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Author | Topic: Can you disprove this secular argument against evolution? | |||||||||||||||||||||||||||||||||||||||
dwise1 Member Posts: 6076 Joined: Member Rating: 7.2 |
Percy writes: Wrong. Evolutionary programs all have something that is called active information(fitness function) which is a form of intelligent guidance. Evolutionary programs are written by people, but they model evolution, not intelligent design. The programmer defines the "natural environment" so as to model the real world to the degree of accuracy necessary.Just as an experimental biologist doesn't change selection into an intelligent process by manipulating an organism's environment, neither does a programmer by manipulating a program's "environment". The process modeled is still one of descent with modification and selection. Sorry, but you're the one who got it wrong. The fitness function is there as part of the evolutionary model, to model fitness. Which also makes your later denial of fitness in evolution also wrong. The evaluation of an organism's fitness in evolution is based on its ability to reproduce and for its offspring to survive long enough to reproduce as well. Of course, said evaluation is implicit rather than a explicit step. In the vast majority of evolutionary programs, while the fitness test is an explicit step, the target is not. For example, if you are trying to solve for a mathematical problem involving a large number of variables, then the fitness function would involve plugging each set of possible solutions (that being one "genotype") in which case the set closest to the solution would be rated with higher fitness. In the case of programming an FPGA to perform a function (such as a high-performance amplifier as in one experiment) then you program each possible solution into an FPGA and the one that works best would have higher fitness and would be selected to spawn the next generation of attempts. Or there could just be no explicit fitness test. When you read my MONKEY page, at the bottom I discuss a few such programs. TBUGS sets up a feeding ground for a bunch of bugs who inherit feeding behaviors which can mutate. Toss the bugs into different kinds of environments where food growth differs and you soon see certain kinds of behaviors dominate all based on how the bugs can survive best. BTW, as I recall the "genes" for behavior are very simple, so behavior like "twirling" is not programmed in but rather is an expression of those "genes". TIERRA is much more ambitious. It's a computer environment filled with virtual CPUs which start off with the same code for using computer resources (memory and clock cycles) to survive and reproduce. There was no explicit fitness function, as I recall. After a while, some of the CPUs became parasites who fed off of other CPUs in reproducing. Non-parasite CPUs evolved defenses against the parasites and there even arose a type of hyper-parasite which fed off of the parasites. In designing the original code and analyzing it for what could happen, the researchers determined the minimum length that code could be and still work. The CPUs evolved a far shorter code which worked, something that was far beyond the capability of their "intelligent designers". Evolutionary programs are written to solve problems that people either cannot solve or find very difficult to solve. Many of them are engineering problems. All that the programmer can do is set up the environment and provide a fitness test, but that fitness test just measures performance, basically what happens in nature. As both Dawkins and I pointed out quite explicitly, neither WEASEL nor MONKEY is an evolutionary simulation, so one might question calling them evolutionary programs. Rather, they implement two forms of selection, one of which is indeed modeled after evolution (ie, cumulative selection), in order to demonstrate and compare their capabilities. And I should point here yet again that both selection methods use the exact same fitness test, so why does single-step selection fail miserably while cumulative selection cannot fail?
In evolution this active information does not exist, meaning evolution can select only those individuals who manage to reach the right corner of the field. In other words, in the real world the path towards this corner is not guided but is carried by random means.
I don't know what your proposed simulation is supposed to simulate, but it certainly isn't evolution. Fitness does indeed exist in evolution, so your statement is false. And your assessment of what it would take for evolution to succeed is also wrong.
So yes, evolutionary programs model intelligent design. No, they do not. To start with, IDists look at something complex in nature and call it "design". Well, I happen to be an intelligent designer, a software engineer, so I happen to know a few things about design. Intelligent designers do not create complex designs. For that matter, we try our best to reduce the level of complexity in our intelligent designs, striving instead of elegance. Complexity just breeds trouble and makes it so much harder to maintain the design and the product. Too much complexity is the sign of an incompetent designer. As I said, one of the uses of evolutionary programs is to use evolutionary processes to create a design. From that work, we discovered something rather interesting, that a striking characteristic of the products of evolutionary programs is complexity. That amplifier FPGA code I mentioned. The product was highly complex, irreducibly complex since you couldn't make a single change without breaking it. It was so complex that it made use of the analog electrical characteristics of the FPGA's digital circuitry (hence it was not portable). In software design, we joke that we use evolution. We want to create a product that does something, so we use as our baseline an existing program which does most of what we want, but which a few changes. Or we want to add a feature that's similar to an existing feature, so we copy that code and modify it. How does evolution create a new structure? By modifying an existing structure or by copying and modifying that copy (eg, creating a new protein). And as a result of using evolutionary design techniques, our programs become increasingly complex and increasingly difficult to debug or to maintain. Don't you just love the irony? Complexity actually disproves intelligent design. So the next time you see something complex in nature, realize that that's a sure sign that it had evolved. Edited by dwise1, : tweeked the final line
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dwise1 Member Posts: 6076 Joined: Member Rating: 7.2 |
My objections mean that in evolutionary programing, targets are a priori selected by intelligent agents. Without this information about the search space structure evolutionary programing does no better than blind search. Wrong! As we have already discussed, the vast majority of evolutionary programming experiments do not rely on a priori selected targets. Some do not even have fitness functions, but rather let the environment they had created do that as would happen in nature. Evolutionary programming involves applying evolutionary processes to solving complex problems. Problems for which we do not know the solution and hence could not specify the solution in advance even if we wanted to. You really need to learn something about evolution and about evolutionary programming. Then at least you might be able to raise an actual objection. As it is, you sound like you're just parroting what some IDiots like Dembski have written. For example, I quote from Dembski's presentation at the April 2000 "Nature of Nature" conference at Baylor University, "Can Evolutionary Algorithms Generate Specified Complexity", because in it he grossly misrepresents who WEASEL (and hence MONKEY) work, falsely claiming that the program cheats. Practicing geologist and former young-earth creationist (former because of his field work in geology) Glenn R. Morton also attended that conference and reported on it. He reported that in the subsequent question and answer session, Dembski was faced with "[h]ands ... upraised all over the room" by people who worked with genetic algorithms and knew better than what Dembski had told them. Dembski's response? "Dembski had the deer in headlights look." forexhr, do not be that deer. Learn something about the subject matter.
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dwise1 Member Posts: 6076 Joined: Member Rating: 7.2 |
My objections mean that in evolutionary programing, targets are a priori selected by intelligent agents. You haven't answered my question. If that "target ... a priori selected by intelligent agents" is your "explanation" for the phenomenal success of WEASEL and MONKEY when they use cumulative selection, then why do they fail abysmally when they use single-step selection? I'm an engineer (software), which means that I am frequently involved in troubleshooting problems and debugging code. One of the cardinal rules is to change one and only one thing at a time, then test for whether that solved the problem. The use of control groups in science experiments is for the same reason. If you change several things and the problem is solved, you still don't know which change was responsible. MONKEY (actually, I forget whether WEASEL had a single-step selection mode) offers such a controlled experiment. You can choose single-step or cumulative selection. That's the only thing that changes. Everything else is the same, including having a pre-determined target string. So if there is any difference in the program's performance, it must be because of the only thing that changed, the selection method used. So yet again, since they both use the exact-same fitness test, why does single-step selection fail so abysmally while cumulative selection succeeds so spectacularly?
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forexhr Member (Idle past 2320 days) Posts: 129 Joined: |
dwise1 writes: Yes! Finally! You are finally starting to understand what's wrong with your stupid use of the Texas Sharpshooter Fallacy! You are finally starting to understand what a lie it is to take something that already happened and then prattle on about how it could not have possibly happened because the probability is so low, despite the simple fact that it did indeed happen. Of course, being a creationist you will deny that you understand what you've done. I really feel sorry for you, man. I really do. You have become so desperate, that you are now trying to project your ignorance of basic mathematics on me, by using an infantile sarcasm. Of course that we can take an event that already happened and then calculate the probability of it happening, but only if parameters for calculating the probability were definable before the event. The probability of an event A is defined as P(A) = number of favorable outcomes/ total number of possible outcomes. Although nobody witnessed the formation of a particular bio-structure, and therefore being able to define the "number of favorable outcomes" before its formation, this number is definable with reference to a particular environment. If this environment is the operator of Lambda phage genome to which lambda repressor binds, then the "number of favorable outcomes" are all functional lambda repressor folds that are capable to regulate the transcription of lambda phage genome, while the "total number of possible outcomes" are all possible 92-residue sequences. Given the study referenced in the O.P., there are 10^56 "favorable outcomes" and 10^119 possible outcomes (20^92) which gives P(A) = 10^56/10^119 =10^-63. On the other hand, if we are looking at all the cards dealt one after another and ask what was the probability for this arrangement, then this is a nonsensical question because we cannot relate this arrangement back to an "environment" before the cards were dealt, for example to something that someone said or wrote about favorable arrangements of cards. Since the "favorable outcomes" parameter is missing we cannot use the probability formula and determine the probability.
dwise1 writes: You really need to learn something about evolution and about evolutionary programming. Then at least you might be able to raise an actual objection. Instead of proving that you are wrong by refuting your extensive responses point by point, I have much more interesting and effective way. I will set up an experiment that will allow you to test your suggestion that there is a link between Darwinian evolution and evolutionary programming in a sense that the path towards the right solution in the real world in not carried by random means. Through an analogy, this experiment will simulate the environmental condition to which an organism must adapt. Since you are educated about evolutionary computation, all you'll need to do is explain how would you solve this adaptation problem via evolutionary programming. Ok, let us begin. From the perspective of Darwinian evolution, enviotnmental condition is something to which an organism must adapt in order to improve its chances of survival and reproduction. If this enviotnmental condition is intron-exon gene structure then adapting to this condition simply means, gaining the ability to remove introns and join exons. Since this ability comes from the group of proteins(RNA splicing machine), while the information that codes for them is written on the DNA, adapting to an environment simply means finding the right arrangement of nucleotides in the DNA. This realtionhip between enviotnmental condition and the right arrangement of nucleotides is similar to question-answer relationship - inton-exon gene structure is the question, while the arrangement of nucleotides in the DNA that codes for RNA splicing is the correct answer to this question. Hence, adapting to a particular environmental condition is like providing the right answer to a particular question. Given this analogy, in this experiment you'll need to explain how to find the correct answer to a particular question via evolutionary programs. Of course, you have one constraint - you are required to use mechanisms of Darwinian evolution. Since we know, that evolution has no intelligence and no mind, so it cannot see, read, think, percieve, ....it cannot grasp the question... here is the question for you: "____________________________________________________?" I have this question written in my Word document. Now, all you have to do it to use your evolutionary programs and provide the right answer to it. At your disposal you have all possible evolutionary mechanisms: functional shift, exaptation, co-option, selection, duplication. In other words, you are alowed to combine existing letters, words and sentences that exist in books, newspapers, magazines,dictionaries, internet or in your mind. You can do whatever you want in creating new combinations of linguistic elements. The only constraint is your inability to use engineering and inteligent design principles in solving the problem. You are unable to notice or become aware of the question, or in other words, you are unable to create a mental representation of perceived question and then, by using your cognitive faculties, to co-opt the right combination of letters, words and sentences according to this mental representation. In short, no intelligence is allowed. Also, you cannot communicate with me about a partial accuracy of the answer since communication is an intelligent activity, and we know that evolution is not intelligent and therefore it is not able to communicate. Let us explain this by using above mentioned enviotnmental condition of intron-exon gene structure. Adaptation to this environment consists of at least four subprocesses: to recognize mRNA and its intron-exon boundaries, then to cut the introns out, to rearrange exons and finally to release the mRNA molecule. Only when combination of nucleotides in the DNA that codes for all for subprocesses exists, only then natural selection can act, and not before. For example: If we assume the existance of splicing helper proteins that assembly at the intron-exon borders to guide small nuclear ribo proteins to form a splicing machine, this partial correctness of the splicing process won't cause introns to magically disappear without a complete splicing machine. Meaning, evolution is able to select an organism only when all four subprocesses are in existance. That is why you cannot communicate with me to determine a partial accuracy of the answer. In this experiment, we will presuppose that all functional words already exist in the "word pool". So, you don't need to create new words from scratch but you can co-opt the right answer from words that already exist in the books, newspapers, dictionaries.... We are told by evolutionists that co-option is a powerful evolutionary mechanism. They say that the parts nessecary to build new molecular machines could be taken from other molecular machines and combined into the new machine being constructed. Hence, in answering the question you are allowed to take words from existing books, newspapers, dictionaries, etc. Now just thing about the extent of the problem. The subject of the question can be any aspect of the reality that can be expressed in words. So there is a potential for nearly infinite number of potential questions. And since you don't know what the question is you don't know what words or letters to use, how to combine them, you don't know what amount of words constitute the correct answer. But, you opposed my claim that in the real world the path towards the right solution is carried by random means. So, there you go... try to solve this problem via whatever non-random evolutionary means you choose in your evolutionary programming. Let me know when you find out the correct answer. Edited by Admin, : Shorten long line. Edited by forexhr, : No reason given. Edited by forexhr, : No reason given. Edited by forexhr, : No reason given.
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Percy Member Posts: 22940 From: New Hampshire Joined: Member Rating: 6.9 |
Dwise1 is correct that fitness assessment is how the adaptive pressures of the environment are modeled in evolutionary programs. Your example problem seems to be insisting that there be no fitness assessment, as if evolution were no more than random change. You're leaving selection pressures out of the process. Any valid evolutionary program has to include both random change and selection, and selection will always be according to some criteria.
--Percy
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bluegenes Member (Idle past 2730 days) Posts: 3119 From: U.K. Joined:
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forexhr writes: What does functional protein that binds to ATP has to do with the development of all specific bio-functions that we observe at all levels of bio-organization. Well, you like targets, so we can think up an experiment together. We are asking ourselves the general question: if a biological species was randomly shuffling around amino acids via mutations on its genome, about how many attempts might it take to come up with a new protein that is useful to it? So, we could pick out a random specific target, and test a whole bunch of random AA sequences for it. We don't have to test our AA sequences out for any other of the many specific functions they might perform in order to infer that our result gives an approximation of the general answer to our question.
Functional Proteins from a random sequence library Fortunately, Szostak and Co. have done it for us. It's ~1 in 10^11.
forexhr writes: If we consider ATP binding function as "general" what would this generality have to do with this specific structural niches: intron-exon gene structure, female reproductive system, lactose, ability of the lambda repressor to regulate expresion of cI protein? Can you fulfil these niches with ATP binding function? No. Can you fulfil the 'contraction' niche of striated muscle tissues, with this protein composed of 80 amino acids? No. You need a specific protein protein composed of ~27,000 to ~33,000 amino acids that is called Titin. So the Szostak paper demostrated that there has been enough sampling to cover the entire functional landscape of ATP binding protein. But what follows from that in the context of above mentioned niches? Absolutely nothing. The Szostak sample wasn't tested for other functions. It hit its target, and would have hit many others. It tells you a lot. Amongst other things, because four completely unrelated proteins that had some ATP binding function were found in the sample, you should no longer be using the 1 in 10^63 figure (from the first paper linked to in the O.P.) in the way that you are, because it's a very rough estimate on the proportion of search space that could be derived from variations on the current Lamda protein, and doesn't include any completely unrelated ones that could cause the function, although the authors do indirectly touch on this possibility in the paper. Also, when you talk about protein size, you need to understand degeneracy in proteins. As the protein increases in size, the number of functional variants increases exponentially, so if we could derive, for example, 10^50 functional variants on one of the 80AA ATP binding proteins found in the Szostak experiment, we'd expect ~10^100 functionals on a 180AA protein.
forexhr writes: You committed the same mistake again. Let me use this example for illustration. If you have 1000 generally meaningful 10 letter words, whose sequence space is 26^10=141167095653376, and if you have enough resources(trials) to explore this sequence sapce, for example 150000000000000 trials, than by your logic, 150000000000000 trials would stumble across all 1000 generally meaningful 10 letter words.But here is the problem. How would you define what is meaningful in a specific context? In a language, meaningful is defined by other words in a sentence. For example: this 10 letter word - "authorized" is meaningful in the context of this sentence: The defender admits that he authorized all operations, but this 10 letter word - "birthplace" is not. Since you don't know in advance what sentence or context will appear, it is impossible to "select" a specific meaningful 10 letter word if you stumble across it during a random search. I was assuming context. What I was saying, using your analogy, was more like if about 1412 combinations are potentially functional (in this context) then 10^11 searches would find one, and that the same principle would apply as an approximation for all contexts (in general). One of the reasons it doesn't appear to work in your analogy but does for function in biology is that life is a terrible speller, so there would be about 10^20 ways of spelling a 30 letter word (according to the paper in your O.P.) and about 10^6 ways of spelling "authorized" instead of the two we have in modern English. So, we actually would hit on authorized itself fairly easily, inside Szostak's estimate, without depending on ten letter synonyms. So, you could get an average length 100,000 word/protein novel with 10^16 "resources", and you could have 1,000,000,000,000,000,000,000,000,000 novels if all the search resources available to life so far (according to your second O.P. paper) had been available. The first paper you linked to in the O.P. covers the different "spellings", but not the different "synonyms" and all their different spellings. The Szostak experiment didn't test its sample for all functions, only one. The thinking goes, "if the organism could benefit from function x, then about how many random arrangements of AAs might it take to get it, so let's test random arrangements for an x." They could have chosen other specific functions, and the same thing could be tried for the Lamda repressor to give a much more accurate picture of the proportion of proteins of all families that would give some minimal selectable function.
forexhr writes: This is even more obvious in the context of biology. Proteins are meaningful only in the context of currently opened structural or environmental niches. If we want to add just one completely new functional protein to our life system today, how many such niches are available to all species? One? An uncountable number? How many per. species?
forexhr writes: For example if this niche arises "The defender admits that he .......... all operations", then to fulfil the niche you need to search for the word "authorized". And to find this word you need to spend almost all of your resources since sequence space of 10 letter words is 26^10=141167095653376. If another niche arises where the word "birthplace" is meaningful, then again, you need 150000000000000 resources to find it. But you alredy spend all your resources for the previous search and you cannot proceed. You could have written:
For example if this niche arises "ATP binding" then to fulfil the niche you need to search for a protein that binds ATP, and to find this you need a fraction of your resources (10^11 out of 10^43). If another niche.. But that would be real biology, wouldn't it? You still seem to be reading your novel backwards and assuming there can only be one novel and that life systems have a plot with an ending. Once again, the resources for "authorized" in biology would be a lot less because there would be about 10^6 functional spellings on a ten letter stretch. A 30 letter word would have about 10^20 spellings, according to the first paper in your O.P.
forexhr writes: To conclude, in your reasoning you presupposed that evolution has a foresight and knows what structural or environmental niches will emerge in the future, so when the random search stumbles across the protein that is meaningful in the context of these future niches, evolution would simply select it, then put it aside and wait for niches to emerge in some random point in the future. In short, you presupposed that evolution has the supernatural powers. Not at all. A species could have many possible structural or environmental niches open to it at any one time, and these can be available for long periods of time. Instead of "authorized" or one of its synonyms, you get "designed" or "loved" or "hated" and the story goes off in a different direction, as it would if you substituted "denied" for "admits" and so on. It's because you like targets that I dug up the Szostak paper for you. It has one, so I thought you'd like it....
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dwise1 Member Posts: 6076 Joined: Member Rating: 7.2
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You have become so desperate, that you are now trying to project your ignorance of basic mathematics on me, by using an infantile sarcasm. Maybe the idea is foreign to you, but not everybody enjoys Schadenfreude. Many of us actually feel empathy for the person who is incapable of understanding a simple and obvious concept like the Texas Sharpshooter Fallacy. And when that person does finally start to get it, we actually feel good for him and want to cheer him on. And unfortunately for you, you are a creationist. The reason why that causes you problems is because creationists don't have any evidence to support their claims nor any valid arguments, so they have to rely on fallacies and false and misleading claims. A strong reason for your inability to understand the Texas Sharpshooter Fallacy is because you cannot allow yourself to understand it. It's either that or resort to deliberate lying because you do understand why your argument is false -- we have seen far too much of that from creationists. BTW, my third bachelor's degree was in math. So if you think that I am so ignorant of basic mathematics, then do please explain what I got so wrong in my "MONKEY Probabilities" (MPROBS) document. You know that one, where I analyze the probabilities of single-step selection and cumulative selection in order to understand and explain why cumulative selection succeeds so spectacularly while single-step selection fails so abysmally. Interesting how hard you are working to avoid that question. Oh, and could you please remind me what your qualifications are?
Although nobody witnessed the formation of a particular bio-structure, and therefore being able to define the "number of favorable outcomes" before its formation, this number is definable with reference to a particular environment. If this environment is the operator of Lambda phage genome to which lambda repressor binds, then the "number of favorable outcomes" are all functional lambda repressor folds that are capable to regulate the transcription of lambda phage genome, while the "total number of possible outcomes" are all possible 92-residue sequences. Given the study referenced in the O.P., there are 10^56 "favorable outcomes" and 10^119 possible outcomes (20^92) which gives P(A) = 10^56/10^119 =10^-63. You are still talking about single-step selection. We already know that the probability of single-step selection succeeding is abysmally small. Using as an example MONKEY's default target, the alphabet in alphabetical order, the probability of success using single-step selection is 1.6244E-37 (1.62210-37). I calculated how many attempts would be needed to bring that up to one chance in a million and arrived at 6.1561027 attempts. To provide some perspective, I suggested using a supercomputer that can perform one million attempts per second (that was written in 1989 when the XT clone (Norton Factor 2) could only do about 200 attempts per second, but personal computers now are maybe a thousand times faster; I'll need to determine that). It would take that hypothetical supercomputer about 195 trillion (1012) years to perform those 6.1561027 attempts. That would be nearly 10,000 times longer than the accepted age of the universe. Hence abysmal. But nobody except a creationist would think that single-step selection applied to your "argument against evolution", since that is far more descriptive of creation ex nihilo. Rather, we would argue that it had evolved, which would mean that an evolutionary probability model would be in order, one that used cumulative selection. If you want to come up with an argument against evolution, then it has to deal with evolution, not with something completely different as you have done. And to accomplish that, you really do need to learn something about evolution. Until you do that, you will only succeed in making yourself, your position, and your religion look foolish. I am asking you to not do that.
I will set up an experiment that will allow you to test your suggestion that there is a link between Darwinian evolution and evolutionary programming in a sense that the path towards the right solution in the real world in not carried by random means. It isn't. Anybody who has any degree of actual understanding of evolution would know that. That you do not know that is obvious just from that statement. And if you don't understand evolution, then how could you possibly propose a valid simulation of it?
From the perspective of Darwinian evolution, enviotnmental condition is something to which an organism must adapt in order to improve its chances of survival and reproduction. That's not how it works. This is not starting out very well.
If this enviotnmental condition is intron-exon gene structure then ... Wrong. DNA is not the environment. DNA is not what is selected, but rather what would be what is produced when the DNA is expressed. Haven't you ever learned the difference between the genotype and the phenotype? Yet again, this is why you really need to learn something about evolution. But, you opposed my claim that in the real world the path towards the right solution is carried by random means. Because it's wrong, as we have all tried to explain to you over and over again while you employed the typical creationist mistake of stubbornly keeping yourself from learning anything, because if you were to learn then you would realize how wrong you are. Yes, there are random elements in mutation and recombination, but selection is very deterministic -- stochastic, but very deterministic. Yes, we would not be able to predict the actual results of evolution, because there are many different solutions that can arise. But every one of those different solution will work in very deterministic ways. We will be able to examine them after the fact and understand why they work and how they evolved, but if we were to start the whole process all over again, we would undoubtedly get a different result. You keep trying to remove selection and fitness, which further demonstrates that you do not understand evolution. Please try to learn something about evolution. And also learn something about evolutionary programming. Not from the creationists and IDiots, because they are lying to you.
So, there you go... try to solve this problem What problem? You did not present one. And do please stop trying to divert our attention away from my question to you and answer my question. You claim that my MONKEY works so well because it knows the target string. But that is also true of the single-step selection test that MONKEY also performs. So why does what you claim makes the cumulative selection test work also fails to work for the single-step selection test? My answer is that it is the nature of the selection methods themselves that accounts for that vast difference in performance. What is your answer?
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bluegenes Member (Idle past 2730 days) Posts: 3119 From: U.K. Joined:
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forexhr writes: Since you don't know in advance what sentence or context will appear, it is impossible to "select" a specific meaningful 10 letter word if you stumble across it during a random search. This is even more obvious in the context of biology. Proteins are meaningful only in the context of currently opened structural or environmental niches. Amazing how quickly the impossible can be achieved. The bacterial phage of the O.P. paper, Lambda, hits a target for forexhr.
Lambda achieves the impossible in about 8 days! The new function often arrives between 8 and 20 days, The whole paper's well worth a read, but here's an extract for the lazy.
quote: All four mutations are required. As I said before forexhr, I know you like targets, so here's your favourite organism hitting a target with a new four letter word.
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caffeine Member (Idle past 1277 days) Posts: 1800 From: Prague, Czech Republic Joined: |
You are calling out for red herrings because this does not change the essence of my argument. But anyway here's the link: Protein-free spliceosomal snRNAs catalyze a reaction that resembles the first step of splicing - PMC "Splicing of introns from mRNA precursors is a two-step reaction performed by the spliceosome, an immense cellular machine consisting of over 200 different proteins and five small RNAs (snRNAs)." That's the first sentence of the paper. You should have kept reading, as the bulk of it seems to be about an experimental demonstration that splicing may be possible without using any of these proteins. If I understand it right, what the writers were trying to acheive was to understand what sort of simpler structure this spliceosome could have evolved from. This is where your whole probability calculations are going wrong. There isn't only one way to acheive a result; and you rarely need perfection on the first attempt. The processes acheived with such efficiency by complicated protein machinery which is the product of thousands of millions of years of cumulative evolution can be acheived with less efficiency by simpler systems.
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forexhr Member (Idle past 2320 days) Posts: 129 Joined: |
bluegenes writes: Functional Proteins from a random sequence libraryFortunately, Szostak and Co. have done it for us. It's ~1 in 10^11. Isn't it interesting how your entire response is focusing on issues that we both agree with. I agree that the Szostak sample wasn't tested for other functions and that it hit its target. I agree that as the polypeptide chain grows in size, the number of functional combinatorial possibilities increases exponentially. I agree that there are many functional amino acid sequences with respect to the vast combinatorial space of possibilities - I stated this explicitly few times already - this is from my last post: "Given the study referenced in the O.P., there are 10^56 "favorable outcomes" and 10^119 possible outcomes". So, you spent your entire post to repeat something that has already been said and that nobody disagrees with. But, you din't even consider to critically examine the logical consequences of the paper you talked about - if 10^11, out of 10^43 evolutionary resources must be spent just to extract one simple binding function, then how much resources must be spent to extract complex and structurally independent functionas like: conformational complementarity of enzyme and substrate, fertilisation, editing of the nascent precursor messenger RNA transcript, synchronization of the kicks of the hopping insect's legs via gear mechanism, blood pumping, visual perception,.... According to Wikipedia, whose editors savage anyone who criticizes the theory of evolution, "... Complex, image-forming eyes have evolved independently some 50 to 100 times." On the other hand, you need 10^11 evolutionary resources to evolve something so simple as ATP binding protein. But even that simple function is not useful biologically. In the real world, proteins that bind to ATP must also be able to release it to complete their function. That may substantially reduce functional sequences. Szostak and Co didn't test to see if their protein was able to perform that feat in vivo. Their experiment was done in vitro. That is a very large difference. Adding that extra requirement will substantially worsened their 1 in 10^11 ratio. But this, of course, isn't something to critically think about. Instead, let us repeat something that nobody disagrees with to prove that those who think critically about the above issues are just stupid creationists.
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forexhr Member (Idle past 2320 days) Posts: 129 Joined: |
bluegenes writes:
I don't know what the fuss is all about. Amazing how quickly the impossible can be achieved. The bacterial phage of the O.P. paper, Lambda, hits a target for forexhr.Lambda achieves the impossible in about 8 days! The new function often arrives between 8 and 20 days, The whole paper's well worth a read, but here's an extract for the lazy. This paper has nothing to do with the issue at hand since it talks neither about evolution of new functional protein folds nor about the ratio of functional amino acid sequences versus the vast combinatorial space. It just talks about the pre-existing viral J protein that acquired the ability to bind a different protein on E. coli, called OmpF, once the LamB protein, to which it normally binds, was turned off. Since both, LamB and OmpF have similar three-dimensional structures, a few mutations in the viral gene fortuitously led to ability of J protein to bind to OmpF. Hence, one out of three functional pre-existing proteins gained the ability to bind a protein similar to one it normally binds, without any change in its native 3-dimensional structure. And this binding ability proves what exactly? That you can produce circulatory system, all joints and bones in your body, or complex, image-forming eyes 100 times independently with 10^43 changes in the spatial positions of molecules?It is really amazing how you evolution believers explain how everything could have happened: due to few mutations, the pre-existing protein can now stick to something it coud not before - therefore it is a fact that mutations produced all bio-structures that that we observe.
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forexhr Member (Idle past 2320 days) Posts: 129 Joined: |
dwise1 writes: BTW, my third bachelor's degree was in math. So if you think that I am so ignorant of basic mathematics, then do please explain what I got so wrong in my "MONKEY Probabilities" (MPROBS) document. I don't care about your bachelor's degree in math. You demonstrated ignorance of basic math when you stated that we cannot calculate the probability in biology. In that regard, you've had two options in responding to my post. You could have said A) "I was wrong", or B) BS-ing. I see you chose the latter. Why? Because you are dogmatic believer in evolution who would rather destroy its academic reputation than accept some argument against evolution. P.S. In your previous posts, you opposed my claim that in the real world the path toward the right solution is carried by random means. So, I am waiting. You have one practical problem to solve. I am not interested in your bachelor's degree and philosophy about MONKEY probabilities, but in your ability to sove biologically derived problem via evolutionary programming.
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New Cat's Eye Inactive Member |
It just talks about the pre-existing viral J protein that acquired the ability to bind a different protein on E. coli, called OmpF, once the LamB protein, to which it normally binds, was turned off. Since both, LamB and OmpF have similar three-dimensional structures, a few mutations in the viral gene fortuitously led to ability of J protein to bind to OmpF. Hence, one out of three functional pre-existing proteins gained the ability to bind a protein similar to one it normally binds, without any change in its native 3-dimensional structure. Holy shit, that's what we're screaming! So you do see that the whole protein doesn't have to be built from scratch, and that pre-existing structures can be slightly modified. That's why your math is wrong: it assumes each protein has to be built from scratch, whole cloth. That why you can convince exactly nobody that your argument has merit.
This paper has nothing to do with the issue at hand since it talks neither about evolution of new functional protein folds nor about the ratio of functional amino acid sequences versus the vast combinatorial space. Actually, as it turns out, that "issue at hand" has nothing to do with the Theory of Evolution. As has been explained to you ad nauseum.
And this binding ability proves what exactly? That you can produce circulatory system, all joints and bones in your body, or complex, image-forming eyes 100 times independently with 10^43 changes in the spatial positions of molecules? Well, that number is bullshit. And you're missing quite a few steps in the logic. But ultimately, yeah, that is a ramification.
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forexhr Member (Idle past 2320 days) Posts: 129 Joined: |
The ability to stick to something has nothing to do with biology. This is the general property of matter that even a pile of wet dirt has. Biology is characterised by the ability to have a proper 3D shape. Conformational complementarity of enzyme and substrate, fertilisation, editing of the nascent precursor messenger RNA transcript, synchronization of the kicks of the hopping insect's legs via gear mechanism, blood pumping, visual perception,.... all these functions in biology arise from the interactions of the proper 3D shapes. Non of the 3D shapes where changed in the experiment that produced those 4 mutations. "My math" is not wrong because it talks about the extraction of specific 3D shapes from particles, while most of your arguments and practical examples boils down to either - the ability to stick to something, or - circular reasoning.
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vimesey Member (Idle past 325 days) Posts: 1398 From: Birmingham, England Joined: |
all these functions in biology arise from the interactions of the proper 3D shapes. Non of the 3D shapes where changed in the experiment that produced those 4 mutations. Are you seriously expecting anyone, with even the most rudimentary understanding of evolution, to go along with such a blatantly simplistic attempt to discredit it ? To attempt to argue that evolution must be false, because it can't, in a few lab experiments, produce the dramatic physiological changes which took place when, say, a gill based respiratory system became lung based - when evolution incorporates, at its most fundamental level, billions of incremental changes over billions of years ? You will run away from this point, and try to hide behind mathematical symbols and scientific terminology, in order to try to impress your target audience. But with your above quotation, you have completely laid bare your utter misunderstanding of the field.Could there be any greater conceit, than for someone to believe that the universe has to be simple enough for them to be able to understand it ?
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