Optima in Evolution

By saying that there are many local optima, I think you are shooting yourself in the foot for it probably means that evolution would be unable to find absolute optimal solutions as is arguably the case for many proteins, ATP synthase, which works at near 100% efficiency, being a good example. This is also what teaches us the rugged fitness landscape paper by Hayashi et al.

Bill, you can have the last laugh when gpuccio publishes his work in a peer reviewed journal not backed by the DI. However, if it doesn’t pass peer review, please be more open to their criticisms than what you have demonstrated here.

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I don’t think evolution has to find absolutely optimal global solutions in general. I think it just has to find something adaptive, and then if it gets stuck on some local optimum, well then it’s got stuck. What matters is it found an adaptive function, not whether it’s the best possible solution to the problem faced by the organism.

I have not come across any evidence that shows that it is generally the case that the globally optimal functions are what exist in extant life. Have you? I’m asking for some sort of review article that shows this, not for a particular handful of examples you might be able to find. We are talking about what we should believe about life in general, for which a handful of examples won’t do.

Then argue it, because I think there’s little to no evidence that evolution has generally found the global optimum. I’d be happy to be corrected on this if you can find evidence to the contrary. As in evidence that this has generally occurred, not just a particular handful of examples.

What does “100% efficiency” mean in this context, and how does that relate to ATP synthase occupying the global optimum?

Please explain in more detail why you think so. I’ve read that paper before many times and discussed it with IDcreationists of various stripes, and I continue to discover they don’t understand what it really says. But I’ll let you explain here first what you take that paper to imply, and then we can talk about that.

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Note to @moderators , I flagged Gil’s post above as off-topic because I’d like to request this be split into a new thread on it’s own. I’d be happy to continue discussing with him there but this thread is already extremely long and difficult to keep track of.

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Hi Rich
Gpuccio is not going to publish peer reviewed papers however others have published papers that use similar techniques such as Kirk Durston. The real utility of gpuccio’s method is you can use available tools and generate data quickly using blast and align tools. This tool is available through the NIH and the blast bit score is an industry standard.

BTW I did a blast of alpha actin 1 and MYH 7 and the maximum bitscore of alpha actin 1 and minimum bit score of MYH 7. MYH 7 minimum score exceeded alpha actin 1 maximum score by 6000 bits. Here is the pub med article count searching blast.

BLAST - Basic Local Alignment Search Tool

A tool for comparing an amino acid or nucleotide sequence to an entire sequence library, identifying regions of high sequence similarity.

blastnblastpblastxPrimer- BLAST

Search results

Items: 1 to 20 of 32390

If you would like a more detailed description of what this means let me know.

I will take the arguments seriously when they follow real data and real statistical methods. If you think I am in error here please let me know specifically where and we can discuss it.

Gil, that is precisely what we know to be the case. We don’t think evolution finds absolute optimal solutions. It only finds local ones.

Talk about shooting yourself in the foot…

Have you ever considered trying to better understand evolutionary biology before frantically attacking it?

Please define “efficiency” biochemically and explain why “near 100% efficiency” represents an absolute optimum.

It teaches you that you’ve been attacking a straw man. Aren’t you embarrassed?

Why not? Why don’t you publish your own impression of Nigel Tufnel in Bio-Complexity? What are you afraid of?

The problem with gpuccio’s method is that it ignores known sequences that work. The other problem is that sequence conservation doesn’t correlate with functional information.

Yet another “This one goes to 11” from Nigel.

You didn’t BLAST MYH7 against all of the variants found in healthy people, so your calculation has nothing to do with functional sequence space.

You don’t even know how many there are. You’re clearly afraid to look.

No, it is not a “pub med article.” The site is blast.ncbi.nlm.nih.gov. That’s not PubMed. I am nitpicking in this case, but only to show how incredibly unobservant you are.

You’ve made it clear that you don’t understand what it means.

Real data: the enormous polymorphism of MYH7 in humans. You won’t follow it.

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It’s not data used in Gpuccio’s method. Again his method measures changes that are fixed in the population. I don’t see any real reason to use non fixed mutations.

I don’t see how it would possibly affect the the calculation in any meaningful way but if you do please start another thread and I will participate.

Do you really think that these additions would effect the bit score over 500 alignments when I am using comparisons of the best match for alpha actin and the worst match for MYH 7.

Again with the “This one goes to 11.” Really, Nigel?

I know that.

That fact, which we agree upon, means that gpuccio’s method does not consider all of the different protein sequences that work–by definition. IT IGNORES HIGHLY RELEVANT DATA. I don’t see how I can make that simple fact any more clear.

Even if it did, it still does not measure functional information. There is no correlation between sequence conservation and functional information.

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Oh, yes, you do, or you would have looked for the number. Instead you’ve evaded for what, two weeks now?

I already started a thread and you didn’t.

Think? No, Bill, I know. I know this stuff. I worked on it for 5 years.

Why do you have such total contempt for others’ expertise?

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Then his method is pointless for it won’t be used, or even considered, outside a few ID researchers. If he really believed his method was valid, it would be selfish not to publish it.

I don’t. I continue to discuss with you because of your expertise. The issue I have is your lack of support for new ideas when they rub up against the design hypothesis. BTW alpha actin 1 has many variants also in humans that are not getting fixed in the mammal population.

I agree it would be best if he attempted to publish.

You are misrepresenting my reason, predictably. I do not support this idea because it has no basis in reality. The fact that sequence conservation does not correlate with functional information has nothing to do with design. It’s just reality.

!) How many ACTA1 variants have been found in healthy humans?
2) How many MYH7 variants have been found in healthy humans?

Can you suppress your Nigellian impulses for a bit?

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How do you explain the following:

Good question. I will take a look.

This appears to be projection at this point. Maybe you have a good reason for your skepticism about correlation between FI and preservation but it is not obvious at this point.

My count from unitprot of variants that don’t have an associated disease identified is.

-Alpha actin 1 20 out of 375 Amino Acids

-Myosin 7. 17. out of 1935 Amino Acids

That’s a pathetic dodge, Bill. The criterion is variants that are found in healthy people, not those that are not associated with disease.

And I suspect that you’re looking at the wrong myosin.

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The anabolic role of ATP synthase is to convert a proton motrice force into biochemical energy, ie., ATP. And it appears that the chemomechanical coupling performed by this molecular machine is perfect.

The paper shows that

  • RV+NS is able to find simpler solutions (local optima) that can implement some degree of function retrieval
  • RV+NS is unable to find a solution as efficient as the wild type solution.
  • Once RV + NS has found a simpler solution (local optimum), the system is trapped there for ever with still less possibility to reach the wild type optimal solution.

IOW, the rugged fitness landscape paper supports the idea that natural functions are implemented by optimal solutions that can’t be reached by RV + NS, the ability of RV+NS to find local optima representing the main obstacle for finding these optimal solutions.

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By what thought process did you reinterpret that to mean that it must therefore be at a single optimum? How did you exclude the possibility that other optima would also provide 100% efficiency?

In what time period? Forever?

How does one do an experiment that lasts forever?