The Extra Face in Mount Rushmore

Tim you have become a useless Troll and are adding no value to the conversation. You either are dishonest or you have comprehension problems. The anti ID rhetoric is getting old.

The have been attempts in the literature to define FI. Here is my attempt Measuring the functional sequence complexity of proteins - PMC and here is another from Hazen et al. https://www.pnas.org/content/104/suppl_1/8574

I would not want to automatically reject natural processes as being able to produce FI. Rather, I think we need to limit our options to what we can empirically verify as having an ability to produce FI. If there is a natural process that we can empirically verify as doing it, then my earlier hypothesis is falsified. We would then be left with two options, rather than just one.

EAs conclusively demonstrate the natural processes in evolution can produce new FI. The excuse the amount produced is not statistically significant is like claiming erosion can’t have produced the Grand Canyon because any erosion we see in real time from water and wind is statistically insignificant compared to the depth of the canyon.

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If we could find a natural biological process that could generate FI in quantities to produce complex adaptions such as a birds wings or an eye then do you think we could build a model for evolution?

It’s already been done Bill. Go read a basic biology or genetics textbook sometime instead of AIG.

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@Kirk

From my understanding, both of those papers measure mutual information, not functional information.

You may want to check out my thread on catalytic antibodies:

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No, I am using the “testable hypothesis” approach.

You’ve got it backwards, Kirk. To be useful, your hypothesis needs to make empirical predictions that allow you no interpretive wiggle room. You’re supposed to be trying to falsify it, not keep it alive. If, after you bash it diligently and ruthlessly, it is still standing, then you’ve got something!

I really don’t get this idea that the hypothesis is the product.

I’m afraid that I would disagree and note that what you wrote makes no sense. I respectfully suggest that you consult my publication record to see that I do have a clue:

As you’ve presented it, cataloguing is all it is. There’s nothing mechanistic.

Why me?

I would rigorously test hypotheses regarding when the hypothetical design occurred.

I agree that you should stop doing that. :smile:

It’s nonsensical. Absolutely nonsensical. Why the fork and knife should the “functional sequence complexity” of a protein depend on how many such similar proteins can be found in PFAM? What does that tell us? That just makes FSC a measure of a completely arbitrarily defined quality, partly constrained by historical circumstance (how many genomes have been sequenced and properly annotated, for example).

Why not count how many birds passed by your window in the last hour before, dividing it by the length of the protein in picometers, multiplied by the logarithm of it’s weight on Jupiter, and then have that be it’s “functional sequence complexity”? What is that a measure of? What does it tell us, other than you have created some nonsensical mathematical relationship to … look clever?

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Stealing this

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I can’t claim credit. Got it from The Good Place.

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The evolution of cancer can produce FI, quite a bit of it. So, therefore, we know intelligence is not the only source. I’m not sure, for that matter, you have even demonstrated intelligence can produce FI. See here: Computing the Functional Information in Cancer.

@Kirk what if the task you are undertaking is (logically or practically) impossible? What if it is like trying to make a perpetual motion machine? How would you know? Perhaps continuing to try is just waisting you effort. How would you be able to avoid that waiste?

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Rum he is measuring conservation of a site similar to the way gpuccio measures it. Does a particular site tolerate substitution.

@Rumraket is correct @colewd. FSC is not FI, and FSC is essentially an arbitrarily defined quantity. It is not the measure of how many functional sequences there are, nor is it a measure of how difficult it is to evolve something.

Are you saying that the paper Kirk posted should have called it FI instead of FSC?

I’m saying he measured an arbitrary quantity that he called FSC, and posited that it equaled FI despite overwhelming evidence to the contrary. FSC is not and never has been a measure of FI.

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So you believe that his method of measurement and calculation does not estimate FI?

That is correct.

I’ve also provided an alternative approach to computing the difficulty of evolvability, that does not suffer from the same problems as FSC (and, for that matter, FI).

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Is this in the cancer discussion?

Yes. The right measure is KL divergence, not delta entropy (which is what @Kirk uses for FSC). KL divergence measure how close new functions are to the starting point. Notice how @Agauger and Axe make their argument:

  1. Evolution of new protein functions requiires design IF…
  2. Function is exceedingly RARE in sequence space AND…
  3. Function is exceedingly ISOLATED in sequence space.

FSC, at best, only measures #2, the rarity of function in sequence space. (Note: It does not even accomplish this). It does not measure #3, how separated functions are in sequence space. Neither FI or FSC, even in principle, measure #3. Both #2 and #3 have to be true for the Axe-@agauger argument to work.

KL-divergence measures #3, which is a better way to measure how difficult it is to evolve a new function, and it is measured in bits too. So we can understand it as the amount of information required to evolve a new function. Using KL divergence, we find out that new functions require far less bits than @Kirk calculates, and we can demonstrate dramatic increases in functional information (as measured by KL-divergence) in natural systems (such as cancer). So less bits are required, and we have direct evidence that natural process can produce them.

The argument fails. I think @kirk is sincere and doing his best here, but it seems he is working towards a desired conclusion, regardless of the evidence. It looks very much like a quest for a perpetual motion machine to me.

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No, I’m saying he hasn’t measured a property of the protein at all. The relationship by which he defines FI, or FSC, or whatever you might want to call it, is fictional and arbitrary.

No conclusion about the protein’s origin or function can be reached from the data used to compute it’s “FSC”. Because it is computed from measures that aren’t actually measures of the proteins attributes, but things entirely unrelated to it. Like how many OTHER proteins with similar sequences that humans have decided to put into the PFAM database.

In other words, Kirk is essentially saying that we can infer something about the protein’s origin and history by, among other things, seeing how industrious human biochemists have been at sequencing and annotating genomes.

Clearly, CLEARLY we aren’t measuring a property of one my genes by considering how often biochemists have sequenced similar genes in other species and uploaded their sequences to PFAM. That should go without saying. That’s before we even begin to consider whether such a value (whatever it might be) should be multiplied, taken the square-root or base-2 log of, or divided by yet another measure.

It’s numerology. It looks fancy and technical, but it’s nonsense.

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