|
Prophets or
Evolution - An LDS Perspective Chapter 28 Copy Genes
and Evolution Genes Mathematical Note It was
noted in the prior chapter on mathematics that 1 / 100 or 10‑2
was equal to .01. .01 can also be
written as 1%. In other words, if we
have a percentage, such as 14.65%, we can move the decimal over two places to
the left and write it as
.1465. Likewise,
if we have a number such as .0045, we can convert this to a percentage by
moving the decimal point two places to the right. Thus, .0045 is equal to: .45%. In this
chapter, sometimes a small number will be represented as a decimal (such as: .000004616)
and sometimes this same exact number will be represented as a percentage (i.e.
.0004616%). They are the same thing. The Different Kinds of
Mutations There are
actually several different kinds of mutations. For
example, there are mutations where entire genes are copied more than once;
which is called: duplication. There are also
mutations where entire chromosomes are copied more than once. And so on. When a gene
is copied, the copy of the gene has no function. It is felt that if one of these extra copies
of a gene are bombarded with point mutations; that a new
gene (actually a new gene complex is needed) may be able to be created by random
mutations of nucleotides. In other
words, you start with a worthless, extra copy of a gene, mutate its nucleotides
many times and end up with a new gene for a new species. This is
important to the theory of evolution because creating a gene from scratch is a
very slow process and is riddled with statistical problems. But let us
consider the problems created by starting with a copy of a gene complex and
trying to modify it; using numerous point mutations; to become a new functional
gene complex for a new species. In fact,
this very thing had to have happened about 200
million times for the theory of evolution to be true (assuming each of
the 10 million unique species has 20 unique gene complexes on average). With 200 million unique gene complexes formed
by evolution, it should be easy to convert a copy of a gene into a new gene
with a new biological function. For
example, suppose a complex animal, such as a female chimpanzee, had an extra
copy of a gene in one of her germ cells, as the result of a mutation. Suppose the female chimpanzee mates. We will ignore male and female issues. Is it
possible this second, useless copy of a gene can mutate to the point that it is
a new, fully functional gene, which leads to a new species (a new species requires
at least one new gene complex, but generally has dozens of unique gene
complexes)? Suppose we
consider the potential evolution (via point mutations) of the extra copy of
this chimpanzee gene. Suppose,
for example, that 50% of the nucleotides of this extra copy were identical to a
gene which does not exist in chimpanzees, but which does exist in a more
advanced primate. Could the extra copy
of a chimpanzee have been the source of a new gene complex for a more advanced
primate? Let us
consider that "Gene A" is an extra copy of an existing gene, meaning
it is a "copy gene" of a valid gene complex. Let us say
that the claim is made that "Gene A," in the old species, via random point
mutations, becomes "Gene B" in the new species. Remember, in
this discussion 50% of the nucleotides of "Gene A" start out to be
identical to the nucleotides in "Gene B," which does not yet
exist. "Gene A" is believed to
"evolve" by random point mutations to become "Gene B." Let us
consider a nucleotide in a position of Gene A (say a 'T' is in nucleotide position
2,576). Suppose a 'T' is also in
position 2,576 of Gene B since B is a copy of A. We will call this 2,576th nucleotide in Gene
A a "right" nucleotide since it does not
need to be changed to equal the 2,576th nucleotide in Gene B. If a
nucleotide in another position of Gene A is not equal to the same position of
Gene B, we will call it a "wrong" nucleotide. Thus, using
this terminology, in our example 50% of the nucleotides in Gene A start out as "right"
nucleotides and 50% of the nucleotides in Gene A start out as "wrong"
nucleotides. Let us
study point mutations as they occur to Gene A. How Point Mutations
Affect "Wrong" and "Right" Nucleotides First of
all, any point mutations to this extra copy of the gene could affect any of the
nucleotides, not just the "wrong" nucleotides. Thus, a mutation would be just as likely to
affect a "right" nucleotide as it would a "wrong"
nucleotide. Thus, you
would have a never-ending battle trying to preserve the "right"
nucleotides from mutating into "wrong" nucleotides while you are
simultaneously trying to "fix" wrong nucleotides. Furthermore,
even when there is a mutation to a "wrong" nucleotide, there is still
a 67% chance that the new mutation will still be a "wrong" nucleotide. To understand this, suppose there is a
nucleotide in position 3,000 which is an 'A' (which is a "wrong"
nucleotide). Let us assume the
"right" nucleotide is a 'G'. There are
three possible mutations of this 'A' nucleotide. It can mutate into a 'C' a 'G' or a 'T'. Note that two of these three mutations are
still "wrong" (i.e. the 'C' and 'T' are still wrong). Thus, 2 out of the 3 possible mutations (i.e.
67%) are still "wrong" even if there is a mutation to a
"wrong" nucleotide. There are
thus three categories of mutations: First, if
the mutation changes a "wrong" nucleotide into a "right"
nucleotide, we will call it a "good" mutation. If the
mutation simply changes one "wrong" nucleotide into a different
"wrong" nucleotide, we will call it a "neutral" mutation
(because it does not change the overall number of "right"
nucleotides). If the
mutation changes a "right" nucleotide into a "wrong"
nucleotide, we will call it a "bad" mutation. Law #1: When
there is a mutation to a "wrong" nucleotide, there is only a 33%
chance the mutation will lead to a "right" nucleotide. Law #2: When
there is a mutation to a "right" nucleotide, there is a 100% chance
it is replaced by a "wrong" nucleotide because any nucleotide other
than the "right" nucleotide will be a "wrong" nucleotide
(Note: a "mutation" implies the nucleotide is changed). Since 50%
of the nucleotides in the "copy gene" are correct, and because 50% of
the nucleotides in the "copy gene" are wrong; there is a 50% chance a
"wrong" nucleotide is changed.
But only 33% of these changes create a "right"
nucleotide. Thus, only 16.67% of the
early point mutations (i.e. 50% times 33%) will convert a "wrong"
nucleotide into a "right" nucleotide. The other
33.33% of the early mutations of a "wrong" nucleotide will convert a
"wrong" nucleotide into a different "wrong"
nucleotide. This is the
"neutral" mutation. Thus, only
16.67% of the early mutations will be beneficial. On the
other hand, 50% of the early mutations will convert a "right"
nucleotide into a "wrong" nucleotide.
Every time you change a "right" nucleotide, it will become a
"wrong" nucleotide. Thus, 50%
of the early mutations will be detrimental. Do you see
what is happening? 16.67% of the early
mutations are "good" mutations.
33.33% of the early mutations are "neutral" mutations and do
not affect the total number of "right" nucleotides, thus they can be
ignored. But 50% of the early mutations
are "bad" mutations. Thus, computer simulations would show a
deterioration of the nucleotide sequence (i.e. a deterioration of the
percentage of "right" nucleotides) as time passed. No matter what percentage of
"right" nucleotides you start with; a stable 25% "good"
mutation level (i.e. only 25% of the nucleotides would be "right"
nucleotides) will eventually result. Let us
analyze why the DNA will deteriorate until 25% of the nucleotides are
"right" nucleotides. Assuming
25% of the nucleotides are correct, all of the "right" nucleotides
(25%) in this sequence, if they are changed by a mutation, will represent a
"bad" mutation. Thus, 25% of
the mutations are "bad" mutations, which convert a "right"
nucleotide into a "wrong" nucleotide. 25% of the
"wrong" nucleotides (75% "wrong" nucleotides times a 33.33%
chance the new nucleotide is a "right" nucleotide) are
"good" mutations. 50% of the
"wrong" nucleotides (75% "wrong" nucleotides times a 66.67%
chance the new nucleotide is also a "wrong" nucleotide) are
"neutral" mutations. Thus the
25% "right" nucleotides will be a very stable percentage of
"right" nucleotides once it is achieved. You would
eventually end up with 25% "right" nucleotides whether Gene A started
out with 95% of its nucleotides identical to Gene B or if Gene A started out
with 10% of its nucleotides identical to Gene B. The bottom
line is that regardless of the beginning percentage of "right"
nucleotides, as more and more nucleotides were randomly mutated, the percentage
of "right" nucleotides would slowly adjust up or down to 25%. Of course,
a gene complex which is only 25% "right," will perform no function
and will be useless. Even if you
started out with no nucleotides, and simply added nucleotides, the 25%
"right" nucleotides will be a very consistent percentage right from
the beginning. Let us
understand why all of this is true by looking at computer simulations. Understanding Point
Mutations to Gene Copies Let us
suppose that Gene A is a medium-sized gene complex with 20,000 nucleotides. Let us further suppose that when this gene
complex is copied, an extra copy of the gene complex is created. This extra copy has no function. Let us
further suppose that 95% of the nucleotides of the extra copy of Gene A are
identical to Gene B, which does not exist yet, but is the goal of evolution
(i.e. via random point mutations). We will
call the extra copy of Gene A the "copy gene," and we will call the
goal of mutations by evolution the "evolution gene." The "copy gene" starts out, in this
example, with 95% "right" nucleotides. The goal is for the "copy gene" to
become the "evolution gene" by random point mutations, which has 100%
"right" nucleotides by definition. First of
all, only 5% of the nucleotides (meaning 1,000 of them) start out to be
"wrong" nucleotides. This
means that only 5% of the "first mutation" (i.e. the very first point
mutation we are considering) will affect a "bad" mutation. 5% of 20,000 nucleotides is
1,000 nucleotides (this is the number of nucleotides which start out as
"wrong" nucleotides, which is 5% of 20,000). However, as
mentioned above, 66.67% of any "first mutation" on a
"wrong" nucleotide would also be a "wrong" nucleotide. This would be a "neutral" mutation. This means
only 33.33% of the mutations, on the 5% of "wrong" nucleotides, would
yield an improvement in the total number of "right" nucleotides. Multiplying
.05 times .33333... yields a .01666... probability; meaning a 1.666...% probability, that the
"first mutation" will convert a "wrong" nucleotide into a
"right" nucleotide. In other
words, since we started out with 1,000 "wrong" nucleotides (i.e. 5%
of 20,000 nucleotides), there is only a 1.666...% probability that the first
mutation will increase the total number of "right" nucleotides to 19,001. On the
other hand, we know immediately that there is a 95% probability that the first
mutation will be a "bad" mutation because 95% of the initial
nucleotides are "right" nucleotides, and if one of these is mutated,
it will automatically be a "bad" mutation. We can
summarize these probabilities thusly: 1st
mutation is a "bad" mutation: 95% (i.e. a
"right" nucleotide is changed into a "wrong" nucleotide) 1st mutation
is a "neutral" mutation: 3.333...% (i.e. a
"wrong" nucleotide is affected, but it is still a "wrong"
nucleotide) 1st
mutation is a "good" mutation: 1.666...% (i.e. a
"wrong" nucleotide is changed into a "right" nucleotide) First Simulated Point
Mutation Let us
consider 500,000 computer simulations. A
"computer simulation" is a situation where a computer randomly picks
a number and applies this number to the beginning condition (i.e. the
simulation starts with 19,000 out of 20,000 nucleotides are "right"
nucleotides). Out of
500,000 cases where a "copy gene" is attempting to become an
"evolution gene (i.e. 500,000 simulations) where a Gene A started out as
95% equal to Gene B, we would only expect 8,333 cases (i.e. 500,000 times
.01666...%) where there were
19,001 "right" nucleotides after the first mutation (i.e. there was
one additional "right" nucleotide added to the initial 19,000
"right" nucleotides). Here is the
calculation of how many "good" mutations we could expect in the very
first mutation: 1) 1,000
"wrong" nucleotides at beginning of simulation 2) 1,000 /
20,000 = .05 or 5% of the initial nucleotides start as "wrong"
nucleotides (these are the nucleotides we are hoping to change into
"right" nucleotides) 3) However,
even when a "wrong" nucleotide is affected, in only 33.333...% of the
cases is a "wrong" nucleotide actually converted into a
"right" nucleotide. 4) Thus in
.05 times .3333... = 1.666...% of the initial mutations is a "wrong"
nucleotide changed into a "right" nucleotide 5) Thus, in
500,000 simulations of the first point mutation, we would expect: 500,000
times .01666... = 8,333.33 instances where the number of "right"
nucleotides increased. Thus, 8,333
of the 500,000 simulations would be expected to be "good"
mutations. In other words, after 1 point
mutation, in 8,333 of the 500,000 simulations there will be 19,001
"right" nucleotides. The Second Simulated
Point Mutation What is the
probability that both the first and
second mutations will be "good" mutations and there will be 19,002
"right" nucleotides after the second point mutation? Here is the
calculation: 1) We
assume the first point mutation was a "good" mutation (i.e. one of
the 1,000 initial "wrong" nucleotides was converted into a
"right" nucleotide), leaving 999 "wrong" nucleotides after
the first mutation (i.e. before the second mutation). 2) 999 /
20,000 = .04995 (probability one of the 999 "wrong" nucleotide is
affected by a point mutation) 3) .04995
times .3333... (probability "wrong" is
converted to "right) = .01665 4) Now we
need to multiply the probability of the 1st "good" mutation with the
probability of a 2nd "good' mutation: .01666... times .016665 = .0002775 5) 500,000
times .0002775 = 139 cases out
of 500,000 will have two consecutive "good" mutations in the first
two attempts. Thus, out
of 500,000 cases where a Gene A started out as 95% equal to Gene B, we would
only expect 139 of the 500,000 cases to create 19,002 "right"
nucleotides after 2 mutations. The Third Simulated Point
Mutation What is the
probability that the first 3 mutations would all be "good" mutations? Try to figure this out for yourself before
looking at the answer. For the
third mutation, there are 19,002 "right" nucleotides and 998 "wrong"
nucleotides to start with (i.e. after the second mutation). Here is the
calculation: 1) 998
"wrong" nucleotides at beginning (i.e. before the third mutation) 2) 998 /
20,000 = .0499 (probability a "wrong" nucleotide is affected) 3) .0499
times .3333... = .0166333... there is a
"good" mutation applied to a "wrong" nucleotide 4) Now we
need to multiply the probability of the 1st two "good" mutations with
the probability of the 3nd consecutive "good'
mutation: .0166333...
times .0166500 times .0166666... = .000004616 500,000
times .0000046156 = 2 In summary,
out of 500,000 computer simulations of the first 3 point mutations, we would
only expect 2 of them to have the first three consecutive mutations be
"good" mutations, ending up with 19,003 "right" nucleotides. Conclusions of First 3
Simulations Thus
we have these statistics for the first 3 mutations for 500,000 simulations: 1) Expected number
with one "good" mutation: 8,333 (.01666...) 2) Expected number
with two consecutive "good" mutations: 139 (.0002775) 3) Expected number
with three consecutive "good" mutations: 2 (.000004616) Do you see
a trend? The probability of getting
consecutive "good" mutations drops very quickly and will continue to
drop. But even if
there were three "good" mutations in the first three attempts, there
would still be only 19,003 "right" nucleotides and 997
"wrong" nucleotides. It would
be ludicrous to think that the first 1,000 mutations would all be good mutations
because the probability drops so quickly. However,
there are many different way to get to 19,003 "good" mutations. Consider this scenario: Start out
with 19,000 "good" mutations, First
Mutation: a "neutral" mutation (still 19,000 "right"
nucleotides) Second
Mutation: a "good" mutation (19,001 "right" nucleotides) Third
Mutation: a "bad" mutation (19,000 "right" nucleotides) Fourth
Mutation: a "good" mutation (19,001 "right" nucleotides) Fifth
Mutation: a "neutral" mutation (19,001 "right" nucleotides) Sixth
Mutation: a "good" mutation (19,002 "right" nucleotides) Seventh
Mutation: a "good" mutation (19,003 "right" nucleotides) In this
case it took seven mutations to get to the goal of 19,003 "good"
mutations. However, there are still 997 "bad"
mutations to fix before getting to where evolution wants to get. Rather than
consider all of the possible paths to 20,000 "good" mutations, and
the probability of each path, there is a much easier way to grasp the problems
with converting a "copy gene" (i.e. a copy of an existing gene) into
an "evolution gene" (i.e. a gene which has a nucleotide sequence
which is the goal of evolution, meaning the goal of random mutations). This far
better method is called computer simulations.
Computer simulations have a great deal of advantages to highly complex
statistical analysis in a situation like this one. A Single Simulation Let us
consider the two kinds of genes we have been talking about (which will be
simulated in a computer program): "copy gene" is an accidental mutation copy of an entire
"old gene," "evolution gene" is the gene which the "copy
gene" is attempting to mutate into. One theory
of evolution is that new genetic material comes from mutations affecting copies
of existing genes. The "evolution
gene" represents this new genetic material and is, by definition, a new
"gene complex" of one of the new genes in a new species. The goal of evolution in this example is for
the "copy gene" to mutation, one nucleotide at a time, into the
"evolution gene." Let us
assume the "copy gene" and "evolution gene" are each 20,000 nucleotide pairs long. Let us
further assume the "copy gene" starts out being 95% identical to the
"evolution gene." The 95%
represents the 19,000 nucleotide pairs of the "copy gene" which are identical
to the same nucleotides, in the same positions, in the "evolution gene." This means
that evolution must fix the other 5% of the nucleotide pairs to create a new,
fully functional gene complex. In other
words, evolution only has to fix 1,000 nucleotides (i.e. 5%) on the copy gene
to equal the evolution gene. Sounds
easy, doesn't it. Let's see if it is
easy. It is the
job of evolution to "fix" the 1,000 "wrong" nucleotide
pairs. Evolution does this by mutating
one nucleotide pair at a time. Actually
we don't worry about "pairs" of nucleotides; we only care about one
side of the "pair" because the other side automatically follows the
main side (e.g. if an 'A' is on one side a 'T' is automatically on the other
side). Thus, we are only concerned about
the main side of the DNA in the gene complex. The
computer simulation starts out with a "copy gene" with 20,000
nucleotides on one side of the DNA. Of
course this gene complex only exists in a computer. The
simulation randomly mutates one of the "nucleotides" (i.e. nucleotide
positions) at a time. Given the
speed of computers, even home computers, a computer can simulate tens of
thousands of random, sequential mutations fairly quickly. After each
random mutation, we can assess how many "right" nucleotides there are
in the "copy gene." For
example, using just one randomly chosen computer simulation of 75,000
sequential point mutations (we are only dealing with one DNA strand and
applying 75,000 consecutive point mutations to this one "copy gene"). These are
the results of the first ten mutations: Column 1 is
the mutation number (i.e. 1 equals the first mutation) Column 2 is
the number of "right" nucleotides after the latest mutation Column 3 is
the percentage of "right" nucleotides after the latest mutation Column 4 is
the type of mutation Results of
a Single Computer Simulation, Where 10 Randomly Selected Mutations Were
Sequentially Applied to the Copy Gene: 1 19001 95.005% "good" mutation 2 19000 95% "bad" mutation 3 18999 94.995% "bad" mutation 4 18998 94.99% "bad" mutation 5 18997 94.985% "bad" mutation 6 18996 94.98% "bad" mutation 7 18995 94.975% "bad" mutation 8 18994 94.97% "bad" mutation 9 18993 94.965% "bad" mutation 10 18992 94.96% "bad" mutation Here are
some other selected mutation points of this simulation so the reader can see
the overall trend. The first column is
the mutation number (i.e. 1000 means the 1,000th consecutive point mutation as
applied to this "copy gene").
The second column is the number of "right" nucleotides. The third column is the percentage of
"right" nucleotides. Results of
a Single Computer Simulation, Where 100,000 Randomly Selected Mutations Were
Sequentially Applied to the Copy Gene: Column #1:
Simulation # (only the first 10,000 are shown) Column #2:
# of "right" nucleotides after the number of simulations) 1000 18082 90.41% 2000 17229 86.145% 3000 16438 82.19% 4000 15701 78.505% 5000 14998 74.99% 6000 14347 71.735% 7000 13741 68.705% 8000 13181 65.905% 9000 12666 63.33% 10000 12154 60.77% Note the overall
downward trend. This is because most of
the nucleotides start out as "right" nucleotides, thus most of the
early mutations turn a "right" nucleotide into a "wrong"
nucleotide. Continuing
on after 10,000 simulations, somewhere between the 15,000th mutation and the
16,000th mutation, the percent of "right" nucleotides dropped below
50%. 15000 10043 50.215 16000 9736 48.68 Somewhere
between the 37,000th and 38,000th mutation the percentage of "right"
nucleotides dropped below 30%. 37000 6073 30.365 38000 5953 29.765 As
predicted, eventually the percentage of "good" mutations stabilized
around 25%. Multiple Simulations A single
simulation may tell us the trend of degeneration, but it doesn't really prove
anything. But the power of the computer
again comes to our aid. My home computer
can do 50,000 simulations, similar to the one above, in less than four hours. However,
each simulation only runs to the point that the percentage of "good" mutations
drops below 85% (which is 10% less than the starting percentage). At this point it is considered
"impossible" that future mutations will ever raise the
"good" mutations above the initial level of 19,000 "good"
mutations. The reader will understand
why in a moment. On average,
the number of "right" nucleotides dropped below 85% on the 2,313th
mutation (i.e. simulation). Note that
10% of the total number of nucleotides is 2,000 and 15% of the nucleotides is
3,000. Thus, within an average of only 2,313
mutations, the total number of "right" nucleotides had dropped by
2,000 to a total of 3,000 wrong nucleotides.
This should give the reader an idea of how quickly the number of
"right" nucleotides drops when starting out with 95%
"right" nucleotides. To insure I
was getting consistent data, I actually ran the 500,000 simulations in 10 sets
of 50,000 simulations. It is actually
best to do it this way to make sure your patterns are consistent. These ten groups of 50,000 simulations tell
us a lot about mutating a "copy gene" into an "evolution gene." Let us
consider the results of the computer simulations. First, let
us consider only the first mutation of these 500,000 simulations: 1st
mutation was a "bad" mutation: 475,123 95.02% 1st
mutation was a "neutral" mutation: 16,470 3.29% 1st
mutation was a "good" mutation: 8,407 1.68% These are very
consistent with our predicted results above: 1st
mutation predicted to be a "bad" mutation: 95% 1st
mutation predicted to be a "neutral" mutation: 3.33% 1st
mutation predicted to be a "good" mutation: 1.67% Now let us
look at the "maximum" percentage of "good" mutations
achieved for each simulation. To gather
this information, for each simulation, and after each and every mutation, the
"maximum" percentage of "good" mutations was kept track of. The "highest" "maximum"
percentage, for each simulation, was recorded. Out of the
500,000 simulations, the maximum percentage of "good" mutations that
was ever achieved was 95.015%. This was
19,003 "right" nucleotides. In
other words, among the 500,000 simulations, none
of these simulations ever achieved 19,004 "right"
nucleotides!! And the
19,003 level of "right" nucleotides was achieved in only 4 of the
500,000 simulations. Here is the
complete table of the maximum achieved percentage of "right"
nucleotides among the 500,000 different simulations: Above
95.015% (above 19,003) 0 0% # Achieved
95.015% (19,003) 4 0.0008% # Achieved
95.01% (19,002) 167 0.03% # Achieved
95.005% (19,001) 8,648 1.7% # Achieved
95% (19,000) 24,496 4.9% # Achieved
94.995% (18,999) 466,685 93.3% In the
above discussion, we predicted that only 2 simulations, out of 500,000, would
have the first 3 consecutive mutations all be "good" mutations. There were actually 4 simulations which
achieved 19,003 "right" nucleotides.
This is not surprising because there are multiple ways to reach 19,003
"right" nucleotides other than just the first 3 mutations being
correct. Nevertheless,
achieving 19,003 "right nucleotides" would be an "outlier,"
meaning it would be a very rare event, and the number of outliers is always
hard to predict. In order
for the "copy gene" to randomly mutate into an "evolution gene,"
it would be necessary to achieve 20,000 "right" nucleotides. Yet, not even 19,004 "right"
nucleotides (starting with 19,000 "right" nucleotides!!) were
achieved in 500,000 attempts (i.e. 500,000 simulations). Another
interesting result of the 500,000 simulations was how quickly the total number
of "right" nucleotides dropped below 19,000, never to rise to the
19,000 level again. This is critical to understand: by the time the 11th mutation was
calculated, in all 500,000 simulations, the total number of "right"
nucleotides was below 19,000, and never achieved 19,000 "good" mutations
again. In other
words, after the 11th mutation, every one of the 500,000 simulations was below
19,000 "right" nucleotides, and never achieved 19,000
"right" nucleotides after the 11th mutation. Only once
in 500,000 simulations was the 10th mutation at 19,000 "right"
nucleotides. Here is the progress of
that one simulation. Simulation
number 29,058 in the seventh set (of ten sets) of 50,000 simulations: 1st
mutation (good) 95.005% 19,001 "right" nucleotides 2nd
mutation (bad) 95% 19,000 "right" nucleotides 3rd
mutation (good) 95.005% 19,001 "right" nucleotides 4th
mutation (bad) 95% 19,000 "right" nucleotides 5th
mutation (bad) 94.995% 18,999 "right" nucleotides 6th
mutation (bad) 94.99% 18,998 "right" nucleotides 7th
mutation (bad) 94.985 18,997 "right" nucleotides 8th
mutation (good) 94.99 18,998 "right"
nucleotides 9th
mutation (good) 94.995 18,999 "right" nucleotides 10th
mutation (good) 95% 19,000 "right" nucleotides Even though
this simulation "kept its head above water" longer than any other
simulation, it only achieved 19,001 "right" nucleotides. This shows
just how quickly the overwhelming problems created by the vast number of
"right" nucleotides (which always mutate into a "wrong"
nucleotide) prevented a significant net accumulation of "right"
nucleotides. While there
is some flexibility in the exact sequence of an "evolution gene,"
these numbers make it very, very clear that even taking into account a
reasonable amount of flexibility, converting a "copy gene" into an
"evolution gene" is impossible, even starting at 95% identical
nucleotides. Starting At Even Higher
Percentages If we had
started at 97% "right" nucleotides, instead of 95% "right"
nucleotides, an even higher percentage of the first mutations would be
"bad" mutations. This is
because there is a higher percentage of "right"
nucleotides to mutate into "wrong" nucleotides. There is
actually a paradox involved. Study this
next sentence very, very carefully because it will become important in future
discussions: The higher the percentage of
"right" initial nucleotides, the lower the probability that the first
few mutations will result in a net gain in the number of "right"
nucleotides. Let us
consider some comparison statistics. Simulations Where
"Plus Two" or Above Was Achieved "Plus
two" means the simulation achieved 2 nucleotides higher than were it
started. For example, if it started at
19,000, "plus two" means a simulation achieved 19,002
"right" nucleotides or above. At 95%
(initial percentage of "right" nucleotides), 19,000 nucleotides
started as "right" nucleotides.
Among 500,000 simulations, 167 simulations achieved "plus two"
nucleotides or above (i.e. 19,002).
Also, 4 simulations achieved "plus three" nucleotides (i.e.
19,003). At 97%,
19,400 nucleotides started as "right" nucleotides. Among 500,000 simulations, only 51 simulations achieved
"plus two" (as opposed to 167) nucleotides (i.e. 19,402). Also, only 1 simulation achieved "plus
three" (as opposed to 4) nucleotides (i.e. 19,403). At 99%,
19,800 nucleotides started as "right" nucleotides. Among 500,000 simulations, only 4 simulations achieved
"plus two" nucleotides (i.e. 19,802).
Also, none of the simulations achieved "plus three"
nucleotides (i.e. 19,803). Clearly, as
the initial percentage of nucleotides start out as "right"
nucleotides, it is harder to achieve a "plus two" and "plus
three" condition. How Quickly Simulations
Dropped Below Initial The next
question to answer is how many mutations did it take
for the simulation to drop below the initial "right" nucleotide
level, never to rise above it again. At 95%, by
the 11th mutation, every simulation was below the initial number of
"right" nucleotides, never to achieve the initial number of
"right" nucleotides again. At 97%, by
the 9th mutation, every simulation was below the initial number of
"right" nucleotides, never to achieve the initial number of
"right" nucleotides again. At 99%, by
the 6th mutation, every simulation was below the initial number, never to
achieve the initial level of "right" nucleotides. We conclude
from this set of data that the higher percentage of initial "right"
nucleotides, the faster the DNA will deteriorate. How Many Simulations
Never Achieved the Initial Condition In each
simulation there was a "first mutation." In most cases this first mutation was a
"bad" mutation. The question
becomes, in what percentage of the simulations was the first mutation a "bad"
mutation, and the simulation
was never able to achieve the initial condition of "right" nucleotides. For example, at 95%, what percent of the time
was the first mutation a "bad" mutation and subsequent mutations never achieved the initial 19,000
"right" nucleotide level? At 95%,
93.3% of the simulations never achieved the initial number of "right"
nucleotides. At 97%,
96.0% of the simulations never achieved the initial number of "right"
nucleotides. At 99%,
98.7% of the simulations never achieved the initial number of "right"
nucleotides. How Quickly Did
Simulations Reach an Unrecoverable Condition When the
deterioration of the DNA dropped 10% below the initial level of
"right" nucleotides, it was considered impossible for the simulation
to ever recover enough to reach the initial level. The simulation was terminated at this point. At 95%, by
the 2,313th mutation, the percentage of "right" mutations had, on
average, dropped by 10% (i.e. from 95% to below 85% or from 19,000
"right" nucleotides to below 17,000 "right" nucleotides). Note that a
drop of 10% amounted to the total number of "right" nucleotides
deteriorating by 2,000. Thus, within
2,313 mutations, the number of "right" nucleotides had dropped by
2,000!! At 97%, by
2,244 mutations, the number of "right" nucleotides had deteriorated
by 2,000. At 99%, by
2,179 mutations, the number of "right" nucleotides had deteriorated
by 2,000. We can
clearly see that the higher the initial number of "right"
nucleotides, the faster the DNA will deteriorate by 10%. All of this
results in a paradox for evolution: Kehr's Paradox: The higher the percentage of initial correct nucleotides,
the more quickly the DNA will deteriorate because of random mutations. While this
paradox may seem obvious after our discussion, it actually is far more
significant to the evolution debate than appears on the surface. Looking At This Another
Way The above
numbers reveal very, very critical concepts.
The overall concept is that the higher the initial percentage of
"right nucleotides," the faster the DNA will deteriorate. Eventually, the DNA will deteriorate to 25%,
no matter what percentage of "right" nucleotides it starts with. But what if
we don't know the actual percentage of "right" nucleotides? How can we get an idea of the initial percentage
of "right" nucleotides? Ponder that
last question before reading on because the answer should be obvious from Kehr's Paradox. The answer
is by studying the ratio (i.e. percentage) of "good" mutations to
"bad" mutations. What the
above data tells us is that if, for a particular species, the percentage of
"good" mutations is very, very rare; then we can logically conclude
that this DNA has a very, very high percentage of "right"
nucleotides. In other
words, if we know the percentage of initial "right" nucleotides, we
can take a good guess at calculating the probability that early mutations will
be "good" or "bad." However, if
we don't know the initial percentage of "right" nucleotides, we can
look at the percentage of "good" mutations versus "bad"
mutations and take a good guess at how many "right" nucleotides there
are at any given time. In the next
chapter, this concept will be discussed in more detail. Conclusion The theory
of evolution depends heavily on new genetic material. Without new genetic material there are no new
species and there is no evolution. Period. Random,
pointless, directionless mutations are at the heart and soul of neo-Darwinism. With the
discovery of DNA the debate between the theory of evolution and creation
science should have made a major turn. Suddenly,
fossil morphology should have taken a "back seat" to the analysis of
DNA in terms of studying mutations to determine the probability of evolution. However,
that didn't happen. The reason is that a
study of DNA mutations is a massive, massive embarrassment to the theory of
evolution for several reasons. When
science sees something that is not favorable to the theory of evolution, the
discovery gets buried. Thus,
instead of DNA and probability analysis, which is embarrassing to the theory of
evolution, nineteenth century morphology is still the main tool of
evolutionists. The next
chapter will further explain why science has avoided any mathematical discussion
of how new genetic material is created. |