Evolutionry/religious nonsense

Behe, that on-line anti-evolution rag and the Discovery Institute all have an agenda to push so-called intelligent design because of a religious bias. You just want to whine about non-existent name-calling and about atheists, and once again demonstrate your ignorance. .

There are many scientists who are men and women of faith, and the Catholic church in particular accepts evolution as the best explanation for the diversity of life on this planet--except perhaps for a Catholic lunatic fringe

Whats really hilarious here is that your claim includes a reference to statistical probability, so that if the claim were true, demonstrating the case would in fact be very easy. So stop attempting to sidetrack this part of the discussion and put up or withdraw that ridiculous claim

So stop attempting to sidetrack this part of the discussion and put up or withdraw that ridiculous claim

By the way, history does not repeat itself. The only historical commonality is human nature, which includes, among a host of other failings, self-delusion, confirmation bias, lying and bigotry. Those failings are particularly noticeable in religious fanatics.

Such numbers seem prohibitive. However, we must be cautious in interpreting the calculations. On the one hand, as discussed previously, these values can actually be considered underestimates because they neglect the time it would take a duplicated gene initially to spread in a population. On the other hand, because the simulation looks for the production of a particular MR feature in a particular gene, the values will be overestimates of the time necessary to produce some MR feature in some duplicated gene. In other words, the simulation takes a prospective stance, asking for a certain feature to be produced, but we look at modern proteins retrospectively. Although we see a particular disulfide bond or binding site in a particular protein, there may have been several sites in the protein that could have evolved into disulfide bonds or binding sites, or other proteins may have fulfilled the same role. For example, Matthews’ group engineered several nonnative disulfide bonds into lysozyme that permit function (Matsumura et al. 1989). We see the modern product but not the historical possibilities.

We should also notice which parameters the model is particularly sensitive to and which not. The model is least sensitive to the point mutation rate v and the selection coefficient s because both of those appear only as linear terms in equation 4. Thus, for example, if we consider an organism where the point mutation rate is increased by a factor of 103, then the numbers calculated from equation 4 will decrease by only that factor. For the case discussed earlier in which six nucleotide changes were required, the population size needed to fix the feature in 108 generations would then decrease from 1022 to just 1019.

The model is more sensitive to the value ofρ, because ρ appears with an exponent in equation 4. If ρ were less by a factor of 10 (100 instead of 1000), then the population size needed to fix the feature in the preceding example in 108 generations would decrease from 1022 to 1016. The number of possible null mutations—the numerator ofρ—arises from basic considerations of protein structure so that it is unlikely to vary significantly. The number of possible compatible mutations λ—the denominator ofρ—is more difficult to estimate. However, the value of one thousand that we use for ρ in Figure 6 ▶ is conservative compared with the range of values used by other workers (Walsh 1995). It should be noted that as λ becomes larger, the number of possible null mutations—and thus implicitly the length of the gene—must increase to maintain a constant value ofρ.

The model is most sensitive to the value of λ—the number of loci that must mutate before a new MR function occurs—which appears as an exponent in equation 4. If in the case just mentioned, because of the particular initial sequence of the parent gene, either three or nine nucleotide changes were necessary instead of six, then the population sizes required to fix the feature in 108 generations would vary from 1011 to 1031 organisms. The dependence on λ may encourage speculation that perhaps MR mutations could develop by point mutation in duplicate genes if the parent gene giving rise to the duplication were serendipitously poised to lead to the new feature with only one mutation in the precursor gene. Although this is certainly possible, it is unlikely to be the general case. As one example, Li (1997) has argued that the precursor to modern hemoglobins that can bind diphosphoglycerate did not have any of the three amino acid residues involved in the interaction. As shown in Figure 5 ▶, for the average case, pre-equilibration, which allows for the occurrence of rare, fortunate alleles, does not affect the expected time Tf in the linear portion of the curve.

The lack of recombination in our model means it is most directly applicable to haploid, asexual organisms. Nonetheless, the results also impinge on the evolution of diploid sexual organisms. The fact that very large population sizes—109 or greater—are required to build even a minimal MR feature requiring two nucleotide alterations within 108 generations by the processes described in our model, and that enormous population sizes are required for more complex features or shorter times, seems to indicate that the mechanism of gene duplication and point mutation alone would be ineffective, at least for multicellular diploid species, because few multicellular species reach the required population sizes. Thus, mechanisms in addition to gene duplication and point mutation may be necessary to explain the development of MR features in multicellular organisms.

Although large uncertainties remain, it nonetheless seems reasonable to conclude that, although gene duplication and point mutation may be an effective mechanism for exploring closely neighboring genetic space for novel functions, where single mutations produce selectable effects, this conceptually simple pathway for developing new functions is problematic when multiple mutations are required. Thus, as a rule, we should look to more complicated pathways, perhaps involving insertion, deletion, recombination, selection of intermediate states, or other mechanisms, to account for most MR protein features.

Dr. Miller presented evidence, based upon peer-reviewed studies, that they are not in fact irreducibly complex.