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Sean Pitman on evolution of mitochondria

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mitochondria/Louisa Howard

From Detecting Design:

Now, it is true that mitochondrial organelles are quite unique and very interesting. Unlike any other organelle, except for chloroplasts, mitochondria appear to originate only from other mitochondria. They contain some of their own DNA, which is usually, but not always, circular – like circular bacterial DNA (there are also many organisms that have linear mitochondrial chromosomes with eukaryotic-style telomeres). Mitochondria also have their own transcriptional and translational machinery to decode DNA and messenger RNA and produce proteins. Also, mitochondrial ribosomes and transfer RNA molecules are similar to those found in bacteria, as are some of the components of their membranes. In 1970, these and other similar observations led Dr. Lynn Margulis to propose an extracellular origin for mitochondria in her book, Origin of Eukaryotic Cells (Margulis, 1970). However, despite having their own DNA, mitochondria do not contain anywhere near the amount of DNA needed to code for all mitochondria-specific proteins. Over 99% of the proteins needed for mitochondrial function are actually produced outside of the mitochondria themselves. The DNA needed to code for these proteins is located within the cell’s nucleus and the protein sequences are assembled in the cytoplasm of the cell before being imported into the mitochondria (Endo and Yamano, 2010). It is hypothesized that these necessary genes were once part of the mitochondrial genome, but were then transferred and incorporated into the eukaryotic nuclear DNA over time. Not surprisingly then, none of the initial mtDNAs investigated by detailed sequencing, including animal mtDNAs, look anything like a typical bacterial genome in the way in which genes are organized and expressed (Michael Gray, 2012).

It is interesting to note at this point that Margulis herself wasn’t really very Darwinian in her thinking. She opposed competition-oriented views of evolution and stressed the importance of symbiotic or cooperative relationships between species. She also argued that standard neo-Darwinism, which insists on the slow accrual of mutations by gene-level natural selection, “is in a complete funk” (Link).

But what about all of those similarities between mitochondria and bacteria? It would seem like these similarities should overwhelmingly support the theory of common ancestry between bacteria and mitochondria.

Well, the problem with Darwinian thinking in general is that too much emphasis is placed on the shared similarities between various creatures without sufficient consideration of the uniquely required functional differences. These required differences are what the Darwinian mechanism cannot reasonably explain beyond the lowest levels of functional complexity (or minimum structural threshold requirements). The fact of the matter is that no one has ever observed nor has anyone ever published a reasonable explanation for how random mutations combined with natural selection can produce any qualitatively novel protein-based biological system that requires more than a few hundred specifically arranged amino acid residues – this side of trillions upon trillions of years of time. Functionally complex systems that require a minimum of multiple proteins comprised of several thousand specifically-coded amino acid residue positions, like a rotary flagellar motility system or ATPsynthase (illustrated), simply don’t evolve. It just doesn’t happen nor is it remotely likely to happen in what anyone would call a reasonable amount of time (Link). And, when it comes to mitochondria, there are various uniquely functional features that are required for successful symbiosis – that bacteria simply do not have. In other words, getting a viable symbiotic relationship established to begin with isn’t so simple from a purely naturalistic perspective. More.

See also: Cells were complex even before mitochondria?: Researchers: Our work demonstrates that the acquisition of mitochondria occurred late in cell evolution, host cell already had a certain degree of complexity

and Life continues to ignore what evolution experts say (symbiosis can happen)

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Comments
seanpit: Your false claims that I drew the line at 7-letter words is nothing but an obvious strawman since it’s quite clear that I’ve always drawn the line at 1000-character sequences. So you retract your claim that “If I want to evolve a new 7-letter word starting with meaningful 7-letter word, I will have to swim through this ocean of meaningless words" (evolution as characterized by stepwise selectable steps)?Zachriel
April 18, 2016
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See also the latest from James Tour: https://youtu.be/_zQXgJ-dXM4seanpit
April 13, 2016
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Zachriel,
A rotating ion pump could have been coopted for motility.
Not true. There are simply too many required modifications (that are not sequentially beneficial) for such a successful cooptation - as described in detail at: DetectingDesign.com/flagellum.html And, as far as your Word evolution algorithm, it shows an exponential decline in evolutionary potential, just like I said it would, with each increase in the minimum size. This particular program of yours actually supports my main argument! Your false claims that I drew the line at 7-letter words is nothing but an obvious strawman since it's quite clear that I've always drawn the line at 1000-character sequences. Your Phrase evolution algorithm, on the other hand, is based on template matching to any portion of a long sequence regardless of it's own meaning/function - which doesn't resemble the Darwinian mechanism of RM/NS.seanpit
April 13, 2016
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seanpit: Improved meaning/function is based on context. That's fine. You said @36, “Select based on changes in beneficial function”. It's up to you to provide an operational definition. Mung: Correct me if I am wrong, but I thought you were the one with the word mutagenesis program. Yes, it was designed to test this claim:
Sean Pitman: If I want to evolve a new 7-letter word starting with meaningful 7-letter word, I will have to swim through this ocean of meaningless words.” Turns out there are stepping stones.
Mung: What does your objective function look like? Words in the dictionary per this statement:
Sean Pitman: say you start with a short sequence, like a two or three-letter word that is defined or recognized as beneficial by a much larger system of function, such as a living cell or an English language system. Try evolving this short word, one letter at a time, into a longer and longer word or phrase. See how far you can go. Very quickly you will find yourself running into walls of non-beneficial function.
bill cole: I have looked at the explanations for interim functionality of flagellum proteins and find them very unconvincing. A rotating ion pump could have been coopted for motility.Zachriel
March 27, 2016
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Zachriel
It’s thought that the components of the flagellum had other functions that were coopted for motility. In any case, there is ample evidence of complex biological morphological evolution, such as in vertebrates, so the argument about complexity is already contradicted by substantial evidence.
I have looked at the explanations for interim functionality of flagellum proteins and find them very unconvincing. In the case of this motor you have to get 20 to 30 proteins to fit together in form and function so highly sequence specific. There is no good explanation for this based on the laws of chemistry in physics. Why would an existing protein be just by chance a charge and form fit to build this highly specified molecular machine? You have at least 4^50000 of sequential space in the genome to organize. What mechanism do you propose?bill cole
March 26, 2016
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Zachriel: Notably, Sean Pitman still can’t provide an operational definition of “beneficial function” with regards to word evolution. Correct me if I am wrong, but I thought you were the one with the word mutagenesis program. What does your objective function look like?Mung
March 26, 2016
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Zachriel,
Did you want to try some examples? Which has more “beneficial function”? “king” or “gdafmg” “king” or “the king” “the king” or “the king shall” “the king shall” or “the king shall drink” “to be or not to be” or “To thine own self be true”
Improved meaning/function is based on context. What ultimate “goal” is being achieved? For biological evolution, the goal for the Darwinian mechanism is to produce enhanced survival/reproduction. The goal for the Darwinian mechanism is not to produce increased functional complexity – which is supposed to be produced as a sideline to the primary effort of enhancing the primary goal of survival/reproduction. So, when you ask, “Which has more beneficial function?”, the answer must be based on what the changes produce regarding some ultimate goal for the “organisms” in your population. Increasing sequence length, by itself, does not necessarily enhance function toward some particular goal. Keeping this concept in mind, what would be the beneficial difference between phrases like: “to be or not to be” or “To thine own self be true”? The first phrase is 18 characters in length while the second is 25 characters in length. Yet, what is the functional benefit in going from the 18-character sequence to the 25-character sequence here? It’s not obvious to me. What ultimate functional goal is enhanced here? The same thing is true regarding “phrases” marked as selectable in your Phrasenation program, such as: “give the first” “second hit or” “than that which” “me the cups” Upon what basis can you argue that any one of these “phrases” is more functionally beneficial compared to the others? Yet, your algorithm defines them all as selectable based only on their match to the target sequence Hamlet. Now, compare this to a bacterium gaining a flagellar motility system. The ultimate goal of survival/reproduction is clear because such a bacterium would gain superior access to food – and therefore improve its survival/reproductive advantage. If you wish to model the Darwinian mechanism you need to set up such a situation.seanpit
March 26, 2016
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Notably, Sean Pitman still can’t provide an operational definition of “beneficial function” with regards to word evolution.Zachriel
March 26, 2016
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Zachriel,
It’s thought that the components of the flagellum had other functions that were coopted for motility. In any case, there is ample evidence of complex biological morphological evolution, such as in vertebrates, so the argument about complexity is already contradicted by substantial evidence.
Oh please. Again, evolutionists always assume evolutionary ancestry when they see some homology here or there within subsystems of a very complex system. The problem as I’ve already mentioned, is that homologies can be and are often produced by intelligent design. Object oriented computer programming, for example, is extensively based on homologies within subsystems of more complex programming. Therefore, reference to homology, by itself, does not support a default assumption of a Darwinian origin vs. a common designer without a viable Darwinian mechanism. And, when it comes to highly complex systems, like the flagellar motility system, co-opting the required components to produce the subsystems along the proposed evolutionary pathway toward full flagellar motility is not remotely as simple as evolutionists, like Zachriel or Matzke imagine. It is not as simple as a single cut-and-paste translocation mutation. Many additional mutations would be required to get a protein, that was working as part of a different system of function, to then work as part of the new system of function. The fact of the matter is, because of this problem, there are simply no examples of evolution in action at this level of functional complexity. It just doesn't happen and cannot happen, via a Darwinian mechanism, for very good statistical reasons. Details of this problem are discussed on my website: DetectingDesign.com/flagellum.htmlseanpit
March 26, 2016
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bill cole: Zachriel seems to be trying to simulate beneficial function with small words where in the biological world some of those words would have to be 500 characters or more. The proposed test of letterspace was made by Sean Pitman. As he can't provide an operational definition of "beneficial function", his proposed test is undefined. bill cole: Until it can move through the medium whats the benefit? It's thought that the components of the flagellum had other functions that were coopted for motility. In any case, there is ample evidence of complex biological morphological evolution, such as in vertebrates, so the argument about complexity is already contradicted by substantial evidence.Zachriel
March 26, 2016
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Sean Zachriel
So, you see, there is an equivalent spectrum in real life. However, as one moves step-by-step up the ladder of functional complexity that exists within this spectrum, the average time needed for the Darwinian mechanism to achieve the next level increases exponentially… which is what Zachriel is having difficultly getting his mind around. He really is having trouble getting his mind around the reality of the truly “big numbers” involved with this situation for the Darwinian mechanism – and what they really mean.
I agree, in the debate you cited Nick was having trouble conceptualizing the magnitude of the sequential space. I believe this is why GA's fail without a target. Zachriel seems to be trying to simulate beneficial function with small words where in the biological world some of those words would have to be 500 characters or more. In the biological world beneficial function is a moving target as you evolve complexity. There is flexibility in the sequence of the first protein of the flagellum the following proteins get more and more specific to have beneficial function i.e. the bacteria can move though a medium. Until it can move through the medium whats the benefit?bill cole
March 26, 2016
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bill cole: I will think about beneficial function but at first blush as you add complexity the definition may move i.e. a stand alone enzyme vs a nuclear protein that binds with 14 different proteins. We're looking for an operational definition of "beneficial function" appropriate to letter sequences. That way we can explore phrasespace and see if it behaves as seanpit has asserted. seanpit: You didn’t falsify my position regarding an exponential increase in the average time required to make additional steps of the ladder We falsified that you had to cross oceans to reach a 7-letter word. There are stepping stones. seanpit: If you want to model this type of evolution with English literature ... It's your model @36. seanpit: then selection based on entire sentences with additional selection based on entire paragraphs, then entire chapters, etc. You forgot phrases, per your original contention. seanpit: In such a situation, remember that the mutations within genomes can cut and paste anywhere within a sequence Been there, done that. You forgot to provide an operational definition of "beneficial function" per @36. That means being return a measure of "beneficial function" for a given sequence. Did you want to try some examples? Which has more "beneficial function"? "king" or "gdafmg" "king" or "the king" "the king" or "the king shall" "the king shall" or "the king shall drink" "to be or not to be" or "To thine own self be true"Zachriel
March 26, 2016
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bill cole,
I do think you have an argument with a 7 letter word but in nature the words are much longer.
In nature there are very short proteins that are beneficially functional (equivalent to short 7-letter words). And, there is a spectrum between these very short functional proteins and much longer functional single-protein systems (going well over 1000aa in size). Then, beyond this, there are systems that are based on multiple specifically arranged proteins (like ATPsynthase or flagellar motility systems that collectively require a minimum of several thousand fairly specified amino acid residues). So, you see, there is an equivalent spectrum in real life. However, as one moves step-by-step up the ladder of functional complexity that exists within this spectrum, the average time needed for the Darwinian mechanism to achieve the next level increases exponentially... which is what Zachriel is having difficultly getting his mind around. He really is having trouble getting his mind around the reality of the truly "big numbers" involved with this situation for the Darwinian mechanism - and what they really mean.seanpit
March 26, 2016
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Zachriel,
seanpit: Why then do you act like you’ve somehow disproved my original argument regarding a need for exponentially greater amounts of time with each step up the ladder of functional complexity?
What we directly falsified was “If I want to evolve a new 7-letter word starting with meaningful 7-letter word, I will have to swim through this ocean of meaningless words.”
Just the opposite is true. You didn’t falsify my position regarding an exponential increase in the average time required to make additional steps of the ladder; you actually supported it! Don’t you see that? Just because the “swim” appears to be relatively short at the 7-character level doesn’t mean that the swim doesn’t take exponentially greater amounts of time compared to 1 or 2-letter words. The entire point of this exercise was to get you to see the exponential nature of the problem with each step up the ladder. Again, why do you think that I drew the line at 1000? - instead of at 7?
seanpit: Your real issue, then, is in regard to your “Phrasentation” algorithm – an algorithm that does not show an exponential decline in evolutionary potential because the targets are pre-defined as being just a very short Levenshtein step away from the next target.
Shakespeare wrote Hamlet to minimize Levenshtein distances as well as in blank verse?! What a genius!
Yes, Shakespeare was a genius! Unfortunately, the Darwinian mechanism is not. Intelligent design can cross large Levenshtein distances (because of an ability to imagine the future) while a mindless Darwinian algorithm simply cannot do this in a reasonable amount of time.
seanpit: Of course, if any match, however small, to a template sequence is defined as “selectable”, there will in fact be a linear relationship between time and the length of the sequence!
No. They have to be whole word snips of Hamlet based on the idea that any consecutive words of Shakespeare are more “beneficial” than random words or letters. You expressed displeasure with the use of Hamlet as a phrase dictionary, even though your original claim concerned a word dictionary. That’s fine… Just provide an operational definition of “beneficial function” for letter sequences so we can test your claim by your proposed process @36.
Richard Dawkins’ “Methinks it is like a weasel” algorithm worked based on template matching where each additional letter match was defined as selectable. The only difference between his algorithm and yours is that you moved it up a bit too single words instead of single letters. However, the basic concept of template matching remains the same between his algorithm and yours. That is why both of your multi-word evolution algorithms work in a linear manner instead of an exponential manner – as is always the case for template matching algorithms for very simple statistical reasons. Again, when selection is defined based on a match of fairly consistent size to a pre-defined larger target sequence, regardless of the enhanced or non-enhanced function of the smaller evolving sequences, the amount of time required will increase in a linear manner as the size of the target sequence increases. That is why your Phrasenation algorithm worked in a linear manner while your word-evolution algorithm worked in an exponential manner. In real life, selection is not made based on template matching where a match of a small sequence of a particular size to a larger sequence is defined as selectable. That simply isn’t how natural selection works in real life. Natural selection works based on an improvement in beneficial function compared to what already exists in the genome – where a beneficial function is defined, in the context of Darwinian evolution, as enhanced survival/reproductive fitness among one’s peers within a given environmental context. There simply is no other definition for “beneficial function” when it comes to Darwinian-style evolution. If you want to model this type of evolution with English literature (without having to actually base selection on improved beneficial meaning/function according some kind of survival/reproductive advantage), as I mentioned earlier, you cannot use essentially the same size of sequence (i.e., an average of 5-letter words) to base your selection compared to some large meaningful target sequence (like Hamlet). A better model could start out with selection for English word matches as in your word-evolution algorithm (but with more reasonable reproductive/mutation rates and the allowance for random walks), then selection based on entire sentences with additional selection based on entire paragraphs, then entire chapters, etc. In such a situation, remember that the mutations within genomes can cut and paste anywhere within a sequence – not just between select “words” or intact “paragraphs”. Given such a situation, you might have all the words you need to produce an entire sentence, but getting them cut and pasted together in the proper order would take exponentially longer than it did to make individual words. The same would be true when it comes to making entire paragraphs, with a bit of a twist. Your genome size may not be large enough to store all the sentences that might be needed to produce any selectable paragraph at a particular point in time. This would only increase the exponential nature of the problem and would reflect a similar problem for evolution within living things. With living things, as the size of the needed translocation(s) increases in a linear manner, the odds that just the right sequence will already exist (preformed somewhere within the overall genome of sequence options) will decrease in an exponential manner. This is because the size of the gene pool is limited because of the finite environmental limitations on population sizes. After a certain point, the population size can no longer increase and this puts a finite limitation on the size of the population’s library of stored sequence options. Once this library size limitation is maxed out, it becomes dramatically more and more difficult to evolve functionally complex systems at higher and higher levels.seanpit
March 26, 2016
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Zachriel This one is complex. I will think about beneficial function but at first blush as you add complexity the definition may move i.e. a stand alone enzyme vs a nuclear protein that binds with 14 different proteins. Honestly in my mind the sequential space enormity over 100 aa sequence makes the current mechanisms highly unlikely. I do think you have an argument with a 7 letter word but in nature the words are much longer.bill cole
March 25, 2016
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bill cole: From reading the discussion I do not believe Zachriel or Nick understands the enormity of the problem. We're more than willing to explore the landscape in question to make that determination. For instance, we know that you don't have to cross an ocean of meaningless sequences to find 7-letter words. bill cole: Once you leave the island you are forever lost in an infinite ocean. That's the claim. We'd be happy to explore the wordscape to verify that prediction. We just need a valid measure of "beneficial function".Zachriel
March 25, 2016
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seanpit Thanks again for siting your website discussions with Nick regarding sequential space and the bacterial flagellum potential evolution. I think that the difficulty of convincing someone of the problem you have surfaced is that the size if sequential space of sequences is not easy to comprehend. From reading the discussion I do not believe Zachriel or Nick understands the enormity of the problem. For all practical purposes we are dealing with exploration through infinity. Once you leave the island you are forever lost in an infinite ocean. There may be many functional possible proteins but because the space of the ocean in infinite you never land on one. In order to solve the problem through random change you need the target sequence as a guide. Any thing short of having a direct target to reference and you spent eternity in the ocean. Sequences are the largest mathematical spaces in the universe. They are great for creating almost infinite possibilities, but they are terrible for finding function through a random search.bill cole
March 25, 2016
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seanpit: Of course, if any match, however small, to a template sequence is defined as “selectable”, there will in fact be a linear relationship between time and the length of the sequence! No. They have to be whole word snips of Hamlet based on the idea that any consecutive words of Shakespeare are more "beneficial" than random words or letters. You expressed displeasure with the use of Hamlet as a phrase dictionary, even though your original claim concerned a word dictionary. That's fine... Just provide an operational definition of “beneficial function” for letter sequences so we can test your claim by your proposed process @36.Zachriel
March 25, 2016
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seanpit: Why then do you act like you’ve somehow disproved my original argument regarding a need for exponentially greater amounts of time with each step up the ladder of functional complexity? What we directly falsified was “If I want to evolve a new 7-letter word starting with meaningful 7-letter word, I will have to swim through this ocean of meaningless words.” seanpit: Your real issue, then, is in regard to your “Phrasentation” algorithm – an algorithm that does not show an exponential decline in evolutionary potential because the targets are pre-defined as being just a very short Levenshtein step away from the next target. Shakespeare wrote Hamlet to minimize Levenshtein distances as well as in blank verse?! What a genius! In any case, you don't like the use of Hamlet as a phrase dictionary. That's fine. Please provide an operational definition of “beneficial function” for letter sequences so we can test your claim by your proposed process @36.Zachriel
March 25, 2016
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Zachriel,
The time it takes to evolve words (based on the parameters repeatedly discussed, including use of the dictionary to determine fitness), increases rapidly with word length. Indeed, it will inevitably reach a limit — because words are only so long!
Hello! This was my original claim! I originally told you that, as an illustration of the problem, an evolutionary algorithm based on single words would show an exponential increase in the time required to achieve success. And, this is exactly what your word-evolution algorithm demonstrates. Why then do you act like you’ve somehow disproved my original argument regarding a need for exponentially greater amounts of time with each step up the ladder of functional complexity? – with a complete stalling out effect before the level of 1000 saars is reached? Why do you try to act like I claimed some kind of limit to evolutionary potential at just 7-letter words? That’s a ridiculous misrepresentation of my position – especially given that your own algorithm actually support my original claim! The exponential increase that your own algorithm shows is the result of the exponential increase in the “ocean” of non-target vs. target sequences where the target sequences are not pre-defined as being just one Levenshtein step away from the next target sequence (unlike how your “Phrasentation” algorithm works). Your real issue, then, is in regard to your “Phrasentation” algorithm – an algorithm that does not show an exponential decline in evolutionary potential because the targets are pre-defined as being just a very short Levenshtein step away from the next target. This is because your algorithm here isn’t based on the Darwinian mechanism. It’s based on Dawkins-like template matching – as I’ve explained to you many times. Of course, if any match, however small, to a template sequence is defined as “selectable”, there will in fact be a linear relationship between time and the length of the sequence! However, when you base your selection on changes in the actual beneficial function of the sequence, the average time required to find higher level systems increases exponentially.seanpit
March 25, 2016
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seanpit: I’ve already given you the definition of beneficial function many times in this thread. We must have missed it. Please provide it again, or provide a reference. Thanks! seanpit: I’m sorry that you were unable to get your algorithms to actually work based on sequential changes in beneficial function. It's YOUR algorithm @36.Zachriel
March 25, 2016
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Zachriel,
Cytochrome b evolves too fast in the primate lineage to return an accurate tree. To reconstruct a phylogeny, you have to look at the entirety of the evidence. When you do, there is a strong congruence between morphology and molecular nested hierarchies.
As already noted in my post above, this simply isn't true. The more one considers the "entirety of the evidence" the less congruence there is between trees based on morphology vs. genetics. And, this isn't just the position of IDists - this is the position of more and more modern evolutionists who study cladistics!
Now please provide an operational definition of “beneficial function” for letter sequences so we can test your claim by your proposed process @36.
Asked and Answered - I've already given you the definition of beneficial function many times in this thread. Clearly, for anyone candidly evaluating your algorithms, your selection method is not based on a sequential functional advantage. I'm sorry that you were unable to get your algorithms to actually work based on sequential changes in beneficial function.seanpit
March 25, 2016
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seanpit: Why don’t you ever respond to my questions regarding the pattern of exponential decline in evolutionary potential with each step up the ladder of functional complexity? We have. Repeatedly. The time it takes to evolve words (based on the parameters repeatedly discussed, including use of the dictionary to determine fitness), increases rapidly with word length. Indeed, it will inevitably reach a limit — because words are only so long! seanpit: Again, even you have to admit that I never said that evolution was “impossible” or even unlikely at such low levels of functional complexity. You said, “If I want to evolve a new 7-letter word starting with meaningful 7-letter word, I will have to swim through this ocean of meaningless words.” Based on your statement @1, that means a 7-letter word will never evolve. seanpit: What do you think would happen once you add in the limitation of selection based on sequentially beneficial changes? You explicitly defined "beneficial" as words found in the dictionary. We proposed using a phrase dictionary, but for some reason, you rejected this. That's fine. Please provide an operational definition of “beneficial function” for letter sequences so we can test your claim by your proposed process @36.Zachriel
March 25, 2016
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Here is some relevant commentary on this discussion from Douglas Axe: __________________ The reported range [for functional vs. non-functional sequences is one in 10^77 (based on data from the more complex beta-lactamase fold; ? = 153) to one in 10^53 (based on the data from the simpler chorismate mutase fold, adjusted to the same length: ? = 153). As remarkable as these figures are, particularly when interpreted as probabilities, they were not without precedent when reported. Rather, they strengthened an existing case for thinking that even very simple protein folds can place very severe constraints on sequence... Rescaling the figures to reflect a more typical chain length of 300 residues gives a prevalence range of one in 10^151 to one in 10^104... On the one hand, this range confirms the very highly many-to-one mapping of sequences to functions. The corresponding range of m values is 10^239 (=20^300/10^151) to 10^286 (=20^300/10^104), meaning that vast numbers of viable sequence possibilities exist for each protein function. But on the other hand it appears that these functional sequences are nowhere near as common as they would have to be in order for the sampling problem to be dismissed. The shortfall is itself a staggering figure—some 80 to 127 orders of magnitude (comparing the above prevalence range to the cutoff value of 1 in 5×10^23). So it appears that even when m is taken into account, protein sequences that perform particular functions are far too rare to be found by random sampling... Two possibilities for mitigating the problem need to be considered. One of these has been mentioned already. It is the possibility that the multiplicity of sequences capable of performing the requisite functions, m, might be large enough for working sequences to be found by random searches. The second possibility is that functional protein sequences might bear a relationship to one another that greatly facilitates the search. In the desert metaphor, imagine all the different gems being together in close proximity or perhaps lined up along lines of longitude and latitude. In either of these situations, or in others like them, finding the first gem would greatly facilitate finding the others because of the relationship their positions bear to one another... When structurally unrelated protein domain sequences are aligned optimally, the resulting alignment scores are very similar to the expected scores for randomized sequences with the same amino acid composition. Since random sequences produced in this way are widely scattered through sequence space, this means that dissimilar natural sequences are as well. In fact, because amino acid composition correlates with structural class, we would expect random sequences with average compositions to align somewhat better than dissimilar natural sequences do. Indeed, such wide dispersion of natural domain sequences throughout sequence space is not surprising considering the great variety of domain structures that these sequences form (Figure 6)... It therefore seems inescapable that considerable distances must be traversed through sequence space in order for new protein folds to be found. Consequently, any shortcut to success, if it exists, must work by traversing those distances more effectively rather than by shortening them. The only obvious possibility here is that new folds might be assembled by recombining sections of existing folds. If modular assembly of this kind works, it would explain how just one or two gene fusion events might produce a new protein that differs substantially from its ‘parents’ in terms of overall sequence and structure. Of course, probabilistic limitations would need to be addressed before this could be deemed a likely explanation (because precise fusion events are much less likely than point mutations), but the first question to ask is whether the assumed modularity is itself plausible... Consequently, self-contained structural modules only become a reality at the domain level, which makes them unhelpful for explaining new folds at that level... Because structural reorganization requires elements of secondary structure to be grouped spatially in new ways, it necessarily involves new binding interfaces where the exteriors of helices and/or sheets must adhere to each other in new ways. But since these interfaces consist largely of side chains, they are necessarily sequence-dependent and therefore non-generic. This is important enough to be restated: The binding interfaces by which elements of secondary structure combine to become units of tertiary structure are predominantly sequence dependent, and therefore not generic. This presents a major challenge for the idea of modular assembly of new folds, at least as a general explanation... For those elements to work as robust modules, their structures would have to be effectively context-independent, allowing them to be combined in any number of ways to form new folds. A vast number of combinations was made by random ligation of the gene segments, but a search through 10^8 variants for properties that may be indicative of folded structure ultimately failed to identify any folded proteins. After a definitive demonstration that the most promising candidates were not properly folded, the authors concluded that “the selected clones should therefore not be viewed as ‘native-like’ proteins but rather ‘molten-globule-like’”, by which they mean that secondary structure is present only transiently, flickering in and out of existence along a compact but mobile chain. This contrasts with native-like structure, where secondary structure is locked-in to form a well defined and stable tertiary fold. Their finding accords well with what we should expect in view of the above considerations. Indeed, it would be very puzzling if secondary structure were modular. In fact, although whole structural domains may be self-contained in the sense of carrying complete information for their own folding, even they may fail to meet the second criterion for structural modularity given above, simply because they do not have generic exteriors. I describe here an experimental demonstration of this that was performed years ago but not previously reported. Again it uses beta lactamases, which are an attractive model system because of the abundance of published structures and the ease of measuring their activity in vivo. This test used the two natural beta lactamases shown in Figure 9, which have highly similar backbone structures despite the fact that their sequences match at only 26% of aligned positions. Both structures consist of two domains, the larger of which was referred to previously (Figure 5B). Sections of the two genes were recombined to encode a chimeric protein that combines the domains colored green and red in Figure 9. The overall structural and functional similarity of the parent enzymes suggests that this kind of domain recombination should work. But the non-generic nature of the interface between the two domains in combination with the substantial sequence dissimilarity indicates otherwise—a point confirmed by the lack of detectable function for the chimeric construct. Douglas Axe: http://biocomplexity.org/ojs/index.php/main/article/view/BIO-C.2010.1/BIO-C.2010.1seanpit
March 25, 2016
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seanpit: Take cytochrome b for example Cytochrome b evolves too fast in the primate lineage to return an accurate tree. To reconstruct a phylogeny, you have to look at the entirety of the evidence. When you do, there is a strong congruence between morphology and molecular nested hierarchies. Now please provide an operational definition of “beneficial function” for letter sequences so we can test your claim by your proposed process @36.Zachriel
March 25, 2016
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Zachriel, Why don't you ever respond to my questions regarding the pattern of exponential decline in evolutionary potential with each step up the ladder of functional complexity? - present even within your own algorithm between 1 and 7+ letter sequences? - especially highlighted when you use smaller steady-state populations and more reasonable mutation and reproductive rates? What do you think would happen once you add in the limitation of selection based on sequentially beneficial changes? Wouldn't the exponential nature of the problem only become more dramatic? Why not at least address this fundamental problem for your theory? Again, even you have to admit that I never said that evolution was “impossible” or even unlikely at such low levels of functional complexity. It is very possible and very likely at such low levels. What I said is that there would be an exponential decline in evolvability with each step up the ladder of functional complexity – which is clearly true. It is this pattern that is important to recognize. And, I made it very clear that because of this exponential decline that there would be a complete stalling out of evolutionary progress, not at the level of 7-character sequences, but at the level of 1000.seanpit
March 25, 2016
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Zachriel, It shows a general congruence between morphological and molecular phylogenies. Hardly. Depending upon which genetic sequence you pick you get very very different phylogenies that often disagree with standard morphologic phylogenies. Take cytochrome b for example:
"The mitochondrial cytochrome b gene implied... an absurd phylogeny of mammals, regardless of the method of tree construction. Cats and whales fell within primates, grouping with simians (monkeys and apes) and strepsirhines (lemurs, bush-babies and lorises) to the exclusion of tarsiers. Cytochrome b is probably the most commonly sequenced gene in vertebrates, making this surprising result even more disconcerting." Michael S. Y. Lee, “Molecular phylogenies become functional,” Trends in Ecology and Evolution, Vol. 14:177-178 (1999)
Even as far back as 1998 it was known that there were serious problems within the highest branches of the "Tree of Life". Amazingly enough, a 1998 article entitled, "Molecules remodel the mammalian tree", Je Jong (in Trends in Ecology and Evolution) concluded:
"The wealth of competing morphological, as well as molecular proposals [of] the prevailing phylogenies of the mammalian orders would reduce [the mammalian tree] to an unresolved bush, the only consistent clade probably being the grouping of elephants and sea cows."
And, this major problem doesn't seem to have gotten any better over time. In 2009, Syvanen compared two thousand genes that are common to humans, frogs, sea squirts, sea urchins, fruit flies and nematodes. In theory, he should have been able to use the gene sequences to construct an evolutionary tree showing the relationships between the six animals. He failed. The problem was that different genes told contradictory evolutionary stories. This was especially true of sea-squirt genes. Conventionally, sea squirts—also known as tunicates—are lumped together with frogs, humans and other vertebrates in the phylum Chordata, but the genes were sending mixed signals. Some genes did indeed cluster within the chordates, but others indicated that tunicates should be placed with sea urchins, which aren't chordates. “Roughly 50 per cent of its genes have one evolutionary history and 50 per cent another." This led Syvanen to conclude: "We’ve just annihilated the tree of life." Likewise, Carl Woese, a pioneer of evolutionary molecular systematics, observed that these problems extend well beyond the base of the tree of life:
"Phylogenetic incongruities [conflicts] can be seen everywhere in the universal tree, from its root to the major branchings within and among the various taxa to the makeup of the primary groupings themselves."
Likewise, in 2006, biologist Lynn Margulis wrote in her article, The Phylogenetic Tree Topples:
"Many biologists claim they know for sure that random mutation (purposeless chance) is the source of inherited variation that generates new species of life and that life evolved in a single-common-trunk, dichotomously branching-phylogenetic-tree pattern! Especially dogmatic are those molecular modelers of the ‘tree of life’ who, ignorant of alternative topologies (such as webs), don’t study ancestors."
Striking admissions of troubles in reconstructing the "Tree of Life" also came from a 2006 paper in the journal PLOS Biology entitled, Bushes in the Tree of Life. The authors acknowledge that, "A large fraction of single genes produce phylogenies of poor quality," observing that one study "omitted 35% of single genes from their data matrix, because those genes produced phylogenies at odds with conventional wisdom." The paper suggests that, "Certain critical parts of the [tree of life] may be difficult to resolve, regardless of the quantity of conventional data available." The paper even contends that, "The recurring discovery of persistently unresolved clades (bushes) should force a re-evaluation of several widely held assumptions of molecular systematics." Then, Elie Dolgin, in a June, 2012 article in Nature reported that short strands of RNA called microRNAs are, "tearing apart traditional ideas about the animal family tree." Dartmouth biologist Kevin Peterson who studies miRNAs lamented, "I've looked at thousands of microRNA genes, and I can't find a single example that would support the traditional tree." According to the article, miRNAs yielded "a radically different diagram for mammals: one that aligns humans more closely with elephants than with rodents." Peterson put it bluntly: "The microRNAs are totally unambiguous ... they give a totally different tree from what everyone else wants." A 2013 paper in Trends in Genetics reported that, "The more we learn about genomes the less tree-like we find their evolutionary history to be." What is also interesting is that this information isn't entirely new - yet it is still treated by many with a great deal of surprise. Even as far back as 2000 Trish Gura argued, also in the journal Nature, that there appeared to be no consistent agreement between genetic phylogenies and those based on more traditional morphological characteristics:
"On one side stand traditionalists who have built evolutionary trees from decades of work on species' morphological characteristics. On the other lie molecular systematists, who are convinced that comparisons of DNA and other biological molecules are the best way to unravel the secrets of evolutionary history. … So can the disparities between molecular and morphological trees ever be resolved? Some proponents of the molecular approach claim there is no need. The solution, they say, is to throw out morphology, and accept their version of the truth. 'Our method provides the final conclusion about phylogeny,' claims Okada. Shared ancestry means a genetic relationship, the molecular camp argues, so it must be better to analyse DNA and the proteins it encodes, rather than morphological characters that can end up looking similar as a result of convergent evolution in unrelated groups, rather than through common descent. But morphologists respond that convergence can also happen at the molecular level, and note there is a long history of systematists making large claims based on one new form of evidence, only to be proved wrong at a later date."
For further information on this topic see: http://www.detectingdesign.com/geneticphylogeny.html In short, your popular claim that, “The nested hierarchy, including the fossil succession, remains as strong evidence of common descent regardless of any explanatory mechanism” simply doesn’t hold water – beyond the fact that such nested hierarchical patters are found all over the place within human-designed systems – especially within computer operating systems. It simply doesn’t follow that just because a NHP is identified that this automatically indicates a mindless evolutionary origin – especially when there is no viable evolutionary mechanism to explain the phenomenon in question.seanpit
March 25, 2016
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seanpit: However, what the evidence actually shows is some sequence homologies of subparts within high-level systems. It shows a general congruence between morphological and molecular phylogenies. seanpit: However, if you cannot demonstrate the consistent effectiveness of your naturalistic mechanism at various levels of functional complexity, your conclusion of evolutionary ancestry simply isn’t scientific or rational. The nested hierarchy, including the fossil succession, remains as strong evidence of common descent regardless of any explanatory mechanism. seanpit: I haven’t “retreated” from my original positions at all – not at all. Then you are still wrong that you have to cross oceans of meaningless sequences to evolve seven-letter words. Now please provide an operational definition of “beneficial function” for letter sequences so we can test your claim by your proposed process @36.Zachriel
March 24, 2016
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Zachriel,
The stepping stones are in the lines of descent from the common ancestor.
That’s certainly one interpretation of the evidence. However, what the evidence actually shows is some sequence homologies of subparts within high-level systems. So, what does this evidence actually mean? The standard conclusion is that these homologies must represent a common evolutionary ancestry over and above the alternative interpretation of common design. However, if you cannot demonstrate the consistent effectiveness of your naturalistic mechanism at various levels of functional complexity, your conclusion of evolutionary ancestry simply isn't scientific or rational. Given that the evidence currently in hand strongly supports the conclusion of an exponential decline in effectiveness, your automatic conclusion that homology must mean common evolutionary ancestry simply doesn’t follow. The alternate conclusion of common intelligent ancestry becomes much more likely. A very similar situation exists for computer programs that also consistently show sequence homologies within subparts of larger and more complex codes that have the same programmer. Yet, no one argues that such homologies could only have been produced by a mindless mechanism acting over time. Why then, when the very same situation is discovered within living things, that the only possible hypothesis that is allowed to be considered is a mindless Darwinian mechanism? – despite any and all evidence that such a mechanism is very very limited?
In any case, while you have retreated from your position concerning having to cross oceans of meaningless sequences to evolve 7-letter words, you still claim that longer sequences can’t be similarly crossed. You proposed a process which entails determining the “beneficial function” of a letter sequence. Please provide an operational definition of “beneficial function” so that we can test your claim.
I haven’t “retreated” from my original positions at all – not at all. Rather, you’ve misstated my original argument and made claims that I never made. The fact remains that there is an exponential decline in evolvability even at the level of sequence spaces going from meaningful 1-character sequences to 7-character sequences! That’s what I was trying to get you to see way back in 2004! I never said that evolution was “impossible” or even unlikely at such low levels of functional complexity. It is very possible and very likely at such low levels. What I said is that there would be an exponential decline in evolvability with each step up the ladder of functional complexity – which is clearly true. It is this pattern that is important to recognize. And, I made it very clear that because of this exponential decline that there would be a complete stalling out of evolutionary progress, not at the level of 7-character sequences, but at the level of 1000. What do you not understand about this argument? It’s really a very simple and straightforward argument. I’m at a loss as to why you’re still so confused? It seems like you simply don’t want to present my argument as it really is. You seem to actually want to try to distort so that it appears to be something silly and nonsensical – like a strawman version of the real thing.seanpit
March 24, 2016
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seanpit: The actual landscape for protein-based systems has been examined in fair detail and very clear patterns have emerged – as higher and higher levels of functional complexity are evaluated. Your claim concerned letter sequences. seanpit: These same features also exist within the English language system That's your claim. Now provide an operational definition of "beneficial function" for letter sequences so we can test your claim by your proposed process @36.Zachriel
March 24, 2016
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