Gpuccio: Functional Information Methodology

[gpuccio]

@glipsnort

I would rather focus on larger issues that I think do matter.

Right. I do agree.

You raise classical objections, that I know very well. Probably, I have not had to time to discuss them here, up to now.

@glipsnort

Sequence conservation is not a valid estimator of the ratio you’re interest in. Conservation tells you nothing about most of the search space; it tells you only about the immediate mutational neighborhood of the existing sequence, which is a vanishingly small fraction of the total. More importantly, it does not give information about the number of nearby states that possess the function in question. Instead, it gives information about the number of states with higher function. (Higher fitness, actually, which need not be the same thing, but that’s a minor concern here.) But in standard evolutionary theory, the theory you’re challenging, adaptation involves passing through states with lower fitness (less functional states) until the local maximum is reached, and not returning to those states. Conservation cannot tell you whether there are less functional but still selectable states nearby in mutation space, and therefore cannot tell you anything about the size of the target space. This alone invalidates conservation as a proxy for the ratio you’re interested in.

This objection can be summarized as follows: we are looking to optimized functions, and they are conserved in such an optimized form. But, of course, we belive that they started much simpler, and evolved gradually to the optimized state by RV + NS. Therefore, the target space for the initial, simpler form of the function must be much bigger". Is that fine?

This objection is very reasonable from the point of view of a fervent believer in the powers, of NS, but irrelevant if we consider what NS can really do according to facts. Optimizations are short and scarcely important. New fucntions are complex already in their starting form. Even if some optimization can cerianly occur, and does indeed occur, a complex function is complex, and cannot be decostructed into simpler stpes, each of them naturally selectable.

I cannot enter into details about that just now, given the bulk of things that I still have to clarify, but I have discussed this point in great detail in my OP about the limits of NS, alredy lnked here. I would also recommend Behe’s last book, Darwin devolves, which is essentially about this problem (how NS really works).

I know that these brief statements will immediately draw hundreds of fierce attacks here. So be it.

@glipsnort

The target space you’ve considered consists of a single function, the function of the gene you’re looking at. To the extent that evolution can be considered a search algorithm, though, it is not a search for “the function performed by gene X”. It is a search for any function that will increase fitness. The only target space that will let you assess whether evolution could have produced some gene without design, then, is the space of all possible beneficial functions. Considering the probability post facto of achieving the function that did arise is indeed the Texas sharpshooter fallacy.

No, the Texas sharpshooter has nothing to do with this. This objection is often referred by me as the “any possible function” objection. In brief, evolution is not searching for anything, so it can find any possible function. Therefore, it must be much more powerful than a search for a specific function.

Again, this seems to be a reasonable objection from the point of view of a good believer in the neo-darwinian algorithm, but it is irrelevant when we consider facts.

First of all, it is not “any possible function”: it is any change that gives some definite reproductive advantage, and can therefor be expanded and fixed, with reasonable probability, by positive NS. That is much more restricted than “any possible function”.

Moreover, the number of functions that can really be useful in a context is sverely limited by the complex organization of the context itself. An existing set of complex functions, well organized, can use only a few new functions, which must anyway be well integrated into what already exists. Behe’s book, and known facts, show clearly that in known cases of NS acting, the variation is very simple, and it is variation of some already existing complex structure, with some impairment of its original function, but at the same time some collateral advantage in a specific environment. Like in antibiotic resistance.

Again, the main point is, again, that complex functions are already complex even in their minimally complex form. And adding the target space of many complex functions changes only trivially the target space - search space ratio, when we are already above the 500 bits, even a lot above. The key point is: these are logarithmic - exponential values. But, again, I cannot deal with this point in greater detail, for lack of time.

@glipsnort

The claim that only processes that incorporate design can generate 500 bits of FI has been challenged by two examples of two biological processes that observably produce large amounts of FI, in cancer and the immune system. Those challenges have not been addressed.

The claim remains valid. The examples offered for non biological objects are simply wrong. I am trying to ahow why, even if I don’t expect to convince anyone here. This is, it seems, a very sensitive points, and it evokes terrible resistances. Again, so be it.

Regarding cancer and the immune system, I will treat those two cases in great detail. If I survive. After all, that is my field, much more than meteorology!