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On the non-evolution of Irreducible Complexity – How Arthur Hunt Fails To Refute Behe

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I do enjoy reading ID’s most vehement critics, both in formal publications (such as books and papers) and on the, somewhat less formal, Internet blogosphere. Part of the reason for this is that it gives one something of a re-assurance to observe the vacuous nature of many of the critics’ attempted rebuttals to the challenge offered to neo-Darwinism by ID, and the attempted compensation of its sheer lack of explicative power by the religious ferocity of the associated rhetoric (to paraphrase Lynn Margulis). The prevalent pretense that the causal sufficiency of neo-Darwinism is an open-and-shut case (when no such open-and-shut case for the affirmative exists) never ceases to amuse me.

One such forum where esteemed critics lurk is the Panda’s Thumb blog. A website devoted to holding the Darwinian fort, and one endorsed by the National Center for Selling Evolution Science Education (NCSE). Since many of the Darwinian heavy guns blog for this website, we can conclude that, if consistently demonstrably faulty arguments are common play, the front-line Darwinism defense lobby is in deep water.

Recently, someone referred me to two articles (one, two) on the Panda’s Thumb website (from back in 2007), by Arthur Hunt (professor in Department of Plant and Soil Sciences at the University of Kentucky). The first is entitled “On the evolution of Irreducible Complexity”; the second, “Reality 1, Behe 0” (the latter posted shortly following the publication of Behe’s second book, The Edge of Evolution).

The articles purport to refute Michael Behe’s notion of irreducible complexity. But, as I intend to show here, they do nothing of the kind!

In his first article, Hunt begins,

There has been a spate of interest in the blogosphere recently in the matter of protein evolution, and in particular the proposition that new protein function can evolve. Nick Matzke summarized a review (reference 1) on the subject here. Briefly, the various mechanisms discussed in the review include exon shuffling, gene duplication, retroposition, recruitment of mobile element sequences, lateral gene transfer, gene fusion, and de novo origination. Of all of these, the mechanism that received the least attention was the last – the de novo appearance of new protein-coding genes basically “from scratch”. A few examples are mentioned (such as antifreeze proteins, or AFGPs), and long-time followers of ev/cre discussions will recognize the players. However, what I would argue is the most impressive of such examples is not mentioned by Long et al. (1).

There is no need to discuss the cited Long et al. (2003) paper in any great detail here, as this has already been done by Casey Luskin here (see also Luskin’s further discussion of Anti-Freeze evolution here), and I wish to concern myself with the central element of Hunt’s argument.

Hunt continues,

Below the fold, I will describe an example of de novo appearance of a new protein-coding gene that should open one’s eyes as to the reach of evolutionary processes. To get readers to actually read below the fold, I’ll summarize – what we will learn of is a protein that is not merely a “simple” binding protein, or one with some novel physicochemical properties (like the AFGPs), but rather a gated ion channel. Specifically, a multimeric complex that: 1. permits passage of ions through membranes; 2. and binds a “trigger” that causes the gate to open (from what is otherwise a “closed” state). Recalling that Behe, in Darwin’s Black Box, explicitly calls gated ion channels IC systems, what the following amounts to is an example of the de novo appearance of a multifunctional, IC system.

Hunt is making big promises. But does he deliver? Let me briefly summarise the jist of Hunt’s argument, and then briefly weigh in on it.

The cornerstone of Hunt’s argument is principally concerned with the gene, T-urf13, which, contra Behe’s delineated ‘edge’ of evolution, is supposedly a de novo mitochondrial gene that very quickly evolved from other genes which specified rRNA, in addition to some non-coding DNA elements. The gene specifies a transmembrane protein, which aids in facilitating the passage of hydrophilic molecules across the mitochondrial membrane in maize – opening only when bound on the exterior by particular molecules.

The protein is specific to the mitochondria of maize with Texas male-sterile cytoplasm, and has also been implicated in causing male sterility and sensitivity to T-cytoplasm-specific fungal diseases. Two parts of the T-urf13 gene are homologous to other parts in the maize genome, with a further component being of unknown origin. Hunt maintains that this proves that this gene evolved by Darwinian-like means.

Hunt further maintains that the T-urf13 consists of at least three “CCCs” (recall Behe’s argument advanced in The Edge of Evolution that a double “CCC” is unlikely to be feasible by a Darwinian pathway). Two of these “CCCs”, Hunt argues, come from the binding of each subunit to at minimum two other subunits in order to form the heteromeric complex in the membrane. This entails that each respective subunit have at minimum two protein-binding sites.

Hunt argues for the presence of yet another “CCC”:

[T]he ion channel is gated. It binds a polyketide toxin, and the consequence is an opening of the channel. This is a third binding site. This is not another protein binding site, and I rather suppose that Behe would argue that this isn’t relevant to the Edge of Evolution. But the notion of a “CCC” derives from consideration of changes in a transporter (PfCRT) that alter the interaction with chloroquine; toxin binding by T-urf13 is quite analogous to the interaction between PfCRT and chloroquine. Thus, this third function of T-urf13 is akin to yet another “CCC”.

He also notes that,

It turns out that T-urf13 is a membrane protein, and in membranes it forms oligomeric structures (I am not sure if the stoichiometries have been firmly established, but that it is oligomeric is not in question). This is the first biochemical trait I would ask readers to file away – this protein is capable of protein-protein interactions, between like subunits. This means that the T-urf13 polypeptide must possess interfaces that mediate protein-protein interactions. (Readers may recall Behe and Snokes, who argued that such interfaces are very unlikely to occur by chance.)

[Note: The Behe & Snoke (2004) paper is available here, and their response (2005) to Michael Lynch’s critique is available here.]

Hunt tells us that “the protein dubbed T-urf13 had evolved, in one fell swoop by random shuffling of the maize mitochondrial genome.” If three CCC’s really evolved in “one fell swoop” by specific but random mutations, then Behe’s argument is in trouble. But does any of the research described by Hunt make any progress with regards to demonstrating that this is even plausible? Short answer: no.

Hunt does have a go of guesstimating the probabilistic plausibility of such an event of neo-functionalisation taking place. He tells us, “The bottom line – T-urf13 consists of at least three ‘CCCs’. Running some numbers, we can guesstimate that T-urf13 would need about 10^60 events of some sort in order to occur.”

Look at what Hunt concludes:

Now, recall that we are talking about, not one, but a minimum of three CCC’s. Behe says 1 in 10^60, what actually happened occurred in a total event size of less that 10^30. Obviously, Behe has badly mis-estimated the “Edge of Evolution”. Briefly stated, his “Edge of Evolution” is wrong. [Emphasis in original]

Readers trained in basic logic will take quick note of the circularity involved in this argumentation. Does Hunt offer any evidence that T-urf13 could have plausibly evolved by a Darwinian-type mechanism? No, he doesn’t. In fact, he casually dismisses the mathematics which refutes his whole argument. Here we have a system with a minimum of three CCCs, and since he presupposes as an a priori principle that it must have a Darwinian explanation, this apparently refutes Behe’s argument! This is truly astonishing argumentation. Yes, certain parts of the gene have known homologous counterparts. But, at most, that demonstrates common descent (and even that conclusion is dubious). But a demonstration of homology, or common ancestral derivation, or a progression of forms is not, in and of itself, a causal explanation. Behe himself noted in Darwin’s Black Box, “Although useful for determining lines of descent … comparing sequences cannot show how a complex biochemical system achieved its function—the question that most concerns us in this book.” Since Behe already maintains that all life is derivative of a common ancestor, a demonstration of biochemical or molecular homology is not likely to impress him greatly.

How, then, might Hunt and others successfully show Behe to be wrong about evolution? It’s very simple: show that adequate probabilistic resources existed to facilitate the plausible origin of these types of multi-component-dependent systems. If, indeed, it is the case that each fitness peak lies separated by more than a few specific mutations, it remains difficult to envision how the Darwinian mechanism might adequately facilitate the transition from one peak to another within any reasonable time frame. Douglas Axe, of the biologic institute, showed in one recent paper in the journal Bio-complexity that the model of gene duplication and recruitment only works if very few changes are required to acquire novel selectable utility or neo-functionalisation. If a duplicated gene is neutral (in terms of its cost to the organism), then the  maximum number of mutations that a novel innovation in a bacterial population can require is up to six. If the duplicated gene has a slightly negative fitness cost, the maximum number drops to two or fewer (not inclusive of the duplication itself). One other study, published in Nature in 2001 by Keefe & Szostak, documented that more than a million million random sequences were required in order to stumble upon a functioning ATP-binding protein, a protein substantially smaller than the transmembrane protein specified by the gene, T-urf13. Douglas Axe has also documented (2004), in the Journal of Molecular Biology, the prohibitive rarity of functional enzymatic binding domains with respect to the vast sea of combinatorial sequence space in a 150 amino-acid long residue (Beta-Lactamase).

What, then, can we conclude? Contrary to his claims, Hunt has failed to provide a detailed and rigorous account of the origin of T-urf13. Hunt also supplies no mathematical demonstration that the de novo origin of such genes is sufficiently probable that it might be justifiably attributed to an unguided or random process, nor does he provide a demonstration that a step-wise pathway exists where novel utility is conferred at every step (being separated by not more than one or two mutations) along the way prior to the emergence of the T-urf13 gene.

The Panda’s Thumb are really going to have to do better than this if they hope to refute Behe!

Comments
Indium, "This is a strange conversation, almost bizarre." Okay, I admit to it ... I was the prompter of the bizarre. :) I am just bizarre enough to realize that the symbols under discussion, changing in sequence as they must, mean absolutely nothing (mathematically or otherwise) without them being mapped to the objects they represent. Without those associations, which cannot be taken for granted under any rigorous examination, the entire issue falls apart and becomes moot. That includes the calculations she is attempting to make. After seeing that Mathgrrl had already been given the calculations she sought, I was prepared to bring this fact (as in "empirical reality") to her fleeting attention, but she referenced her Darwinian Flowchart and has since responded that such empirical realities are not important. I thought they were. My mistake.Upright BiPed
March 10, 2011
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This is a strange conversation, almost bizarre. I think it´s very easy to show Mathgrrl a few examples of CSI, so why don´t you just do it? Before it starts to look as if there would not be some easy calculations I will better go ahead. So, Mathgrrl, if you persist and continue to ask for calculations at some point you will probably be told something like this: 1AFOK1HI917ZHG0LQBMNHJI4FGHE67HZ82HJT5RT8U54FV How would you want to elvolve this? Impossible. And still it is the WPA2 password of my wireless network. It therefore has digital specified functional complex information (DSFCI) which is easily above the probability threshold. No evolutionary algorithm would be able to compute this code. (in a reasonable time, say 6000 years). At this point you will probably answer that replicating organisms don´t have to match a specific key. KF will tell you that life sits on sparse islands of functionality, which you will dispute based on papers which show binding action in random libraries of proteins. Slowly the discussion will move away from the original question. Be prepared to be asked where the proteins come from and what´s up with the fine tuning of our universe.Indium
March 10, 2011
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vjtorley,
Please see the following papers: New Peer-Reviewed Paper Challenges Darwinian Evolution by Jonathan M. Does Gene Duplication Perform As Advertised? by Jonathan M.
I'm afraid your response doesn't address the issue. What, specifically, is wrong with the specification I provide for the gene duplication scenario? If there is nothing wrong with the specification, what is the exact definition of CSI that precludes it from being generated by such an event?
On ev, please see: Evolutionary Synthesis of Nand Logic: Dissecting a Digital Organism by Winston Ewert, William A. Dembski and Robert J. Marks II.
That paper is actually about Avida, a different GA platform. In any case, none of the excerpts you provide address the core questions, namely: Can GAs in principle generate CSI? If so, how can we measure it? If not, what is the definition of CSI that precludes it?
Regarding Tierra, I suggest you re-read PaV’s original comment at https://uncommondescent.com.....ent-366926
That comment does not address the core questions either.
inally, I suggest you have a look at the mathematically rigorous paper, Life’s Conservation Law: Why Darwinian Evolution Cannot Create Biological Information by William A. Dembski and Robert J. Marks II.
I've read that paper, but it isn't pertinent to this conversation. The claim from ID proponents is that CSI is an indicator of intelligent agency. I would like to test this claim. To do so, I need a rigorous mathematical definition of CSI and some examples of how to calculate it. That information has been surpisingly difficult to obtain. References like these are interesting, but do not address my core questions.
Are you claiming that Abel’s functional sequence complexity metric is a reliable indicator of the involvement of intelligent agency?
Yes.
Thank you for the direct response. If a few other ID proponents agree with you, I'll look into testing that claim. In the meantime I'll continue to focus on CSI since that is the more widely recognized metric. I hope you can understand that I'm hesitant to spend much time on analyzing Abel's metric if it is possible that after testing it my results will be dismissed with "That's not what we mean by CSI."
I strongly suggest you read the paper, Life’s Conservation Law: Why Darwinian Evolution Cannot Create Biological Information by William A. Dembski and Robert J. Marks II
I've read it. I would be happy to discuss the issues I have with it once we've reached some resolution on the CSI issue. Unfortunately, I don't have an unlimited amount of time for discussions such as these.MathGrrl
March 10, 2011
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CJYman, Thank you for your very detailed response. I read through the threads on the links you provided (hence my delay in replying). I found the "What is intelligence?" UD thread a bit frustrating, since it petered out just as the questions were getting interesting. Based on your calculations for titin, it seems to me that your calculation of CSI suffers from a similar problem as does that suggested by kairosfocus (two raised to the power of the length of the artifact). As I said in my response to him, if this is your definiition of CSI, known evolutionary mechanisms are demonstrably capable of generating it in both real and simulated environments. I'll repeat the rest of that response for convenience here: [begin repetition] Consider the specification of "Produces X amount of protein Y." A simple gene duplication, even without subsequent modification of the duplicate, can increase production from less than X to greater than X. By your definition, CSI has been generated by a known, observed evolutionary mechanism with no intelligent agency involved. Schneider's ev uses the specification of "A nucleotide that binds to exactly N sites within the genome." Using only simplified forms of known, observed evolutionary mechanisms, ev routinely evolves genomes that meet the specification. The length of the genome required to meet this specification can be quite long, depending on the value of N. By your definition, CSI has been generated by those mechanisms. (ev is particularly interesting because it is based directly on Schneider's PhD work with real biological organisms.) Ray's Tierra routinely results in digital organisms with a number of specifications. One I find interesting is "Acts as a parasite on other digital organisms in the simulation." The length of the shortest parasite is at least 22 bytes. By your definition, CSI has been generated by known, observed evolutionary mechanisms with no intelligent agency required. The Steiner Problem solutions described at the site linked above use the specification "Computes a close approximation to the shortest connected path between a set of points." The length of the genomes required to meet this specification depends on the number of points, but can certainly be hundreds of bits. By your definition, these GAs generate CSI via known, observed evolutionary mechanisms with no intelligent agency required. [end repitition] Could you help me to understand how to calculate CSI by taking me through how you would do so for each of these four scenarios? I appreciate your time.MathGrrl
March 10, 2011
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Virtually all of the recent descriptions of CSI added to this conversation were already covered (in one way or another) by KF earlier in the thread. The indulgence on display here is is not about definitions. This should be obvious to anyone.Upright BiPed
March 9, 2011
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P.S. The question marks in front of log2 in my previous post are minus signs. I don't know why they turned out like that. Sorry.vjtorley
March 9, 2011
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MathGrrl (#246) Thank you for providing a reference (#177) to the four scenarios whereby known evolutionary mechanisms allegedly generate information in real and simulated environments. The four mechanisms you propose are: gene duplication leading to increased protein production, ev evolving binding sites, Tierra evolving parasites, and Genetic Algorithms evolving solutions to the Steiner Problem. Let's look at each of them. (1) Gene duplication. Please see the following papers: New Peer-Reviewed Paper Challenges Darwinian Evolution by Jonathan M. Does Gene Duplication Perform As Advertised? by Jonathan M. Jonathan M. discusses a paper entitled "Is gene duplication a viable explanation for the origination of biological information and complexity?," by Joseph Esfandier Hannon Bozorgmeh, in the journal Complexity. The author defines a gain in exonic information as "[t]he quantitative increase in operational capability and functional specificity with no resultant uncertainty of outcome." The paper concludes that:
Gene duplication and subsequent evolutionary divergence certainly adds to the size of the genome and in large measure to its diversity and versatility. However, in all of the examples given above, known evolutionary mechanisms were markedly constrained in their ability to innovate and to create any novel information. This natural limit to biological change can be attributed mostly to the power of purifying selection, which, despite being relaxed in duplicates, is nonetheless ever-present.
Moreover,
...the various postduplication mechanisms entailing random mutations and recombinations considered were observed to tweak, tinker, copy, cut, divide, and shuffle existing genetic information around, but fell short of generating genuinely distinct and entirely novel functionality.
(2) ev evolving binding sites, (3) Tierra evolving parasites, and (4) Genetic Algorithms evolving solutions to the Steiner Problem. On ev, please see: Evolutionary Synthesis of Nand Logic: Dissecting a Digital Organism by Winston Ewert, William A. Dembski and Robert J. Marks II. Key definitions:
A. Information Measures To assess the performance of a search, we use the following information measures [4], [5], [7]. 1) The endogenous information is a measure of the difficulty of a search and is given by I_omega = ?log2(p) (1) where p is a reference probability of a successful unassisted random search. 2) Let the probability of success of an assisted search under the same set of constraints be q. Denote the exogenous information of a search program as I_s := ?log2(q). 3) The difference between the endogenous and exogenous information is the active information. I+ := I_omega ? I_s = ?log2(p/q).
From the abstract:
Avida uses stair step active information by rewarding logic functions using a smaller number of nands to construct functions requiring more. Removing stair steps deteriorates Avida's performance while removing deleterious instructions improves it. Some search algorithms use prior knowledge better than others. For the Avida digital organism, a simple evolutionary strategy generates the Avida target in far fewer instructions using only the prior knowledge available to Avida.
See also EV Ware: Dissection of a Digital Organism by Baylor Bear. Regarding Tierra, I suggest you re-read PaV's original comment at https://uncommondescent.com/evolution/can-you-say-weasel/#comment-366926 in the earlier thread, https://uncommondescent.com/evolution/can-you-say-weasel (a long thread in which you mysteriously bowed out of the discussion about mid-way - exams?):
MathGrrl: I've looked at a powerpoint presentation of Tierra. First, we have intelligent agents intelligently trying to duplicate what they see in life on a logical framework. It is interesting that when it comes to trying to imitate life, so much logical thought is involved. Second, the entire program is setup in a very simplified way–very little complexity, and then it is set up so that nothing can really die, or, worse yet, cause the program itself to come to a halt. So, this program will live come hell or highwater. Third, from their results it looks like the only thing that has happened are: (1) the size of the program diminishes with time [akin to a loss of complexity], and (2) a "parasite" evolves. But the parasite is simply an organism that has lost its ability to copy its own program and so must rely on some other ‘cell’ to duplicate its ‘genome’. If one compares the "parasite" to the original "ancestor", half of its instructions have been lost. So it seems that the upshot of this experiment in life via Darwinian processes results in a loss of size and a loss of complexity. This, of course, fits in perfectly with ID's claims that claimed examples of Darwinian evolution generally amount to a loss of information and never a gain. I don't see anything there in Tierra land that is of much interest. And apparently T.Ray doesn't either since he worked on it from 1990-2001 and then quit.
Finally, I suggest you have a look at the mathematically rigorous paper, Life's Conservation Law: Why Darwinian Evolution Cannot Create Biological Information by William A. Dembski and Robert J. Marks II. The authors use the same definitions of information as used in the paper above by Ewert, Dembski and Marks (Dissecting a Digital Organism). Some excerpts:
Simulations such as Dawkins's WEASEL, Adami's AVIDA, Ray’s Tierra, and Schneider's ev appear to support Darwinian evolution, but only for lack of clear accounting practices that track the information smuggled into them... Information does not magically materialize. It can be created by intelligence or it can be shunted around by natural forces. But natural forces, and Darwinian processes in particular, do not create information. Active information enables us to see why this is the case. Let's be clear where our argument is headed. We are not here challenging common descent, the claim that all organisms trace their lineage to a universal common ancestor. Nor are we challenging evolutionary gradualism, that organisms have evolved gradually over time. Nor are we even challenging that natural selection may be the principal mechanism by which organisms have evolved. Rather, we are challenging the claim that evolution can create information from scratch where previously it did not exist. The conclusion we are after is that natural selection, even if it is the mechanism by which organisms evolved, achieves its successes by incorporating and using existing information. Mechanisms are never self-explanatory. For instance, your Chevy Impala may be the principal mechanism by which you travel to and from work. Yet explaining how that mechanism gets you from home to work and back again does not explain the information required to build it. Likewise, if natural selection, as operating in conjunction with replication, mutation, and other sources of variation, constitutes the primary mechanism responsible for the evolution of life, the information required to originate this mechanism must still be explained. Moreover, by the Law of Conservation of Information, that information cannot be less than the mechanism gives out in searching for and successfully finding biological form and function. It follows that Dawkins’s characterization of evolution as a mechanism for building up complexity from simplicity fails... Conservation of information therefore points to an information source behind evolution that imparts at least as much information to the evolutionary process as this process in turn is capable of expressing by producing biological form and function. As a consequence, such an information source has three remarkable properties: (1) it cannot be reduced to purely material or natural causes; (2) it shows that we live in an informationally porous universe; and (3) it may rightly be regarded as intelligent. The Law of Conservation of Information therefore counts as a positive reason to accept intelligent design. In particular, it establishes ID's scientific bona fides. Just as information needs to be imparted to a golf ball to land it in a hole, so information needs to be imparted to chemicals to render them useful in origin-of-life research. This information can be tracked and measured. Insofar as it obeys the Law of Conservation of Information, it confirms intelligent design, showing that the information problem either intensifies as we track material causes back in time or terminates in an intelligent information source. Insofar as this information seems to be created for free, LCI calls for closer scrutiny of just where the information that was given out was in fact put in. (Emphases mine - VJT.)
You also ask:
Are you claiming that Abel’s functional sequence complexity metric is a reliable indicator of the involvement of intelligent agency?
Yes.
Are you admitting that there is no rigorous mathematical definition of CSI?
I didn't say that, although I'd say it's less rigorous than Abel's definition of functional sequence complexity, because CSI assumes two kinds of complexity - probabilistic complexity and descriptive complexity - the latter of which is difficult to quantify. But in any case, Dembski didn't rely on his definition of CSI to demonstrate his Law of the Conservation of Information in the paper I cited. He used mathematically unobjectionable formulations. So your objection that CSI isn't rigorously defined seems to be irrelevant. I strongly suggest you read the paper, Life's Conservation Law: Why Darwinian Evolution Cannot Create Biological Information by William A. Dembski and Robert J. Marks II. It's ideal for someone with a mathematical background, and it should clear up your difficulties. As far as I can tell, none of the four scenarios you have provided generate new information. Well, I'm a philosopher, not a mathematician (although I completed a maths degree about 30 years ago) and certainly not a biologist. I hope what I've uncovered is of assistance to you. Anyway, I think I've done quite enough sleuthing for this evening, and it's now 2:05 a.m. Time to catch 40 winks.vjtorley
March 9, 2011
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Thanks again CJYman, I will reference your work for future reference.bornagain77
March 9, 2011
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Hello bornagain77, I have briefly looked over Szoztak's method for calculating functional information and upon first inspection it actually appears to be very similar to Dembski's calculation for CSI. However, I think Dembski's calculation is a little more detailed, since it measures functional information against both sequence space (as Szostak does) and a universal probability bound.CJYman
March 9, 2011
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MathGrrl, Also, within the last link I provided above is my continued response (especially comment #116 and #223) to the comment at the end of the first link.CJYman
March 9, 2011
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Thanks CJYman, for a more detailed definition of CSI, and my apologies to you Mathgrrl for 'venting' on your unreasonableness. As CJYman shows, there is a far more nuanced way to determine CSI than Szostak's 'rough' measure and so I was wrong to think that it was 'enough' for you MathGrrl.bornagain77
March 9, 2011
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MathGrrl, I also re-join the discussion within my first link at comment #94. Please try to skim/read through the full provided comments from where I start each link. There is a lot of good back-and-forth discussion.CJYman
March 9, 2011
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MathGrrl, I gave you a rigorous probability calculation for determining functional information bits in a sequence! That you want to falsely say it is 'metrically' different than Dembski's CSI matters not one iota for me, for 'functional information' as defined in the equation of Szostak, does in fact contain complexity, and specification, in its arrival of functional information bits (FITS). You have been given papers that derive FITS for various protein families in various scenarios! But your blatant unreasonableness, just to support your atheism, is all beside the point anyway for it is IMPOSSIBLE, even in principle, for neo-Darwinism to explain the 'higher dimensional' information of quantum entanglement in biology! That you ignore this central point clearly demonstrates that you are not concerned with finding the truth of the matter in the least, but are instead primarily concerned with trying to establish the legitimacy of your atheistic/Darwinistic beliefs no matter how many deceptive tactics you have to repeatedly employ!!! And exactly what for MathGrrl??? Do you think that your shallowness is not clear for all to see? Do you somehow think that hiding in lies will make life better for you??? I just don't get, why in the world would you put your eternal soul in so much jeopardy with such childishness ???bornagain77
March 9, 2011
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MathGrrl: vjtorley @ 241 has provided the link to "Specification: The Pattern That Signifies Intelligence." This is all one needs to read to understand CSI. I'm surprised it took so long for this link to be provided. My own understanding of CSI is based on that paper, and the calculations and explanations I provide in the following links are based on the concept of CSI as found in the that paper. Calculating CSI of a protein Continued defense and explanation of CSI Showing no CSI in a chaotic pattern Further Discussion of CSI I apologize that the discussions are so long, but that is the depth that needs to be provided sometimes. Also, that is why I am linking to those discussions. It could take just as long to explain everything all over again. If you are seriously interested in understanding CSI, read through, understand and seriously engage in the math and examples provided by Dembski in "Specification: The Pattern That Signifies Intelligence" and then compare that to my above linked calculations and discussions above. A non-complicated definition and breakdown of CSI is provided at the beginning of my first link. I am quite busy studying at the moment, so I apologize if I take a while getting back to you to answer any questions. However, now you should have enough material to peruse through for a while and hopefully most of your questions will be answered.CJYman
March 9, 2011
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MathGrrl- We say we have met your challenge. That you refuse to accept that is your problem, not ours. And to prove my claim that we have met your challenge you have failed to produce something you position offers that also fulfills your requirements so that we can compare. That we we can see if you are just a troll or do you really have an valid criticism. Also even if CSI didn't exist as a concept you still wouldn't have any positive evidence for your position- just look at this thread- the best you have is T-URF 13, which is next to nothing. So until you provide something from your position as a standard you will always be in the position to say "that just ain't good enough", which is childish.Joseph
March 9, 2011
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bornagain77,
None of your references address CSI and the calculations related to them show that they are not the same metric that Dembski describes.
And sequences that demonstrate functionally are fundamentally (metrically) different from Dembski’s CSI exactly how???
It is CSI that is claimed by ID proponents to be an indicator of intelligent agency. It is therefore CSI that I am interested in understanding to a sufficient level of detail in order to test those claims. If you and other ID proponents agree that one of these other metrics is also an indicator of intelligent agency, I will be happy to look at it. I would much prefer, however, to use Dembski's metric since that is the one referenced most often in these discussions. I don't understand the difficulty in getting a response to what I believe are very reasonable questions. I just want to know the definition of CSI and see some examples of how to calculate it for the scenarios I detailed. Please assist me if you can.MathGrrl
March 9, 2011
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Joseph, I'll simply repeat what I said to Upright BiPed: If you can't define your metric with mathematical rigor and can't demonstrate how to calculate it for a few simple scenarios, it is useless and any claims based on it are unfounded. I look forward to continuing the discussion with you when you have provided the necessary mathematical rigor and examples.MathGrrl
March 9, 2011
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MathGrrl, put it this way, you want to establish the legitimacy of neo-Darwinism right? And neo-Darwinism is built on materialistic presuppositions right?? And materialism (local realism) is falsified by quantum entanglement right??? And quantum entanglement is found in molecular biology right??? Thus neo-Darwinism cannot be the explanation for quantum entanglement in molecular biology!!! Probability calculations, on which neo-Darwinism would depend if it were true, do not even apply for this 'highest dimensional' information displayed by entanglement!!bornagain77
March 9, 2011
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correction,, it is every 10^14 sequences that will produce a meaningful 10 LETTER word in the English languagebornagain77
March 9, 2011
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it is every 10^14 sequences that will produce a meaningful word in the English language,,, You can use that for a ballpark figure to ascertain information (Functional information bits) from Szostak's equation,,I(Ex)= -log2 [F(Ex)] ,,, but since we are dealing with proteins, you must find rarity of proteins in sequence space,,, to which I referred to Axe's work. But none the less Mathgrrl the crushing point or 'information' to you, which you will deny the validity of anyway because of your atheistic bias, is that quantum entanglement is found in molecular biology!bornagain77
March 9, 2011
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MathGrrl you state; 'None of your references address CSI and the calculations related to them show that they are not the same metric that Dembski describes.' And sequences that demonstrate functionally are fundamentally (metrically) different from Dembski's CSI exactly how??? you then state; 'Are you asserting that one of these other metrics is an indicator of intelligent agency?' Actually It is much easier than the math I cited. You see mathgrrl in your short post you have generated more functional information than anyone has ever seen generated by purely material processes: In your post,,,,
bornagain77, None of your references address CSI and the calculations related to them show that they are not the same metric that Dembski describes. Are you asserting that one of these other metrics is an indicator of intelligent agency? Are you admitting that there is no mathematically rigorous definition of CSI that you can reference?
,,, not counting punctuation, spacing, capital letters, and all,, you have approx. 282 alphabetic letters (the length of a fairly average protein). How many possible arrangements of those letters: 26^282 or approx. 10^1562 possible combinations. How much 'functional information' is that? Well if I recall right from this video,,, Stephen Meyer - Functional Proteins And Information For Body Plans - video http://www.metacafe.com/watch/4050681/ ,,,,bornagain77
March 9, 2011
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MathGrrl, You obviously have serious issues. My quote of CJYMan did not say anything about generating Shannon Information. Shannon didn't really care about information:
The word information in this theory is used in a special mathematical sense that must not be confused with its ordinary usage. In particular, information must not be confused with meaning.- Warren Weaver, one of Shannon's collaborators
Also you are still confused- CSI argues against BLIND WATCHMAKER mechanisms- and your use of evolutionary mechanisms is nothing more than an equivocation. That said: Until either of you provide a defined metric with matematical rigor for your poition thre isn’t any need for you to whine about CSI. Ya see until we know what you accept your whining is meaningless and makes you both look like little cildren who can’t get their way. So have at it- no one is impressed with your whining. We need somthing that you accept from your position we can compare CSI to. Are you up to it? My prediction is you are not and will continue to whine- that prediction is based on the fact I have asked mny times and have not received. And that tells me your complaints are without merit.Joseph
March 9, 2011
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Joseph,
If it is Shannon information, not algorithmically compressible, and can be processed by an information processor into a separate functional system, then it is complex specified information.- CJYMan
Tom Schneider's ev demonstrates how simple evolutionary mechanisms can generate Shannon Information. Do you therefore agree that those evolutionary mechanisms can generate CSI?MathGrrl
March 9, 2011
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bornagain77, None of your references address CSI and the calculations related to them show that they are not the same metric that Dembski describes. Are you asserting that one of these other metrics is an indicator of intelligent agency? Are you admitting that there is no mathematically rigorous definition of CSI that you can reference?MathGrrl
March 9, 2011
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I think it is pretty clear that Mathgrrl doesn't have anything. That point has been made abundantly clear. So, let's get on with showing her that our side has the rigorous calculations she says we don't.jon specter
March 9, 2011
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If it is Shannon information, not algorithmically compressible, and can be processed by an information processor into a separate functional system, then it is complex specified information.- CJYMan
Joseph
March 9, 2011
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Moreover, though current mathematical measures of the information in life are all ultimately based on extreme improbability against functionality arising from purely material processes, there is actually a more satisfactory proof against neo-Darwinism, as far as 'measuring' information is concerned,,, It is now shown that 'higher dimensional' information is its own unique entity! A entity which is completely separate from matter and/or energy,,, The Failure Of Local Realism - Materialism - Alain Aspect - (Quantum Entanglement) video http://www.metacafe.com/w/4744145 Moreover this higher dimensional 'transcendent' information, which is not reducible to a material basis, in fact this higher dimensional information which falsifies the very materialistic presuppositions which undergird the neo-Darwinism framework, is found to extend into molecular biology; Quantum Information/Entanglement In DNA & Protein Folding - short video http://www.metacafe.com/watch/5936605/ Further evidence that quantum entanglement/information is found throughout entire protein structures: https://uncommondescent.com/intelligent-design/rescue-proteins-leave-evolutionists-in-the-ditch/#comment-373214 It is simply ludicrous to appeal to the materialistic framework, which undergirds the entire neo-Darwinian framework, that has been falsified by the very same quantum entanglement effect that one is seeking an explanation to! To give a coherent explanation for an effect that is shown to be completely independent of any time and space constraints one is forced to appeal to a cause that is itself not limited to time and space! Probability arguments, which have been a staple of the arguments against neo-Darwinism, simply do not apply! further notes; Quantum entanglement holds together life’s blueprint - 2010 Excerpt: “If you didn’t have entanglement, then DNA would have a simple flat structure, and you would never get the twist that seems to be important to the functioning of DNA,” says team member Vlatko Vedral of the University of Oxford. http://neshealthblog.wordpress.com/2010/09/15/quantum-entanglement-holds-together-lifes-blueprint/ Information and entropy – top-down or bottom-up development in living systems? A.C. McINTOSH Excerpt: It is proposed in conclusion that it is the non-material information (transcendent to the matter and energy) that is actually itself constraining the local thermodynamics to be in ordered disequilibrium and with specified raised free energy levels necessary for the molecular and cellular machinery to operate. http://journals.witpress.com/journals.asp?iid=47 Another 'mathematical' measure for information, which I find to be a more accurate measure for 'total' information content in a cell, is, Information theory. Relation between information and entropy. Excerpt: the total information content (of a bacterial cell) is then 1.3 x 10^12 or, in round numbers, 10^12 bits. http://www.astroscu.unam.mx/~angel/tsb/molecular.htm 'The information content of a simple cell has been estimated as around 10^12 bits, comparable to about a hundred million pages of the Encyclopedia Britannica." Carl Sagan, "Life" in Encyclopedia Britannica: Macropaedia (1974 ed.), pp. 893-894 Also of interest; 'genetic entropy', the true principle for all biological adaptations, has never been violated; Is Antibiotic Resistance evidence for evolution? - 'The Fitness Test' - video http://www.metacafe.com/watch/3995248 Evolution Vs Genetic Entropy - Andy McIntosh - video http://www.metacafe.com/watch/4028086 “The First Rule of Adaptive Evolution”: Break or blunt any functional coded element whose loss would yield a net fitness gain - Michael Behe - December 2010 Excerpt: In its most recent issue The Quarterly Review of Biology has published a review by myself of laboratory evolution experiments of microbes going back four decades.,,, The gist of the paper is that so far the overwhelming number of adaptive (that is, helpful) mutations seen in laboratory evolution experiments are either loss or modification of function. Of course we had already known that the great majority of mutations that have a visible effect on an organism are deleterious. Now, surprisingly, it seems that even the great majority of helpful mutations degrade the genome to a greater or lesser extent.,,, I dub it “The First Rule of Adaptive Evolution”: Break or blunt any functional coded element whose loss would yield a net fitness gain.(that is a net 'fitness gain' within a 'stressed' environment i.e. remove the stress from the environment and the parent strain is always more 'fit') http://behe.uncommondescent.com/2010/12/the-first-rule-of-adaptive-evolution/bornagain77
March 9, 2011
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Mathgrrl and DrBot, Functional information and the emergence of bio-complexity: Robert M. Hazen, Patrick L. Griffin, James M. Carothers, and Jack W. Szostak: Abstract: Complex emergent systems of many interacting components, including complex biological systems, have the potential to perform quantifiable functions. Accordingly, we define 'functional information,' I(Ex), as a measure of system complexity. For a given system and function, x (e.g., a folded RNA sequence that binds to GTP), and degree of function, Ex (e.g., the RNA-GTP binding energy), I(Ex)= -log2 [F(Ex)], where F(Ex) is the fraction of all possible configurations of the system that possess a degree of function > Ex. Functional information, which we illustrate with letter sequences, artificial life, and biopolymers, thus represents the probability that an arbitrary configuration of a system will achieve a specific function to a specified degree. In each case we observe evidence for several distinct solutions with different maximum degrees of function, features that lead to steps in plots of information versus degree of functions. http://genetics.mgh.harvard.edu/szostakweb/publications/Szostak_pdfs/Hazen_etal_PNAS_2007.pdf Mathematically Defining Functional Information In Molecular Biology - Kirk Durston - short video http://www.metacafe.com/watch/3995236 Entire video: http://vimeo.com/1775160 Measuring the functional sequence complexity of proteins - Kirk K Durston, David KY Chiu, David L Abel and Jack T Trevors Excerpt: We have extended Shannon uncertainty by incorporating the data variable with a functionality variable. The resulting measured unit, which we call Functional bit (Fit), is calculated from the sequence data jointly with the defined functionality variable. To demonstrate the relevance to functional bioinformatics, a method to measure functional sequence complexity was developed and applied to 35 protein families. Considerations were made in determining how the measure can be used to correlate functionality when relating to the whole molecule and sub-molecule. In the experiment, we show that when the proposed measure is applied to the aligned protein sequences of ubiquitin, 6 of the 7 highest value sites correlate with the binding domain. http://www.tbiomed.com/content/4/1/47 Intelligent Design: Required by Biological Life? K.D. Kalinsky - Pg. 10 - 11 Case Three: an average 300 amino acid protein: Excerpt: It is reasonable, therefore, to estimate the functional information required for the average 300 amino acid protein to be around 700 bits of information. I(Ex) > Inat and ID (Intelligent Design) is 10^155 times more probable than mindless natural processes to produce the average protein. http://www.newscholars.com/papers/ID%20Web%20Article.pdf Three subsets of sequence complexity and their relevance to biopolymeric information - Abel, Trevors Excerpt: Shannon information theory measures the relative degrees of RSC and OSC. Shannon information theory cannot measure FSC. FSC is invariably associated with all forms of complex biofunction, including biochemical pathways, cycles, positive and negative feedback regulation, and homeostatic metabolism. The algorithmic programming of FSC, not merely its aperiodicity, accounts for biological organization. No empirical evidence exists of either RSC of OSC ever having produced a single instance of sophisticated biological organization. Organization invariably manifests FSC rather than successive random events (RSC) or low-informational self-ordering phenomena (OSC). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1208958/ Estimating the prevalence of protein sequences adopting functional enzyme folds: Doug Axe: Excerpt: Starting with a weakly functional sequence carrying this signature, clusters of ten side-chains within the fold are replaced randomly, within the boundaries of the signature, and tested for function. The prevalence of low-level function in four such experiments indicates that roughly one in 10^64 signature-consistent sequences forms a working domain. Combined with the estimated prevalence of plausible hydropathic patterns (for any fold) and of relevant folds for particular functions, this implies the overall prevalence of sequences performing a specific function by any domain-sized fold may be as low as 1 in 10^77, adding to the body of evidence that functional folds require highly extraordinary sequences. http://www.ncbi.nlm.nih.gov/pubmed/15321723bornagain77
March 9, 2011
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MathGrrl 17: A simple gene duplication, even without subsequent modification of the duplicate, can increase production from less than X to greater than X. How was it determined that gene duplications are blind watchmaker pocesses? You do realize that a duplicated ene is nothing without the proper binding sites- right? And even then all you have is another protein that you already have- if it doesn't have a place to go, it is useless and can jus get in the way of existing proteins.Joseph
March 9, 2011
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Dr Boy=t and mathGrrl- Until either of you provide a dfined metric with matematical rigor for your poition thre isn't any need for you to whine about CSI. Ya see until we know t you accept yourwhining is meaningless and makesyou both look like little cildren who can't get their way. So have at it- no one is impressed with your whining. We need somthing that you accept from your position we can compare CSI to. Are you up to it? My prediction is you are not and will continue to whine- that prediction is based on the fact I have asked mny times and have not received. And that tells me your complaints are without merit.Joseph
March 9, 2011
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