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

seanpit @ 118
Here’s a relevant math question for you:
It is not relevant. Please see comment @ 119Me_Think_{March 20, 2016
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seanpit @ 113
Again, sit down and actually do the math… Here, I’ll help you: For 2D space: The number of possible positions at a Distance of D = circumference of a circle (2*?*R) or 2*?*D. Volume: ?*R^2 Odds of hitting a particular point at Distance D = 1/(2*?*D). A rough calculation of the average number of steps (N) to find a particular point at Distance D = D^2 / 1/(2*?*D) If D = 5 the answer would be: 52 / 1/ (2 * 3.14 * 5) = 25 / (1 / 31.42) = 25 / 0.0318 = ~785.5 steps For 3D space: The number of possible positions at Distance D = surface area of a sphere (4*?*R^2) or 4*?*D^2 Volume: 4/3 ?*R^3
First, points which lay on edge in smaller dimensions will come nearer in higher dimension - it will no longer be on edge. Second, you don't search the space- you search the 'search space'. If there are totally 5,000 metabolic pathways, it is the 'search space' in both small and higher dimensions. IOW 5,000 metabolic pathways are spread across volume in higher dimension. So, in a 1 unit circle, those 5,000 metabolisms are spread across area of 3.14159 (Pix1^2). In a 10 dimension sphere, those 5,000 pathways are spread across (2Pi^(10/2)/Gamma[10/2])/10 volume of 2.55016. In 20 dimensions, the 5,000 metabolic pathways are spread across a volume of just 0.0258069; in a 30 dimension, the metabolic pathways are spread across a volume of just 0.0000219154 ! It takes more than 4 steps to reach edge of a unit circle but in higher dimension, it is not even a single step. In fact, in every dimensions after 5, the volume will keep decreasing and hence the random walk step to reach edge or anywhere inside will decrease too. P.S: I have taken unit circle as the template to avoid any ambiguity in calculating random walk steps.Me_Think_{March 20, 2016
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Me_Think, Here's a relevant math question for you: 2D space: Draw a circle around a random walk starting point at a given radius within a space that has a radius at least twice as large as the circle. How many random walk steps are expected to reach the edge of the circle? Now, how many random walk steps are expected to reach a particular point on the edge of the circle? Please show your math... 3D space: Draw a sphere around a random walk starting point at a given radius within an overall space that has a radius at least twice as large as the sphere. Make sure the volume of the sphere contains the same number of potential locations as contained with the 2D circle. How many random walk steps are expected to reach the edge of the sphere? Now, how many random walk steps are expected to reach a particular point on the edge of the sphere? Please show your math...seanpit_{March 19, 2016
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Zachriel,
seanpit: Not, it’s not because natural selection cannot select, in a positive manner, until a new beneficial sequence is found by purely random chance.
Which is why selective evolution does not work the same. The question is whether a random walk and selective evolution work the same on a vast landscape with just two viable points, depending on dimension. They do not. A random walk works whether the landscape is two dimensions or with vast dimensions. Selective evolution only works with the latter case. Random walks and selective evolution do not work the same.
Your version of “selective evolution” appears to be nothing more than a random sampling of the sequence space surrounding a starting position until a target is found. Such random sampling of sequence space is not affected by the dimensions of that space given the same ratio of potential targets vs. non-targets within that space. In other words, random sampling would be just as effective in higher dimensional space at it would be in 2D space. The same is true for random walks - on a bounded k-dimensional lattice (or hypercube). You see, “When it comes to locating small targets in large spaces, random sampling and random walks are equally ineffective.” There simply is no advantage for using one vs. the other. http://billdembski.com/documents/2005.03.Searching_Large_Spaces.pdf
In our previous conversations, we published voluminous data concerning the behavior of selective evolution, including the increasing time to reach longer sequences. It took a few billion mutations to reach sequences of several hundred bits (5 bits per character).
And, in real life, each step up the ladder of functional complexity (i.e., minimum structural threshold requirements) will continue to require exponentially greater and greater amounts of time to realize via the Darwinian mechanism of random mutation and function-based selection. Again, this is why evolutionary progress stalls out, this side of a practical eternity of time before the level of systems of function that require a minimum of 1000 specifically arranged characters can be reached.
Even simple statistical tests show that texts of English are not randomly distributed in sequence space. In any case, you seemingly can’t provide an unambiguous measure “beneficial function” of letter sequences.
You’re only using these “statistical tests” at very low levels of functional complexity within the English language system. Of course short words and even phrases in English do not have an entirely uniform random distribution within sequence space! I’m the one who pointed this out to you back in 2004! However, if you analyze the pattern at higher and higher levels of functional complexity, you will notice that functionally meaningful/beneficial sequences start to take on a more and more randomly uniform distribution. The very same thing happens with functional DNA and proteins in living things. Very quickly these sequences take on a uniformly random appearance in their locations within sequence space. As far as an “unambiguous measure” of a beneficial function, I’m not sure it is possible to be clearer than the definition used for biological systems – i.e., a system that produces a survival/reproductive advantage for the organism in a given environment vs. the rest of its peers. If you wish to simulate this situation with computer code or the English language, you will have to set up an environment with organisms that function based on coded sequences where certain types of sequences produce functions that allow for better survival/reproduction in that environment relative to the other “organisms” in the population. In other words, you have to create a competitive environment where different sequences provide some kind of functional advantage. Then, you have to see if organisms that have these sequences will evolve additional beneficial sequences that have even greater minimum size/specificity requirements.
seanpit: It is just that at such a high level of functional complexity, the potential beneficial targets in this space are so extremely rare that the minimum Levenshtein distances between what exists within the current gene pool and the next closest potentially beneficial sequence within 5000aa sequence space is simply too far to cross this side of a practical eternity of time.
That would imply that such sequences can’t sustain any mutations, which is simply not the case.
You’re mistaken. Such 5000 character systems and sequences can and do sustain many mutations in real life - and even undergo random walks in real life. However, none of these mutations or random walks ever comes across another qualitatively novel beneficial system at such a high level of functional complexity nor is this likely to happen this side of a practical eternity of time.
We’re not talking about biological evolution, but your claim about words. “Beware a war of words …”
If we’re not talking about biological evolution, then our conversation is over. For me, the conversation has always been about the potential and limits of biological evolution via the Darwinian mechanism of random mutations and function-based selection. You falsely claim, here and on your website, that I drew the limit for evolutionary progress at the level of single words. You know that’s not true, but you keep saying it anyway. You knew full well that I’ve always drawn the line for a limit to evolutionary progress at the level of 1000 specifically arranged characters. Of course, the same limitations with the Darwinian mechanism would also exist for any other system of meaningful/functional information – to include the English language system. This is why the Darwinian mechanism is never used to create masterpieces of literature – like the works of Shakespeare for instance. Such masterpieces are always produced by intelligent design and could only be produced by intelligent design this side of a practical eternity of time.seanpit_{March 19, 2016
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seanpit: Not, it’s not because natural selection cannot select, in a positive manner, until a new beneficial sequence is found by purely random chance. Which is why selective evolution does not work the same. The question is whether a random walk and selective evolution work the same on a vast landscape with just two viable points, depending on dimension. They do not. A random walk works whether the landscape is two dimensions or with vast dimensions. Selective evolution only works with the latter case. Random walks and selective evolution do not work the same. seanpit: A mutation that provides a survival/reproductive advantage in a given environment – as you already know. Rewording the question isn't an answer. seanpit: Select based on changes in beneficial function – not template-matching which doesn’t happen in real life. Your basic claim concerns words, so words in the dictionary are a reasonable way to determine whether a sequence of letters is a valid word. If we were to use a similar dictionary of phrases, then it would be the equivalent. A play of Shakespeare comes to mind. Any consecutive words in Shakespeare are certainly "more beneficial" than random words strung together. However, you didn't like this method. That's fine; however, as you made the claim, it is up to you to provide an unambiguous way to determine the "beneficial function" of strings of characters. Or you could simply abandon your claim as an ill-chosen analogy. seanpit: Now, you know that longer beneficial sequences take longer to find, but you have yet to admit that the relationship isn’t a linear relationship, but an exponential relationship. In our previous conversations, we published voluminous data concerning the behavior of selective evolution, including the increasing time to reach longer sequences. It took a few billion mutations to reach sequences of several hundred bits (5 bits per character). seanpit: The credibility of this claim is backed up by multiple lines of evidence – to include the observed nature of sequence space where known beneficial sequences have an essentially uniformly random distribution ... Even simple statistical tests show that texts of English are not randomly distributed in sequence space. In any case, you seemingly can't provide an unambiguous measure "beneficial function" of letter sequences. seanpit: It is just that at such a high level of functional complexity, the potential beneficial targets in this space are so extremely rare that the minimum Levenshtein distances between what exists within the current gene pool and the next closest potentially beneficial sequence within 5000aa sequence space is simply too far to cross this side of a practical eternity of time. That would imply that such sequences can't sustain any mutations, which is simply not the case. seanpit: I’ve never seen any other evolutionist even attempt to argue that random walks (neutral evolution over time) weren’t a significant part of Darwinian evolution. We're not talking about biological evolution, but your claim about words. "Beware a war of words ..."Zachriel_{March 19, 2016
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Zachriel,
They’re available, they’re just not viable. Consider again the two-dimensional case. There are two frisbees separated on a vast plain. We can’t leave the first frisbee because every point in the neighborhood is unviable. That doesn’t mean there aren’t two dimensions. The availability of viable points is contingent on the particular landscape. If there happens to be a frisbee in the neighborhood, then we can move to that point. If not, we can’t.
While it is correct to argue that highly beneficial sequences will be maintained by nature selection, thereby limiting the random walk potential from this starting point, it is completely wrong to argue that therefore everything within a genome will be so constrained by natural selection. I'm really amazed that you keep presenting this argument as it is completely off base. In fact, it is so off base that I've never heard it presented by any other evolutionist before. The fact is that there are many sequences that provide no significant functional advantage within genomes as far as their specific sequence structure is concerned (i.e. they are truly "neutral sequences" with respect to function). Such neutral sequences can and do in fact undergo true random walks without any significant influence or restraint from natural selection. And, that means, of course, that all of sequence space is open to potential discovery by these random walks.seanpit_{March 19, 2016
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Origenes,
Sure, I agree that from a viable position an organism can do a search. However, my self-evident point is that it cannot do so from an unviable position. IOWs unviable positions cannot serve as pathways/connections in Wagner’s hypercube. From a viable position 5000 ‘dimensions’ are searchable, from an unviable position zero dimensions are searchable.
Not quite true since a genome, like the human genome, can and does maintain essentially random sequences of DNA that can undergo random walks and “search” within all 5000 dimensions.seanpit_{March 19, 2016
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Me_Think
I think you are confusing Root Mean Square distance of random walk with the mean distance of random walk. The formula for root mean square is square root of the steps, but in higher dimensions, the mean distance is represented by the formula Sqrt[(2 N)/d]*([CapitalGamma]*((d + 1)/2))/([CapitalGamma]*(d/2))where d is the number of dimension and N is the number of steps. so for 10,000 steps, the mean random walk distance in 4,5,6,7,8,9 and 10 dimensions will be 88.3883,75.8947,67.3575,61.0883,56.25,52.3783 and 49.1935 respectively. (Reference:http://math.stackexchange.com/.....andom-walk)
Again, your logic is mistaken here. You see, I’m not confusing the root mean distance with the mean distance of a random walk. These are calculations as to how far, in absolute terms, a random walker is likely to be from the starting point after N number of steps. However, this isn’t the right formula for calculating the average number of steps to find a particular target location in sequence space at various dimensions. In other words, this isn’t the formula you need to calculate the average time to find our Frisbee within various dimensions where the number of potential locations remains the same. Again, this your formula here is for the expected absolute distance that a random walker would be from the original starting point after a given number of steps – without regard to finding a specific location within sequence space. In other words, this is not the formula for determining the average time needed for a random walker to hit upon a particular point in sequence space that is a particular distance or radius from the starting point. So, while it is quite clear, even intuitive, that the absolute linear distance a random walker will likely travel away from a particular starting point will be shorter in higher dimensions of sequence space (given the same number of random walk steps), it is not true that this therefore means that a rare target will be found faster in higher dimensions of sequence space (given the same ratio of targets vs. non-targets). In other words, this by no means helps you find the Frisbee any faster in higher dimensional space. Yet again, I advise you to actually sit down and do the math with regard to the average number of steps our random walker will take to find our rare Frisbee within sequence spaces at different dimensions given the same ratio of targets vs. non-targets.
What is interesting is that the Polya Constants decreases as the dimensions increase(0.19,0.13,0.10,0.08 and 0.07 in 4,5,6,7 and 8 dimensions respectively ) , which indicates that the random walk’s probability of returning to same point (or even origin) decreases. This means the chance of finding new genotype/metabolism increases
Not true. This does not mean that the chance of finding our rare Frisbee, or any other rare target, increases with increasing dimensions of sequence space. The “Polya Constant” simply means that for sequence spaces of dimension 1 or 2, the odds of returning to the starting point for an infinite random walk are 100%. However, for higher dimensions than 2, the odds of returning to the starting point, even for an infinite random walk distance, are less than 100%. In other words, the random walker can get “lost” in higher dimensional sequence space and never return home. This reality, unfortunately, does not improve the odds of success when it comes to finding a rare target in higher dimensions of sequence space (given the same ratio of targets vs. non-targets). Again, sit down and actually do the math… Here, I’ll help you: For 2D space: The number of possible positions at a Distance of D = circumference of a circle (2*?*R) or 2*?*D. Volume: ?*R^2 Odds of hitting a particular point at Distance D = 1/(2*?*D). A rough calculation of the average number of steps (N) to find a particular point at Distance D = D^2 / 1/(2*?*D) If D = 5 the answer would be: 52 / 1/ (2 * 3.14 * 5) = 25 / (1 / 31.42) = 25 / 0.0318 = ~785.5 steps For 3D space: The number of possible positions at Distance D = surface area of a sphere (4*?*R^2) or 4*?*D^2 Volume: 4/3 ?*R^3 A rough calculation of the average number of steps (N) to find a particular point at Distance D = D^2 / 1/(4* ?*D^2) If D = 5 the answer would be: 25/ (1/314) = 25 / 0.0032 = ~7850 steps Notice how expected number of random walk steps required to find a specific target in higher dimensional space increases exponentially if the radius remains the same. But, what happens if the absolute volume of sequence space remains the same? – if the number of possible positions within sequence space remains the same between lower and higher dimensions? The radius between starting point and target would be reduced from 5 to 2.924. So, how many random walk steps should be expect now to reach the target now? If D = 2.924 the answer would be: (2.924)^2 / 1 / (4*?*(2.924)^2) = 8.549776 / (1 / 107.44) = 918 steps The difference here between the 2D and 3D numbers is due to the rough nature of the calculation. Using larger distances would produce more similar values. But, you get the idea. Searching for rare targets within higher dimensions simply offers no advantage compared to lower dimensions.
This will help conceptualize why sparse landscape doesn’t matter: Draw a line of 100 cm on a piece of paper. At the end of the line draw a green circle to represent a new genotype. Now, in one dimension, the number of random walk steps to reach that circle will be more than 100 (since random walk can also be backward in 1 dimension). Crush the paper into a ball to represent higher dimension. The circle is now just a step or at most few step away from the start. It doesn’t matter that the landscape is sparse in higher dimension. Look at how dense a 7 and 10 dimension Hypercube is here : http://imgur.com/a/eZzZA Now imagine how much area will be covered in just 1 step as the dimensions grow.
Again, it doesn’t matter the linear distance that is traveled from your starting point. Your random walker is likely to travel the entire distance of higher dimensional sequence space many times over as compared to lower dimensional sequence space (given the same number of steps in both situations). However, this reality will not help the random walker find a rare target within higher dimensional sequence space any more efficiently. Again, do the math yourself if you don’t believe me. Actually sit down and calculate the odds of finding a particular location within sequence spaces within various dimensions of sequence space where the total number of possible positions remains the same.seanpit_{March 19, 2016
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Origenes: How can one coherently claim that there are so many dimensions if the vast majority of those dimensions are unavailable? They're available, they're just not viable. Consider again the two-dimensional case. There are two frisbees separated on a vast plain. We can't leave the first frisbee because every point in the neighborhood is unviable. That doesn't mean there aren't two dimensions. The availability of viable points is contingent on the particular landscape. If there happens to be a frisbee in the neighborhood, then we can move to that point. If not, we can't. Generally, we define the dimensions by the number of independent variables, such as genetic bases. Origenes: How can dimensions, where an organism cannot go simply because those dimensions are not viable, still function as pathways/connections to other viable points? If they're not viable, then they can't connect us to other viable points. Again, in two-dimensions, just because we can't go north, doesn't mean we can't go east or west, if there happens to be a frisbee on that spot. Phinehas: So, was there any intelligence involved in the construction of the above or not? Of course. However, it answers the objection that there are no connections through phase-space.Zachriel_{March 19, 2016
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Me_Think,
MT: The network is made of ‘viable’ and ‘unviable’ pathways.
Indeed. And the vast majority of 'pathways' is unviable, which renders them not-functional. IOWs the vast majority of "pathways" are actually no pathways at all.
MT: Why do you think all pathways should be viable?
Well, obviously because unviable pathways imply sickness and death of an organism. What do you think that unviable means?
MT: A random walk doesn’t need viable pathway to traverse across the network.
Why not? Do you hold that unviable mutations can function as stepping stones? In Wagner's hypercube a series of unviable metabolic sequences may provide a shortcut to evolutionary success for an organism. However its obvious that an organism cannot tolerate those steps. Even Zachriel understands this:
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.
IOWs "unviable pathways" are no pathways at all, do not provide shortcuts and do not connect anything, they function as stop signs instead.Origenes_{March 19, 2016
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Origenes @ 109
In order to connect the circle with the start, in order for all those dimensions to serve as pathways and to be interconnected, one needs unviable positions to function as pathways, and this they cannot do.
The network is made of 'viable' and 'unviable' pathways. Why do you think all pathways should be viable? A random walk doesn't need viable pathway to traverse across the network.Me_Think_{March 19, 2016
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Seanpit: A dimension space of 5000 isn’t really that difficult to achieve. For example, a protein-based system that uses a minimum of 5001aa is contained within a sequence space of 5000 dimensions. And, all of those dimensions are in fact searchable by random walk mutations.
Sure, I agree that from a viable position an organism can do a search. However, my self-evident point is that it cannot do so from an unviable position. IOWs unviable positions cannot serve as pathways/connections in Wagner’s hypercube. From a viable position 5000 ‘dimensions’ are searchable, from an unviable position zero dimensions are searchable.
Me_Think: Draw a line of 100 cm on a piece of paper. At the end of the line draw a green circle to represent a new genotype. Now, in one dimension, the number of random walk steps to reach that circle will be more than 100 (since random walk can also be backward in 1 dimension). Crush the paper into a ball to represent higher dimension. The circle is now just a step or at most few step away from the start.
In order to connect the circle with the start, in order for all those dimensions to serve as pathways and to be interconnected, one needs unviable positions to function as pathways, and this they cannot do.Origenes_{March 19, 2016
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This will help conceptualize why sparse landscape doesn't matter: Draw a line of 100 cm on a piece of paper. At the end of the line draw a green circle to represent a new genotype. Now, in one dimension, the number of random walk steps to reach that circle will be more than 100 (since random walk can also be backward in 1 dimension). Crush the paper into a ball to represent higher dimension. The circle is now just a step or at most few step away from the start. It doesn't matter that the landscape is sparse in higher dimension. Look at how dense a 7 and 10 dimension Hypercube is here : http://imgur.com/a/eZzZA Now imagine how much area will be covered in just 1 step as the dimensions grow.Me_Think_{March 19, 2016
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Origenes @ 100
It seems to me a matter of logic that all those countless unviable dimensions cannot function as pathways/connections. IOWs also for this reason Wagner’s concept of a 5000-dimensional cube doesn’t make sense.
The 5000 dimensions is based on 5000 (now more) actual metabolic pathways. You can explore the pathways at http://biocyc.org/biocyc-pgdb-list.shtml#tier1 If you really want to understand his concept, you have to download the free Math Lab package and explore. the MathLab package - Hyperspace is at : http://www.ieu.uzh.ch/wagner/publications-software.htmlMe_Think_{March 18, 2016
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seanpit @ 93
You’re mistaken. Again, even though the linear distance is much much shorter within a higher dimensional room, the average random walk distance needed to find a Frisbee is not shorter at all because, in our illustration, there are the same number of non-target options regardless of the dimensions of the search space being considered.....Why don’t you just sit down and actually do the math?
I think you are confusing Root Mean Square distance of random walk with the mean distance of random walk. The formula for root mean square is square root of the steps, but in higher dimensions, the mean distance is represented by the formula Sqrt[(2 N)/d]*([CapitalGamma]*((d + 1)/2))/([CapitalGamma]*(d/2)) where d is the number of dimension and N is the number of steps. so for 10,000 steps, the mean random walk distance in 4,5,6,7,8,9 and 10 dimensions will be 88.3883,75.8947,67.3575,61.0883,56.25,52.3783 and 49.1935 respectively. (Reference:http://math.stackexchange.com/questions/103142/expected-value-of-random-walk) What is interesting is that the Polya Constants decreases as the dimensions increase (0.19,0.13,0.10,0.08 and 0.07 in 4,5,6,7 and 8 dimensions respectively ) , which indicates that the random walk's probability of returning to same point (or even origin) decreases. This means the chance of finding new genotype/metabolism increasesMe_Think_{March 18, 2016
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Zachriel,
We have seen the explanation. We have also shown why evolution does not work like a random walk. 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.
I’ve never seen any other evolutionist even attempt to argue that random walks (neutral evolution over time) weren’t a significant part of Darwinian evolution. Beyond the fact that you’re simply wrong here (random walks do indeed happen in all gene pools over time) it doesn’t matter when it comes to solving the problem at hand. You’re claiming that when evolution works it always works by single steps off a starting island into the surrounding ocean – sampling to see what might be found from a safe vantage point. Even this view were in fact the only way that evolution could proceed, it simply wouldn’t proceed fast enough beyond very low levels of functional complexity. At the level of 1000 saars, your method would be very very unlikely to find anything this side of trillions upon trillions of years of time. There simply is no statistical advantage to this method of yours when it comes to solving the problem at hand. It just wouldn’t work remotely fast enough when it comes to finding higher level systems. In short, you’re in the same boat as all the other evolutionists. Your mechanism just can’t do the job you claim it did.seanpit_{March 18, 2016
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Origenes,
How can one coherently claim that there are so many dimensions if the vast majority of those dimensions are unavailable? How can dimensions, where an organism cannot go simply because those dimensions are not viable, still function as pathways/connections to other viable points? How can a dimension, where no organism can go, still be of assistance to an organism? It seems to me a matter of logic that all those countless unviable dimensions cannot function as pathways/connections. IOWs also for this reason Wagner’s concept of a 5000-dimensional cube doesn’t make sense.
A dimension space of 5000 isn’t really that difficult to achieve. For example, a protein-based system that uses a minimum of 5001aa is contained within a sequence space of 5000 dimensions. And, all of those dimensions are in fact searchable by random walk mutations. It is just that at such a high level of functional complexity, the potential beneficial targets in this space are so extremely rare that the minimum Levenshtein distances between what exists within the current gene pool and the next closest potentially beneficial sequence within 5000aa sequence space is simply too far to cross this side of a practical eternity of time. It just doesn’t happen…seanpit_{March 18, 2016
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Zachriel,
The behavior is different with evolution by selection.
Not, it’s not because natural selection cannot select, in a positive manner, until a new beneficial sequence is found by purely random chance. As the minimum Levenshtein distance to the next closest potentially beneficial sequences increases in a linear manner, with each step up the ladder of functional complexity, the average time to success increases exponentially. Natural selection cannot solve this problem…
Great! Then you should have no problem providing an unambiguous measure of “beneficial function”.
A mutation that provides a survival/reproductive advantage in a given environment – as you already know.
No need to reinvent the wheel. It does take longer to find longer words. However, you claim this results in an effective wall, but have been unable to even define a valid selection criterion, meaning your claim is not testable.
Yet again, I’ve been quite clear over the years discussing this topic with you and many others that the “effective wall” for the Darwinian mechanism is at the level of systems of function that require a minimum of 1000 specifically arranged characters (amino acids residues when discussing living things). I’ve also been quite clear that evolution works just fine when you’re talking about systems of function that require only a handful of specifically arranged characters – or even a couple hundred! My claim has always been that you will see an exponential stalling effect with each step up the ladder of functional complexity. By the time the level of 1000 saars is reached, evolutionary progress completely stalls out this side of a practical eternity of time. Now, you know that longer beneficial sequences take longer to find, but you have yet to admit that the relationship isn’t a linear relationship, but an exponential relationship. Systems of function that require a greater minimum size (given a certain degree of specificity) take exponentially longer to find in sequence space by random search algorithms. It is this pattern that you can extrapolate quite clearly to higher levels of functional complexity and see that the Darwinian mechanism simply isn’t up to the job that you claim it is. It just doesn’t work, it cannot work, beyond the lowest rungs of the ladder. The credibility of this claim is backed up by multiple lines of evidence – to include the observed nature of sequence space where known beneficial sequences have an essentially uniformly random distribution which becomes more and more prominent at higher levels of functional complexity. It is also known that examples of evolution in action become exponentially rarer with each step up the ladder of functional complexity. And, there are absolutely no observable examples of evolution in action beyond the level of 1000 saars. It just doesn’t happen for very good empirical and statistical reasons. Your vision that high level sequence space is somehow “stacked” just right so that the Darwinian mechanism can work – despite the extreme odds against this notion of yours, is just wishful thinking. There is no evidence to back up this truly fantastic notion. In any case, such an extreme example of stacking the deck, if ever identified, would be very very good evidence of deliberate design – not mindless chance. Unless, of course, you subscribe to the multiverse nonsense where our universe just happened to turn our right in so many extremely unlikely ways “By Sheer Chance”. Please…seanpit_{March 18, 2016
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Z:
Now, please provide an unambiguous way to determine changes in “beneficial function” so we can try the test what you proposed.
Step one: Publish on Amazon the first book your Phrasenation generates. Step two: Note the average number of stars in the book's reviews. Step three: Keep generating and publishing books until you get one with a higher rating. Step four: Once you hit three stars on a book, charge money. Step five: Profit! I hope there isn't anything ambiguous about the above.Phinehas_{March 18, 2016
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O Sean Pitman Beware a war of words ere you err. A man wins the crown, but lowers his helm. A kiss Is a kiss, and a war can be just, but a war of words Just irks the crowd and leads you far astray. Words, you know, can lead to a clash of swords. Why do you think that you alone have it Legit when sages aver another idea? Could it be that you could see the light But choose instead to close your eyes and block The sight? The origin of the life we know Just like this poem rose from simple forms, In meaning, and in kind, step-by-step.
So, was there any intelligence involved in the construction of the above or not?Phinehas_{March 18, 2016
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How can one coherently claim that there are so many dimensions if the vast majority of those dimensions are unavailable? How can dimensions, where an organism cannot go simply because those dimensions are not viable, still function as pathways/connections to other viable points? How can a dimension, where no organism can go, still be of assistance to an organism? It seems to me a matter of logic that all those countless unviable dimensions cannot function as pathways/connections. IOWs also for this reason Wagner's concept of a 5000-dimensional cube doesn't make sense.Origenes_{March 18, 2016
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Origenes: Only if hyper-dimensions would improve things, which it doesn’t; see the explanations by Sean Pitman. We have seen the explanation. We have also shown why evolution does not work like a random walk. 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 18, 2016
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Zachriel: It’s schematic, but shows how complex systems can evolve from less integrated structures.
Only if hyper-dimensions would improve things, which it doesn't; see the explanations by Sean Pitman.Origenes_{March 18, 2016
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Origenes: Unfortunately that doesn’t solve the problem It's schematic, but shows how complex systems can evolve from less integrated structures.Zachriel_{March 18, 2016
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Zachriel: What you are doing is quoting a rhetorical question, posed in the prologue of a book wherein the author answers the question.
Correction: the author attempts to answer the "rhetorical question" by proposing the existence of a metabolic library in a 5000-dimensional hypercube. Unfortunately that doesn't solve the problem; see the explanations by Sean Pitman.Origenes_{March 18, 2016
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Origenes: "And not nearly enough time." Evolution isn't random. What you are doing is quoting a rhetorical question, posed in the prologue of a book wherein the author answers the question. seanpit: That is why each linear increase in the Levenshtein distance will result in an exponential increase in the average random walk distance – regardless of the dimensions of the search spaces you’re considering. The behavior is different with evolution by selection. seanpit: There are many different ways to do this – as I’ve already explained. Great! Then you should have no problem providing an unambiguous measure of "beneficial function". seanpit: In other words, given these parameters, you will see a non-linear increase in the average number of generations needed for your small population to find longer and longer defined words No need to reinvent the wheel. It does take longer to find longer words. However, you claim this results in an effective wall, but have been unable to even define a valid selection criterion, meaning your claim is not testable.Zachriel_{March 18, 2016
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Zachriel
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.
The genomes of highly complex organisms, like humans for instance, consists of functionally beneficial as well as functional neutral genetic sequences. And, even functionally beneficial genetic sequences are not so rigid that they cannot experience random walks within their “neutral nets” as well. So, any reasonable simulation of the evolutionary algorithm will need to model random walks. Beyond this, your notion that evolution must always proceed by individual mutations leaping into the surrounding sequence space from a starting point island until something beneficial is found is complete nonsense. Such a notion would only be successful at very very low levels of functional complexity. It is just extraordinary to me that you actually believe that it is statistically likely, within what anyone would call a reasonable amount of time, to find something beneficial at a level beyond 1000 specifically arranged amino acid residues – with just a single mutation of any kind! That’s complete nonsense! By all means, what makes you think that this situation is remotely likely beyond the lowest levels of functional complexity? What makes you think that sequence spaces at such high levels are so extraordinarily structured as you imagine them to be? Oh, I know, because they would have to be for your evolutionary philosophy to be tenable. However, where's the "science" to support that notion? Where is your actual evidence that sequence space at high levels is set up like this?
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.
There are many different ways to do this – as I’ve already explained. It’s just that no one has ever observed, in computer simulations or biological systems, evolution of qualitatively novel beneficial systems beyond the level of 1000 specifically arranged characters. It’s not even close. Evolutionary progress clearly stalls out, in all of these simulations and in real life, on very low rungs of the ladder of functional complexity. Even in your own word-evolution simulations, with selection only based on a match to the dictionary, you will see early features of this stalling effect - if you: 1) reduce the steady state population size to or two or three, 2) limit the reproduction rate to 2 offspring per individual per generation, and 4) limit the mutation rate to one mutation per individual per generation, and 5) allow for some actual random walk (by starting with a longer randomly-generated sequence for the “genome” of say 20 characters for each individual in your starting population divided into, say, 5 separate sequences, divided by spaces (an extra character to give a total of 27 character options) with a 6) maximum genome size of, say, 50 characters per individual genome – where one of these original sequences is your starting single character “word” – like “O” or “I” or “A”). Positive selection will be based on the discovery, by any part of the genome in any individual within the overall population, of a new longer word. At this point, this particular individual genome will be used to populate the next generation (i.e., the other two genomes will be killed off). Also, remember, “recombination mutations” can only occur within the genomes of a single individual in this situation – not between different individuals in the population. In other words, given these parameters, you will see a non-linear increase in the average number of generations needed for your small population to find longer and longer defined words – even without basing selection on an increase in beneficial function. If you want to move on from this point to modify your “Phrasentation” program to be more reflective of the problems the Darwian mechanism faces in real life, set it up, along the same parameters described above, where selection, in this case, is based, not on an increase in beneficial function, but upon sequence matching to entire sentences, then entire paragraphs, then entire chapters – etc. You can increase the maximum genome size to 50,000 characters per individual as additional levels of success are achieved. In such a situation, you would see a non-linear increase in the number of generations required to achieve the next “level” of evolutionary progress. Using real function-based selection would only add, obviously, to the difficulties for the Darwinian mechanism. Also, it would be helpful to modify your program to also work with 64-bit Excel spreadsheets.seanpit_{March 18, 2016
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Me_Think
Sean Pitman: 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.
You’re mistaken. Again, even though the linear distance is much much shorter within a higher dimensional room, the average random walk distance needed to find a Frisbee is not shorter at all because, in our illustration, there are the same number of non-target options regardless of the dimensions of the search space being considered. That is why it takes just as many steps for a random walker to find the Frisbee regardless of the dimensions of a room with the same number of potential locations. This is also why, at higher dimension, the same linear distance to a target that existed within a lower-dimensional space will result in an exponential increase in the random walk distance within a higher-dimensional space. In this line, the Levenshtein distance is a measure of distance within hyper-dimensional space. That is why each linear increase in the Levenshtein distance will result in an exponential increase in the average random walk distance – regardless of the dimensions of the search spaces you’re considering. Why don’t you just sit down and actually do the math? If you do, you’ll soon recognize your mistake.seanpit_{March 18, 2016
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Zachriel #91, Andreas Wagner makes it sound even worse:
The first vertebrates to use crystallins in lenses did so more than five hundred million years ago, and the opsins that enable the falcon’s vision are some seven hundred million years old.10 They originated some three billion years after life first appeared on earth. That sounds like a helpfully long amount of time to come up with these molecular innovations. But each one of those opsin and crystallin proteins is a chain of hundreds of amino acids, highly specific sequences of molecules written in an alphabet of twenty amino acid letters. If only one such sequence could sense light or help form a transparent cameralike lens, how many different hundred-amino-acid-long protein strings would we have to sift through? The first amino acid of such a string could be any one of the twenty kinds of amino acids, and the same holds for the second amino acid. Because 20 × 20 = 400, there are there are 400 possible strings of two amino acids. Consider also the third amino acid, and you have arrived at 20 × 20 × 20, or 8,000, possibilities. At four amino acids we already have 160,000 possibilities. For a protein with a hundred amino acids (crystallins and opsins are much longer), the numbers multiply to a 1 with more than 130 trailing zeroes, or more than 10^130 possible amino acid strings. To get a sense of this number’s magnitude, consider that most atoms in the universe are hydrogen atoms, and physicists have estimated the number of these atoms as 10^90, or 1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000,000,000,000,000,000,000,000,000,000. This is “only” a 1 with 90 zeroes. The number of potential proteins is not merely astronomical, it is hyperastronomical, much greater than the number of hydrogen atoms in the universe.11 To find a specific sequence like that is not just less likely than winning the jackpot in the lottery, it is less likely than winning a jackpot every year since the Big Bang.12 In fact, it’s countless billions of times less likely. If a trillion different organisms had tried an amino acid string every second since life began, they might have tried a tiny fraction of the 10^130 potential ones. They would never have found the one opsin string. There are a lot of different ways to arrange molecules. And not nearly enough time.
Origenes_{March 18, 2016
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Origenes: 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. Gee whiz! It almost sounds like it might take millions of years for many evolutionary changes to occur!Zachriel_{March 18, 2016
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