The Cat and the Hat - And the Evolution of Code

The Cat and the Hat

And the Evolution of Code

Sean Pitman M.D.

© July 2003

A man walks into a store and tells the clerk, “I’m looking to buy a hat.” The clerk says, “We are all out of hats, but I do have a cat that I will sell to you at a good price.” The man tells the clerk, “Close enough. I’ll take it.”

This is just silly - right? A cat is nothing like a hat despite the fact that the words look and sound similar. The point is, words are just arbitrary symbolic representations of ideas. The letters or symbols in a word mean nothing unless they are assigned a meaning by an outside source (like a dictionary or a codebook). Because of the arbitrary nature of language any symbol or group of symbols can be assigned any definition, as long as it is agreed upon or understood by those who wish to use symbols to communicate ideas. In this way, some very similar ideas can be represented by very different looking words or some very different ideas can be represented by some very similar looking and sounding words. For example, the words “Admire” and “Esteem” have very similar meanings, but look nothing like each other. The words “Vacation” and “Vocation” look and sound very similar, but have very different meanings. Why? Because of the arbitrary nature of language. All languages are arbitrary in that written or spoken symbols (or other symbols such as are used in sign language) are given their meaning and this meaning is independent of and greater than the symbols themselves.

Symbolic languages are not just limited to human communication. Every living thing uses symbolic language to communicate information. How? In the form of genetic words written in the languages of DNA and protein. If you are interested in the details, just look in any basic biology textbook, and you will find that the language of DNA is made up of words. Each of these words is given an arbitrary meaning by a codebook called the “Genetic Code.” Proteins are also “written” using letters in a chemical alphabet called amino acids. There are 20 different amino acids just as there are 26 different letters in the English alphabet. Different arrangements of these letters in proteins spell out protein words, which are given an arbitrary meaning or function by the cell that makes them. Just as in any other symbolic language, there is no inherent meaning for a given protein outside of the how the cell defines it. For example, the protein called “insulin” is a signal to some cells in the human body to uptake sugar (glucose) from the blood stream. The insulin protein (Bovine Insulin) is made up of two protein words that are linked together. One of these words is 21 letters (amino acids) in length. The other word is 30 letters in length.¹ There is nothing special about these words in and of themselves that tells a cell that it needs to uptake sugar. So, how does the cell “know” what to do when it comes in contact with insulin? The cell recognizes insulin. But how does the cell recognize insulin? The cell has a specific receptor protein that senses insulin like a lock recognizes a key. Then, just like when a key turns a lock, this insulin receptor sends a signal to the cell that tells it to uptake sugar. In other words, this lock is linked to an underlying system of function. The key that it recognizes is the insulin key, but this recognition is arbitrary. The same function could in fact be set up to recognize any other protein “word” or “words.” The fact that it recognizes insulin is strictly arbitrary, just as in any other symbolic language. The insulin molecule is simply a symbolic representation of an idea or a function that the cell recognizes. The cell recognizes insulin because it is programmed to recognize the language of the body or “system” that it is a part of. Specialized cells make the insulin protein as a symbolic message to other cells in the body that tell them when and how they need to use the blood sugar that is available to the body. They could just as easily have been programmed to use some other protein molecule or “word” for the same purpose. The fact that living creatures use symbols to send messages and to perform functions is undeniable. The fact that these messages are arbitrary and dependent upon a pre-established code of definition also seems intuitive.

The question now is, how did these arbitrary languages and words of living things come about? For the English language, and all other known languages, the ideas come first, and then the symbolic expressions of the ideas (since the symbols themselves have no inherent meaning). The letters, “cat” mean nothing aside from the attached idea that is arbitrarily given them by the English dictionary (or the English speaking “environment”). Likewise, the letters in the insulin molecule mean nothing outside of the attached meaning given to them by a living cell or system in a particular environment. Do words change their meaning through an evolution of random letter changes, or through an evolution of ideas, which then seeks out some symbolic representation? If I change the letters “cat” to read “hat”, does this change necessitate an evolution of recognition or function in and of itself? Obviously not because if the change read “cct” this change would have no meaning. Why? Because “cct” is not defined in the English dictionary/environment as being meaningful much less beneficial in a given situation. Remember, the symbols or letters themselves have no inherent meaning whatsoever. Definition and recognition must always come before a symbolic representation. So, if I change the letters in the insulin words around, would these changes necessitate a change in cell recognition and function? No, of course not. In fact, if the letters in the insulin words change too much, the cell would not recognize the new molecule at all. Why? Because this new protein may not be defined in the cell’s dictionary of protein words.

However, is it possible to change a letter of a word randomly and have it mean something in the English dictionary? Of course it is, but this change would need to have had a pre-existent definition waiting for it in the dictionary. Changing the “c” in the word “cat” to an “h” in the word “hat” does in fact change the understood definition at the same time - but why? Because, both of the words, “cat” and “hat” were pre-defined by the English dictionary/environment. Similarly, it would be possible to change insulin into another protein that did in fact have function - if the cell or organism had a pre-established system that recognized this “new” protein.

Now, let me pose a scenario. Regis Philbin is the host of a game show called “Millionaire or Not” and you are the next contestant. In front of you is a safety deposit box with a million dollars in it. On the front of the box is an apparatus that looks like a slot machine. It has 15 rotating wheels, each with the 26 letters of the alphabet on it. Regis tells you that there are one million different winning combinations of fifteen letters that will open the safety deposit box. You can rotate each wheel at will and then press a button to see if the combination that you chose is one of the one million winning combinations. You can keep doing this until you give up. You think that this game is a synch. With one million winning combinations possible, you are practically guaranteed to win. However, if you never choose the same combination twice and if you test a new combination every second, how long will it take you on average to find any one of the one million correct combinations? It would take you a bit over 53 million years on average. It is definitely not as easy as it looks anymore is it? It sure would help if you could figure out which combination that you chose was “closer” to any of the winning combinations now wouldn’t it? However, there is no function except the winning “function” to any combination that you try. There is no “close” function. No lights go off when you are getting “warmer” or “colder.” You see, without some indication, without some intelligible function or signal attached to the losing combinations, you are completely in the dark as far as your ability to know if you are even getting close to a winning combination.

Likewise, in living cells, there are far less usable or recognized proteins than there are possible proteins as one moves up the ladder of functional complexity. Obviously, like simple words with simple functions, there are also simple proteins with simple or very general functional capabilities. All real-time examples of evolution in action point to functional changes in proteins that are very simple in functional complexity (such as examples of antibiotic resistance and other types of drug resistance). However, as with the English language, there are levels of functional complexity when it comes to cellular functions.

Getting from one "beneficial" protein to another "beneficial" protein by random mutation is next to impossible above a certain level of specified complexity (which is determined by both the cellular makeup as well as the environment). There is a gulf of neutral or even detrimental protein sequences between potentially beneficial proteins that expands exponentially with increasing levels of functional complexity. During the crossing of such neutral oceans of function, there is no way for nature to detect when a neutral protein is getting closer to a beneficial sequence. The reason for this is because nature is blind without an ability to detect a change in function with a neutral change in symbolic sequence just as a player of “Millionaire or Not” would be blind without an ability to detect any change in function between each new non-winning change in the 15-letter sequence.

Certain functions, like antibiotic resistance functions, are extremely simple to evolve because they are based on changes that interfere with or destroy other pre-established functions or interactions. An antibiotic's interaction with it's target is very specific. Often, very slight changes or mutations are all that are needed to interfere with this interaction. Obviously, the ratio of interfering proteins as compared to the total number of potential protein sequences (the total number of possible protein words) is very high. In other words, there exist in the potential pile of protein words a very large number of proteins that would not react very well with the antibiotic. Because of this high ratio of interfering proteins, the odds that a change in the original protein would end up on an interfering protein are very high. Thus, the evolution of resistance to this antibiotic is very likely. This phenomenon is clearly supported by the real life ability of bacteria to overcome just about any antibiotic that comes their way in very short order. Clearly, this is a real time example of evolution in action, but the new function that was evolved here was obviously of the lowest level of functional complexity. Consider that it was much easier to break Humpty Dumpty than it was to put him back together again. Every child knows this law of nature. It is far easier to destroy than it is to create.

Why is this? Why is it easier to destroy than it is to create from scratch? The reason is that there are a lot more ways to destroy than there are to create. There are a zillion more ways to break a glass vase than there are to fix or create that glass vase to begin with. The same thing holds true for the various functions in living things. They are like glass vases.

The evolution of antibiotic resistance via changes to a target sequence was easy because it involved the breaking of an established function/interaction. However, many more functions exist in living things that cannot be created by breaking some pre-established function. The relatively simple function of single protein enzymes is a good example of this. There is certainly no way to get the penicillinase enzyme (another method of antibiotic resistance - but the penicillinase enzyme does not evolve in real time) to evolve by disrupting some other interaction. The same is true for the lactase and nylonase enzymes. The functions of these enzymes cannot be realized by interfering with other pre-established functions. Experiments which include the evolution of the lactase enzyme in E. coli (a study done by Professor Barry Hall), and the evolution of the nylonase enzyme (from a paper by Kinoshita, et. al.), to name just two of many such examples, demonstrate this phenomenon nicely.^10,11,12 What Hall showed is that if some genes are deleted in living cells like E. coli, they are simply incapable of evolving certain specified functions, such as the lactase ability, despite strong selection pressures and thousands upon thousands of generations of time. Professor Hall himself described such colonies of bacteria as having, "limited evolutionary potential." What is especially interesting about such experiments is that these same colonies of bacteria would be able to evolve antibiotic resistance to just about any antibiotic presented to them in short order. And yet, they simply could not evolve the relatively simple lactase function over the course of tens of thousands of generations?

So it is clear that although single protein enzymes might be fairly simple when compared to other cellular functions, they are still fairly complex in that the ratio of sequences with a particular or specified type of enzymatic function is fairly low when compared to the total number of potential protein sequences or "words". Even so, the ratio is often high enough so that on relatively rare occasions, large populations of bacteria can and have been shown to evolve unique beneficial functions that are based on the potential of single protein enzymes (i.e., the lactase and nylonase functions - as well as many other such examples are clearly examples of the de novo evolution of brand new functions that are not dependent upon the loss of any other known cellular function). Despite their demonstrated evolution, this evolution is obviously much more difficult to come by than examples of the evolution of functions of lower levels of specified complexity (such as the de novo evolution of antibiotic resistance).

The problem for the theory of evolution is found in the fact that the ladder of function complexity keeps going up. The next rung up this ladder of functional complexity includes those functions that require multiple proteins all working together at the same time in a specified arrangement with each other. Examples of this level of cellular function include functions like bacterial motility systems (like the flagellar apparatus which requires around 50 or 60 parts all working together at the same time in a specified arrangement or order). Interestingly enough, when it comes to this level of specified functional complexity there simply are no examples of evolution in action - period. No one has ever demonstrated the evolution of a multi-protein system of function (of even a few proteins) where all of the protein parts are working together at the same time in a specified arrangement. I propose that the reason for this is that ratio of protein sequences that could give rise to such a specified function at this level of functional complexity is truly miniscule when compared to the total number of proteins in the vast ocean of potential protein "sentences/paragraphs". This tiny ratio of what will work as compared to what won't work creates a neutral gap between various potentially beneficial functions at this level of complexity that is simply too wide for the random walk of random mutations to overcome - even in trillions upon trillions of years.

The Theory of Evolution is in serious crisis because of this very problem although many have tried to explain away this problem. One valiant attempt was made by the famous British evolutionary biologist Richard Dawkins. In his 1986 book called “The Blind Watchmaker” Dawkins described an experiment of his that showed how evolution is supposed to work. He programmed a computer to generate random sequences of letters to see if the computer would, over time, generate the line from Hamlet, “METHINKS IT IS LIKE A WEASEL.” This line has 28 characters (including spaces), so the computer was programmed to make 28 selections using the 26 letters of the alphabet plus a space to make 27 possible characters to pick from. A typical output was “MWR SWTNUZMLDCLEUBXTQHNZVJQF.” With this information, a calculation of the probability of picking the “correct” sequence can be made, as well as how long it would take, on average, to find this correct sequence. Dawkins figured that it would take his computer a million million million million million million years (or a trillion trillion trillion years… 1 x 10³⁶ years), on average. Well, this is clearly way too long for the current theory. So, how could evolution possibly take place? Dawkins now put some “natural selection” into the computer program to simulate “real life” more closely. The computer made multiple copies of “MWR SWTNUZMLDCLEUBXTQHNZVJQF” (Offspring) while introducing random “errors” (mutations) into the copies. The computer then examined all the mutated “offspring” and selected the one that had the closest match to, “METHINKS IT IS LIKE A WEASEL.” This selection by the computer (nature) was now used to make new copies and random mutations (in a “new generation”), from which the best copy was selected again… and so on. By ten “generations” the sequence had “evolved” to read, “MDLDMNLS ITJISWHRQEZ MECS P.” By the thirtieth generation it read, “METHINGS IT ISWLIKE B WECSEL.” Instead of taking many trillions and zillions of years this time, the computer came up with the “fittest” phrase in only forty-three generations.²

Yes, Dawkins does make a disclaimer about this experiment saying that it is not intended as a demonstration how real evolution works. He says that it is only an example to show how a selection mechanism gives an advantage over time. Even so, I still fail to see the relevance to the theory of evolution. The most obvious problem is that the computer already had the “correct” phrase programmed into it ahead of time, which it could use to compare any future phrases to see if they were getting closer. Evolving something that is already there is not the evolution of anything new at all. If nature already has what it wants or needs, then it does not need to “evolve” it. I mean really, if computers could be so easily creative in the way Dawkins describes, then we would not need Shakespeare now would we?

Another problem with Dawkins’s illustration is that nature cannot select for what is not functioning. Nature does not “see” the actual letters of words (in DNA or Protein). All that nature can see is what function results. Since function is arbitrarily attached to words by an outside source of information as previously described, a gradual change in the letters of the words themselves is not going to result in a gradual evolution of their meaning or function beyond the lowest levels of functional complexity. A gradual letter by letter change in a beneficial collection of words/sentences will most likely destroy their original collective meaning well before any new beneficial function of the same level of complexity is realized. The reason for this limitation can be found in the neutral gap problem that is created by the exponential expansion of the pile of "junk proteins" as the level of complexity increases. This neutral gaps blinds the abilities of natural selection to guide evolution to new functions of increasing complexity. Neutral changes do not result in functional changes. Without functional changes along the entire path toward a new function, natural selection is blind. Without natural selection as a driving force, even Dawkins will admit that evolution is statistically impossible.

Michael Behe, a professor of biochemistry at Lehigh University, says that, “Molecular evolution is not based on scientific authority. There is no publication in the scientific literature in prestigious journals, specialty journals, or books that describe how molecular evolution of any real, complex, biochemical system either did occur or even might have occurred. There are assertions that such evolution occurred, but absolutely none are supported by pertinent experiments or calculations.”³

Should these facts be pasted by unacknowledged by the scientific mind? It seems like evolutionary theories have had ample time to prove themselves. “It is good to keep in mind ... that nobody has ever succeeded in producing even one new species by the accumulation of micromutations. Darwin's theory of natural selection [as a creative force beyond the lowest levels of functional complexity] has never had any proof, yet it has been universally accepted.”⁴

If significant evolution could happen in just a few generations as Dawkins indicates, then why is it not being observed in cells like bacteria that have very short generation times? Over the past 50+ years, greater than one million generations of E. coli have been observed, radiated, drugged, burned, frozen, dissected, mutated, selected and manipulated in every conceivable manner (talk about selection pressure), and yet E. coli is still E. coli.? This seems especially strange when one considers that humans supposedly evolved from apes in less than 200,000 generations using a much lower mutation rate (on the order of one mutation per gene per 100,000 generations).^8,9 Dr. Robert Macnab seems to be asking the same question when he comments that “…one can only marvel at the intricacy in a simple bacterium, of the total motor and sensory system which has been the subject of this review and remark that our concept of evolution by selective advantage must surely be an oversimplification.”⁵ Gordon Taylor also observes, “In all the thousands of fly-breeding experiments carried out all over the world for more than fifty years, a distinct new species has never been seen to emerge.”⁶

“...An intelligible communication via radio signal from some distant galaxy would be widely hailed as evidence of an intelligent source. Why then doesn't the message sequence on the DNA molecule also constitute prima facie evidence for an intelligent source? After all, DNA information is not just analogous to a message sequence such as Morse code, it is such a message sequence.”⁷ Has Design Theory come full circle? Many, even among the most respected of scientific minds, seem to be giving it more than another look.

1. Stryer, Lubert. Biochemistry, 3^rd ed., 1988, pp. 153, 744.

2. Dawkins, Richard. The Blind Watchmaker, 1987.

3. Behe, Michael J. Darwin’s Black Box, The Free Press, 1996.

4. Goldschmidt, R. PhD, DSc Prof. Zoology, University of Calif. in Material Basis of Evolution, Yale Univ. Press.

5. Macnab, Robert. Yale University, Bacterial Mobility and Chemotaxis: The Molecular Biology of a Behavioral System, CRC Critical Reviews in Biochemistry, vol. 5, issue 4, Dec., 1978, pp. 291-341

6. Taylor, Gordon. The Great Evolution Mystery, New York: Harper and Row, 1983, pp 34, 38

7. Thaxton, Charles B. Walter L Bradley and Robert L Olsen: The Mystery of Life's Origin, Reassessing Current Theories, New York Philosophical Library 1984, pp 211-212.

8. Dugaiczyk, Achilles. Lecture Notes, Biochemistry 110-A, University California Riverside, Fall 1999.

9. Ayala, Francisco J. Teleological Explanations in Evolutionary Biology, Philosophy of Science, March, 1970, p. 3.

10. B.G. Hall, Evolution on a Petri Dish. The Evolved B-Galactosidase System as a Model for Studying Acquisitive Evolution in the Laboratory, Evolutionary Biology, 15(1982): 85-150.

11. Kinoshita, et. al.,"Purification and Characterization of 6-Aminohexanoic-Acid-Oligomer Hydrolase of Flavobacterium sp. K172," Eur. J. Biochem. 116, 547-551 (1981), FEBS 1981.

12. Susumu Ohno, "Birth of a unique enzyme from an alternative reading frame of the pre-existed, internally repetitious coding sequence", Proc. Natl. Acad. Sci. USA, Vol. 81, pp. 2421-2425, April 1984.

. Home Page . Truth, the Scientific Method, and Evolution

. Methinks it is Like a Weasel . The Cat and the Hat - The Evolution of Code

. Maquiziliducks - The Language of Evolution . Defining Evolution

. The God of the Gaps . Rube Goldberg Machines

. Evolving the Irreducible . Gregor Mendel

. Natural Selection . Computer Evolution

. Which Came First . Antibiotic Resistance

. The Immune System . Pseudogenes

. Genetic Phylogeny . Fossilized DNA

. DNA Mutation Rates . Donkeys, Horses, Mules and Evolution

. The Fossil Record . The Geologic Column

. Early Man . The Human Eye