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Kirk Durston: A common either-or mistake both Darwinists and ID theorists make

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Biophysicist Kirk Durston of the New Scholars Society offers an explanation below:

Note: Kirk Durston is back and this post has been stuck to the front page until late this evening EST, so that further comments and his responses may be noted. – News

There is a common either-or mistake made by most Darwinists and, quite frequently, by ID theorists as well. The mistake, which is an example of the fallacy known as the false dichotomy, can be described as occurring when one assumes that either no functional information encoded in the genomes of life can be produced by natural processes, or all of it was produced by natural processes. A closely related mistake made by Darwinists is the assumption that if natural processes can produce a trivial level of functional information, then we can safely conclude that natural processes can produce all biological information.

There are still challenges in mathematically defining functional information or functional complexity. For my purpose here, however, I will simply use the mathematical descriptions published by Hazen et al., and Durston et al. Both approaches cited are very closely related to an earlier equation published in 1951 by Leon Brillouin, which can be simply represented as

FI = -log nf/nt

Where nf = number of sequences that are functional and nt = the total number of possible sequences. It should be clear from the above equation that if nf is large enough for examples to be generated by random recombinations, then functional information (FI) can be generated by random natural processes, albeit a trivial level. For example, it is clear from work done at the Georgia Institute of Technology, that nf for simple binding pockets is pretty high, which entails that the FI required to code for binding pockets is relatively trivial.

Reflection on the above equation reveals that the FI required for a given function can range anywhere from zero to some very high number. It is, therefore, a mistake to assume that FI can only be generated by intelligence; a trivial level of FI can be produced by completely mindless processes, as should be obvious from the above equation, and as the Georgia Tech results illustrate.

It is also a mistake to assume, as many Darwinists do, that because mindless processes can generate a trivial level of FI, therefore mindless processes can generate high levels of FI. Again, reflection upon the above equation (or the more detailed equations published by Hazen or Durston) reveal that the higher the FI required, the less probable it becomes (i.e., the nf/nt ratio approaches zero).

The fatal mistake made by Darwinists at this point is to invoke what has become the Darwinist god-of-the-gaps, namely selection. As we can illustrate from evolutionary algorithms, selection requires a fitness function which, itself requires FI to encode. Of course, it follows from what I am arguing here that trivial levels of selection can be produced with trivial levels of FI. The question is whether natural selection has sufficient information to locate stable, functional, biological proteins. All our work to date seems to falsify that option and verify the need and actual role for intelligent design (in this case human) when producing artificial proteins of any significant structure. To clarify, recent building of artificial proteins is an example of intelligent design in action.

The Georgia Tech work has led some Darwinists to believe that because binding pockets are relatively trivial to encode in a sequence that, therefore, we have somehow explained how natural processes could have encoded biological proteins. In real life, however, proteins are about a lot more than simple binding. Binding to the right molecule is important, at the right time, at the right location and with the right binding strength so that the bond can be broken at the right time and place, etc. This can often require a larger 3D structure for proper functionality, that has a nf/nt ratio approaching zero. For example, if we take the results published for 35 protein families by Durston et al., and solve for nf/nt, we observe that it is extremely small for many protein families.

My contention is that the ability to generate statistically significant levels of functional information is unique to intelligence. It follows from this that if a function can be achieved with a statistically insignificant level of FI, then intelligence is not required. Statistical significance, therefore, is the safeguard against false positives and can be measured in a variety of ways, such as measuring the adjusted residual of the outcome and choosing a cutoff that represents a very high confidence level, such as 99.9% or greater. With this in mind, an executive summary of my own case for intelligent design in biological life is available here.

Comments
JLAfan
BA77, you always ask to show how nature can produce proteins from scratch using blind processess. Is this good enought for you or are you going to move the goalposts once more? Face it, my freind, you and your theology are losing. Give it up. I must thank ID though. If it wasn’t for ID, the good science might not have worked as hard to refute the bad science.
The issue isn't just producing proteins, but how undirected, natural causes produce proteins the exhibit complex, specified information. Perhaps you could reference a single peer reviewed scientific research study that demonstrates how undirected, natural causes accomplishes this feat. (hint: there isn't one...not one!)DonaldM
July 25, 2013
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JlAfan2001 you claim,
Here is an experiment that shows biological information can be produced by nature, which they have long asked for, an it;s not good enough.
And yet Casey Luskin, in the article I I listed directly above your post (which apparently you did not read), points out that 'nature' had nothing to do with the 'experiment':
Skolnick and Gao’s work is entirely theoretical and computational, and is thus hard-pressed to impinge upon Axe’s experimental results.
Ummm JLAfan2001, not to doubt your unflinching faith in all things Darwinian, but if intelligently designed computational models/programs are required to explain the origination of even 'trivial levels of functional information' (since no one has actually seen it happen in real life) then shouldn't this at least raise a red flag in your book that all may not be well in Darwinland? Especially since the level of functional complexity that needs to be explained in the real world, in real living organisms, greatly exceeds the functional complexity of any computer program man has ever devised, much less these ad hoc computer programs that were intelligently designed to demonstrate the origination of 'trivial levels of functional information since Darwinism cannot offer a real world example?? Notes as to the sheer disconnect between the empirical evidence (of the real world) and what needs to be explained:
"The immediate, most important implication is that complexes with more than two different binding sites-ones that require three or more proteins-are beyond the edge of evolution, past what is biologically reasonable to expect Darwinian evolution to have accomplished in all of life in all of the billion-year history of the world. The reasoning is straightforward. The odds of getting two independent things right are the multiple of the odds of getting each right by itself. So, other things being equal, the likelihood of developing two binding sites in a protein complex would be the square of the probability for getting one: a double CCC, 10^20 times 10^20, which is 10^40. There have likely been fewer than 10^40 cells in the world in the last 4 billion years, so the odds are against a single event of this variety in the history of life. It is biologically unreasonable." - Michael Behe - The Edge of Evolution - page 146 Cells Are Like Robust Computational Systems, - June 2009 Excerpt: Gene regulatory networks in cell nuclei are similar to cloud computing networks, such as Google or Yahoo!, researchers report today in the online journal Molecular Systems Biology. The similarity is that each system keeps working despite the failure of individual components, whether they are master genes or computer processors. ,,,,"We now have reason to think of cells as robust computational devices, employing redundancy in the same way that enables large computing systems, such as Amazon, to keep operating despite the fact that servers routinely fail." http://www.sciencedaily.com/releases/2009/06/090616103205.htm How we could create life: The key to existence will be found not in primordial sludge, but in the nanotechnology of the living cell - Paul Davies - 2002 Excerpt: Instead, the living cell is best thought of as a supercomputer – an information processing and replicating system of astonishing complexity. DNA is not a special life-giving molecule, but a genetic databank that transmits its information using a mathematical code. Most of the workings of the cell are best described, not in terms of material stuff – hardware – but as information, or software. Trying to make life by mixing chemicals in a test tube is like soldering switches and wires in an attempt to produce Windows 98. It won’t work because it addresses the problem at the wrong conceptual level. - Paul Davies http://www.guardian.co.uk/education/2002/dec/11/highereducation.uk Passing the baton of life - from Schrödinger to Venter - July 2012 Excerpt: "All living cells that we know of on this planet are 'DNA software'-driven biological machines comprised of hundreds of thousands of protein robots, coded for by the DNA, that carry out precise functions," said Venter. "We are now using computer software to design new DNA software." - Craig Venter http://www.newscientist.com/blogs/culturelab/2012/07/passing-the-baton-of-life---from-schrodinger-to-venter.html "We have always underestimated cells. Undoubtedly we still do today,,, Indeed, the entire cell can be viewed as a factory that contains an elaborate network of interlocking assembly lines, each which is composed of a set of large protein machines." Bruce Alberts: Former President, National Academy of Sciences; Problems with the Metaphor of a Cell as "Machine" - July 2012 Excerpt: Too often, we envision the cell as a "factory" containing a fixed complement of "machinery" operating according to "instructions" (or "software" or "blueprints") contained in the genome and spitting out the "gene products" (proteins) that sustain life. Many things are wrong with this picture, but one of the problems that needs to be discussed more openly is the fact that in this "factory," many if not most of the "machines" are themselves constantly turning over -- being assembled when and where they are needed, and disassembled afterwards. The mitotic spindle...is one of the best-known examples, but there are many others. Funny sort of "factory" that, with the "machinery" itself popping in and out of existence as needed!,,, http://www.evolutionnews.org/2012/07/problems_with_t062691.html To Model the Simplest Microbe in the World, You Need 128 Computers - July 2012 Excerpt: Mycoplasma genitalium has one of the smallest genomes of any free-living organism in the world, clocking in at a mere 525 genes. That's a fraction of the size of even another bacterium like E. coli, which has 4,288 genes.,,, The bioengineers, led by Stanford's Markus Covert, succeeded in modeling the bacterium, and published their work last week in the journal Cell. What's fascinating is how much horsepower they needed to partially simulate this simple organism. It took a cluster of 128 computers running for 9 to 10 hours to actually generate the data on the 25 categories of molecules that are involved in the cell's lifecycle processes.,,, ,,the depth and breadth of cellular complexity has turned out to be nearly unbelievable, and difficult to manage, even given Moore's Law. The M. genitalium model required 28 subsystems to be individually modeled and integrated, and many critics of the work have been complaining on Twitter that's only a fraction of what will eventually be required to consider the simulation realistic.,,, http://www.theatlantic.com/technology/archive/2012/07/to-model-the-simplest-microbe-in-the-world-you-need-128-computers/260198/ Three Subsets of Sequence Complexity and Their Relevance to Biopolymeric Information - David L. Abel and Jack T. Trevors - Theoretical Biology & Medical Modelling, Vol. 2, 11 August 2005, page 8 "No man-made program comes close to the technical brilliance of even Mycoplasmal genetic algorithms. Mycoplasmas are the simplest known organism with the smallest known genome, to date. How was its genome and other living organisms' genomes programmed?" http://www.biomedcentral.com/content/pdf/1742-4682-2-29.pdf
JlAfan2001, perhaps you have no problem using intelligently designed computer programs to demonstrate that unguided Darwinian processes can produce computer programs of unfathomed complexity, but others not so enamored with all things Darwinian, might question your sanity for claiming as such! Just saying!bornagain77
July 25, 2013
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JLAfan2001- There isn't any evidence that blind and undirected chemical processes can construct multi-protein configurations. And seeing tat living organisms contain many of those it appears that your position can't explain jack. Look at Lenski's long-running experiment- no new proteins were produced. You have a better chance of showing that nature produced Stonehenge than you do of showing nature can produce a living organism from non-living matter.Joe
July 25, 2013
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It stands to reason that if nature can produce information, amino acids, nucleotides etc then somehow it produced the first cell with all of this.
Not at all. To produce rocks and to generate cathedrals made of rocks are two different things. That nature produce the first cell from molecules is yet more difficult than that. Durston says that natural processes produce *trivial* FI and that real proteins contain far higher levels of FI. Moreover from proteins to organisms there is a deeper gap than from rocks to cathedrals. The bottom line is that Darwinian molecules-to-man is pure absurdity.niwrad
July 25, 2013
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This is pretty damn typical of IDists. They always ask for evidence that natural processes can do the trick and when they are shown it, they stick their fingers in their ears. "It's just a flesh wound", they cry. Here is an experiment that shows biological information can be produced by nature, which they have long asked for, an it;s not good enough. The line gets pulled back and they want evidence that nature can produce REAL information, not just any information. We just haven't found how yet but it's coming. It stands to reason that if nature can produce information, amino acids, nucleotides etc then somehow it produced the first cell with all of this. We will figure it out soon. I would suggest that the IDists here start sticking your fingers in your ears even deeper now to save you the trouble in the future. BA77, you always ask to show how nature can produce proteins from scratch using blind processess. Is this good enought for you or are you going to move the goalposts once more? Face it, my freind, you and your theology are losing. Give it up. I must thank ID though. If it wasn't for ID, the good science might not have worked as hard to refute the bad science.JLAfan2001
July 25, 2013
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correction: Related note as to the Georgia Tech work and how it relates to reality: sorry guys, I really need to get into the habit of giving my posts the once over before I submit.bornagain77
July 25, 2013
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Related note as to the Google Tech work and how it relates to reality: Why Skolnick and Gao (2013) Doesn't Refute ID Research on the Origin of Proteins Casey Luskin July 23, 2013 Excerpt: (1) Skolnick and Gao don't cite any work of ID proponents nor do they claim their paper pertains to it. (2) Skolnick and Gao's work is entirely theoretical and computational, and is thus hard-pressed to impinge upon Axe's experimental results. (3) Many other researchers working on protein structure disagree with their conclusion, and believe "there are many more than 500 unique sites among the human proteome." (4) Skolnick and Gao only studied the behavior of an artificial set of proteins which were assumed to already be capable of folding into a stable structure, and thus their paper doesn't address Axe's research which investigated the likelihood of producing stably folded proteins in the first place. (5) If Skolnick and Gao are correct that proteins are clustered closely in sequence space, it still doesn't show that closely related proteins can always evolve from one function to another: Axe and Gauger (2011) experimentally found that even with proteins very close in sequence and structure, moving from one function to another can require more simultaneous mutations than could arise through a Darwinian evolutionary process over the entire history of the earth. (6) Skolnick and Gao only studied proteins that bind small molecules like ligands. They did not study the feasibility of evolving much more complex protein shapes required for many common enzyme functions, like enzyme catalysis, involving much larger and more complex molecules. (7) Their paper attempts to theoretically bolster the "promiscuity hypothesis" of protein evolution, a hypothesis that has not fared well in experimental tests. Skolnick and Gao ask, "How can one demonstrate that the ability to engage in a variety of low-level biochemical functions without selection is an intrinsic property of proteins... ?" In other words, what should convince us that the functions of natural proteins are abundantly present in random polypeptide chains? The answer is obvious: Go into the lab and impress us with what you find in a mixture of random polypeptides. Neither they, nor other critics, have done this. http://www.evolutionnews.org/2013/07/why_skolnick_an074761.htmlbornagain77
July 25, 2013
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