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On FSCO/I vs. Needles and Haystacks (as well as elephants in rooms)

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Sometimes, the very dismissiveness of hyperskeptical objections is their undoing, as in this case from TSZ:

Pesky EleP(T|H)ant

Over at Uncommon Descent KirosFocus repeats the same old bignum arguments as always. He seems to enjoy the ‘needle in a haystack’ metaphor, but I’d like to counter by asking how does he know he’s not searching for a needle in a needle stack? . . .

What had happened, is that on June 24th, I had posted a discussion here at UD on what Functionally Specific Complex Organisation and associated Information (FSCO/I) is about, including this summary infographic:

csi_defnInstead of addressing what this actually does, RTH of TSZ sought to strawmannise and rhetorically dismiss it by an allusion to the 2005 Dembski expression for Complex Specified Information, CSI:

χ = – log2[10^120 ·ϕS(T)·P(T|H)].

–> χ is “chi” and ϕ is “phi” (where, CSI exists if Chi > ~ 1)

. . . failing to understand — as did the sock-puppet Mathgrrrl [not to be confused with the Calculus prof who uses that improperly appropriated handle) — that by simply moving forward to the extraction of the information and threshold terms involved, this expression reduces as follows:

To simplify and build a more “practical” mathematical model, we note that information theory researchers Shannon and Hartley showed us how to measure information by changing probability into a log measure that allows pieces of information to add up naturally:

Ip = – log p, in bits if the base is 2. That is where the now familiar unit, the bit, comes from. Where we may observe from say — as just one of many examples of a standard result — Principles of Comm Systems, 2nd edn, Taub and Schilling (McGraw Hill, 1986), p. 512, Sect. 13.2:

Let us consider a communication system in which the allowable messages are m1, m2, . . ., with probabilities of occurrence p1, p2, . . . . Of course p1 + p2 + . . . = 1. Let the transmitter select message mk of probability pk; let us further assume that the receiver has correctly identified the message [[–> My nb: i.e. the a posteriori probability in my online discussion here is 1]. Then we shall say, by way of definition of the term information, that the system has communicated an amount of information Ik given by

I_k = (def) log_2  1/p_k   (13.2-1)

xxi: So, since 10^120 ~ 2^398, we may “boil down” the Dembski metric using some algebra — i.e. substituting and simplifying the three terms in order — as log(p*q*r) = log(p) + log(q ) + log(r) and log(1/p) = log (p):

Chi = – log2(2^398 * D2 * p), in bits,  and where also D2 = ϕS(T)
Chi = Ip – (398 + K2), where now: log2 (D2 ) = K
That is, chi is a metric of bits from a zone of interest, beyond a threshold of “sufficient complexity to not plausibly be the result of chance,”  (398 + K2).  So,
(a) since (398 + K2) tends to at most 500 bits on the gamut of our solar system [[our practical universe, for chemical interactions! ( . . . if you want , 1,000 bits would be a limit for the observable cosmos)] and
(b) as we can define and introduce a dummy variable for specificity, S, where
(c) S = 1 or 0 according as the observed configuration, E, is on objective analysis specific to a narrow and independently describable zone of interest, T:

Chi =  Ip*S – 500, in bits beyond a “complex enough” threshold

  • NB: If S = 0, this locks us at Chi = – 500; and, if Ip is less than 500 bits, Chi will be negative even if S is positive.
  • E.g.: a string of 501 coins tossed at random will have S = 0, but if the coins are arranged to spell out a message in English using the ASCII code [[notice independent specification of a narrow zone of possible configurations, T], Chi will — unsurprisingly — be positive.

explan_filter

  • S goes to 1 when we have objective grounds — to be explained case by case — to assign that value.
  • That is, we need to justify why we think the observed cases E come from a narrow zone of interest, T, that is independently describable, not just a list of members E1, E2, E3 . . . ; in short, we must have a reasonable criterion that allows us to build or recognise cases Ei from T, without resorting to an arbitrary list.
  • A string at random is a list with one member, but if we pick it as a password, it is now a zone with one member.  (Where also, a lottery, is a sort of inverse password game where we pay for the privilege; and where the complexity has to be carefully managed to make it winnable. )
  • An obvious example of such a zone T, is code symbol strings of a given length that work in a programme or communicate meaningful statements in a language based on its grammar, vocabulary etc. This paragraph is a case in point, which can be contrasted with typical random strings ( . . . 68gsdesnmyw . . . ) or repetitive ones ( . . . ftftftft . . . ); where we can also see by this case how such a case can enfold random and repetitive sub-strings.
  • Arguably — and of course this is hotly disputed — DNA protein and regulatory codes are another. Design theorists argue that the only observed adequate cause for such is a process of intelligently directed configuration, i.e. of  design, so we are justified in taking such a case as a reliable sign of such a cause having been at work. (Thus, the sign then counts as evidence pointing to a perhaps otherwise unknown designer having been at work.)
  • So also, to overthrow the design inference, a valid counter example would be needed, a case where blind mechanical necessity and/or blind chance produces such functionally specific, complex information. (Points xiv – xvi above outline why that will be hard indeed to come up with. There are literally billions of cases where FSCI is observed to come from design.)

xxii: So, we have some reason to suggest that if something, E, is based on specific information describable in a way that does not just quote E and requires at least 500 specific bits to store the specific information, then the most reasonable explanation for the cause of E is that it was designed. The metric may be directly applied to biological cases:

Using Durston’s Fits values — functionally specific bits — from his Table 1, to quantify I, so also  accepting functionality on specific sequences as showing specificity giving S = 1, we may apply the simplified Chi_500 metric of bits beyond the threshold:
RecA: 242 AA, 832 fits, Chi: 332 bits beyond
SecY: 342 AA, 688 fits, Chi: 188 bits beyond
Corona S2: 445 AA, 1285 fits, Chi: 785 bits beyond

Where, of course, there are many well known ways to obtain the information content of an entity, which automatically addresses the “how do you evaluate p(T|H)” issue. (As has been repeatedly pointed out, just insistently ignored in the rhetorical intent to seize upon a dismissive talking point.)

There is no elephant in the room.

Apart from . . . the usual one design objectors generally refuse to address, selective hyperskepticism.

But also, RTH imagines there is a whole field of needles, refusing to accept that many relevant complex entities are critically dependent on having the right parts, correctly arranged, coupled and organised in order to function.

That is, there are indeed empirically and analytically well founded narrow zones of functional configs in the space of possible configs. By far and away most of the ways in which the parts of a watch may be arranged — even leaving off the ever so many more ways they can be scattered across a planet or solar system– will not work.

The reality of narrow and recognisable zones T in large spaces W beyond the blind sampling capacity — that’s yet another concern — of a solar system of 10^57 atoms or an observed cosmos of 10^80 or so atoms and 10^17 s or so duration, is patent. (And if RTH wishes to dismiss this, let him show us observed cases of life spontaneously organising itself out of reasonable components, say soup cans. Or, of watches created by shaking parts in drums, or of recognisable English text strings of at least 72 characters being created through random text generation . . . which last is a simple case that is WLOG, as the infographic points out. As, 3D functional arrangements can be reduced to code strings, per AutoCAD etc.)

Finally, when the material issue is sampling, we do not need to generate grand probability calculations.

The proverbial needle in the haystack
The proverbial needle in the haystack

For, once we are reasonably confident that we are looking at deeply isolated zones in a field of possibilities, it is simple to show that unless a “search” is so “biased” as to be decidedly not random and decidedly not blind, only a blind sample on a scope sufficient to make it reasonably likely to catch zones T in the field W would be a plausible blind chance + mechanical necessity causal account.

But, 500 – 1,000 bits (a rather conservative threshold relative to what we see in just the genomes of life forms) of FSCO/I is (as the infographic shows) far more than enough to demolish that hope. For 500 bits, one can see that to give every one of the 10^57 atoms of our solar system a tray of 500 H/T coins tossed and inspected every 10^-14 s — a fast ionic reaction rate — would sample as one straw to a cubical haystack 1,000 LY across, about as thick as our galaxy’s central bulge. If such a haystack were superposed on our galactic neighbourhood and we were to take a blind, reasonably random one-straw sized sample it would with maximum likelihood be straw.

As in, empirically impossible, or if you insist, all but impossible.

 

It seems that objectors to design inferences on FSCO/I have been reduced to clutching at straws. END

Comments
KF Is it easy to commute between Antigua and Montserrat on ferryboats? What about Barbuda? If one goes there, is it better to stay in Antigua and use it as a base from where to tour the other islands too? Would the locals understand my heavy Hispanic accent? Is the best season to visit at the beginning of the hear? Probably the most expensive too? Thank you. P.S. sorry, just realized this post is very OT for the current thread, but I needed a mental break after trying to understand the FSCO/I and dFSCI concepts well. Imagining myself in one of those islands is quite a refreshing exercise ;-) Maybe that's why there are so many nice smiling people there? Probably a more layback 'don't worry, be happy' less agitated, slower paced, friendlier, less stressful lifestyle ? :) Except, as you said, for those few who have never understood your fellow Britton's old song "can't buy me love"? :(Dionisio
August 30, 2014
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Gpuccio, thank you for your conformation :) I hold that this 'balancing act' of organisms is highly underrated and utterly mysterious. How is it that organisms maintain dynamic equilibrium? The life of even one cell is a constant flux from one equilibrium into the next - an ever shifting context. Somehow 'equilibrium information' relative to 'whatever the context is' is available, but cannot be explained 'bottom-up'.Box
August 30, 2014
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KF I like the 'no ice' part of the story ;-)Dionisio
August 30, 2014
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For those interested in reading gpuccio's interesting explanation of the dFSCI concept, here are the post #s within this thread: 133, 140, 146, 149, 152.
That could be a separate OP, very related to this thread, but started from scratch.Dionisio
August 30, 2014
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For those interested in reading gpuccio's interesting explanation of the dFSCI concept, here are the post #s within this thread: 133, 140, 146, 149, 152.Dionisio
August 30, 2014
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Denton: >> To grasp the reality of life as it has been revealed by molecular biology, we must magnify a cell a thousand million times until it is twenty kilometers in diameter [[so each atom in it would be “the size of a tennis ball”] and resembles a giant airship large enough to cover a great city like London or New York. What we would then see would be an object of unparalleled complexity and adaptive design. On the surface of the cell we would see millions of openings, like the port holes of a vast space ship, opening and closing to allow a continual stream of materials to flow in and out. If we were to enter one of these openings we would find ourselves in a world of supreme technology and bewildering complexity. We would see endless highly organized corridors and conduits branching in every direction away from the perimeter of the cell, some leading to the central memory bank in the nucleus and others to assembly plants and processing units. The nucleus itself would be a vast spherical chamber more than a kilometer in diameter, resembling a geodesic dome inside of which we would see, all neatly stacked together in ordered arrays, the miles of coiled chains of the DNA molecules. A huge range of products and raw materials would shuttle along all the manifold conduits in a highly ordered fashion to and from all the various assembly plants in the outer regions of the cell. We would wonder at the level of control implicit in the movement of so many objects down so many seemingly endless conduits, all in perfect unison. We would see all around us, in every direction we looked, all sorts of robot-like machines . . . . We would see that nearly every feature of our own advanced machines had its analogue in the cell: artificial languages and their decoding systems, memory banks for information storage and retrieval, elegant control systems regulating the automated assembly of components, error fail-safe and proof-reading devices used for quality control, assembly processes involving the principle of prefabrication and modular construction . . . . However, it would be a factory which would have one capacity not equaled in any of our own most advanced machines, for it would be capable of replicating its entire structure within a matter of a few hours . . . . Unlike our own pseudo-automated assembly plants, where external controls are being continually applied, the cell's manufacturing capability is entirely self-regulated . . . . [[Denton, Michael, Evolution: A Theory in Crisis, Adler, 1986, pp. 327 – 331. This work is a classic that is still well worth reading. Also see Meyer's Signature in the Cell, 2009.] >>kairosfocus
August 30, 2014
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Box: You are perfectly right: I was holding back. My usual shy attitude! :) When Szostak used all his (remarkable) knowledge and understanding to build a new protein in a context of bottom up intelligent selection, pretending that it emerged randomly, the best he could do was to generate an artificial protein with a strong binding for ATP. When someone tried to put that protein in a real cellular context, the effect was devastating. So yes, I was holding back.gpuccio
August 30, 2014
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Gpuccio #149: And the important point is, the more a context is complex, the more difficult it is to integrate a new function in it, unless it is very complex. In a sense, for example, it is very unlikely that a single protein, even if it has a basic biochemical function, may be really useful in a biological context unless it is integrated in what already exists. That integration usually requires a lot of additional information: transciptional, post transcriptional and post translational regulation, transport and localization in the correct cellular context and, usually, coordination with other proteins or structures. IOWs, in most cases we would have an additional problem of irreducible complexity, which should be added to the basic complexity of the molecule.
The problem you raised here is in IMHO devastating for any attempt of naturalistic explanation of life. The most 'blessed' DNA mutation is dangerous without proper regulation (epigenetics). You go even further when you write: "(...) it is very unlikely that a single protein, even if it has a basic biochemical function, may be really useful in a biological context unless it is integrated in what already exists. "/ In fact, I believe you are holding back here. An improperly regulated protein, irrespective of its potential usefulness, is almost certainly detrimental to the organism. When we see the inner workings of an organism we stand in awe of the intricate display of balance and harmony.Box
August 30, 2014
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GP: Yes, adaptation to specific context is important. KF PS: I am happy to see you being able to take the lead in the main technical discussion in-thread. The local silly season has me struggling to try to keep or get eyes on the ball: it's the POLICY, stupid! Backed up by, if you do not have a sustainable, integrated framework of priority initiatives, a valid world class implementer-oriented project cycle management process and adequate implementing and expediting capacity to a timeline, you are dead, Fred. Now I understand what my dad was going through when he stood contra mundum for policy soundness decades ago. He was right but was brushed aside, that's why Jamaica is in the mess it is in. Oh, that we would learn the lesson of soundness in decision, instead of in regret . . .kairosfocus
August 30, 2014
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D: Good to hear of progress. And yes, the scenery is quite wonderful. Apart from those caught up in the deceitfulness of riches and the agendas of folly-tricks the people are even better, never mind the usual foibles. Best of all, no ice . . . except from the 'fridge! KFkairosfocus
August 30, 2014
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KF: Yes, I believe that part of what is not conserved in proteins can correspond to "information which changes because it must change for functional reasons", and not only to "information which changes because it is not essential to function". I have tried to make that distinction in one OP. For example, the same protein can have some sequence which differs among species, because it is regulatory, and it interacts differently in different species. I have observed something suggestive of that in very complex transcription factors. They are very long proteins, but only a small part of the sequence corresponds to known conserved domains. The rest is less understood, and less conserved. And you observe that trend exactly in the most complex and important regulators!gpuccio
August 30, 2014
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Gordon Davisson: Your next argument is the following:
But not all sequences are equally likely, because selection biases the search toward the sequence space near other successful solutions, and functional sequences seem to cluster together (e.g. gene families). Let’s say (again, all numbers made up for the sake of illustration) that this increases the “hit rate” for functional sequences by a factor of 10^100. That means that while functional sequences make up only 1/2^200 of the sequence space, evolution stumbles into one every 1/2^100 or so “tries”.
No. This argument is simply wrong if you consider my premises. My aim is not to say that all proteins are designed. My aim is to make a design inference for some (indeed, many) proteins. I have already said that I consider differentiation of individual proteins inside a superfamily/family as a "borderline" issue. It has no priority. The priority is, definitely, to explain how new sequences emerge. That's why I consider superfamilies. Proteins from different superfamilies are completely unrelated at sequence level. Therefore, your argument is indeed in favor of my reasoning. As I have said many times, assuming an uniform distribution is reasonable, but is indeed optimistic in favor of the neo darwinian model. There is no doubt that related or partially related states have higher probability of being reached in a random walk. Therefore, their probability is higher that 1/N. That also means, obviously, that the probability of reaching an unrelated state is certainly lower than 1/N, which is the probability of each state in a uniform distribution. For considerations similar to some that I have already done (the number of related states is certainly much smaller than the number of unrelated states), I don't believe that the difference is significant. However, 1/N is a higher threshold for the probability of reaching an unrelated state, which is what the dFSCI of a protein family or superfamily is measuring. Then you say:
Or rather, it would be the calculation I was talking about if it had real numbers, rather than just made-up-out-of-thin-air ones. And I have no idea what the real numbers are. I don’t think there’s any way to get a good handle on them until we know far more about the large-scale shape of the fitness function (i.e. the mapping between sequence and function) than we do now. But if you want to do a probability argument, you pretty much need them.
As I have tried to show, that is not the case. dFSCI is a tool which works perfectly even if it is defined for a specific function. The number of really useful functions, that can be naturally selected in a specific cellular context, is certainly smnall enough that it can be overlooked. Indeed, as we are speaking of logarithmic values, even if we considered the only empirical number that we have: 2000 protein superfamilies that have a definite role in all biological life as we know it today, that is only 11 bits. How can you think that it matters, when we are computing dFSCI in the order of 150 to thousands of bits? Moreover, even if we consider the probabiliti of finding one of the 2000 superfamilies in one attempt, the mean functional complexity in the 35 families studied by Durston is 543 bits. How do you think that 11 bits more or less would count? And there is another important point which is often overlooked. 543 bits (mean complexity) means that we have 1:2^543 probabilities to find one superfamily in one attempt, which is already well beyond my cutoff of 150 bits, and also beyond Dembski's UPB of 520 bits. But the problem is, biological beings have not found one protein superfamily once. They have found 2000 independent protein superfamilies, each with a mean probability of being found of 1:2^543. Do you want to use the binomial distribution to compute the probability of having 2000 successes of that kind? Now, some of the simplest families could have been found, perhaps. The lowest value of complexity in Durston's table is 46 bits (about 10 AAs). It is below my threshold of 150 bits, so I would not infer design for that family (Ankyrin). However, 10 AAs are certainly above the empirical thresholds suggested by Behe and Axe, from different considerations. But what about Paramyx RNA Polymerase (1886 bits), or Flu PB2 (2416 bits), or Usher (1296 bits)? If your reason of "aggregating" all useful functional proteins worked, we should at most find a few examples of the simplest ones, which are much more likely to be found, and not hundreds of complex ones, which is what we observe. More in next post.gpuccio
August 30, 2014
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GP: It is indeed true that function is inter alia specific to context. The wrong part, will not work on a given car even if it will work fine on another. And if the right part is not properly oriented and placed then coupled, trouble. KFkairosfocus
August 30, 2014
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KF Doing much better, though still a little swollen left side of face, but almost unnoticeable. Thank you for asking! BTW, was looking at some maps of the islands Antigua and Montserrat. Pretty spectacular scenery in that area.Dionisio
August 30, 2014
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Gordon Davisson: Some further thoughts on the argument of "any possible function", continuing from my previous post. b) Another big problem is that the "any possible function" argument is not really true. Even if we want to reason in that sense (which, as explained in my point a, is not really warranted), we should at most consider "any possible function which is really useful in the specific context in which it arises". And the important point is, the more a context is complex, the more difficult it is to integrate a new function in it, unless it is very complex. In a sense, for example, it is very unlikely that a single protein, even if it has a basic biochemical function, may be really useful in a biological context unless it is integrated in what already exists. That integration usually requires a lot of additional information: transciptional, post transcriptional and post translational regulation, transport and localization in the correct cellular context and, usually, coordination with other proteins or structures. IOWs, in most cases we would have an additional problem of irreducible complexity, which should be added to the basic complexity of the molecule. Moreover, in a beings which is already efficient (think of prokaryotes, practically the most efficient reproductors in the whole history of our planet), it is not likely at all that a single new biochemical function can really help the cell. That brings us to the following point: c) Even the subset of useful new functions in the context is probably too big. Indeed, as we will discuss better later, if the neo darwinian model were true, the only functions which are truly useful would be those whihc can confer a detectable reproductive advantage. IOWs, those which are "visible" to NS. Even if we do not consider, for the moment, the hypothetical role of naturally selectable intermediates (we will do that later), still a new single functional protein which is useful, but does not confer a detectable reproductive advantage would very likely be lost, because it could not be expanded by positive selection (be fixed in the population) nor be conserved by negative selection. So, even if we reason about "any possible function", that should become "any possible function which can be so useful in the specific cellular context in which it arises, that it can confer a detectable, naturally selectable reproductive advantage. That is certainly a much smaller subset than "any possible function". Are you sure that 2^50 is still a reasonable guess? After all we have got only about 2000 basic protein superfamilies in the course of natural history. Do you think that we have only "scratched the surface" of the space of possible useful protein configurations in our biological context? And how do you explain that about half of those superfamilies were already present in LUCA, and that the rate of appearance of new superfamilies has definitely slowed down with time? d) Finally, your observation about the "many different ways that a gene might perform any of these functions". You give the example of different types of flagella. But flagella are complex structures made of many different parts, and again a very strong problem of irreducible complexity applies. Moreover, as I have said, I have never tried to compute dFSCI for such complex structures (OK, I have given the example of the alpha-beta part of ATP synthase, but that is really a single structure that is part of a single multi-chain protein). That's the reason why I compute dFSCI preferably for single proteins, with a clear biochemical function. If an enzyme is conserved, we can assume that the specific sequence is necessary for the enzymatic reaction, and not for other things. And, in general, that biochemical reaction will be performed only by that structure in the proteome (with some exceptions). The synthesis of ATP from a proton gradient is accomplished by ATP synthase. That is very different from saying, for example, that flight can be accomplished by many different types of wings. More in next post.gpuccio
August 30, 2014
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D: How's progress? KFkairosfocus
August 30, 2014
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GP: Very well put. Mung: snappy as usual. Joe: On the money, too. GD: GA's are in effect hill climbers within islands of function with well behaved fitness functions -- by very careful design. The FSCO/I challenge is to find islands of function that are deeply isolated in seas of non-function, with relatively extremely limited resources. That's why the 500 - 1,000 bit threshold is important, and it is why the contrast between blind, chance and necessity search and intelligent injection of active information is also important. KFkairosfocus
August 30, 2014
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Gordon Davisson: Now, let's go to your arguments, in detail. You say:
Let me give a simple (and completely made-up) example to illustrate the issue. Suppose we had a 750-base-pair gene (I know, pretty small, but as I said this is a simple example), meaning its total information content was 1500 bits. Let’s say that 250 of those bases are fully required for the gene’s function, and the other 500 could be anything without changing its function. That means we should get around 500 Fits for this gene (assuming the sequence space is fully explored etc). But that doesn’t mean that the probability of something like this evolving is 1 in 2^500, because there are other factors you need to take into account.
OK. I will follow your example, but with the further specification that the scenario is the one I have suggested in my premises: a new protein, of a new superfamily, with new domain (or domains), IOWs a new functional sequence, unrelated to previously existing proteins, which emerges in a definite time span in the course of natural history. And which has a specific biochemical activity, possibly a new enzymatic activity, well defined and measurable in the lab. You go on:
First, there are many different functions that a gene might have, and we’re only looking at the probability of that particular one evolving. Let’s say, for the sake of illustration, that there are 2^50 possible functions that a gene might perform. Also, there are likely to be many different ways that a gene might perform any of these functions. I’m not talking about minor sequence variation (Durston’s method accounts for those), but e.g. the difference between the bacterial and archaeal flagella — very different structures, but essentially the same function. Again for the sake of illustration, let’s say there are 2^250 possible structures corresponding to each of those functions (and to oversimplify even further, assume each has the same degree of functional restriction, i.e. the same Fits). That means that while each possible gene’s “functional island” corresponds to only 1/2^500 of the sequence space, a total of 1/2^200 of the sequence space corresponds to some function.
OK. This is a very common objection, but I must say that you put it very well, very concretely. I will try to explain, as I have already made in past occasions, why that is not really a problem, even if the argument has certainly some relevance. I usually call this objection the "any possible function" argument. In brief, it says that it is wrong to compute the probability of a specific function (which is what dFSCI does, because dFSCI is specific for a defined function), when a lot of other functional gens could arise. IOWs, the true subset of which we should compute the probability is the subset of all functional genes, which is much more difficult to define. You add the further argument that the same gen can have many functions. That would complicates the computation even more, because, as I have said many times, dFSCI is computed for a specific function, explicitly defined, and not for all the possible functions of the observed object (the gene). I don't agree that these objections, however reasonable, are relevant. For many reasons, that I will try to explain here. a) First of all, we must remember that the concept of dFSCI, before we apply it to biology, comes out as a tool to detect human design. Well, as I have tried to explain, dFSCI is defined for a specific function, not for all possible functions, and not for the object. IOWs, it is the complexity linked to the explicitly defined function. And yet, it can detect human design with 100% specificity. So, when we apply it to biological context, we can reasonably expect a similar behaviour and specificity. This is the empirical observation. But why does that happen? Why doesn't dFSCI fail miserably in detecting human design? Why doesn't it give a lot of false positives, if the existence of so many possible functions in general, and of so many possible functions fro the same object, should be considered a potential hindrance to its specificity? The explanation is simple, and it is similar to the reason why the second law of thermodinamics works. The simple fact is, if the ration between specified states and non specified states is really low, no specified state will ever be observed. Indeed, no ordered state is ever observed in the molecules of a gas even if there are potentially a lot of ordered states. The subset of ordered states is however trivial if compared to the subset of non ordered states. That's exactly the reason why dFSCI, if we use an appropriate threshold of complexity, can detect human design with 100% specificity. The number of functionally specified states are simply too rare, is the total search space is big enough. I will give an example with language. If we take one of Shakespeare's sonnets, we are absolutely confident that it was designed, even if after all it is not a very long composition, and even if we don't make the necessary computations of its dFSCI. And yet, we could reason that there are a lot of sequences of characters of the same length which have meaning in english, and would be specified just the same. And we could reason that there are certainly a lot of other sequences of characters of the same length which have meaning in other known languages. And certainly a lot of sequences of characters of the same length which have meaning in possible languages that we don't know. And that the same sequence, in principle, could have different meanings in other unknown languages, on other planets, and so on. Does any of those reasonings lower our empirical certainty that the sonnet was designed? Not at all. Why? Because it is simply too unlikely that such a specific sequence of characters, with such a specific, and beautiful meaning in English, could arise in a random system, even if given a lot of probabilistic resources. And how big is the search space here? My favourite one, n. 76, is 582 characters long, including spaces. Considering an alphabet of about 30 characters, the search space, if I am not wrong, should be of 2800 bits. And this is the search space, not the dFSCI. If we define the function as "any sequence which has good meaning in English", the dFSCI is certainly much lower. As I have argued, the minimal dFSCI of ATP synthase alpha+beta subunit is about 1600 bits. Its search space if about 4500 bits, much higher than the Shakespeare sonnet's search space. So, why should we doubt that ATPsyntase alpha+beta subunit was designed? For lack of time, I will discuss the other reasons against this argument, and the other arguments, in the following posts. By the way, here is Shakespeare's sonnet n. 76, for the enjoyment of all! Why is my verse so barren of new pride, So far from variation or quick change? Why with the time do I not glance aside To new-found methods, and to compounds strange? Why write I still all one, ever the same, And keep invention in a noted weed, That every word doth almost tell my name, Showing their birth, and where they did proceed? O! know sweet love I always write of you, And you and love are still my argument; So all my best is dressing old words new, Spending again what is already spent: For as the sun is daily new and old, So is my love still telling what is told.gpuccio
August 30, 2014
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Gordon Davisson:
This part is just an assumption on my part, based on Occam’s razor and what seems to me to be a lack of evidence for intelligent assistance.)
I don't think that's a legitimate appeal to Occam's razor.
Let me give a quick sketch of my theoretical argument that this sort of information can come from unintelligent sources. Basically, it comes from the success of genetic algorithms: they show that random variation + selection for function can produce functional information.
So your best evidence for unintelligent design comes from programs that are intelligently designed? That seems just a tad odd.Mung
August 29, 2014
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For gpuccio- FYI only With genetic engineering scientists have taken the protein coding sequence from one organism and transferred that to another. Only in rare cases did the newly implanted sequence produce a functioning protein, eg insulin. Most times the polypeptide was transcribed and translated but it failed to fold, meaning it was totally useless. Semonti discusses this in his book. Then there are prions which change the shape of a protein just by contact. If the sequence of AAs produced the shape that should not happen, yet it does. Sermonti discusses that also. As for monogenic disease, well sure, especially if the change is in the active site of the protein. It should only affect folding if the protein is small enough to fold without a chaperone. But anyway, just food for thought and not an attempt to argue...Joe
August 29, 2014
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rich:
I think both CSI and FSCO/I are flawed. This is evidenced by the fact that no-one is using them for design detection.
CORRECTION- No evolutionary biologists and no materialists are using them. But that is because they say design is accounted for by nature. However I will note that not one evolutionary biologist nor materialist has any evidence for their claims nor do they have a methodology to test their claims.
FSCO/I seems to only argue against spontaneous assembly,...
Spontaneous just means without a designer. It doesn't mean "instantaneous".Joe
August 29, 2014
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gpuccio, I don't expect my comments to be responded to, unless they pose serious questions, which is not the case this time. In some cases I repost parts of your comments, that I like so much, that I want to ensure others read them too. I'd rather see you using your limited spare time (a) working on your upcoming OP, (b) commenting on the difficult subjects being discussed here and (c) responding to the posts written by the interlocutors. Others -including myself and many anonymous visiting onlookers- can learn from your very insightful posts. Mile grazie caro amico Dottore!Dionisio
August 29, 2014
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KF, Dionisio: Thank you for your attention and contributions. As you can see, for the moment I am fully focused on answering Gordon Davisson's arguments, but be sure that I really appreciate your interventions! :)gpuccio
August 29, 2014
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Gordon Davisson: OK, now let's go to biology at last. Biological objects are the issue: were they designed (as has been believed for centuries, and many still believe) or do they only appear to be designed, but can be reasonably explained by some RV + NS algorithm? That many biological objects exhibit a strong appearance of design is rather obvious: even Dawkins admits that. Now, as we have developed a formal property which works so well for human designed things (at least in the sense of specificity), it is perfectly natural to apply it to biological objects. As our tool, dFSCI, is easily applied to digital sequences, the natural objects to apply it are certainly genes, and in particular protein coding genes. We can work with the gene or (as I usually do) with the corresponding protein sequence. There is not a great difference. I will take for granted, for the moment, that dFSCI can be approximated for a specific protein family by the Durston method. You seem to accept that, so I will go on fro the moment to address your specific objections. But, here again, I need a few premises about the scenario I will consider: a) My aim is not (and never has been) to demonstrate that all existing proteins exhibit dFSCI, and therefore must be considered designed. First of all, some short peptides can be so simple that a design inference cannot be made for them. Second, I have often stated that variation in the active site in a protein family, even with great differences on the final function, can be in principle compatible with the neo darwinian model. That could be the case, for example, for nylonase. In these cases, the transition is rather simple (a few aminoacids), and it would not be categorized as dFSCI. There are a lot of transitions, in protein families, which are somewhere in the middle. They could be considered as borderline cases, in which it is difficult at present to make a judgement. My aim is, definitely, to show that there are some proteins (indeed, a lot of them) for which a design inference is at present, by far, the best explanation. b) For the above reasons, I will focus on a very definite scenario: the emergence in natural history of a new protein superfamily, with an original sequence and a new biochemical function. As there are about 2000 superfamilies, all of them quite unrelated at sequence level, all of them with different functions, there is a great abundance of cases to be considered. c) Another important note is that I will define as "function" what I call "the local function", that is the specific biochemical activity that characterizes the proteins. So, I will deal with proteins which have a very clear functional biochemical activity, such as enzymes, or ATP synthase, rather than with proteins with a regulatory function, or which work in a more complex setting, like a protein cascade. IOWs, I will not deal, for now, with cases of irreducible complexity: just single proteins, with a clear local function, which can be well defined and characterized. Enzymes are a very good example: an enzyme is a wonderful machine, which realizes a "biochemical miracle": a reaction which would never occur (or would occur too slowly to be useful) in the absence of the enzyme itself. An enzymatic activity can be well defined, measure in the lab in appropriate contexts, and a threshold of activity can easily be established. All that is of great help for our discussion. d) Finally, I will consider as possible non design explanation only the neo darwinian model: RV + NS. Why? Because I am not aware of any other non design explanation on the market. All other "explanations" (beginning with neutral theory) don't explain anything, at least not anything pertinent to our problem (how functional information emerges against probabilistic barriers). So, as I see it, the game, at present, is between two explanations only: design and neo darwinism. However, I am fully available to consider any other non design explanation: I just don't know any such thing.gpuccio
August 29, 2014
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gpuccio
There are obviously, [...], ” big messy unknowns”, but they are in the whole issue of biological information and of how it emerged. They are not in my formula or in my reasoning, they are in all formulas and in all reasonings about the issue, because the issue is much more complex than we can imagine, even with all that we know, and that grows daily.
Agree. This is a very good quotable declaration. :)Dionisio
August 29, 2014
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GP: I suggest referring to the string data structure: . . . -*-*-*-*- . . . This allows us to see how digitally coded info is normally laid out, whether bits, hex code, octal code, decimal digits, sexagesimal ones [why 60 mins in the hour etc . . . ask the Babylonians!], alphabetic glyphs, or ASCII or even UNICODE, etc. Where D/RNA and Amino Acid chains are also capable of bearing such info. I add, as 3-D functionally organised entities may be represented by using coded strings (cf. AutoCAD etc), this is WLOG. KFkairosfocus
August 29, 2014
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gpuccio
I would like to remind here that ID is trying, at least, to offer a quantitative approach to the problem of probabilities in biological evolution. That is not only the duty of ID: it is the duty of anyone who is interested in the problem, most of all the duty of those who believe that the neo darwinian model is a good solution.
Does this apply to 'the third way' folks too? ;-)Dionisio
August 29, 2014
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gpuccio
What I mean is that, for me, “The jury will always be out”.
Unending revelation of the ultimate reality :)Dionisio
August 29, 2014
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gpuccio
What I mean is that, form me, “The jury will always be out”.
Agree, although that's a challenging statement. :O P.S. I encounter the same issue with the type-ahead feature, which keeps rewriting some words in my comments, and many times I don't notice the change until after I have posted the text. Pretty annoying sometimes.Dionisio
August 29, 2014
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Dionisio: Yes, that was "leave", definitely. Thank you for the correction. :)gpuccio
August 29, 2014
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