) Couldn’t happen! There are only 10120 protons in the universe. But yet it did; Here is my friend Marty in the land of the living. And here, too, is Chris Falter, with the same inifinitesimal probability of existing (10-7526).
What are we to make of this? Simply that there is no need to explore the entire probability space. Once it is functioning, the stochastic process we call inheritance has a probability of basically 1.0 of producing really complex proteins. Our limit (our edge) is that we cannot predict in advance which exact protein chains will emerge, because any individual protein has such an infinitesimal probability. Just as the probability of 360,000 babies being born every day is basically 1.0, even though the probability of the birth of any of those 360,000 is on the order of 10-7526.
Here’s still another helpful analogy: suppose you have a bin of 357 fair dice. Each die is stamped with a tiny, unique ID. You stand on the top of an A-frame ladder in the middle of a basketball court with the bin. You shake it around as thoroughly as you can without compromising your safety, then you dump all 357 of the dice on the court. You carefully note the ID and face-up value of each die. What is the probability of your having observed the exact throw of the dice that you just observed?
The answer is 6-357, the same as the probability of the amino acid composition of that median human protein you cited.
So the conclusion of the matter is…you are special, Marty! And so am I! Let’s watch every episode of Mr. Rogers’ Neighborhood, while we’re at it.
(*) Assuming that the 2 alleles of a gene are uniformly distributed in the population.