You were asked for evidence. You didn’t produce any. What Behe says about the evidence isn’t evidence, it’s hearsay.
What does the Bible say about using hearsay for judgment?
3 Likes
That does not show that multiple simultaneous mutations are required. Perhaps you did not understand the question?
2 Likes
Then Behe is wrong. In a group of a few billion B-cells there are many, many new protein-protein binding sites in the randomly produced antibodies within each B-cell lineage.
5 Likes
Yes, that’s a valid perspective, the problem arises when we mix them up.
Well, I don’t think that the probability that something that doesn’t go extinct will evolve new features is 1, for starters.
Well, the probability of a double-CCC (CCC: chloroquine-complexity-cluster) is 1 in 10^40, which is about the number of organisms in the history of life. So if two new binding sites are required for a transition, that would place it outside the bounds of evolution’s ability.
An argument is a way of examining the evidence, it is not just hearsay.
Five or six mutations for a new binding site would be multiple simultaneous mutations needed for a new function, though.
Wow, that’s a pretty poor argument since B-cells are part of the immune system, a highly complex subsystem in systems that are already highly differentiated. B-cells are designed to test multiple combinations of molecules.
Sending it back your way! Use randomness, not molecule generating machines, and most importantly, show the math. Thanks!
@lee_merrill debating with these guys can be fun, but they have no interest in understanding your points. They will assume you are either ignorant, stupid, or a scoundrel, because they have been taught to think that way. It may be impossible for them to even consider that a bright, thoughtful person with integrity could research issues and then find fault with their deep and distinguished Faith. You are not merely wrong to them, but a blasphemer. Good luck!
2 Likes
Behe discusses the immune system, and is interested in changes outside of this system, how fast can we expect changes there?
T-URF13.
1 Like
No. You clearly haven’t examined any evidence for yourself. It’s all hearsay. You can’t even relate Behe’s argument in your own words.
Antibodies evolve in two weeks from recombination (which you and your hero ignore), mutation, and selection.
This “simultaneous mutation” stuff is fantasy.
That works by genetic variation and strong selection.
B-cells don’t do the testing. They produce the antibodies. Selection involves multiple cell types.
Can you explain why you and Lee, who seems desperate to deny that antibodies are produced in only two weeks by genetic variation and selection, obviously don’t understand the most elementary aspects of immunology worked out more than half a century ago?
Remember what you wrote below about “a bright, thoughtful person with integrity”? It seems to me that such a person would be eager to learn basic immunology, even if the goal was to disprove evolutionary theory. Instead, both of you are fabricating evidence. Why?
The genetic variation generated to produce antibodies is random with respect to fitness, just like it is in whole organisms. If evolution is so ridiculous and God directly designed us, why did He design the immune system to use Darwinian evolution? Did He do it to mock IDcreationists?
Have you or Lee shown any math?
Pure projection.
But your and Lee’s false descriptions of basic immunology reveal that you aren’t researching much of anything. Please stop pretending that you are independent on this.
More projection!
3 Likes
So stop mixing them up.
Omitted text restored:
Not even a spark.
3 Likes
The adaptive immune system employs textbook blind and unguided Darwinian evolution to find new adaptive antibodies that bind to their targets.
There is a population of B-cells.
The population of B-cells has many different variants of the antibody protein.
The variants are produced by random mutation and recombination in total blindness to their phenotypic effects(the different b-cells don’t know whether the particular mutations and VDJ combinations they produce will be adaptive or not).
The majority of newly generated variants are deleterious (if the cells somehow knew how to make adaptive changes there’d be no need to generate a huge population of variants to increase the probability an adaptive one is made by chance).
The immune system some times fails to make adaptive variants fast enough, and the host dies.
B-cells that produce adaptive variants by chance are selected for, and get to reproduce more.
B-cells that produce deleterious variants by chance are selected against and get to reproduce less(some are even killed).
By this process are more and more adaptive variants evolved over many generations of B-cells, and they take over the B-cell population through a change in allele frequency.
Again. Textbook blind and unguided Darwinian evolution.
5 Likes
Do you think if you repeat this falsehood enough times it will magically come true? Sorry, that’s not how science works.
2 Likes
The poor argument is the bare assertion that B-cells are designed.
V(D)J recombination produces random peptide sequences within the antigen binding domain of antibodies. Those random peptides regularly bind to other proteins. Therefore, protein-protein binding sites can’t be that rare.
5 Likes
It may be hopeless to try to convince the true believers here that their B cell example is specious, but for the sake of others who read this thread, something needs to be said.
First, Behe’s argument is about the math. Whether his “two simultaneous mutations” hypothesis is the correct answer or not, the actual cause of Chloroquine resistance is going to fit the math of evolution.
Run the math on B cells, and it becomes obvious why our immune system wins the lottery most of the time. There are hundreds of thousands of examples of microevolution where the results fit the math. B cells is one.
But B cells are a highly evolved “binding site generating” subsystem which was not around for well over 3 billion years while proteins were binding into complexes. The B cell argument is, to me, like claiming, “Because there are now factories that manufacture car parts, we know car parts evolved.” Not convinced.
Behe applies his math argument to protein complex binding sites. Evolution apologists do not try to run the math on protein complex binding sites. The caveats and special pleading needed would make it into a fish story. So instead, they criticize and demonize those who point it out.
There is one math argument they use as in quotes from this thread:
The above is an emotional probability argument, with just one term: deep time. Not the least bit rigorous.
But then a more complete probability argument is rejected because it doesn’t fit the theory. The emotional math argument of “deep time” is used where it supports, but more rigorous math arguments are rejected because they don’t support.
This next is common also, the first to @lee_merrill and the second to me:
@lee_merrill is criticized for failing to use his own words, then when I use my own words, I get criticized about anal technical use of a word. Doesn’t matter what we say, we’ll be nitpicked to death.
There’s nothing I can say to the true believers here. For the others who read this, please consider the points above.
1 Like
Well, Behe counts 2 mutations of the 5 or 6 needed, as neutral, leaving 3 or 4 mutations as deleterious, which must occur together.
Do you think if you repeat this falsehood enough times it will magically come true?
3 Likes
It’s more about the false assumptions that go into his simplistic math. Behe misrepresents the evidence itself.
It’s been objectively disproven.
And Behe ignores many evolutionary mechanisms to do his simplistic math.
Have you?
The same math applies to Behe’s assertion about binding. It’s worth noting that antibody-antigen binding tends to be more tight and more specific than almost all other protein-protein binding in biology.
And they generate those binding sites from random peptides produced by random recombination, a major source of inherited variation that Behe’s simplistic math completely ignores.
The parts are manufactured by genetic variation and selection, so your analogy makes no sense.
And ignores the complexes of antibodies binding to antigens, which evolve in two weeks.
Objectively false, Marty.
We reject Behe’s math because it isn’t based on evidence.
Making you and Lee use your own words reveals that you have no idea how biology works in real time. Therefore your skepticism about how those things evolve in deep time is unfounded.
More projection. Science isn’t about belief. It’s about the evidence that you can’t bring yourself to examine.
2 Likes
Nobody is disputing the math, what we’re disputing is the assumption that any known adaptation lies beyond the so-called edge of evolution.
Behe’s calculation is GIGO. Garbage in->garbage out. The math is fine, it’s the garbage put into the formula that’s the problem. Specifically the idea that new protein-protein binding sites require one or more deleterious mutations to evolve.
We are disputing all the assumptions about the rarity of mutations that contribute to binding-site evolution, we are disputing the assumption about their selection coefficients(we don’t think they require deleterious mutations), and we are disputing the assumption about how many are required to produce efficient binding.
We are not disputing that X is very unlikely to evolve if all those assumptions check out. It’s just that nothing supports those assumptions, so that’s why we dispute them.
Do it then. Run the math. I mean you say it’s obvious, so you must have done the math right? Do the math for the immune system evolving new binding sites and compare it to a population of free living organisms. Do it, show the math.
Well you seem to have misunderstood what the argument from the adaptive immune system is supposed to show here.
The point of the analogy here is to show it is a blind and unguided Darwinian process both in the immune system and in populations of free living organisms, not that the numbers are identical for things like population size, genome size, mutation rate, generation time etc. etc.
The population of B cell is analogous to a population of individual organisms that make up some species. So rather than having a population of B cells within an individual evolve a new antibody protein by VDJ recombination and somatic hypermutation of only the antibody encoding gene, we instead have (for a population of free living organisms) their entire genomes being subject to mutation, recombination, natural selection and genetic drift.
And so when novel protein-protein binding sites are favored in free living organisms the same mechanisms are in operation there as are working in the adaptive immune system. A population of individuals showing variation in their characteristics, each of which are subject to mutation, recombination, natural selection and genetic drift. And that a successful adaptive response depends on producing a large amount of random variation in blindness to their phenotypic effects.
But that’s just an outright falsehood. Not only have they done the math on those, they’ve simulated their evolution using combined biophysics and population genetics and shown it’s easy to select for new protein-protein binding sites.
Read it and weep:
Abstract
A majority of cellular proteins function as part of multimeric complexes of two or more subunits. Multimer formation requires interactions between protein surfaces that lead to closed structures, such as dimers and tetramers. If proteins interact in an open-ended way, uncontrolled growth of fibrils can occur, which is likely to be detrimental in most cases. We present a statistical physics model that allows aggregation of proteins as either closed dimers or open fibrils of all lengths. We use pairwise amino-acid contact energies to calculate the energies of interacting protein surfaces. The probabilities of all possible aggregate configurations can be calculated for any given sequence of surface amino acids. We link the statistical physics model to a population genetics model that describes the evolution of the surface residues. When proteins evolve neutrally, without selection for or against multimer formation, we find that a majority of proteins remain as monomers at moderate concentrations, but strong dimer-forming or fibril-forming sequences are also possible. If selection is applied in favor of dimers or in favor of fibrils, then it is easy to select either dimer-forming or fibril-forming sequences. It is also possible to select for oriented fibrils with protein subunits all aligned in the same direction. We measure the propensities of amino acids to occur at interfaces relative to noninteracting surfaces and show that the propensities in our model are strongly correlated with those that have been measured in real protein structures. We also show that there are significant differences between amino acid frequencies at isologous and heterologous interfaces in our model, and we observe that similar effects occur in real protein structures.
II. CALCULATION OF INTERFACE ENERGIES
We consider two opposing faces of the protein, denoted A and B, as potential binding surfaces (as shown in Fig. 1). There are two possible isologous interfaces (AA and BB) and one heterologous interface (AB). The energies of the three interfaces E AA, E BB, and E AB depend on the sequences of residues on the surfaces. Nonsurface residues play no role in this model. A surface is modeled as a 4 × 4 array of amino acids. The energy of an interface is modeled as the sum of the 16 pairwise interactions between amino acids that are formed when two surfaces are brought together (see Fig. 1). We consider four possible 90° rotations of two surfaces. The three energies E AA, E BB, and E AB are defined to be the lowest of the four energies that arise from the four possible rotations.
FIG. 1. Model of a protein with two opposing surfaces, A and B, which may interact, shown as blue and red, respectively. There are 16 amino acids on each surface. Interface energy is determined by the sum of the energies of the 16 pairwise contacts that are formed when the two surfaces are brought together, as indicated by arrows. (a) An AA interface is shown with the two proteins in the same rotational configuration. (b) An AA interface in which one protein has been rotated by 90°. The energy E AA of the AA interface is defined as the lowest energy of the four possible rotations. (c) When proteins aggregate in different configurations, the energy of the multimer is given by the sum of the energies of all the interfaces in the multimer structure.
III. CALCULATION OF AGGREGATION PROBABILITIES
For any given sequence of surface residues, we calculate the interface energies as in Sec. II. We then use the interface energies to calculate the probabilities of protein-protein interactions. We consider a solution of a single kind of protein with total concentration ϕ moles per unit volume. We determine the equilibrium concentration of monomers c and of aggregates of n subunits, C n, in the following way.
For each of the three types of interface ij ∈ {AA, AB, BB}, we define
(1)where ω is the number of possible orientational configurations of one protein relative to its neighbor. For the simple cubic lattice considered here, ω = 24, which is the number of possible orientations of a cubic object on a cubic lattice. In the calculations below, the statistical weight of an interface of type ij is given by a ij**c/c 0, where c 0 = 1M is the reference concentration. (…)
V. PHENOTYPE DISTRIBUTIONS
The two most useful quantities to summarize the phenotype of a sequence are the frequency of AA dimers, 𝑃∗2P2*, and the frequency of fibrils, P f ib. Figure 2(a) shows the distribution of a sample of sequences generated by the MCMC evolutionary simulation in the neutral case with a total concentration of ϕ = 0.01M. The MCMC routine ran for 300 000 generations, and the first 5000 generations were discarded to allow for equilibration. As all sequences have equal frequency under this mutational model when there is no selection, the sequences generated are simply random amino acid sequences.
Figures 2(b) and 2(c) show the way the phenotype distribution shifts when selection is applied for dimers and for fibrils. When selection is applied for dimers, the distribution shifts close to the bottom right corner, with ⟨𝑃∗2⟩=0.89⟨P2*⟩=0.89 and ⟨P f ib⟩ = 0.01. When selection is applied for fibrils, the distribution shifts close to the top corner, with ⟨𝑃∗2⟩=0.04⟨P2*⟩=0.04 and ⟨P f ib⟩ = 0.90. This means that sequences that are either very strong fibril-formers or very strong dimer-formers are possible in this model and that they arise easily when selection favors them. Nevertheless, they are relatively rare compared to the large number of random sequences with weaker interface interactions, so they do not arise frequently in the mixture of random sequences generated under neutral evolution.
An “emotional probability” argument eh? You’ve also altered my statement, which is a direct response to what @lee_merrill is paraphrasing Michael Behe to have stated:
So here @lee_merrill is taking Michael Behe to have stated that Chloroquine resistance is as difficult to evolve as a new protein-protein binding site. That’s HIS statment, not mine. I simply run with it. You complain we don’t do math, but fine let me do a simple back of the envelope calculation with the same assumptions @lee_merill says Behe uses.
Say chloroquine resistance (CQR) took 100 years to evolve (it didn’t, but let’s just be extremely conservative). Life has existed on Earth for approximately 4 billion years. It just has. You can cry about that but it just has. Life, the planet, and the entire univers is really really old. Get over it.
Anyway, assuming there has only been one population of cells roughly of the size of the plasmodium population, for that entire history, one new protein-protein binding site could have evolved once every 100 years. Right? I mean if it takes 100 years to evolve CQR, and if a protein-protein binding site is of an equal difficulty to CQR, then evolving a new protein-protein binding site would also take 100 years. Right? What’s 4 billion divided by 100? That’s right, 40 million. 40 million new protein-protein binding sites could have evolved in that single population then. And I’ve extremely conservatively assumed they would have to evolve sequentially. One only begins evolving after another has already finished.
There you go buddy. It’s that math you wanted. Using your hero’s assumptions, not mine. 
5 Likes
Yes. Behe is very good at making up scenarios that would not work, and for which he give no reason to believe is actually exists in real life.
As I said before, one could also make up a scenario in which bacteria evolve jetpacks that allow them to travel at the speed of sound, but this is impossible because we never see it, therefore Intelligent Design is true. Would you also agree that is a good argument?
5 Likes