'Junk DNA' Intelligent Design

Vodka! Jean Claude Perez, the golden ratio, dragon curve fractals and musical design in “junk DNA”

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Jean Claude Perez is a self-organizational theorist, he is not a creationist. He has also published papers with an occasional visitor to UD, Andras Pellionisz.

If the mathematical/musical patterns Perez has found in DNA are improbable relative to laws of physics and chemistry, then he may have found yet another design feature of DNA, and this feature is found by combining coding DNA with non-coding DNA and viewing it holistically.

Here is the simplest explanation I found of his work:

When cells replicate, they count the total number of letters in the DNA strand of the daughter cell. If the letter counts don’t match certain exact ratios, the cell knows that an error has been made. So it abandons the operation and kills the new cell.

Failure of this checksum mechanism causes birth defects and cancer.

Jean-Claude Perez discovered an evolutionary mathematical matrix in DNA, based on the Golden Ratio 1.618

Dr. Jean-Claude Perez started counting letters in DNA. He discovered that these ratios are highly mathematical and based on “Phi”, the Golden Ratio 1.618. This is a very special number, sort of like Pi. Perez’ discovery was published in the scientific journal Interdisciplinary Sciences / Computational Life Sciences in September 2010.

Jean-Claude Perez discovered an evolutionary mathematical matrix in DNA, based on the Golden Ratio 1.618

Before I tell you about it, allow me to explain just a little bit about the genetic code.

DNA has four symbols, T, C, A and G. These symbols are grouped into letters made from combinations of 3 symbols, called triplets. There are 4x4x4=64 possible combinations.

So the genetic alphabet has 64 letters. The 64 letters are used to write the instructions that make amino acids and proteins.

Perez somehow figured out that if he arranged the letters in DNA according to a T-C-A-G table, an interesting pattern appeared when he counted the letters.

He divided the table in half as you see below. He took single stranded DNA of the human genome, which has 1 billion triplets. He counted the population of each triplet in the DNA and put the total in each slot:

sym1

When he added up the letters, the ratio of total white letters to black letters was 1:1. And this turned out to not just be roughly true. It was exactlytrue, to better than one part in one thousand, i.e. 1.000:1.000.

Then Perez divided the table this way:

sym2

Perez discovered that the ratio of white letters to black letters is exactly 0.690983, which is (3-Phi)/2. Phi is the number 1.618, the “Golden Ratio.”

He also discovered the exact same ratio, 0.690983, when he divided the table the following two alternative ways:
sym3
sym4

Perez discovered that the ratio of white letters to black letters is exactly 0.690983, which is (3-Phi)/2. Phi is the number 1.618, the “Golden Ratio.”

He also discovered the exact same ratio, 0.690983, when he divided the table the following two alternative ways:


Above: Total ratio of white:black letters = 1:1

So for three ways of dividing the table, the ratio of white to black is 1.000:1.000.

And for the other three ways of dividing it, the ratio is 0.690983 or (3-Phi)/2.

When you overlay these 6 symmetries on top of each other, you get a set of mathematical stairs with 32 golden steps. Then an absolutely fascinating geometrical pattern emerges: The “Dragon Curve” which is well known in fractal geometry. Here it is, labeled with DNA letters in descending frequency:

You can see other non-DNA, computer generated versions of this same curve here.

Other interesting facts:
•Similar patterns with variations on these same rules are seen across a range of 20 different species. From the AIDS virus to bacteria, primates and humans
•Each character in DNA occurs a precise number of times, and each has a twin. TTT and AAA are twins and appear the most often; they’re the DNA equivalent of the letter E.
•This pattern creates a stair step of 32 frequencies, a specific frequency for each pair.
•The number of triplets that begin with a T is precisely the same as the number of triplets that begin with A (to within 0.1%).
•The number of triplets that begin with a C is precisely the same as the number of triplets that begin with G.
•The genetic code table is fractal – the same pattern repeats itself at every level. The micro scale controls conversion of triplets to amino acids, and it’s in every biology book. The macro scale, newly discovered by Dr. Perez, checks the integrity of the entire organism.
•Perez is also discovering additional patterns within the pattern.

I am only giving you the tip of the iceberg. There are other rules and layers of detail that I’m omitting for simplicity. Perez presses forward with his research; more papers are in the works, and if you’re able to read French, I recommend his book “Codex Biogenesis” and his French website. Here is an English translation.

(By the way, he found some of his most interesting data in what used to be called “Junk DNA.” It turns out to not be junk at all.)

OK, so what does all this mean?
•Copying errors cannot be the source of evolutionary progress, because if that were true, eventually all the letters would be equally probable.
•This proves that useful evolutionary mutations are not random. Instead, they are controlled by a precise Evolutionary Matrix to within 0.1%
•When organisms exchange DNA with each other through Horizontal Gene Transfer, the end result still obeys specific mathematical patterns
•DNA is able to re-create destroyed data by computing checksums in reverse – like calculating the missing contents of a page ripped out of a novel.

No man-made language has this kind of precise mathematical structure. DNA is a tightly woven, highly efficient language that follows extremely specific rules. Its alphabet, grammar and overall structure are ordered by a beautiful set of mathematical functions.

More interesting factoids:

The most common pair of letters (TTT and AAA) appears exactly 1/13X as often as all the letters combined – consistently, the genomes of humans and chimpanzees.

If you put the 32 most common triplets in Group 1 and the 32 least common triplets in Group 2, the ratio of letters in Group1:Group2 is exactly 2:1. And since triplet counts occur in symmetrical pairs (TTT-AAA, TAT-ATA, etc), you can group them into four groups of 16.

When you put those four triplet populations on a graph, you get the peace symbol:

dna_peace_symbol

Does this precise set of rules and symmetries appear random or accidental to you?

My friend, this is how it is possible for DNA to be a code that is self-repairing, self-correcting, self-re-writing and self-evolving. It reveals a level of engineering and sophistication that human engineers could only dream of. Most of all, it’s elegant.

Cancer has sometimes been described as “evolution run amok.” Dr. Perez has noted interesting distortions of this matrix in cancer cells. I strongly suspect that new breakthroughs in cancer research are hidden in this matrix.

I submit to you that the most productive research that can possibly be conducted in medicine and computer science is intensive study of the DNA Evolution Matrix. Like I said, this is just the tip of the iceberg.

There is so much more here to discover!

When we develop computer languages based on DNA language, they will be capable of extreme data compression, error correction, and yes, self-evolution. Imagine: Computer programs that add features and improve with time. All by themselves.

What would that be like?

Perry Marshall

Shift Frequency Jean Claude Perez

Elizabeth Liddle with her love of music might actually appreciate the possibility that life and musical notes are possibly intertwined.

I can’t do justice to the topic by quoting snippets, so I will have to provide a link:
Phi and Music DNA Here is diagram from that article:

Recall the discussion of ID and tuning of musical instruments here.

Perez bio is in creation wiki:

Jean-Claude Perez, Ph.D., is a French interdisciplinary scientist born on June 26, 1947 in Bassens, Gironde near Bordeaux (France). An engineer and French scholar from Bordeaux university[1], Perez worked principally with IBM in both the areas of Biomathematics and Artificial Intelligence (the first time, showing evidence of high level self-organization in cellular automata networks [2] and the second time creating neural networks with “FRACTAL CHAOS”(Fractal geometry), his holographic-like memory system and novelty detector). Then, in 1990, Jean-claude Perez published strong links between the world of fractals and numbers of the Fibonacci sequence which are based on the Golden ratio[3]. In this last area, with the “DNA supracode”[4], he proved that DNA coding for genes is structured by proportions related to Fibonacci numbers[5] [6] [7]. He verified this discovery in the field of the HIV genome by partnerships with Professor Luc Montagnier[8], the discoverer of the HIV virus. He has worked for 20 years in the fields of whole genome numerical analysis and numerical decoding of genes as coding or non-coding DNA sequences (as demonstrated particularly by the last publications: Five last publications/conferences ).
Particularly, in “Interdisciplinary Science” September 2010 issue, J.C. Perez published a peer-reviewed paper proving that the whole human genome codon populations are managed by a “DRAGON fractal paper folding curve” fine-tuned around the “Golden ratio”. Particularly, this main paper entitled “Codon populations in single-stranded whole human genome DNA are fractal and fine-tuned by the Golden Ratio 1.618.” shows that the Universal Genetic Code Table not only maps codons to amino acids, but serves as a global checksum matrix at the whole genome macro-structural scale.[9]

Golden ratio.jpg

Draft of the paper Final published paper
A complete summary of J.C Perez’s research was published in Pellionisz A., Graham R., Pellionisz P., Perez J.: Genome Function of the Cerebellum: Geometric Unification of Neuroscience and Genomics. In: Manto M., Gruol D., Schmahmann J., Koibuchi N., Rossi F. (Ed.) Handbook of the Cerebellum and Cerebellar Disorders: SpringerReference (www.springerreference.com). Springer-Verlag Berlin Heidelberg, -1. DOI: 10.1007/SpringerReference_310386 2012-03-12 14:15:14 UTC. Details in [10] and full paper available in [11]

In october 2013 jean-claude perez published a peer reviewed major article in [APPLIED MATHEMATICS]http://www.scirp.org/journal/am/ (BIOMATHEMATICS issue) entitled “The 3 genomic numbers discovery”. This article show – for the first way – that complete human genome single stranded DNA constitutes a WHOLE… [12]

http://creationwiki.org/Jean-claude_Perez

The “Vodka” designation means a highly speculative topic.

43 Replies to “Vodka! Jean Claude Perez, the golden ratio, dragon curve fractals and musical design in “junk DNA”

  1. 1
    scordova says:

    I submit to you that the most productive research that can possibly be conducted in medicine and computer science is intensive study of the DNA Evolution Matrix. Like I said, this is just the tip of the iceberg.

    There is so much more here to discover!

    When we develop computer languages based on DNA language, they will be capable of extreme data compression, error correction, and yes, self-evolution. Imagine: Computer programs that add features and improve with time. All by themselves.

    Self-directed evolution. If I were an evolutionist, I’d probably be a self-organizational neutral evolutionist. It’s at least more sophisticated than Darwinism.

    Nevertheless, I think Perez has possibly found a fruitful area of design research. 😎

  2. 2
    Jehu says:

    How you checksum DNA without intelligence?

  3. 3
    Dionisio says:

    Jehu @ 2

    How you checksum DNA without intelligence?

    You don’t need any intelligence to create any algorithm.

    It’s done ex-nihilo very simple, through the magical D formula Evo = RV + NS + T

    You just don’t understand evolution 😉

    P.S. please, don’t mention this to engineers or computer scientists, because they don’t agree at all, so they will say this is stupid.

  4. 4
    Dionisio says:

    “DNA researcher, Andras Pellionisz gives favorable review to a shredding of Dawkins and TalkOrigins”

    January 11, 2007 Posted by scordova

    40 Comments

    (Visited 120 times, 4 visits today)

    Obviously things have changed since scordova posted the above OP on January 11, 2007. That thread accumulated 120 visits until now.
    This current thread, which apparently scordova just posted late last night (GMT-5), has already 127 visits.
    120 visits in over 7 years vs. 127 visits in less than a day!

    Does that tell us anything? Maybe not, but I found it sorta-kinda interesting. Perhaps it just shows that internet has become more popular and accessible worldwide?
    Another possible implication?

  5. 5
    Dionisio says:

    This current thread, which apparently scordova just posted late last night (GMT-5), has already 127 visits.
    120 visits in over 7 years vs. 127 visits in less than a day!

    Well, by the time I posted my comment the visitors counter had changed:

    (Visited 146 times, 146 visits today)

  6. 6

    I wonder what anti-ID advocates would say is the better evolutionary model in terms of predicting this sort of finding about DNA? Random/Neutral, or Designed?

  7. 7
    jstanley01 says:

    In the financial markets, a lot of technical traders use Fibonacci numbers for their entry, failure, and profit-taking levels. Meanwhile, a lot of non-technical traders claim that fibs only work in hindsight, sort of like voodoo. In my experience, although I have seen fib levels work in real time a’plenty, I’m not ready to risk real money yet. In the near future I’m going to have some time available to test out technical trading using a dummy account. Personally, I see no reason why human intentionality as delineated by financial market meanderings couldn’t behave according to fibs, like so much else in nature does. (As a matter of fact, now I’m seeing how the golden mean patterns down to the level of DNA. Wow! Thanks for the post.) While there are many approaches to utilizing fibs, based on my research I’d say that David Halsey’s merits an honorable mention to anyone interested in checking out fib techniques.

  8. 8
    scordova says:

    I don’t believe in technical trading as a general rule ESPECIALLY fibonnaci because the parameters are too loose. A lot of it is after the fact. If it were that good, it would be predicting stock prices. It doesn’t.

    There have been a few successful technical traders like Richard Dennis. I prefer statistical arbitrage.

    However, some statistical analyses really do resist after-the-fact non-randomness like 500 fair coins heads. Perez analysis looks like he has detected a non-random pattern because of its simplicity. It can be and should be tested.

    Another non-random pattern is the genetic code itself (the association of codons to amino acid residues). The relationship exists because of the protein making machinery.

    I strongly suspect Perez pattern also exists because of machinery! And if there is machinery to make these patterns, there is a machine maker.

  9. 9
    jstanley01 says:

    Any system that requires better than a 50% success rate will fail precisely because price cannot be predicted. Successful trading (which has a 99% failure rate among those who try their hand) based upon whatever — technical, fundamental, or astrological (lol) — therefore requires the maximizing of profits and the minimizing of losses, a.k.a. “money management.” In light of which, fibs are not used to predict price, but rather, to describe how markets trend with a view to identifying entry, failure, and profit-taking levels. Anyway, independent of boosters and detractors, I’ve got the perfect empirical test for Fibonacci retracements: whether or not they make me money… 😀

  10. 10
    quark1 says:

    Well I´m not a biologist so I don´t now if this really means anyting but however I find something interesting about these numbers. You can find number pi from this genetic maps.

    1) Take subtraction diagonal like

    A = (TCT)-(TTC) = 251604556 – 225669775 = 25934781
    B = (TAT)-(TTA) = 234734484 – 260313647 = -25579163
    C = (TGT)-(TTG) = 229405484 – 190719226 = 38686258
    D = (TAC)-(TCA) = 169023944 – 202932695 = -33908751
    E = (TGC)-(TCG) = 96112792 – 91988158 = 4124634
    F = (TGA)-(TAG) = 202932695 – 149333215 = 53599480

    2) Alright, thereafter do a summation like

    A + B + C + D + E + F = 62857239

    3) Divide with 10.000.000 and thereafter

    (1/10.000.000)(A + B + C + D + E + F) = 6,2857239

    This is approximately 2pi = 2*3,14159 = 6,283185

    Do the same for the other three intervall:

    The second one: I get -6pi
    The third one: I get 3pi
    The fourd one: I get pi

    What do this means? Do the biological systems use circles and the number of turns in the genetic codes? The negative maybe means backword turn of the circle and will go three turns?

    I´m also find other strange formula!

    (TTT) – (TAA) = abs(A) + abs(B) + abs(C) + abs(D) + abs(F) = 10((TCC) – (TGG)) = 10pi*(7/2)^(1/4)*abs(E) = 177265160

    There for example abs(B)=abs(-25579163) = 25579163

    The interesting numbers can also be

    TCC = CCT = AGG = GGA = 188628365

    Thereafter

    (188628365 – 188000000)/100000 = 6,28365 = 2pi

    Alright enought from me. However it is very awesome with the music octave discovery from these numbers, wow.

  11. 11
    anthropic says:

    Actually, prices can be predicted with a better than random chance of success. Fama won a Nobel Prize for demonstrating this in the 1970s, I believe. My father demonstrated it ten years earlier in his doctoral dissertation, The Implications of Trend Persistency in Portfolio Management.

  12. 12
    Gordon Davisson says:

    Umm, isn’t this mostly just the occurrence ratios of the different DNA bases, with a lot of over-interpretation?

    Perez’s original article is paywalled, but according to the Phi and Music in DNA article:

    In his work Jean-Claude Perez analysed the entirety of the whole human genome (not just the coding 2%) from the 2003 “BUILD34” finalized release [2]. He considered only a single strand of the DNA sequence. Given a sequence of bases, for example TCAATGTCA, if the starting point is unknown there are three possible ways of grouping the bases into codons; these three codon groupings are known as the codon reading frames:

    1) TCA|ATG|TCA
    2) CAA|TGT|CAT
    3) AAT|GTC|ATC

    Using one whole DNA strand, Jean-Claude Perez computed, for each of the 3 possible codon reading frames, the cumulative number of each of the 64 genetic code codons. In his book “Codex Biogenesis” [3] he also provides the cumulative populations of codons obtained joining the 12 possible reading combinations (2 strands x 2 senses per strand x 3 reading frames). My results were obtained while trying to replicate his results on this bigger population table (Table 2).

    …so each DNA base contributes to 6 entries in the table: twice as the first base of a triplet (once read in each direction), twice as the second base, and twice as the third base. Similarly, that base’s pair on the other strand also shows up 6 times.

    Say that at a particular position there happens to be an adenine (A) base. It’ll contribute two counts somwehere in the third column (exectly where depends on its neighboring bases). From the base pairing rules, we know that its pair on the other strand will be a thymine (T) base, will contribute two counts to the first column. If the bases were reversed, the counts would be the same. And a guanine-cytosine pair would similarly contribute two counts each to colums two and four.

    So the base pairing rule trivially explains why the totals in columns 1+2 equals that in 3+4. The same principle applies when you look at the rows by first and third bases. No magic needed, just the standard chemistry of DNA base pairing.

    But what about the other ways of breaking the table down, e.g. columns 1+3 vs 2+4? In that case, we’re just comparing the total number of A-T pairs (times 4) with the number of G-C pairs (also times 4). I’ll get to its relationship to Phi in a minute, but first notice that when he breaks the table into rows based on the first base, he’s actually making the same calculation, just in a different format. And the same thing when he breaks it down by third base.

    There’s nothing special about the fact that he gets the same ratio each time; that’s an inevitable result of the way the table was constructed. The overlaid symmetries he gets all excited about (and draws the dragon curves etc based on) have nothing to do with the structure of the DNA, they’re entirely due to the way he constructed the table. The structure is entirely a figment of his analysis method!

    But what about that ratio, and its relationship to Phi? According to Perez, his ratio comes out to exactly 0.690983, which is (3-Phi)/2. But I ran the calculations on the table in the “Phi and Music in DNA” paper, and got 0.691457 instead. Pretty close, but not exact…

    The ratio he’s looking at is essentially a nonstandard way to describe the GC content of the genome — that is, the fraction of all base pairs that are G-C instead of A-T. Perez is looking at (C+G)/(A+T), while the standard GC ratio is defined as (C+G)/(C+G+A+T). The GC ratio is known to vary a great deal — between regions in the same genome, between different genomes… You can see the variation in different regions in the human genome in the figures in “An isochore map of human chromosomes” by Maria Costantini, Oliver Clay, Fabio Auletta, and Giorgio Bernardi, Genome Res. 2006:16:536-541. It’s pretty clear there’s no checksumming to keep this ratio consistent.

    Here are some examples of different species, based the Wikipedia article on Chargaff’s rules of DNA base frequencies, where I calculated the Perez-style ratio based on the listed CG ratio (sorry about the sloppy formatting):

    Species    CG Ratio   Perez ratio
    ?X174     44.8%   0.812
    Maize       46.1%   0.855
    Octopus   35.2%   0.543
    Chicken   43.7%   0.776
    Rat          42.9%   0.751
    Human    40.7%   0.686 (note that this doesn’t quite match either Perez’ or my figure)
    Grasshopper   41.2%   0.701
    Sea Urchin   35%   0.538
    Wheat    45.5%   0.835
    Yeast     35.8%   0.558
    E. coli    51.7%   1.070

    Again, note that the ratio varies quite a bit. The fact that the human ratio happens to be close to (3-Phi)/2 looks more like a coincidence than anything else. This is numerology, not science.

    So to sum up… we’ve got patterns injected by the analysis method, massive overinterpretation, and some numerology to top it off. Is there anything of any value here?

  13. 13
    scordova says:

    Gordon,

    Thanks for the 2nd pair of eyes on this.

    First off, it is acknowledged on several levels there is non-randomness in the DNA.

    For example, in coding regions there is codon bias.

    There is also 3 base periodicity.
    http://www.sciencedirect.com/s.....9307001543

    One way to test your claim is to throw the same test on randomly generated ATCG. Maybe there is a chemical constraint, maybe it is a biological constraint in the way the organism structures itself.

    As long as the tallying isn’t too complex, I think there is room for legitimate detection of checksum, and detection of polyconstraints on the DNA, etc.

    Thanks for you input, that’s why I posted it. I’ll revisit this again when I have a chance to discuss with others who are more qualified than I.

    Thanks again for your criticism.

  14. 14
    scordova says:

    Where I was heading with all this is that I think there are non-random pattern in the DNA as a whole including the “junk DNA”. It makes possible this:

    http://www.asknature.org/strat.....12sm41OXrc

    Whether Perez is right or wrong, the quest for non-random patterns is on. How Chargaff’s rules didn’t detect codon bias or 3 periodicity is an important question. Something has to give!

    Now if the physical “fractal globules” in

    http://www.asknature.org/strat.....12sm41OXrc

    have connection to the sequences, then this is also important, and it also means “junk DNA” has significance.

  15. 15
    Gordon Davisson says:

    Sal, I don’t think there’s any question that there are many types (/levels) of non-randomness in genome sequences. In addition to the ones you mentioned, I’ll throw isochores (regions with similar GC content) on the list. And note that they’re related to other forms of non-randomness, for example gene-rich regions tend to have high GC content, while gene deserts have low GC content.

    I think there are two different questions here, though: first, whether natural processes (i.e. evolution) can account for the various forms of non-randomness, and second whether Perez has identified a significant form of non-randomness.

    The first question is huge (in a sense, it’s the question of ID) and complicated, and I’ll mostly duck it here. Well, ok, a really quick summary of my opinion: some of the non-randomness can be accounted for by known processes and mechanisms, and some will be accounted for by processes and mechanisms we’ve yet to fully work out. I haven’t seen a convincing case either way for whether all non-randomness will eventually be explained by evolutionary mechanisms, so I don’t try to draw implications from either assumption.

    As for the second question, it’s also a bit hard to answer, because I don’t have access to the original paper, data, and analysis method. There’s some stuff at golden-ratio-in-dna.blogspot.com that might be by Perez, but I frankly find it incomprehensible. The discussion in “Phi and Music in DNA” is more readable, but I’m not sure it’s describing Perez’s analysis method correctly.

    If the description in “Phi and Music in DNA” is correct, I think my criticism is obviously correct (to the point of not needing to be tested). There is one small correction I need to make, though: bases near the end of a strand will not be counted as many times as I described. A base right at the end of a strand can only be the first or last of a triplet, so it only contributes to 2 entries in the table. Similarly, a base one from the end will only contribute to 4 entries. Bases two or more from the end contribute to the full 6 entries, as I described. Note, however, that the paired base also contributes the same amount, so the symmetry is maintained even in these cases.

    BTW, there’s another, more visible way to see the symmetry due to the analysis method: suppose that at a particular position in the genome, there’s an ACG triplet. It obviously gets counted in the ACG bin. In the reverse order, it also gets counted in the GCA bin. The paired triplet on the other strand, TGC, similarly gets counted in the TGC and (in reverse) CGT bins. This means that the ACG, GCA, TGC, and CGT bins should all have exactly the same count. And if you look at the figure you included, they do: 96112792. Similarly, CAT, TAC, GTA, and ATG all have 169023944; ATT, TTA, TAA, and AAT all have 260313647; etc.

    Note that if the analysis didn’t count both strands in both directions with all three possible starting offsets, we’d expect these symmetry groups to have the same counts on average, but have different statistical variations from that average. With all 12 possibilities counted, these variations are smoothed out and vanish, but if the 12-way overcounting wasn’t done we’d see at least some variation. Maybe not very much, though: if non-random features appear at random starting offsets on random strands, they’ll average out and produce near-symmetries in the table.

    That may sound backward, so I’d better also point out that in this table arrangement, uniformity shows up as symmetries in the table, and non-uniformity (i.e. deviations from uniform randomness) show up as asymmetries. For example, the 3-codon periodicity (actually, correlations between nearby bases in general) show up in the table as differences between bins that are anagrams of each other. Thus the difference in counts between CAT (169023944), CTA (149333215), and ACT (202932695) is an indicator of non-randomness in the sequence. Thus, the symmetries he points out (equalities between different rows and columns) are actually an indication of a lack of pattern in the DNA sequence (if anything).

    Note that this does lead to something that could be tested: generate a random sequence of ACGT with a slight bias toward AT, and see if the various permutation, reorder, and complement bins come out with nearly the same counts. I feel pretty secure that they will…

    I’m pretty sure we can also rule out the checksum claim. If it were correct, mutations wouldn’t happen (they do), and the different chromosomes would all have the same CG ratios (they don’t; look at figure 1 of the isochore map paper, and compare chromosomes 17, 19, and 22 with 4, 5, 13, and X).

  16. 16
    scordova says:

    Gordon,

    If it is okay with you:

    1. I can try to get you a copy of Perez orginal paper, I have several papers I’ll be getting in also, and I can make them available to you.

    2. I’d like to give you an account at Creation Evolution University to record some of our more technical interactions rather than at UD. It will be a better way of archiving some of what you have to say without it going quickly into cyber oblivion. What you have to say is to valuable, and it’s awfully hard to search for some of your comments in the quarter million or so at UD.

    I have my doubts as well, and I was waiting to see if anyone else would chime in. My main issue is how did Perez do an alignment so as to identify triplets. There are huge “NNNN” segments in fasta files, so how does he know where to start recognizing triplets?

  17. 17
  18. 18
    scordova says:

    Gordon,

    I publicly express my thanks for your meticulous work. I’m indebted to you:

    http://www.uncommondescent.com.....d-article/

  19. 19
  20. 20
    jean-claude perez says:

    Gordon
    all this work was reproduced by Dr Jordi sola soler
    http://www.sacred-geometry.es/.....-music-dna

    for human chromosome1 I have all data results…
    details in my book CODEX BIOGENESIS http://www.amazon.co.uk/Codex-.....2874340448

  21. 21
    jean-claude perez says:

    Dear GORDON DAVIDSON,
    you are right talking on PI:
    if the concensus ratio for whole human genome is (3-Phi)/2,

    the limits lower chromosomes are:
    chr4 1/Phi
    and
    chr19 1/Phi + 1/Pi !!!!
    please see details in my BEIJING conference here:
    http://fr.scribd.com/doc/57828.....jing032011

  22. 22
    jean-claude perez says:

    Full J.C. Perez’s interdisciplinary biography in CreationWiki: http://creationwiki.org/Jean-claude_Perez

  23. 23
    jean-claude perez says:

    Complete jc perez interdisciplinary Creation Wiki BIOGRAPHY in: http://creationwiki.org/Jean-claude_Perez

  24. 24
    jean-claude perez says:

    Dear SCORDOVA,
    your last question:

    “I have my doubts as well, and I was waiting to see if anyone else would chime in. My main issue is how did Perez do an alignment so as to identify triplets. There are huge “NNNN” segments in fasta files, so how does he know where to start recognizing triplets?”

    my response:
    on the about billion codon triplets within whole human genome, NNN undetermined nucleotides are unsignifiant. Then to be sure of consistency of analyses, we have analysed the 3 coden reading frames, compressing NNN bases. Then, the 3 kinds of results are highly similar…
    The perfect proportions fine tuning around golden ratio etc… are statistically unaffected by undetermined bases.
    MEANWHILE, in the CODEX BIOGENESIS book, we show how accuracy increase withe the progress of human genome project comparing the successives releases of HGP, with NNN decreasing at each release.
    THEN our ratios converge at each new release…
    Here is a DYNAMIC proof of our discovery!
    Please, see CODEX BIOGENESIS pp 155 fig 12.3 comparing Human Genome Project releases od April 2001, Nivember 2002 and August 2003.
    http://www.amazon.co.uk/Codex-.....2874340448

  25. 25
    Gordon Davisson says:

    Jean-Claude Perez, thank you for joining us! I have some questions that I’d appreciate if it you could answer:

    First, is there anyplace where you detail exactly how you performed your various analyses? I earlier used the description in the “Phi and Music in DNA” (by Jordi Solà-Soler), but it appears that doesn’t match your method. For example, in the “The Number « 4 »” section of your Beijing paper, the counts for TTT and AAA are close, but not exactly equal (while Jordi’s method will always give exactly equal counts for these triplets). How exactly did you do your counting?

    Second, it looks to me like you’re reading a lot of meaning into features of the data that don’t really seem (to me) to show anything significant. For example, in the “-III- Evidence of 2 « attractors »:« 1 » and « (3-Phi)/2 »” section of your Beijing paper, you make a great deal of the fact that ratio of triplets in the even and odd halves of the table (and even and odd octants, etc) are very close to 1. But isn’t this just a trivial result of DNA base pairing rules (every T on one strand matches an A on the other, and every C on one matches a G on the other), together with a lack of bias for either half of the pair to be concentrated on one strand?

    In other words, if you counted the bases on both strands, DNA pairing guarantees that the number of Ts will match the number of As, and the number of Cs will match the number of Gs. And unless there’s some consistent difference between the two strands that led, for example, most of the As to be in one strand and the matching Ts to be in the other, the counts should nearly match for each individual strand as well. Just as we see.

    (Note that your “codon level generalization of Chargaff’s second rule” can also be explained the same way.)

    As for the ratios that come out to (3–Phi)/2, aren’t these just the GC/AT ratios, filtered through the triplet table in various ways?

    Finally, is there any reason to regard the similarity between the GC/AT ratio and (3–Phi)/2 as anything other than a numeric coincidence? Especially since it varies a great deal between different regions of the genome, different chromosomes, and different species? Especially since there are many other formulae involving Phi that it could have come out close to (e.g. Phi/3, Phi/2, Phi-1, 4-2*Phi, 2*Phi/5, etc…)

  26. 26
    Gordon Davisson says:

    Sal, thanks for your offer. I don’t like to publish my regular email address anywhere spammer-searchable, but if you drop me a note at ——-, I’ll send you my real address.

    [e-mail edited to protect commenter privacy]

  27. 27
    scordova says:

    Gordon,

    I sent you an e-mail from my personal account. You’ll get an e-mail from the CEU Admin “Hwang” as well.

    I really don’t need your personal e-mail, just something address where I can privately transmit you username and password.

    I don’t expect there will be a lot of traffic on that discussion board, and that is by design. I prefer it to be a repository of research.

    Sal

  28. 28
    jean-claude perez says:

    Gordon,
    ALL your questions are fine and natural. But all find response in my 2 peer published articles referenced here:
    the 2010 article:
    http://fr.scribd.com/doc/95641.....atio-1-618
    the 2013 article:
    http://file.scirp.org/Html/4-7401586_37457.htm

  29. 29
    jean-claude perez says:

    Gordon,

    on your last main question:
    “Finally, is there any reason to regard the similarity between the GC/AT ratio and (3–Phi)/2 as anything other than a numeric coincidence? Especially since it varies a great deal between different regions of the genome, different chromosomes, and different species? Especially since there are many other formulae involving Phi that it could have come out close to (e.g. Phi/3, Phi/2, Phi-1, 4-2*Phi, 2*Phi/5, etc…)”

    2 responses:
    1/ In my 2013 article, it is show the universal nature of this value ‘quarks etc…°
    2/ applying this same analysis to:
    -whole human genome ==> (3-Phi)/2
    -lower scale chromosome is chr4: ratio = 1/Phi
    -highter scale chromosome is chr19: ratio = 1/Phi + 1/Pi
    strange no?

    Then analysing by others approachs chr4 shows that – if there is “design” – the right research way must be… HUMAN CHROMOSOME4

  30. 30
  31. 31
  32. 32
    quark1 says:

    Hello again.

    Golden ratio formula: Other way to find the thi number.

    (((TTA)-(TTC))^2 + ((TCT)-(TAT))^2 + ((TGC) – (TGA))^2 + ((TAG)-(TCG))^2)/10^16 = 1,618

    General Quadrilateral

    The general quadrilateral can be find from this numbers.

    We name the angles with the corners like e,f,v and w and the two diagonals for E and F. The length of the general quadrilateral can be named as A, B, C, and D

    The length can be calculate as

    A = ((TTT)-(TTC))+((TTG)-(TTA))
    B = ((TTT)-(TCT))+((TGT)-(TAT))
    C = ((TGT)-(TGC))+((TGG)-(TGA))
    D = ((TTG)-(TCG))+((TGG)-(TAG))

    The total angle for this geometry is 2pi, and we want to use the cosinus law to calculate the angles. But we need the diagonal for that and if we look at the numbers in the inner circle which is (TCC),(TAC),(TCA) and (TAA), this numbers maybe can be corresponding to the diagonals. One can see some kind of structure of this thing. However use that the diagonal is

    E = (TAC) and F = (TCA)

    e = cos^-1((E^2-A^2-C^2)/2AC) = 1,6371
    f = cos^-1((E^2-B^2-D^2)/2BD) = 2,011
    v = cos^-1((F^2-C^2-D^2)/2CD) = 0,83
    w = cos^-1((F^2-A^2-B^2)/2AB) = 1,799

    Thereafter,

    e + f + v + w = 2pi

    For this case I only get an error on 0,1%. Other cases this will be between 1 to 2 %.

    Probability other geometrical figures can be find, I´m nearly to find one formula for the circle from this thing but so far the error is about over 5 %.

  33. 33
    jean-claude perez says:

    quark1: no comment… Here is the real limit of a real Scientific!

  34. 34
    jean-claude perez says:

    Quark1: I precise a bit: there is here the distance between SCIENCE and… NUMEROLOGY !

  35. 35
    jean-claude perez says:

    Quark1:
    intersting perhaps:
    our devise of researchers and not of numerologists: in french sorry:
    “il faut separer le bon grain de l’ivraie”

  36. 36
    quark1 says:

    Hello Jean-Claude Perez

    No, I did this only for fun and I like math.

  37. 37
    jean-claude perez says:

    Quark1
    Free Curiisity is on of the first qualities to do best and real …research in science.. Like you do there!!

  38. 38
    jean-claude perez says:

    Dears Pietr, Joe or Gordon, I’m sorry for your unapropried comments on SANDWALK of the Sal Cordova entry entitled Vodka! Jean Claude Perez, the golden ratio, dragon curve fractals and musical design in “junk DNA”…

    The reason is that all (ALL) their comments were done without reading the basic original article:

    I suggest you reading the original basic peer review article of 2010 published in Interdisciplinary Science:
    http://fr.scribd.com/doc/95641.....atio-1-618

    and my 2013 peer review article: http://www.scirp.org/journal/P.....2Mwlfl_trA

  39. 39
    jean-claude perez says:

    To Gordon:
    An important topic showing you run in error: precisely if i analyse SINGLE STRANDED DNA the trivial crick watson base pairing disappear .
    Then your analyse is wrong
    in the native article we discuss this topic…
    I m sorry…

  40. 40
    jean-claude perez says:

    Contrarly
    The first pi analyse of QUARK
    MIST be studied seriously!

  41. 41
    jean-claude perez says:

    2 last remarks on Gordon Davisson remarks 12 and 15:
    Post 12: you are wrong because precisely i analyse single stranded dna whole genome sequence where there id no reason to find trivial crick watson pairing symmetries…
    Post 15: i meet inventor of ISOCHORES PR GIORGIO BERNARDI 25 years ago in his lab paris sorbonne… There are no links between my analysis at codpn population scale and ospchorrs ratips… We no not considere here the same level of henomic infprmation: him. Ratiod cg by species. Me. Codons triplets

  42. 42
    jean-claude perez says:

    ERRATUM
    2 last remarks on Gordon Davisson remarks 12 and 15:

    Post 12: you are wrong because precisely i analyse single stranded dna whole genome sequence where there is no reason to find trivial crick watson pairing symmetries…

    Post 15: i meet inventor of ISOCHORES PR GIORGIO BERNARDI 25 years ago in his lab paris sorbonne…
    There are no links between my analysis at codpn population scale and ISOCHORES ratios…
    We no not considere here the same level of genomic infpamation:
    him. Ratiod CG by species.
    Me. Codons triplets

  43. 43
    craig373 says:

    I have just carried out three experiments to verify the work done by Perez. I am happy to announce that his findings have been confirmed.

    I have created a video showing each of the experiments. You can view the video by going to Youtube and entering the phrase “Mathematical Patterns within DNA”

    Craig Paardekooper

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