Darwinism: Is this a big, fat dogfight in the making? Coyne vs. Matzke?

6 Replies to “Darwinism: Is this a big, fat dogfight in the making? Coyne vs. Matzke?”

1. 1
   bornagain77 says:

   November 1, 2011 at 3:01 pm

   Here is something else you don’t see everyday; Here a Darwinian Psychologist has a actual ‘moment of honesty’ facing the ‘hard problem’ of consciousness;

   Darwinian Psychologist David Barash Admits the Seeming Insolubility of Science’s “Hardest Problem”
   Excerpt: ‘But the hard problem of consciousness is so hard that I can’t even imagine what kind of empirical findings would satisfactorily solve it. In fact, I don’t even know what kind of discovery would get us to first base, not to mention a home run.’
   David Barash – Materialist/Atheist Darwinian Psychologist
   http://www.evolutionnews.org/2.....52491.html
2. 2
   Starbuck says:

   November 1, 2011 at 4:46 pm

   Matzke is a commenter here, and apparently no fan of Jerry and the other gnu atheists, because he has good relationships with Catholic Darwinists.

   lol, naww. I think he’s just a very thoughtful person and not afraid to criticize anyone, whether gnu or not.
3. 3
   CannuckianYankee says:

   November 1, 2011 at 7:26 pm

   Um, how about a near death experience?

   BA, I’m surprised you didn’t mention that. 😉
4. 4
   bornagain77 says:

   November 1, 2011 at 7:39 pm

   CannuckianYankee, here is another beaut of a quote I found today:

   DNA Computer
   Excerpt: DNA computers will work through the use of DNA-based logic gates. These logic gates are very much similar to what is used in our computers today with the only difference being the composition of the input and output signals.,,, With the use of DNA logic gates, a DNA computer the size of a teardrop will be more powerful than today’s most powerful supercomputer. A DNA chip less than the size of a dime will have the capacity to perform 10 trillion parallel calculations at one time as well as hold ten terabytes of data. The capacity to perform parallel calculations, much more trillions of parallel calculations, is something silicon-based computers are not able to do. As such, a complex mathematical problem that could take silicon-based computers thousands of years to solve can be done by DNA computers in hours.
   http://www.tech-faq.com/dna-computer.html

   Remember a few years back when neo-Darwinists denied that information was even in the DNA ??? 🙂
5. 5
   SCheesman says:

   November 2, 2011 at 2:21 pm

   Parallel programming is a good deal more difficult than just finding a subtrate like DNA capable of doing millions or billions of operations simultaneously. You have to find a way for to coordinate all the threads and allow for inter-communication of results as well.

   A sizeable number of difficult problems are hardly even amenable to parallelization, so it’s hardly a magic bullet.
6. 6
   bornagain77 says:

   November 2, 2011 at 2:58 pm

   SCheesman thanks for pointing that out,,, that’s the whole thing though,,, parallel programming, such as we find in the cell’s DNA, is magnitudes of levels higher, in functional integrated complexity, than anything that our top teams of computer programmers have accomplished, and yet, though no one can seem to locate any random mutations/variations that increase functional information, above the completely trivial (and sometimes the trivially contrived) examples we see, we are told, by supposedly rational PhD. holding people, that this level of complexity, that human computer programmers can only dream of imitating, arose by Random material processes. The sheer disconnect between reality and reason would be hard for me to believe if I have not seen such insanity first hand!!!

   notes:

   Scientists Map All Mammalian Gene Interactions – August 2010
   Excerpt: Mammals, including humans, have roughly 20,000 different genes.,,, They found a network of more than 7 million interactions encompassing essentially every one of the genes in the mammalian genome.
   http://www.sciencedaily.com/re.....142044.htm

   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/c.....2-2-29.pdf

   Astonishing DNA complexity update
   Excerpt: The untranslated regions (now called UTRs, rather than ‘junk’) are far more important than the translated regions (the genes), as measured by the number of DNA bases appearing in RNA transcripts. Genic regions are transcribed on average in five different overlapping and interleaved ways, while UTRs are transcribed on average in seven different overlapping and interleaved ways. Since there are about 33 times as many bases in UTRs than in genic regions, that makes the ‘junk’ about 50 times more active than the genes.
   http://creation.com/astonishin.....ity-update

   Scientists Discover Parallel Codes In Genes
   Excerpt: Their work,,, shows that the genetic code — used by organisms as diverse as reef coral, termites, and humans — is nearly optimal for encoding signals of any length in parallel to sequences that code for proteins.
   http://www.sciencedaily.com/re.....230116.htm

   “There is abundant evidence that most DNA sequences are poly-functional, and therefore are poly-constrained. This fact has been extensively demonstrated by Trifonov (1989). For example, most human coding sequences encode for two different RNAs, read in opposite directions i.e. Both DNA strands are transcribed ( Yelin et al., 2003). Some sequences encode for different proteins depending on where translation is initiated and where the reading frame begins (i.e. read-through proteins). Some sequences encode for different proteins based upon alternate mRNA splicing. Some sequences serve simultaneously for protein-encoding and also serve as internal transcriptional promoters. Some sequences encode for both a protein coding, and a protein-binding region. Alu elements and origins-of-replication can be found within functional promoters and within exons. Basically all DNA sequences are constrained by isochore requirements (regional GC content), “word” content (species-specific profiles of di-, tri-, and tetra-nucleotide frequencies), and nucleosome binding sites (i.e. All DNA must condense). Selective condensation is clearly implicated in gene regulation, and selective nucleosome binding is controlled by specific DNA sequence patterns – which must permeate the entire genome. Lastly, probably all sequences do what they do, even as they also affect general spacing and DNA-folding/architecture – which is clearly sequence dependent. To explain the incredible amount of information which must somehow be packed into the genome (given that extreme complexity of life), we really have to assume that there are even higher levels of organization and information encrypted within the genome. For example, there is another whole level of organization at the epigenetic level (Gibbs 2003). There also appears to be extensive sequence dependent three-dimensional organization within chromosomes and the whole nucleus (Manuelides, 1990; Gardiner, 1995; Flam, 1994). Trifonov (1989), has shown that probably all DNA sequences in the genome encrypt multiple “codes” (up to 12 codes).
   Dr. John Sanford; Genetic Entropy 2005

   DNA Caught Rock ‘N Rollin’: On Rare Occasions DNA Dances Itself Into a Different Shape – January 2011
   Excerpt: Because critical interactions between DNA and proteins are thought to be directed by both the sequence of bases and the flexing of the molecule, these excited states represent a whole new level of information contained in the genetic code,
   http://www.sciencedaily.com/re.....104244.htm

   3-D Structure Of Human Genome: Fractal Globule Architecture Packs Two Meters Of DNA Into Each Cell – Oct. 2009
   Excerpt: the information density in the nucleus is trillions of times higher than on a computer chip — while avoiding the knots and tangles that might interfere with the cell’s ability to read its own genome. Moreover, the DNA can easily unfold and refold during gene activation, gene repression, and cell replication.
   http://www.sciencedaily.com/re.....142957.htm

   Mutations : when benefits level off – June 2011 – (Lenski’s e-coli after 50,000 generations;,, approx. equal to 1,000,000 years of human evolution)
   Excerpt: After having identified the first five beneficial mutations combined successively and spontaneously in the bacterial population, the scientists generated, from the ancestral bacterial strain, 32 mutant strains exhibiting all of the possible combinations of each of these five mutations. They then noted that the benefit linked to the simultaneous presence of five mutations was less than the sum of the individual benefits conferred by each mutation individually.
   http://www2.cnrs.fr/en/1867.htm?theme1=7

   The GS (genetic selection) Principle – David L. Abel – 2009
   Excerpt: Stunningly, information has been shown not to increase in the coding regions of DNA with evolution. Mutations do not produce increased information. Mira et al (65) showed that the amount of coding in DNA actually decreases with evolution of bacterial genomes, not increases. This paper parallels Petrov’s papers starting with (66) showing a net DNA loss with Drosophila evolution (67). Konopka (68) found strong evidence against the contention of Subba Rao et al (69, 70) that information increases with mutations. The information content of the coding regions in DNA does not tend to increase with evolution as hypothesized. Konopka also found Shannon complexity not to be a suitable indicator of evolutionary progress over a wide range of evolving genes. Konopka’s work applies Shannon theory to known functional text. Kok et al. (71) also found that information does not increase in DNA with evolution. As with Konopka, this finding is in the context of the change in mere Shannon uncertainty. The latter is a far more forgiving definition of information than that required for prescriptive information (PI) (21, 22, 33, 72). It is all the more significant that mutations do not program increased PI. Prescriptive information either instructs or directly produces formal function. No increase in Shannon or Prescriptive information occurs in duplication. What the above papers show is that not even variation of the duplication produces new information, not even Shannon “information.”
   http://www.bioscience.org/2009.....lltext.htm

   Human DNA is like a computer program but far, far more advanced than any software we’ve ever created.
   Bill Gates, The Road Ahead, 1996, p. 188