- Detecting Design
- The Evolution of Early Man
- Age of the Earth
- Evolution’s Limits
- Personal Profile
- Ignorant, Stupid, or Insane…
- The Detection of Intelligent Design
- The Emperor’s New Clothes
- Methinks it is like a Weasel
- The Geologic Column
- The Cat and the Hat
- Meaningful Information and Artifact
- The Fossil Record
- Defining Evolution and its Boundaries
- Milankovitch Cycles and the Age of the Earth
- Ancient Ice
- Radiocarbon and Tree Ring Dating
- Amino Acid Racemization Dating
- Radiometric Dating Methods
- DNA Mutation Rates and Evolution
- The Evolution of the Flagellum and the Climbing of “Mt. Improbable”
- J Harlen Bretz
Defining Evolution and its Boundaries
Sean D. Pitman M.D.© September 2006
- Charles Darwin
- Gregor Mendel
- Breeding via Human Selection
- Random Mutations
- Functional vs. Neutral Mutations
- Antibiotic Resistance
- Enzyme Evolution
- Limited Evolutionary Potential
- Growing Neutral Gaps
- The Junk Pile and The Ladder of Functional Complexity
- A Construction Foreman Who Cannot Read
- Adding a Stereo System to a Car
- One Last Question
The theory of evolution is based on a very simple and elegant idea. The idea is that all living things have a common ancestor. The differences between living things that exist today are thought to be the result of “common decent with modification” or slight changes over time that have simply added up over many millions of generations to produce the remarkable variety that we see today. These modifications are the result of mindless non-directed random genetic mutations combined with natural selection. Natural selection is able to select between these randomly produced genetic sequences in such a way that those sequences with the greatest attached beneficial function provide their host the the greatest reproductive advantage. In this way, natural selection, while still mindless or non-intentional, is the non-random part of evolution. 3,4,5,6
If the amazing diversity of life forms on this planet arose from the evolutionary potential of a common ancestor life form, the assumption can be made that all or nearly all life forms living today have the same potential for future diversity. If true, this mindless force is a very creative force. But, how does this mindless force work?
Most will agree that if living things change over time, they change because their D.N.A. (deoxyribonucleic acid) changed. The information contained in the DNA is called the “genotype”. The expression of this information in the physical form of the creature is called the “phenotype”. 1, 2 DNA is very much like the paper that the blueprint for a house is written down on. This blueprint is equivalent to the genotype. The actual house, once it is built, is equivalent to the phenotype. The phenotype changes only if the blueprint changes.
The theory of evolution proposes that all the various blueprints of living things are descendents of a single common ancestor blueprint. The diversity of blueprints that exist today is simply the result of variations on a single theme. If true, the power of the mindless evolutionary force of nature is truly astounding. The sheer creativity and magnificent diversity of nature is enough to make even the most cynical stand in silent awe. If humankind could harness this power and speed it up with the aid of our intelligent minds, the implications for advancement seem unlimited.
How then does this mindless evolution work? How does the equivalent of a blueprint for a house change over time to code for phenotypic structures as diverse as an automobile, a battleship, a skyscraper, or a screwdriver? Obviously, at least this degree of diversity is seen in living things in the forms of creatures like bacteria, oak trees, and elephants. In considering this question perhaps we should begin with Darwin and what he saw. (Back to Top)
Charles Darwin (1809-1882) came up with his famous version of the theory of evolution after observing some very interesting differences, such as the variation in the size and shape of finch beaks on the Galapagos Islands. Many other similar changes have also been observed and carefully documented. Certainly these are “changes” and as changes many would call them evolutionary changes. If the theory of evolution is defined as any and every phenotypic change that occurs from parents to offspring, then it might be perfectly fine to say that finches are demonstrating evolution in real time. But, are they demonstrating genotypic evolution? Has the blueprint changed in an informationally unique way? Is it possible to have change in phenotype without a change in genotype? In other words, do the phenotypic changes in the finches that Darwin observed require new meaningful genetic information that the ancestor finches never had? (Back to Top)
Darwin was unable to answer this question although the answer was in fact available in his own day. Gregor Mendel (1822-1884), the father of genetics, came up with the idea of “alleles” or unchanging “traits” after studying pea plants. 3,7 What he discovered is that certain phenotypic traits, such a pea color, texture, shape, and a number of other traits, are passed on by unchanging genotypic alleles. Different combinations of these alleles result in different allelic expressions in the phenotype.
For example, lets say that a house needs colored carpet. Colored carpet is a “trait” or characteristic of the house listed in the blueprint. The blueprint of this particular house is interesting however in that it is a double blueprint. There are two pieces of paper that code for every aspect of the house. The two blueprints are identical as far as the traits that they code for (ie: colored carpet), but they are different as far as the trait variations are concerned (ie: red, yellow, green, blue or white carpet). If one blueprint coded for green carpet and the other coded for white carpet, what color would the carpet be? It will be the “dominant” color.
Mendel found that allelic traits could be either dominant or recessive (We now know that they can also be co-dominant, incompletely dominant, additive, multiplicative etc.) This is made possible because of the fact that many traits have at least two alleles, or two separate codes on two blueprint copies, that code for the same trait. If both alleles are the same, then the expressed phenotype will match both alleles. If the two alleles are different, then the phenotypic expression will match the dominant allele. Each allele is inherited unchanged, one from each parent. During the process of sexual reproduction, the alleles coding for the same trait trade places with each other randomly (from one blueprint copy to the same place on the other blueprint copy) so that the next generation will be uniquely different from the current generation in their phenotypic expression of the same alleles. This is why siblings from the same parents never look exactly alike unless they are twins arising from the same fertilized egg. Siblings can in fact look very different from each other and even their parents. One may have a big nose and the other a small nose. Similarly, a finch may have a bigger or smaller beak than its siblings. Such phenotypic changes do indeed occur, but they need not be based in any change of the common “gene pool” of options. 2 (Back to Top)
Breeding via Human Selection
The variation in allelic expression can be quite dramatic. It is responsible for the majority of changes seen in animal breeding. For example, the main differences between a Chihuahua and a Great Dane are primarily the result of trait selection where desired traits contained by a common gene pool were gathered together over a few generations into one animal. Most of traits themselves already existed, fully formed, in the common ancestral gene pool of dogs. Thus, neither of these breeds has “evolved” much of anything that their common ancestors did not already have in their common gene pool of options.
The ability for great phenotypic variation is obvious, but there are clear limits to this variation. Using genetic recombination alone, a dog cannot be anything but a dog. A dog can never be changed, via genetic recombination alone, into a cat or a chicken or anything else. Why? Because cats, dogs, and chickens are made from different blueprints that code for different trait types and trait options that are not contained by the gene pools of the others. Also, traits that may be similar may not necessarily be located at the same relative positions on their respective genetic blueprints.
Again, using the house blueprint analogy as an example, one house might have a blueprint that codes for carpet at the bottom right-hand corner of the page. Another house might have a blueprint that codes for an electric garage door opener at this location and has no code for carpet at all. Also, the first house might not have a code for a garage much less an electric garage door opener. Neither one of the blueprints for one house will match with the options or order of options for the other house.
So, what does this mean? It means that the blueprints for the different houses in this case cannot talk to each other, mix and match, or “recombine” their collective information in any functional way. They cannot “interbreed” so to speak to produce viable “offspring”. Only blueprints that have the same setup and “trait” types can exchange equivalent information with each other. It is impossible then for blueprints that do not have a position for a garage code to trade equivalent information that results in the formation of a garage. The same is true for different animals such as dogs, chickens and cats. They cannot breed with each other, nor can they be bred to look like each other, using genetic recombination alone.
Although genetic recombination can and does result in some very dramatic phenotypic changes for these creatures, this process is limited by the edges of a large but finite pool of options. Such a gene pool remains fixed while various creatures within the gene pool give phenotypic expression to various aspects of this genotypic pool of options. The pool provides the means for huge phenotypic variation or “change” but the genotypic pool itself may not change from one generation to the next. 8 The changing phenotypic creature is nothing more than a partial reflection of a non-changing or “static” genotypic pool. Thus, it is the genotypic pool and not the phenotypic creature that “real” evolution must act on.
But, if Darwin’s finch beaks are not examples of gene pool evolution is there anything that is? Is there any creature that has unique traits that its ancestors did not have in their gene pool? (Back to Top)
This is where mutations come into play. Genetic mutations are relatively rare random changes that occur in a creature’s genotypic blueprint that were not in the blueprints of that creature’s parents. There are many different types of mutations. There are point mutations where just one letter is changed in the wording of the genetic blueprint. There are translocation mutations where a section of the blueprint is cut out and pasted in another place on the blueprint. There are inversion mutations where a section of the blueprint is cut out and turned upside down and pasted back in the same place. There are duplication mutations where a section of the blueprint is copied and then pasted in another place. The list goes on and on, but basically the mutated blueprint has genes/alleles or genetic sequences that were not in the blueprints of either one of the parent blueprints. 1,2,4 (Back to Top)
Functional vs. Neutral Mutations
As would seem intuitive, most functional mutations are harmful and may even be lethal. Fortunately though, most mutations are not functional. Most mutations are silent or “neutral” and result in no detectable phenotypic change. Very rarely, some mutations are beneficial. The ratio of beneficial vs. detrimental mutations is on the order of 1 in 1,000 for certain types of functions (discussed in more detail below). Common examples of beneficial mutations are those that give bacteria antibiotic resistance or those that cause sickle cell anemia in people who live where malaria is prevalent. But how, exactly, do beneficial mutations achieve their benefits? (Back to Top)
In the case of bacterial antibiotic resistance, certain mutations actually do result in the fairly sudden creation of new as well as beneficial functions that the “parent” bacteria did not have in their collective gene pool of options.
Now, it should be noted that bacteria are not like dogs, cats, and chickens, or anything else that uses sex or genetic recombination as a means of reproduction. Bacteria reproduce themselves by a relatively simple method of cell division. In other words, all of the offspring of a given bacterium will be identical with itself as well as with each other. They are basically clones of each other. Because of this, there are no trait options to choose from. There is only one copy of the blueprint instead of the two copies used in sexual recombination. So, there is only one option for each bacterial “trait.” There is no gene “pool” of options for each trait – so to speak. Of course, many types of bacteria can in fact laterally exchange genetic information via plasmids and the like. But, as a general rule of thumb, bacteria do not undergo genetic recombination. So, for all practical purposes, all bacteria within a given isolated population are identical except if a mutational change occurs. Such mutations, when they do occur, are passed on to all subsequent offspring.
So, back to the notion of beneficial mutations. They do happen in bacterial populations all the time. For example, penicillin resistance is not always gained by the production of the famous β-lactamase enzyme “penicillinase.” There are several other ways that bacteria become resistant to penicillin. A notable example occurs in Streptococcus pneumoniae bacteria. β-lactamases have never been identified in S. pneumoniae and yet they are capable of penicillin antibiotic resistance due to modification of their penicillin binding proteins (PBPs). Since PBPs are the natural target of penicillin, many different point mutations within this target are capable of interfering with the target-antibiotic interaction. It is this interference that results in penicillin resistance. And, importantly, this resistance, combined with what are called “compensatory mutations“, can be achieved without any significant loss of any other functional system within the bacterium. So, the argument that all mutations end up producing at least some detectably harmful effect to gain a beneficial effect simply isn’t true.
Other antibiotics require a specialized transport protein to bring them into the bacterium. Again, many different point mutations can interfere with the ability of the transport protein to interact properly with the antibiotic. This interference results in resistance to this particular antibiotic. Again, this interference can be gained without any significant functional loss of the mutant bacteria relative to their peers.
Other bacteria already make more complex antibiotic enzymes, such as the penicillinase enzyme. Such enzymes do not evolve in previously susceptible bacterial populations. There is not a single documented case where the penicillinase enzyme code has been observed to evolve in a population where it wasn’t already there. The coded sequence or “gene” needed to produce the penicillinase enzyme was already there or it was gained via lateral transfer from other bacteria who already had this code (often via plasmids). The problem is that this coded sequence is usually regulated so that the penicillinase enzyme is not produced in sufficient enough quantities to protect the bacterium from high levels of the penicillin antibiotic. Several different mutations are capable of blocking or interfering with the suppression of penicillinase production so that much greater quantities can be made, which results in enhanced penicillin resistance.
Similarly, Mycobacterium tuberculosis, the cause of the tuberculosis disease, produces an enzyme that (as well as its other useful functions) changes the non-harmful antibiotic “isoniazid” into its active and lethal form. The now active isoniazid proceeds to kill the Mycobacterium. Several different mutations are capable of interfering with the isoniazid-enzyme interaction. And again, this interference results in Mycobacterial resistance to isoniazid.
To give another example, the 4-quinolone antibiotics attack the enzyme “DNA gyrase” inside various bacteria. Again, several different point mutations are capable of interfering with the gyrase-antibiotic interaction.9
Perhaps the most famous and oft quoted example of a beneficial mutation is the point mutation of the hemoglobin molecule that is seen in people affected by a condition known as sickle cell anemia. This single point mutation decreases the effective oxygen carrying capacity of the hemoglobin molecule. It still carries oxygen, just not as well. Now, it just so happens that the malarial parasite needs a high oxygen concentration to survive and so cannot survive in blood with the sickle cell mutation.10 Those people who have only one of their two blueprint DNA copies affected by this mutation do not have significant anemia, but their blood still doesn’t carry oxygen well enough to support the malarial parasite. So, they are resistant to malaria while at the same time having little problem with the hemoglobin mutation. However, those unfortunate individuals who end up with a double mutation, known as a “homozygous” condition, have a severe problem with their hemoglobin molecules crystallizing under low oxygen tension. This crystallization effect dramatically distorts the red blood cells and they no longer fit very well through small vessels in the body. Of course, this means that organs and tissues supplied by these vessels become starved for oxygen. This causes a very painful and debilitating condition with significantly reduced life span.
Now, there are several interesting observations to note. Most beneficial mutations achieve their benefits with just one or rarely two point mutations. Also, it is hard to miss the fact that all of the functions gained, at least those listed here, were the result of a mutation that interfered with a previously established function or specific interaction. And, as we all know from a famous children’s story, it is far easier to break than to create. The reason is that there are so many different ways to break something compared to the relatively few ways to make something work. Why else couldn’t all the King’s men put Humpty Dumpty back together again? (Back to Top)
But, how did such apparently complex enzymes, such as penicillinase, evolve? A bacterium is not going to evolve the enzymatic penicillinase function with just one or two point mutations to some target sequence because the penicillinase function is not based on the loss or hindrance of a pre-existing function or interaction. So, how could such a function evolve?
There are many theories as to how the penicillinase enzyme must have evolved. However, when it comes right down to it, no one has ever demonstrated the evolution of the penicillinase enzyme in the lab. As previously noted, bacteria that produce the penicillinase enzyme were always capable of producing this enzyme or they obtained the code for this enzyme via a plasmid from another bacterium who had this code already formed.11 Sometimes, a point mutation is required to deregulate the production of penicillinase so that much greater quantities are produced, rendering the bacterium (and its subsequence offspring) instantly resistant to greater doses of penicillin. But, this change really has nothing to do with explaining how the rather complex penicillinase function evolved.9 So, are there any documented reports of the evolution of a complex enzymatic function comparable to that of the penicillinase function?
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.“
Others, such as the well known evolutionary biologist Kenneth Miller, disagree. In his 1999 book, Finding Darwin’s God, one of Miller’s challenges of Behe’s position includes a 1982 research study by professor Barry Hall, an evolutionary biologist from the University of Rochester. In this study, Hall deleted a gene (lacZ gene) in a type of bacteria (E. coli) that makes an enzyme (β-galactosidase). This enzyme converts the sugar lactose into the sugars glucose and galactose. The E. coli then use glucose and galactose for energy.
Without this lactase enzyme one would think that these bacteria and their offspring would not be able to utilize lactose. However, what Hall found is that after a short time (just one generation) of exposure to a lactose-enriched environment these bacteria modified a different gene with just one point mutation so that it gained the ability to produce a new lactase enzyme.12 Since the original enzyme was composed of a fairly large tetramer (~1,000 amino acids for each subunit), it seemed like the evolution of the lactase function might require a fair amount of enzymatic complexity (fairly large number and specific arrangement of amino acid residues). In other words, it might be rather difficult to come across very many enzymes with lactase ability out of the vast numbers of potential arrangments within sequence space. So, the demonstration of such rapid evolution of a completely different hexametric lactase enzyme was quite a stunning success for Hall. How did these amazing bacteria evolve a brand new enzyme to do such an apparently complex task?
As it turns out, these E. coli bacteria had something of a spare tire gene that Hall called the “evolved β–galactosidase gene” (ebgA). Just one point mutation was all it took to give this spare tire gene the ability to produce a protein with the beneficial lactase activity. Hall was of course disappointed to find out that only one point mutation was enough to “evolve” this beneficial lactase activity. So, he did a very interesting thing. He deleted both the original lacZ genes as well as the evolved ebgA gene in some rather large colonies of E. coli. Interestingly enough, none of these doubly mutated bacteria nor their offspring never evolved any other gene or DNA sequence into a functional lactase enzyme despite observation for tens of thousands of generations.
Hall was mystified. He described these bacteria as having, “limited evolutionary potential.” 12 The interesting thing is that these same bacteria that were limited in their ability to evolve the lactase function would easily have evolved resistance to just about any antibiotic in just a few generations of sublethal exposure. Compared to antibiotic resistance, the evolution of even a single-protein enzyme is quite a different matter. We are starting to climb the ladder of increasing functional complexity. (Back to Top)
Limited Evolutionary Potential
Now I ask, what exactly was limiting the evolutionary potential of Hall’s bacteria? Does the theory of evolution explain such limits? If so, how are they explained? The theory of evolution claims the power to create incredible diversity via mindless processes if given enough time. Well, how much time, on average, would it take for E. coli, without lacZ and ebgA, to evolve the lactase function? Can this time be estimated, even roughly? If so, upon what basis can it be estimated?
According to Hall’s own calculations, a function that required just two independent (neutral) mutations would take around “100,000 years” to achieve in E. coli.12 It seems as though Hall does not understand the statistics of random walk very well or he would not have been surprised when he did in fact isolate such a double mutant in just a few days. The estimated time for fixation is what caused Hall to estimate a time of 100,000 years for the crossing of a gap of just two neutral mutations. What Hall did not realize is that stepwise fixation of each mutation (spread to all members of a population) is not required for such a gap to be crossed. With populations the size of Hall’s E. coli colonies, such a double mutation would be realized in at least one bacterium in the population in just one or two generations using random walk alone. (Back to Top)
Growing Neutral Gaps
So, is the problem solved? Hardly. With each doubling of the neutral gap between the current genetic real estate and a new potentially beneficial function, the random walk increases by a factor of two. For example, a gap of 2 amino acid residue differences has only 400 different options to fill (20 potentially different residues in each position in a protein sequence). A population of one billion bacteria would quickly distribute itself among all these 400 options in very short order (given that these 400 options were all functionally neutral with respect to each other). However, doubling the gap to 4 differences would increase the number of options to 160,000. Doubling the gap again to 8 differences would increase the number of options to 25.6 billion. A gap of 16 would yield 655 million trillion (6.5e20). In such a case, each bacterium in the population of one billion would be surrounded by a sea of 655 billion non-beneficial options. The time required to traverse this gap, even for a population of one billion bacteria, would run into the trillions of generations. Why? Because the time required for a mutation to hit even one of the residues that form the gap runs into the hundreds of thousands of generations, on average. In other words, each random walk step would take hundreds of thousands of generations. So, finding one specific spot out of 655 billion options would require 6.55e9 x 1e5 = ~ 1e14 or 100 trillion generations.
This is because natural selection cannot preferentially select for any sequence that doesn’t provide an improved function over what was already there to begin with. The only forces for change that can sort through such non-beneficial sequences are random mutations. These random mutations randomly search through sequence space with the use of either short or long random steps. However, regardless of the size of the step/mutation, the odds of success are not changed. Such a random walk takes a whole lot more time than a non-random direct walk would take – exponentially more time. This simple little problem is what messes things all up for evolution.
For instance, consider that there are many bacterial functions that are far more complex than the relatively simple single-protein based enzymatic-type functions of lactase or nylonase. Many single protein enzymes are actually fairly complex, don’t get me wrong. They certainly are far more complex than the function of antibiotic resistance that arises via an interfering mutation. However, their functions are still relatively simple when compared to other cellular functions of higher complexity.
For example, there are about 10130 potential protein sequences 100 residues in length. Of these 10130 potential proteins, how many would have a specific function? Well, it depends on the function. It depends upon how specific the arrangement of residues needs to be. So, as an example, lets pick one of the more specifically arranged functional protein that requires about 100 residues to work – like cytochrome c. Some scientists, like Yockey, have estimated anywhere between 1050 to 1090 different potential cytochrome c proteins exist in sequence space.15-17 To understand how big these numbers are, consider that the total number of atoms in the visible universe is only around 1080. So, one can see that 1090 different cytochrome c sequences is an absolutely huge number. The problem is, this pile of 1090 cytochrome c proteins is absolutely miniscule when compared to 10130, which is the total number of different potential protein sequences 100aa in length. For every one cytochrome c sequence there would be about 1040 non-cytochrome c sequences in sequence space.
And yet, this ratio gets exponentially worse as the complexity of function increases. For example, the function of bacterial motility involves the interactions of many different proteins all working together at the same time – over 20 different structural protein parts in specific arrangement totaling well over 10,000aa that must be specifically coded for in correct sequence. The question is, how many different arrangements of these amino acid residues would produce a motility system (or how many arrangements of 10,000 letters would produce a meaningful, much less beneficial, essay in English)? For argument’s sake, lets say that 102000 different motility systems could be made with such a stretch of amino acids. Despite this apparently astronomical number of different motility systems, 102000 is still a tiny fraction of 1013,010 – the minimum potential protein sequence space at this level of complexity (10,000aa level). Each sequence with a motility function would be surrounded by at least 1011,000 sequences without the motility function. In fact, the beneficial sequence density at this level of complexity seems to be so miniscule that evolution is powerless to evolve any function at such a level of complexity. There simply are no observable examples of any such function evolving in real time – not one example (i.e., a function that requires more than a few thousand fairly specifically arranged residues).
Now, there are a whole lot of stories about how such functions must have evolved. These stories are exclusively based on the notion that sequence similarities of portions of such systems to portions of sequences in other systems of function must mean that they share a common evolutionary ancestor. Certainly the similarities do seem to indicate a common origin of some kind. However, as Behe has repeatedly pointed out, nobody has provided any detailed explanation as to how evolutionary mechanisms of random mutation and function-based selection could give rise to the functional differences at higher levels of minimum functional complexity. The functional differences are what are important here – not so much the similarities. How are these differences explained by any non-deliberate process? Both deliberate and non-deliberate processes can explain the similarities, but how well can non-deliberate evolutionary forces explain the differences?
The problem is that there is always more potential junk than non-junk at any given level of complexity. The real problem though is that this junk pile grows exponentially, relative to the pile of potentially beneficial sequences, with each step up the ladder of functional complexity. (Back to Top)
The Junk Pile and The Ladder of Functional Complexity
The ladder of complexity limits the ability of evolution to evolve beyond its lowest rungs where the most simple functions, such as antibiotic resistance, can be found. The target mutations required to achieve antibiotic resistance are extremely simple to get “right” because there are so many “right” options. However, the evolution of specific enzymatic functions, like the lactase function, are a lot harder to get “right” because far fewer options are “right.” Then again, even these functions are relatively easy to get “right” compared to more complex multipart functions, such as bacterial motility systems, where all the protein parts work together at the same time in a specific 3-D orientation with each other. So, as one moves up the ladder of functional complexity, the difficulty of finding any sequence that does anything beneficial at such a level of complexity becomes exponentially harder and harder to do until not even trillions upon trillions of years are enough. (Back to Top)
A Construction Foreman Who Cannot Read
It is all very much like a construction foreman who never learned how to read a blueprint, but who intuitively knows what works when he sees it in action. His workers are the ones that know how to read blueprints and follow directions exactly. The workers also copy parent blueprints to use as templates for each new house that is to be built. However, although they are extremely careful copyists, the workers make little mistakes every now an then. These little mistakes may not result in any phenotypic change whatsoever, but sometimes they translate into slight or even major variations among the actual houses built (the phenotype). The foreman then comes to inspect the completed houses and picks the one that is the ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œbestÃƒÂ¢Ã¢â€šÂ¬Ã‚Â given the particular needs of that house for that location and housing market. The choice of the foreman is based only on current function.5,6 He knows only what works right now. He has no imagination, memory, or vision for the future. If there is a part of a house that he does not recognize as having current beneficial overall function, he will not select to keep that house and the offending part will be lost from future blueprint options. The foreman goes around saying, “Keeping do-dads around that don’t work is expensive!” He will not maintain what he does not recognize as beneficial right now in hopes that sometime in the future, with some potential change in the housing market/environment, it may develop into something beneficial. Once the selection for the best overall house is made by the foreman, the workers find the blueprint for this house and use it as a template for the next building project.
But what happens if the housing market changes the next year? What if certain changes would benefit the house in this new environment? For example, what if there were a prolonged drought and wild fires became a threat making houses with tile shingles more resistant to fire than houses with wooden or asphalt shingles? Would the foreman be able to make these beneficial changes?
Consider the thought that languages and thus blueprints are arbitrary in that they use arbitrary symbols to represent ideas. A change or evolution of a symbol does not necessarily correlate to an equivalent change in the attached idea. If a symbol changes, even a little bit, the attached idea may simply disappear, leaving the “new” symbol without any recognized function. The symbol is now meaningless. For example, what if the blueprint for our house in question called for “wooden shingles.” Each of the words, “wooden” and “shingles” is an arbitrary group of symbols that represents an idea to an English speaking person. If the blueprint could be changed to read “tile shingles” the understood change in meaning and the resultant change in house building would be a clear advantage in our fire-hazard environment. If no pre-established alleles for “tile shingles” are available in the blueprint pool of trait options, is there any way to create the “tile allele” from anything that already exists in the gene pool?
The problem for gradual change is that each letter change must make sense. If “wood” is changed to “hood” the actual word “hood” has meaning. However, is the meaning for “hood” any closer to the meaning for “tile”? Even though hood has meaning, does it have beneficial meaning in this case? What does “hood shingles” mean?
So, not only does each word of the blueprint have to make sense to the workers, but the combination or location of the words on the blueprint has to make sense as well or else the workers cannot make anything, much less something beneficial. Order is important at all levels of complexity. Amino acid residue order is important for the proper function of a single protein. Also, the order of multiple proteins is important for the formation of a multiprotein system. Again, if the workers build something that the foreman cannot recognize as beneficial right now, it will be rejected. It is as simple as that.
In order to better visualize the problem, put yourself in place of the foreman. You can only select based on functions that work “right now.” With this in mind, consider the phrase, “Methinks it is like a weasel.” 6 Now, add, subtract, or change one letter at a time from the phrase in any position or order that you want. There are just two more little rules to this game. Each change that you make must make sense in English and each change must be beneficial in a particular situation/environment. See how far you can go and how much change in meaning you can get. Changing the meaning very far is a lot more difficult than one might think even if the beneficial nature of the change is not a concern.
Nature runs into this same little problem. Changing genetic sequences too much destroys all phenotypic function before any new function can be reached. Maintaining a functional phenotypic trait along the path towards any uniquely functional trait requires multiple changes (maybe even hundreds or thousands at higher levels of functional complexity) that do not change the original function and do not achieve new function, until all the changes are in place.5,12 This is because many functional genetic elements are isolated from each other like islands on a large ocean of neutral function or even detrimental function.
Consider the fact that most character sequences of a given length have no meaning to an English speaking person. The same is true for sequences of DNA. The vast majority of potential genes of a given length mean nothing to a given cell. Any gene that happens to get mutated into one of these unrecognized or neutral genes becomes suddenly lost in the ocean of neutrality where no guidance is available. Without guidance, evolution drowns in this ocean. (Back to Top)
Adding a Stereo System to a Car
Some say that evolution need not work like this but that the mindless processes of nature evolve new traits and gene pools by simply adding on previously defined genetic elements to an established system of function… like the addition of a stereo system to a car. The addition of these units enhances the function of the established system, even to the point of giving it new functions that it never had before. In this way, a simple system of function can be enhanced in complexity step by tiny step.
Let’s try a thought experiment to illustrate this point. Consider the sentence in a large book of sentences that reads, “I am.” Now add any other word onto this sentence. The only rule is that whatever word you choose must make sense in the context of the other sentences and the book as a whole. For example, I could add the pre-formed word, “pleased” and make the new sentence read, “I am pleased.” This makes sense in English and it adds meaning to the sentence (whether or not it makes sense to the rest of the paragraph is another story). The new word, “pleased” could have been the result of a duplication mutation of a gene somewhere else that just happened to get inserted into our sentence. But, if the mutation had read, “I am very”, this phrase does not necessarily make sense in most situations/environments. The addition of the word “very” probably destroyed the previous function of our sentence without creating a new function. However, if the phrase “I am pleased” was first to evolve, the phrase, “I am very pleased” could evolve next and make sense.
With these rules in mind, try and keep adding on words (or subtracting words) and see how far you can get given a particular situation/environment. Maybe the next mutation could read, “I am very pleased Tom.” Then, “I am pleased Tom.” Then, “I am Tom.” We could also go another route and say, “I am very pleased in Tom.” Then, “I am very pleased in seeing Tom.” Then “I am very pleased in seeing Tom run.” Then, “I am very pleased in seeing Tom run fast.” Then, “I am very pleased in seeing Tom run real fast.” Then, etc. etc. etc. We can evolve quite a few different phrases with quite a few unique meanings with the simple addition or subtraction of previously defined words. Could genes in DNA do the same thing? Technically yes, but there are just a few problems to consider.
Remember that not every defined word that exists in English will make sense when added to the phrase, “I am.” Granted, the odds that one will make sense seem to be fairly good though. However, the longer our sentence gets, the less words there are that make sense when they are added to our sentence in the context of a specific situation/environment. Consider also that the placement of the words within our sentence is extremely vital to the functionality of the sentence. I might say, “I am green.” This phrase makes sense in English. But what if the word “green” had been inserted into the wrong place? The sentence could just as easily have “evolved” to read, “I agreenm.” This makes no sense in English and destroys the function of a previously functional sentence. Consider also that the duplication mutation could have occurred or been inserted into an area of the book of sentences where it was not needed. What are the odds that it would land in exactly the right “evolving” sentence in exactly the right position within that sentence when there are potentially millions of other locations it could have landed? Then consider that the sentence itself, even if it might make sense by itself, must make sense as it relates with the other sentences around it and in the rest of the book. If any of these problems arise, that sentence is lost in the ocean of non-beneficial function. (Back to Top)
One Last Question
If the theory of evolution runs into such apparently difficult statistical problems, how is it that this theory can be so earnestly presented as the only “rational” answer to the question of the origins of living things? (Back to Top)
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