But, omg, you guys called it, he refused to use my name! lolol And then he called @art “some other guy.” Coyne gets all the credit this time. Darn! Okay, I’m having a little too much fun with this. Gotta get back to work.
It has been my experience that one very common way for opponents to try to discredit an argument is to exaggerate it, to ignore distinctions an author makes, and/or to change carefully qualified claims into bizarre absolutes. Why, here’s an example right here:
What Behe is saying is that harming genes is the only way that unguided mutations can ever help an organism.
Matti Leisola, in his review on Barnes and Noble:
Behe introduces new molecular-level facts that sink the Darwinian view of life once and for all: Darwinian mechanism sometimes helps survival of an organism but always by damaging or breaking genes. The conclusion is clear: life is the product of a mind.
Behe assigns a “probably” or “possibly” damaging to all these amino acid substitutions, but does he go into why that is anywhere? What is it we (presumably) know that makes this the more likely option?
I hope he isn’t just begging the question to arrive at the conclusion he is seeking to convince us of.
Also, does he argue anywhere why we should think that all the mutations in the LTEE are “probably” damaging? What is it he thinks we know that make this the most likely possibility?
Even with weak evidence from predictive computer algorithms, only around half are damaging. Behe seems to gloss over this fact. Fig. 4 from that paper is worth checking out. The largest proportion of mutations have a 0-20% probability of being damaging.
He also assigns a “possibly” damaging to 10 out of about 25 of these amino acid substitutions. Well if he don’t actually know, then sure it’s “possible” they’re damaging. But how likely is that? If it’s 0.0000000000003% probable then it’s still “possible”.
This mere possibility of being damaging, if it is to be an argument in favor of his main thesis, should be significantly more likely than not.
apoB serves as a tag on cholesterol particles in the blood stream which bind to receptors on cells. Once bound, the cholesterol can be transported into the cell.
So what is happening in polar bears? If apoB were “damaged” then it would have a harder time filling its role as a tag for cholesterol transport. Instead, apoB appears to be doing a better job in its function.
From what I can see, polar bear apoB isn’t broken or damaged. Instead, it is working better than it did before.
This is a list of seventeen genes described in Liu et al. as being affected by missense mutations, and their classification according to computer predictions. I have made four groupings here so that readers can see how the computer predictions sort out: those genes whose associated misssense mutations are all benign, those bearing either benign or “possibly damaging” mutations, those with at least one “probably damaging” mutation, and those with missense changes are predicted to give differing outcomes (and may have only benign or possibly damaging mutations). Readers are invited to look into, and comment on, these, and especially on the prospects that these may be degraded or destroyed in the polar bear.
It’s a list of all the variants predicted to be “possibly damaging” or “probably damaging” by Liu et al. The problem is that he doesn’t inform his audience that this table is only a subset of Liu et al’s table S7. He calls it the “relevant information” from table S7.
Are we supposed to believe that Behe did that little switcharoo without thinking that a large fraction of his lay readers would just see a big list of entries saying “damaging” and come away with the impression that these were the majority of variants, or even all of them?
This gets to a key point. Polar Bears NEED an APOB to function more effectively than it does for brown bears. So yes, the APOB (in a perverse sense) loses a lower level of function to get an a higher level of function. In what backwards sense is this “damaging”?
I’d point out too that these predictors are just predictors, and they are commonly used in cancer where, by convention, even gain of function mutations are called “damaging.” For this reason, they assume that a FUNCTIONAL mutation = DAMAGING mutation. This, however, is not correct in this case. The purportedly “damaging” mutations could very well be the gain of function mutations. Seee, for example, the score outputs for HDivPred and HVarPred:
Prediction outcome can be one of probably damaging, possibly damaging, or benign. “
Notice, it cannot be “improved function.” The reason why is that the program assumes that change of function mutations are damaging, and this is likely the case in cancer (in that they cause cancer). It is not true, however, in the case we are applying it.
Exactly. All the program can do is flag mutations as “altering things from ‘normal’ somehow”. It is just by accident it is labeled “damaging.” This is well known in the field. The software, for example, does not have a category for “beneficial.”