Sean Pitman on evolution of mitochondria

_{Denyse O'Leary

March 3, 2016

Cell biology, News, Origin Of Life}

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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

Origenes @ 88 ..Which is why he wrote the book to explain how robustness and hyper-dimensional search helps in increasing the probability of locating sparse genotypes / metabolismMe_Think_{March 17, 2016
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You see, you are confusing a linear distance with a random walk distance. They aren’t the same thing. While the shortest linear distance is indeed significantly decreased within higher spacial dimensions, the random walk distance remains the same.
Certainly not. If you lock up a drunkard each in a large and small room, the drunkard in the smaller room will hit the wall ( or, if you like, step on Frisbee thrown on floor) first. This is because the unit size of a random walk step doesn't decrease with decease in linear distance in any dimensions.Me_Think_{March 17, 2016
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Seanpit: If the mechanism involves random mutations and function-based selection, that mechanism, beyond very low levels of functional complexity, will stall out, in an exponential manner, with each step up the ladder of functional complexity.
Intuitively this makes a lot of sense. The more specified a function/organism is, the more specified parts are involved, the more parts are pointed in the same direction, the less evolvable/flexible a function/organism becomes. BTW Andreas Wagner made a similar observation against interest:
Some of nature’s ways to find new metabolic texts are familiar, because they dominate in large multicellular animals like us. They include the changes accompanying sexual reproduction, which shuffles chromosomes like decks of cards, so that each of our children starts with a new deal. Then there are the spontaneous mutations in a DNA’s letter sequence, arising through chance events such as when photons of ultraviolet radiation smash into the genome, or through highly reactive oxygen radicals that are by-products of chemical reactions and burst the chemical bonds of nearby DNA. Neither way to explore the metabolic library is very effective. Since the shuffling of sexual reproduction occurs between highly similar genomes—two human genomes share 99.9 percent of their DNA letter sequence—it is not the most effective way to create new metabolisms.15 It is like trying to write a new play by changing thirty words in Hamlet. And while mutations can create new proteins, including new enzyme catalysts, they are rare, which means the process is rather slow.
Origenes_{March 17, 2016
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seanpit: functionally neutral genetic sequences are often maintained within various gene pools (to include the human gene pool) and these can experience a random walk through multidimensional sequence space without the influence of natural selection. Sure, there are random walks in biological evolution, but never such that the organism is no longer viable. In any case, your original claim concerned selectable stepwise evolution, not random walks. seanpit: I also do not withdraw my claims about the limited evolutionary potential of any functionally-beneficial system via a Darwinian-like mechansim – be that system comprised of characters in the English-language system or computer codes or a biological system. Great! According to your test, we have to “Select based on changes in beneficial function.” Now, please provide an unambiguous way to determine changes in "beneficial function" so we can try the test what you proposed.Zachriel_{March 17, 2016
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Zachriel,
A random walk is not the algorithm under discussion. You should either withdraw your claims about word-space or explain how to “Select based on changes in beneficial function.” Otherwise, your claim is just, well, words.
Why isn’t random walk under discussion? – since random walk is quite real in biological systems and is supposed to be a significant part of biological evolution? I also do not withdraw my claims about the limited evolutionary potential of any functionally-beneficial system via a Darwinian-like mechansim – be that system comprised of characters in the English-language system or computer codes or a biological system. If the mechanism involves random mutations and function-based selection, that mechanism, beyond very low levels of functional complexity, will stall out, in an exponential manner, with each step up the ladder of functional complexity. That is why you base the selection process, within your own evolution algorithms, not on the recognition of the appearance of some kind of new beneficial function, but on template matching to some pre-established sequence irrespective of any change in the underlying function or meaning of the evolving sequence in question. You know as well as I do how to select based on changes in beneficial function. And, you know as well as I do that your evolution algorithms simply don’t do this.seanpit_{March 17, 2016
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Zachriel,
Consider a very simple case, with two viable points separated on a vast two-dimensional landscape. A random walk will eventually lead from one viable point to another viable point. However, evolution will never leave the first viable point because every neighbor to it is unviable. Consider now two viable points on a landscape with a vast number of dimensions such that any two points are now a single step away from another. Yes, you may have to test large numbers of neighbors, but you remain viable the entire time during and until you find the other viable point.
This is a mistaken view of biological evolution - or the evolution of any kind of information-based system for that matter. First off, functionally neutral genetic sequences are often maintained within various gene pools (to include the human gene pool) and these can experience a random walk through multidimensional sequence space without the influence of natural selection. This forms the basis of the neutral theory of evolution. Beyond this, it is quite clear that at higher levels of functional complexity it is effectively impossible to evolve between any one island and the next closest in sequence space with a single mutation of any kind – i.e., a single recombination or point mutation. Multiple rather specific mutations would be required to cross the gap distances that exist beyond very low levels of functional complexity. For example, try going from any one of the proposed subsystems in flagellar evolution to the next with just one mutation of any kind. That notion is extremely unlikely, even given the pre-established existence of homologous structures within the gene pool (which do exist as parts of other systems of function), because of the problem that these subsystems would require significant modifications (numerous mutations) before they would actually work, advantageously, as part of the newly evolving system. This assumption of yours is a key to your misunderstanding of the potential of the Darwinian mechanism. The single-step steppingstones that were so common at very low levels of functional complexity become exponentially less and less common with each step up the ladder of functional complexity until they simply disappear, altogether, beyond the level of 1000 specifically arranged amino acid residues. And, of course, at this point evolutionary progress stalls out complexity this side of a practical eternity of time.seanpit_{March 17, 2016
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bill cole, Thanks Bill. I appreciate your note. As far as what will happen to the theory of evolution? I'm not sure what will happen because, for now, it is being treated more like a religious philosophy than an empirical science. It is very difficult for people, even scientists, to let go of their closely held religious or philosophical positions. I just don't see it happening very soon...seanpit_{March 17, 2016
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Sean Thanks for the post. The sequential space problem is why I became interested in learning about evolution. It originally came up in an origin of life discussion but I soon realized that it left the theory of evolution without a viable mechanism. I have read through the post and watched you patiently work people through it. As the public begins to comprehend the magnitude of this obsticle what do you think will happen to the theory as it is being taught in schools? Will we teach intelligent design or just modify the theory of evolution to an untested hypothesis or stop teaching it all together?bill cole_{March 17, 2016
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Mung: Origines asked if you had any proof of the reality Which is why we referred to genes, rather than a schematic metabolism. Genes are connected in sequence space in as many dimensions as there are bases.Zachriel_{March 17, 2016
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seanpit: It doesn’t matter if the target is only one step away if there are a million non-target options that are also one step away. They randomly walk off a cliff and survive to reproduce at the bottom of the cliff. It's all uphill from there.Mung_{March 17, 2016
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Zachriel: Metabolic genotypes, which consists of on-off switches for chemical reactions. Wagner’s basic concept is the same. Origines asked if you had any proof of the reality, not for a restatement in different words.Mung_{March 17, 2016
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Me_Think
I think it will be easier for you to imagine the search space as lattice. if you consider the search space as lattice, then you can easily see that in higher dimensions, the density of lattice increases, so when you do a random walk, you cover more space in less steps. Some thing which was at the edge of the field will now be only a few steps away.
It would be only a "few steps away" (or far fewer steps away in a higher dimensional space) if your random walker wasn't blind - if he could walk directly toward the target. However, that's not how random walks work. Random walks are, well, random. It doesn't matter if the target is only one step away if there are a million non-target options that are also one step away. You see, you are confusing a linear distance with a random walk distance. They aren't the same thing. While the shortest linear distance is indeed significantly decreased within higher spacial dimensions, the random walk distance remains the same.seanpit_{March 17, 2016
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Mung: Except Wagner’s not talking about genes, he’s talking about metabolisms. Metabolic genotypes, which consists of on-off switches for chemical reactions. Wagner's basic concept is the same. "Any neighbor would differ in exactly one of these letters, one chemical reaction that may be either present or absent"Zachriel_{March 17, 2016
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Zachriel: A gene has neighbors along as many dimensions as it has bases. Except Wagner's not talking about genes, he's talking about metabolisms.Mung_{March 17, 2016
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Zachriel:
What happens is that the organism takes a step, and if that step isn’t viable, the organism *remains where it is*. It continues to check each neighboring step until or if it finds one where it is viable.
LoL. This is worthy of laughter and not much else.Mung_{March 17, 2016
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Origenes: Assuming arguendo that in hyperspace things are somehow only a few steps away, it follows that that there are an enormous amount of things only a few steps away. And this means that there is an enormous number of ways to not find the right “door”. The answer depends on the structure of the fitness landscape. We've been discussing word-evolution, and with words at least, viable words are much more likely to have viable neighbors than do random letter sequences. Origenes: Finally, is there any proof of the reality of the hyperspace as proposed by Wagner? The dimensions are defined by neighborhoods. A gene has neighbors along as many dimensions as it has bases.Zachriel_{March 17, 2016
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Origenes: If 1000 mutations (reactions/letters) is the divide between organism A and organism B, isn’t it so that one needs ± 1000 steps in hyperspace also — given that each step (“visiting a neighbor”) is a change in one reaction/letter? Not quite with regards to evolution. If we were to take a step then another step then another step, a random walk, then yes. However, evolution by natural selection is not a random walk. What happens is that the organism takes a step, and if that step isn't viable, the organism *remains where it is*. It continues to check each neighboring step until or if it finds one where it is viable. Consider a very simple case, with two viable points separated on a vast two-dimensional landscape. A random walk will eventually lead from one viable point to another viable point. However, evolution will never leave the first viable point because every neighbor to it is unviable. Consider now two viable points on a landscape with a vast number of dimensions such that any two points are now a single step away from another. Yes, you may have to test large numbers of neighbors, but you remain viable the entire time during and until you find the other viable point.Zachriel_{March 17, 2016
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Me_Think: Some thing which was at the edge of the field will now be only a few steps away.
I'm trying to understand this concept. Wagner writes that neighbors differ in one reaction/sequence/letter.
Likewise, in a 5,000-dimensional cube, each and every metabolism has as many neighbors as there are dimensions, five thousand in all. You can walk from each metabolic text in five thousand different directions, to find one of its five thousand neighbors in a single step. Each of these neighbors differs from the text in exactly one reaction. Either the neighbor has an additional reaction—in this case one entry of the string changes from 0 to 1—or it has one fewer reaction—one entry changes from 1 to 0. (...) Any neighbor would differ in exactly one of these letters, one chemical reaction that may be either present or absent. (It cannot possibly differ in less than that, and if it differed in more, it would no longer be a neighbor.) There is one neighbor that differs in the first letter of this string, another that differs in the second letter, one that differs in the third letter, and so on, until the very last of these letters. [Andreas Wagner]
If 1000 mutations (reactions/letters) is the divide between organism A and organism B, isn't it so that one needs ± 1000 steps in hyperspace also — given that each step ("visiting a neighbor") is a change in one reaction/letter? Put simply, hyperspace or not, the elephant and the mouse are not neighbors. BTW how many of the inhabitants of hyperspace are supposed to be viable organisms? Is the vast majority dead? If not, why not? "There is one neighbor that differs in the first letter of this string, another that differs in the second letter, one that differs in the third letter, and so on, until the very last of these letters.", (Wagner), obviously such a process cannot produce function and a majority of viable organisms. Assuming arguendo that in hyperspace things are somehow only a few steps away, it follows that that there are an enormous amount of things only a few steps away. And this means that there is an enormous number of ways to not find the right "door". Finally, is there any proof of the reality of the hyperspace as proposed by Wagner? For instance, is there anything in the evolution experiment by Richard Lenski that is suggestive of the existence of such a hyper-dimensional space?Origenes_{March 17, 2016
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seanpit @ 65
The problem with your argument, you see, is that the ratio of potential targets vs. non-targets doesn’t change with an increase in the dimension of the search space. Therefore, the average number of random walk steps required to find a target remains the same.
I think it will be easier for you to imagine the search space as lattice. if you consider the search space as lattice, then you can easily see that in higher dimensions, the density of lattice increases, so when you do a random walk, you cover more space in less steps. Some thing which was at the edge of the field will now be only a few steps away.Me_Think_{March 16, 2016
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seanpit: As I’ve already mentioned, you don’t start on a Frisbee. A random walk is not the algorithm under discussion. You should either withdraw your claims about word-space or explain how to “Select based on changes in beneficial function.” Otherwise, your claim is just, well, words.Zachriel_{March 16, 2016
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Zachriel: The landscape has three points of high fitness, and millions of points of zero fitness on a two-dimensional landscape. If we start on a frisbee, evolution by selectable point-mutation can never leave the first frisbee. You will never find another frisbee.
As I've already mentioned, you don't start on a Frisbee. You start in the middle of the field and try to find the first randomly-placed Frisbee by unguided random walk. You will eventually find the first Frisbee using random walk - and the average number of steps for success is not dependent upon the dimensions of the search space nor the size of the steps being taken. I know that your programs don't model random walk, but random walks can and do take place in biological systems. Functionally neutral DNA sequences, for example, can undergo random walk within sequence spaces without being restricted to a particular starting point by natural selection. Also, random walk can occur within neutral nets where various sequences within the name net or "island" cluster produce the same type of function to essentially the same selectable level of functionality.
seanpit: However, as I’ve explained to you in some detail, this effect is not maintained beyond very low levels of functional complexity.
You mean as you repeatedly claimed.
And as I've repeatedly shown you evidence of the appearance of real protein sequence spaces that show an essentially uniform distribution of target islands throughout... more and more so at higher and higher levels of functional complexity.
The random ones and zeros referred to the landscape.
The "ruggedness" in the Cui paper is in reference to changes in function of a 16-character binary sequence in response to various point mutations. A landscape of random binary sequences would be "flat" with respect to function - i.e., functionally neutral rather than functionally rugged.seanpit_{March 16, 2016
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seanpit: However, as I’ve explained to you in some detail, this effect is not maintained beyond very low levels of functional complexity. You mean as you repeatedly claimed. seanpit: Come on now, a random sequence of zeros and ones is unlikely to have a functional advantage over another random sequence of zeros and ones. Huh? The random ones and zeros referred to the landscape. seanpit: Again, my claim is and has always been that any model that is based on random mutations and function-based selection will stall out, in an exponential manner, beyond very low levels of functional complexity. You claim it applied to word-space, but you can't or won't unambiguously define "functional complexity" for word-space. You should either withdraw your comments about word-space or explain how to "Select based on changes in beneficial function." Otherwise, your claim is just, well, words. seanpit: It would take a long time, but not forever. The landscape has three points of high fitness, and millions of points of zero fitness on a two-dimensional landscape. If we start on a frisbee, evolution by selectable point-mutation can never leave the first frisbee. You will never find another frisbee.Zachriel_{March 16, 2016
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Zachriel,
But if we use a stepwise selection algorithm, how many steps would it take? Forever.
It would take a long time, but not forever. The point is that changing the dimensions doesn't change the average time to success.
Now think about what happens if we increase the number of dimensions to, say, 27,878,400,000,000. The results are not the same, and we haven’t even considered possible structuring, rather than a random distribution.
Yes, they are the same. Regardless of the number of dimensions you use, given the same ratio of potential targets vs. non-targets, the average random walk time will not change. The only way to change the average random walk time is with the addition of some non-random "structure" to the location of our targets within the search space. That's the only way to significantly reduce the average random walk time for a given ratio of targets vs. non-targets. Your only problem is, of course, that the structure necessary to prevent an exponential increase in the average random walk time, with each step up the ladder of functional complexity, just isn't there. Your necessary structure starts to break up, early on, so that by the time you're at a level of say, 1000 saars, your structure is so fragmented that the average random walk time is pretty close to how it would be given a truly random distribution of targets within this higher level search space.seanpit_{March 16, 2016
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Zachriel,
Even simple statistical tests demonstrate that English is not evenly distributed in character-space.
Indeed! And the same is true for proteins as well – at very low levels of functional complexity. However, as I’ve explained to you in some detail, this effect is not maintained beyond very low levels of functional complexity. The “distribution” of beneficial sequences does in fact take on a fairly uniform appearance within sequence space. You just refuse to acknowledge the evidence is all…
A random landscape of ones and zeros is not neutral to function, but is characterized by a multitude of fitness peaks.
This is like saying that a landscape of Zs and Ts is “not a neutral function”. Come on now, a random sequence of zeros and ones is unlikely to have a functional advantage over another random sequence of zeros and ones. That means, of course, that these sequences are “neutral” with respect to their functional advantage.
It’s YOUR citation, and concerned whether recombination was effective in evolutionary search. The paper directly contradicted your position. So now you say it is irrelevant.
I didn’t say it was “irrelevant”. It is quite relevant in that it shows how recombination mutations vs. point mutations work in different situations. If the fitness landscape has sharp increases or decreases in sequence function, recombination mutations don’t provide a significant advantage. However, if the gradient of functionality is fairly smooth, obviously recombination mutations would be able to scale the smooth slopes more efficiently compared to point mutations acting alone. Also, this paper, along with the others I cited for you, show that as the ratio of potential targets vs. non-targets gets very small, that the beneficial islands (including their neutral nets) become more and more isolated from each other and become more and more uniformly distributed within sequence space – just like I’ve been trying to explain to you. Of course, you yourself agree that given a situation where targets are extremely rare within sequence space (like 1 in 1e600 or so), and given that they are fairly evenly distributed within that space, that evolutionary progress would clearly stall out. Yet, you cling to this idea that the structure that exists at very very low levels of functional complexity also exists at very high levels of functional complexity. The problem, of course, is that the available evidence does not support this claim of yours.
You didn’t like our model. You said to “Select based on changes in beneficial function,” but you haven’t been able to provide an unambiguous measure of “beneficial function” with regards to the English language.You made a claim. You point in the general direction, but haven’t been able to support it.
Again, my claim is and has always been that any model that is based on random mutations and function-based selection will stall out, in an exponential manner, beyond very low levels of functional complexity. That’s my claim and that claim is very well supported by the data I’ve already provided you here and on my website. To get you to at least start to think about this concept I told you to think about the English language system and how the ratio of potentially beneficial vs. non-beneficial significantly changes as you move up the ladder of functional complexity. You took this to mean that I saw no structure at low levels of functional complexity within the English language system. That’s nonsense. Of course there is some structure at the lowest levels of functional complexity – both in the English language system as well as in protein-based systems of function. I’ve explained this over and over again – even back in 2004 when we started this conversation. Where we disagree is in your fantastic notion that this structure remains at higher and higher levels so that no exponentially stalling effect is ever realized – regardless of the level of functional complexity under consideration. Your argument is that somehow, somewhat, the underlying structure of sequence space will, without fail, arrange the extremely rare steppingstones in nice little closely spaced rows that are easily found by random large steps into sequence space. Now, I’m sorry that your programs fail to use the Darwinian mechanism. But, they clearly don’t use it. That’s not my problem. I didn’t create these programs of yours. If I knew how to create a computer program that could use the Darwinian mechanism to truly create something like the works of Shakespeare, I’d be a very wealthy man indeed! My argument, of course, is that such a computer program cannot be produced – that intelligent design will always be required to produce meaningful sequences beyond very low levels of meaningful/functional complexity. It just doesn’t happen without intelligent design – as your own website and algorithms demonstrate quite nicely. If anything, your efforts only serve to highlight the truth of my position. If you want to actually demonstrate the creative potential of the Darwinian mechanism, you have to think of a scenario where sequences compete for resources over time and that this competition leads to higher and higher levels of functional complexity. That’s how the Darwinian mechanism is supposed to work. Numerous efforts have been published along these lines. The problem, of course, is that none of them actually generate qualitatively novel functional complexity beyond very very low levels. It just doesn’t happen – not in computer simulations and certainly not in biological systems. There simply are no such examples anywhere where some new system of function is produced that requires a minimum of more than 1000 specifically arranged characters to perform its novel function. And, statistically, it is extremely unlikely to ever happen this side of a practical eternity of time. But, go ahead, prove me wrong. In the mean time, I wouldn’t keep forwarding your current algorithms as having anything to do with the Darwinian mechanism. It makes you look like you don’t know what you’re talking about . . .seanpit_{March 16, 2016
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seanpit: How many steps, on average would it take a random walker to find any one of the Frisbees? – starting from the center of the field? But if we use a stepwise selection algorithm, how many steps would it take? Forever. Now think about what happens if we increase the number of dimensions to, say, 27,878,400,000,000. The results are not the same, and we haven't even considered possible structuring, rather than a random distribution.Zachriel_{March 16, 2016
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Me_Think You don’t seem to understand how increasing the dimensions of a search space really works when it comes to random walks and the odds of successfully finding a rare target within a certain number of options. Consider our large field example again as an illustration. Our field measures 1000 miles on each side for a total of 1,000,000 square miles, which equals 27,878,400,000,000 square feet. Say that we have three Frisbees in the field that each measure 1 square foot scattered randomly within this space. How many steps, on average would it take a random walker to find any one of the Frisbees? – starting from the center of the field? Now, turn these 2.78e13 squares into cubes. Originally each side of our field measured 5,280,000 feet. Now, each side of our cube measures 30,321 feet (just 5.74 miles). So, if I understand you correctly, your argument is that this reduced distance per axis in a higher dimension would significantly reduce the average random walk time to find one of the target positions in that space. Let me ask you, how many potential locations are there in the cube vs. the number of potential locations in the field? It’s the same number, right? What about the ratio of targets to non-targets in the cube vs. the field? It’s also the same. The problem with your argument, you see, is that the ratio of potential targets vs. non-targets doesn’t change with an increase in the dimension of the search space. Therefore, the average number of random walk steps required to find a target remains the same. Nothing changes by moving up to a higher dimension as far as average random walk “distances” or “steps” are concerned.seanpit_{March 16, 2016
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seanpit: And, that is precisely why the Darwinian mechanism stalls out, in an exponential manner, as this lower-level structure starts to break down and the potential target islands become significantly more and more isolated and randomly uniform in their apparent distribution within higher and higher levels of sequence spaces. That's your claim, however, even simple statistical tests demonstrate that English is not evenly distributed in character-space. seanpit: The concept of “ruggedness” is based, in this case, on the degree of increase or decrease in sequence function – described as “fitness” in the paper (as an increase or decrease in the “mortality” or “internal free energy” of the sequence). Yes, it's based on fitness in relation to point-mutations; however, ruggedness is contrasted with smoothness: "exploration is more efficient when the evolutionary landscape is smooth (small ?), but as ruggedness or the average selection gradient increases (larger ?), exploration becomes sluggish. seanpit: A random landscape of “zeros and ones” would be neutral with respect to function and therefore perfectly flat – not “rugged” with respect of function. No. A random landscape of ones and zeros is not neutral to function, but is characterized by a multitude of fitness peaks. seanpit: Consider also that the Cui paper was only talking about short sequences with a chain length of 18 with a total sequence space size of 2^18 = 262,144. It's YOUR citation, and concerned whether recombination was effective in evolutionary search. The paper directly contradicted your position. So now you say it is irrelevant. seanpit: Wait a minute. You’re the one who hasn’t modeled function-based selection with your own algorithms – and you’re trying to blame me for that? You didn't like our model. You said to “Select based on changes in beneficial function," but you haven't been able to provide an unambiguous measure of "beneficial function" with regards to the English language. You made a claim. You point in the general direction, but haven't been able to support it.Zachriel_{March 16, 2016
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There's a serious mistake @ 62. I am leaving it there so you can find out (Just another way of saying, I didn't edit in time :-) !Me_Think_{March 16, 2016
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seanpit @ 56
Why not? Because, it doesn’t matter that the starting point in hyperdimenstional sequence space is surrounded by large numbers of neighbors – or that these next-door-neighbors are in turn surrounded by large numbers of neighbors (so that within a very short Levenshtein distance the total number of neighbors is absolutely enormous. None of that helps find a target sequence any faster via a random search algorithm given a particular ratio of uniformly distributed targets among non-target options.
That's not true for hyperdimensions. Take the frisbee example @ 57- the search area for the 1000 miles field is about 1.609344X10^9 square meters but in hyperdimension of 4, the length of side of the hypercube will be just 200 meter ! (1.609E+9 = a^4, Solving for a you get about 200.). In 10 dimension, it's just 8.33 meter! So any random walk will find the frisbee quickly.Me_Think_{March 16, 2016
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Zachriel,
If words were random letters, then word evolution wouldn’t work… Apparently one in 10^20 is not sparse enough for you.
Yes, it would work at very low levels – just not as well. And, that is precisely why the Darwinian mechanism stalls out, in an exponential manner, as this lower-level structure starts to break down and the potential target islands become significantly more and more isolated and randomly uniform in their apparent distribution within higher and higher levels of sequence spaces. And, no, a ratio of 1 in 1e20 isn’t remotely “sparse enough” to stall evolutionary progress for a reasonably sized population with a reasonable reproductive rate and mutation rate. When you get to the level of 1000 saars, the ratio is more like 1 in 1e600. At this point, the structure needed to support evolutionary progress simply isn’t there.
A vast flat zero landscape with a small gentle hill in the middle is sparse, but not rugged. A random landscape of zeros and ones is rugged, but not sparse.
No. The concept of “ruggedness” is based, in this case, on the degree of increase or decrease in sequence function - described as “fitness” in the paper (as an increase or decrease in the “mortality” or “internal free energy” of the sequence). A random landscape of “zeros and ones” would be neutral with respect to function and therefore perfectly flat – not “rugged” with respect of function. In a “rugged” landscape, “a few random mutations randomize structure ensembles” – and disrupt function (Fontana et al., 1993; Schuster et al., 1994; Bomberg-Bauer, 1996; Tacker et al., 1996; Renner and Bomberg-Bauer, 1997). So, as either the ruggedness of the fitness landscape increases or the structure of the fitness landscape decreases, the helpfulness of recombination mutations vs. point mutations also decreases. And, with each step up the ladder of functional complexity, this is exactly what happens. The structure or location of potentially beneficial island targets becomes more and more uniformly random in appearance. Consider also that the Cui paper was only talking about short sequences with a chain length of 18 with a total sequence space size of 2^18 = 262,144. That’s a tiny sequence space compared to my proposed limiting sequence space size of 20^1000 sequences = 1e1301 sequences. Yet, there the features of this limited model that are interesting - despite the very small size of it's sequence space. “In analogy to protein families, nets [in the HP model] are dense and well separated in sequence space... [Also], although only a small fraction of sequence space yields uniquely folding sequences, sequence space is occupied nearly uniformly. No ‘higher order’ clustering (i.e., except the trivial case of the homologous sequences) is visible… Studies on shape space covering show that it is very difficult to convert one structure into another by a few point mutations.” http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1181141/pdf/biophysj00028-0151.pdf The same thing is true for 100aa proteins when it comes to sequences that can at least produce “stable folds” (which are required for functional proteins). Such sequences are rare and show an essentially random distribution within sequence space. http://www.sciencedirect.com/science/article/pii/S1359027897000370 And, when you’re talking about 1000aa sequence space, the number of stable/viable 1000aa sequences in sequence space is around 1e707. Given the size of sequence space at this level is 20^1000, the ratio of viable vs. non-viable is ~1e-594. And, this isn't the worst of it. This number is "further reduced by the dual requirements of stability and kinetic accessibility and the number of sequences that are biologically competent.” In short, the ratio of 1e-594 potential targets vs. non-targets is being generous for 1000aa sequence space. http://www.detectingdesign.com/flagellum.html#Few
seanpit: in biology, as in the English language system, sequences can be functionally neutral, detrimental, or beneficial relative to what came before. Surely you understand that – right?
Sure. And that’s what we’re asking. In order to test your claim about word evolution, you need to “Select based on changes in beneficial function,” that is, you need an unambiguous function that returns the difference in what you call beneficial function. You haven’t been able to do that, so your claim is essentially undefined.
Wait a minute. You’re the one who hasn’t modeled function-based selection with your own algorithms – and you’re trying to blame me for that? The concepts of beneficial, detrimental, and neutral have been defined, in literature, with respect to evolving sequences. You just haven’t used them in your algorithms is all. Rather, you base your selection on template matching – not any kind of change in beneficial function. I’m sorry, but that’s not my fault nor is it a problem that the relevant concepts haven’t been adequately defined for you. They have been very clearly defined.seanpit_{March 16, 2016
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