Rebuttal To Creationists - "Since We Can't Directly Observe Evolution..."

I already explained those things in previous posts.Why do you think the creation of new alleles is a mystery? Can you explain this? They are amongst the ones that have stuck around and are now found in the human population. We don't need to know what each and every mutation does in order to conclude that the DNA differences between the species are responsible for the physical differences between them. Why are you having such a hard time understanding this? Desai's group did, too. Guess what, their model disagrees with yours, and their model is backed by experimental evidence. Already did that. If fixation requires 100% of the population to have the same base at the same loci, how many fixed mutations do you think there are in the 3 billion base haploid human genome? (hint: not many)On top of that, the Desai paper specifically stated that they observed mutations moving towards fixation independently. How many times do I need to point this out?

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To investigate how sex improves the efficiency of selection, we analyzed the dynamics of adaptation. As in earlier studies21,22, asexual populations exhibit signatures of hitchhiking and clonal interference (Figure 2a–d). Groups of functionally unrelated mutations, linked within the same genetic background, change in frequency together as clonal cohorts. The outcomes of evolution are determined by competition between these cohorts. In contrast, sexual populations are not characterized by cohorts of linked mutations (Figure 2e–h). Instead, the dynamics of each mutation is largely independent of other variation in the population. In these populations, mutations that occur on different backgrounds fix independently, while others briefly hitchhike to moderate frequencies where they persist or are eliminated from the population.
Sex Speeds Adaptation by Altering the Dynamics of Molecular Evolution - PMC

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The Desai paper describes it perfectly:

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As in earlier studies21,22, asexual populations exhibit signatures of hitchhiking and clonal interference (Figure 2a–d). Groups of functionally unrelated mutations, linked within the same genetic background, change in frequency together as clonal cohorts. The outcomes of evolution are determined by competition between these cohorts.

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The problem for you is that sexually reproducing species act differently. I was the one who had to explain fitness landscapes to you. Want to try again?

It may correlate with asexual organisms, but not with sexual organisms. Humans are sexual organisms. You are assuming that each adaptation can be reached by only one mutation in the whole genome. This is a false assumption. You also falsely assume that there is only one possible adaptation. This is why your math doesn't work.For example, there are multiple different mutations in the promoter region of the human lactase gene that result in lactase persistence. In rock pocket mice, we can find two populations of black mice that have different mutations in different genes that both produce the black phenotype.Just a moment...In humans, there are multiple different mutations that modulate skin color. I could go on and on and on. Do you see why your math is wrong? The mutation rate in E. coli can not be directly applied to humans. How many times have I shown you this? So how can you know the probability of a beneficial mutation if you don't know what the environment is? Here's another example for you from the Desai paper:

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Our genetic reconstructions also highlight the potential importance of epistasis. For example, we identified a mutation in MET2 which fixed in a sexual population despite being deleterious in the ancestral background. However, further reconstructions showed that this mutation is beneficial in an evolved background, an example of sign epistasis (Methods). We cannot rule out the possibility of similar epistatic effects involving other mutations; this represents a limitation of the analysis in Figure 3.
Sex Speeds Adaptation by Altering the Dynamics of Molecular Evolution - PMC

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Wow. A mutation that is deleterious in one genetic background but beneficial in another. Where is that in your math?

Taq:

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Always meant to come back to this one:

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Kleinman writes:

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It appears you didn't follow my discussion with Taq very closely. That billion replications is based on the following:
How Many People Have Ever Lived on Earth?
There have been about 100 billion people that have ever lived, 99% have lived in the last 10,000 years. That leaves about a billion humans who have lived from our first appearance until the last 10,000 years. Choose a mutation rate and do the math. Today there are over 7 billion people on earth and only 300,000 chimps.

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So let's do the actual math, in Python (written by me).

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population = 100000
gentime = 25
time = 5000000
mutrate = 50
numgen = int(round(time/gentime, 0))

accumbirths = 0
accummutation = 0
for i in range(numgen):
    accumbirths += population
    accummutation += population*mutrate

print(f'{accumbirths:.2e} total births')
print(f'{accummutation:.2e} total mutations')
print(f'{round(accummutation/6000000000, 1)}x mutational coverage of human genome')

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The code brackets should preserve the tab indentions, so a copy and paste into an online Python interpreter works just fine.

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Online Python Compiler (Interpreter)

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I have a constant human population of 100,000, a mutation rate of 50 mutations per person per generation (which has been empirically verified), a generation time of 25 years, and a time span of 5 million years since common ancestry with chimps. These values can all be changed if you want, say to a constant population of 1 million individuals.

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The outputs give you the number of births, the total number of mutations, and the fold coverage for that number of mutations. The coverage represents the number of mutations that would have happened at each base if all mutations were spread evenly across the 6 billion base diploid human genome.

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With those numbers I get:

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2.00e+10 total births
1.00e+12 total mutations
166.7x mutational coverage of human genome

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Those are a bit different from what Kleinman seems to be claiming. With just a constant population of 100,000 we get 20 billion births, and enough mutations to cover the human genome 166.7 times over, assuming even distribution.

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Kleinman, is my math right?

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The problem with your calculation is that you are assuming neutral evolution. In other words, all you are doing is counting the total number of mutations that could occur in a given number of generations given a mutation rate, population size and genome length. Adaptive evolution requires that the particular mutation occur at the correct site to give an adaptive allele. That takes a lot of random trials (replications) and most of the human replications have occurred in the last 10,000 years (99%) according to the data:
How Many People Have Ever Lived on Earth? | PRBBefore 10,000 years ago, you only have about a billion replications to work with and that's not a lot for descent with modification, even if you throw in recombination. The Desai experiment only shows 44 de novo mutations after 100 million replications and that's in a single selection pressure constant environment. The real environment that we live in has many more selection conditions making for much more complex evolutionary landscapes and trajectories. But don't give up on doing the math. Finding the correct mathematical relationship between the variables in the process you are studying and then correlating it with the experimental data is the way you figure out the problem you are working on.

Where? All I am calculating is the number of mutations that have happened over that time period. There have been enough mutations over the last 5 million years to hit every base in the human genome 166.7 times over, and that is just with a constant population of 100,000. If we shift that to a constant population of 1 million then we get enough mutations to hit every base 1,666.7 times over. That's target thinking. You are getting your probabilities backwards. There is not "correct" mutation. There are only the mutations that occur and the adaptations that are found. Where is my math wrong? I have about 20 billion births prior to the present population boom. Again, where is it wrong? You don't model these either.

Are you saying you don't ignore the child abuse in your church? How are you addressing that? Not passing out math models after mass I hope?

Yes. More and more what your bible says.Step right up, Ladies and Germs, Kleinman will now show us his math and physics on the Eucharist.

Kleinman:

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Considering you don't have an explanation of how drug resistance evolves or why cancer treatments fail but think that you are explaining the evolution of humans and chimps from the same gene pool is the mystery.

Taq:

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I already explained those things in previous posts.
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Why do you think the creation of new alleles is a mystery? Can you explain this?

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I mean show your math. Explain why it takes a billion replications for each adaptive mutation in the Kishony (and Lenski) experiment(s). The creation of new alleles is not a mystery to me. Specify the mutation rate (the probability of a mutation occurring in a single replication) and I can compute the probability of that mutation happening at least once in x number of replications.

Kleinman:

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So which of these 50 to 100 mutations that humans get every replication are the ones that give humans the reproductive fitness advantage over chimps?

Taq:

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They are amongst the ones that have stuck around and are now found in the human population. We don't need to know what each and every mutation does in order to conclude that the DNA differences between the species are responsible for the physical differences between them. Why are you having such a hard time understanding this?

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I'm just yanking your chain, I know you can't answer that question. But you should be able to answer the question of how to compute the probability of a particular mutation occurring at a particular site in the genome given the mutation rate and the number of replications.

Kleinman:

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I'm the only one here that has presented a mathematical model for descent with modification (DNA evolution) and recombination.

Taq:

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Desai's group did, too. Guess what, their model disagrees with yours, and their model is backed by experimental evidence.

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Did they now? Post the equation and the data from their experiment that disagrees with my model. And you still haven't shown us how the Desai team determines fixation in their experiment. Here's how they do it:
Phenotypic and molecular evolution across 10,000 generations in laboratory budding yeast (with asexual reproduction and sexual reproduction) populations

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At each sequenced timepoint, we call mutations fixed if they are at greater than or equal to 40% frequency (diploids) or 90% frequency (haploids) and do not drop below these thresholds at a later timepoint.

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That would explain why their number of generations to fixation is so low. However, 40% frequency for adaptive alleles will give a high probability of an adaptive recombination event occurring. The relative fitness of the more fit variants must be quite a bit higher than the less fit variants for the increase in frequency to occur in so few generations.

Kleinman:

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Why should anyone believe you when you can't even explain the evolution of antimicrobial drug resistance or why cancer treatments fail?

Taq:

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Already did that.

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Only in your dreams.

Kleinman:

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Don't be stupid, I'm the only one here that has presented a mathematical model of recombination. And you know it is correct, that's why you are arguing about multiple alleles fixing simultaneously. You don't even understand what fixation is. You can't have multiple alleles fixing simultaneously in a population. What is the definition of fixation that Desai is using in his paper?

Taq:

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If fixation requires 100% of the population to have the same base at the same loci, how many fixed mutations do you think there are in the 3 billion base haploid human genome? (hint: not many)

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I was under the impression that every human on earth has 99%+ the same genome. Do you have some different data?

Taq:

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On top of that, the Desai paper specifically stated that they observed mutations moving towards fixation independently. How many times do I need to point this out?

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To investigate how sex improves the efficiency of selection, we analyzed the dynamics of adaptation. As in earlier studies21,22, asexual populations exhibit signatures of hitchhiking and clonal interference (Figure 2a–d). Groups of functionally unrelated mutations, linked within the same genetic background, change in frequency together as clonal cohorts. The outcomes of evolution are determined by competition between these cohorts. In contrast, sexual populations are not characterized by cohorts of linked mutations (Figure 2e–h). Instead, the dynamics of each mutation is largely independent of other variation in the population. In these populations, mutations that occur on different backgrounds fix independently, while others briefly hitchhike to moderate frequencies where they persist or are eliminated from the population.
Sex Speeds Adaptation by Altering the Dynamics of Molecular Evolution - PMC

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Biologists need to get some better terminology for this than fixation. Perhaps simply an increase in frequency, or amplification because what Desai is calling fixation is not fixation.

Kleinman:

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Where's your mathematical explanation of the Kishony and Lenski experiments?

Taq:

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The Desai paper describes it perfectly:

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As in earlier studies21,22, asexual populations exhibit signatures of hitchhiking and clonal interference (Figure 2a–d). Groups of functionally unrelated mutations, linked within the same genetic background, change in frequency together as clonal cohorts. The outcomes of evolution are determined by competition between these cohorts.

Taq:

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The problem for you is that sexually reproducing species act differently.

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Now Taq. We know you can do some mathematics. You almost figured out random recombination by multiplying the frequencies, you just didn't include the possible permutations in your math and you know how to do a simple neutral evolution calculation. So, post an equation.

Kleinman:

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When we started this discussion, you didn't even understand that the improvement in fitness with each fixation was decreasing for each adaptive step in the Lenski experiment. The same thing happens in the Desai experiment.

Taq:

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I was the one who had to explain fitness landscapes to you. Want to try again?

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Yeah, right. Just like you explained the addition rule to me. Have you figured out yet the difference between using the addition rule for mutually exclusive events and arbitrary events yet?

Kleinman:

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The math that I've presented correlates very nicely with the Kishony and Lenski experiments.

Taq:

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It may correlate with asexual organisms, but not with sexual organisms. Humans are sexual organisms.

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I don't know what it is going to take for you to understand that descent with modification (DNA evolution) is not the same as recombination. In one case, new alleles are created and in the other case these alleles a shuffled. The Kishony experiment can be modeled with a descent with modification equation alone because competition is negligible. The Lenski experiment has to be modeled with a descent with modification equation and a biological competition equations solved simultaneously. The Desai experiment has to be modeled with a descent with modification equation, a biological competition equation, and a random recombination equation all solved simultaneously.

Kleinman:

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Taq, you are so mathematically incompetent. The reason why it takes about 1/(mutation rate) replications for an adaptive mutation to occur is that math applies to every site in the genome.

Taq:

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You are assuming that each adaptation can be reached by only one mutation in the whole genome. This is a false assumption. You also falsely assume that there is only one possible adaptation. This is why your math doesn't work.

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You still don't get it. This "at least one" equation applies to every site in the genome. And I don't assume that there is only one possible adaptation. Read my paper carefully.
The basic science and mathematics of random mutation and natural selection

Kleinman:

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What this empirical example demonstrates is that the sequence of mutations must occur in an order of ever increasing fitness in order for the evolutionary process to have a reasonable chance of occurring. In addition, this example demonstrates that there is more than a single sequential order, which can occur. In other words, not every member of the population must have the same sequence of mutations in order to evolve resistance to the antibiotic selection pressure. The population of bacteria has subdivided into subpopulations, each taking their own trajectory to achieve resistance to this particular selection pressure.

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Different adaptive mutations lead to different evolutionary trajectories. These are different variants in the population.

Kleinman:

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That's why it takes a billion replications in the Kishony experiment for each adaptive mutation for a mutation rate of 1e-9. In that billion replications will be members with a mutation at every possible site in the genome. In other words, an exhaustive random search is being done of the sample space.

Taq:

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The mutation rate in E. coli can not be directly applied to humans. How many times have I shown you this?

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So use a mutation rate of 1e-8 for humans. That's only requires 100 million replications for that adaptive mutation to have a reasonable probability of occurring. But that's for a single selection pressure environment. It doesn't help to have an adaptive mutation for malaria if you die of influenza, small pox, strep, tetanus, starvation, dehydration, or any of a myriad of other selection pressures in a real environment.

Kleinman:

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The selection conditions of the environment determine if any of those mutations are beneficial.

Taq:

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So how can you know the probability of a beneficial mutation if you don't know what the environment is? Here's another example for you from the Desai paper:

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It doesn't matter, every possible mutation has occurred when the population has done 1/(mutation rate) replications. When Kishony does his experiment with one drug (eg Ciprofloxacin), the member that gets the adaptive mutation for that drug is able to grow in the next higher drug concentration region. However, in that population will also be a variant with an adaptive mutation to a different drug (eg Trimethoprim). But that variant cannot grow in the next higher drug concentration region if the drug used in the experiment is Ciprofloxacin. Only when the drug used in the experiment is Trimethoprim will the Trimethoprim resistant variant become apparent.

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Our genetic reconstructions also highlight the potential importance of epistasis. For example, we identified a mutation in MET2 which fixed in a sexual population despite being deleterious in the ancestral background. However, further reconstructions showed that this mutation is beneficial in an evolved background, an example of sign epistasis (Methods). We cannot rule out the possibility of similar epistatic effects involving other mutations; this represents a limitation of the analysis in Figure 3.
Sex Speeds Adaptation by Altering the Dynamics of Molecular Evolution - PMC

Taq:

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Wow. A mutation that is deleterious in one genetic background but beneficial in another. Where is that in your math?

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The math I've presented includes every possible mutation, beneficial, neutral, or detrimental. Study the equation and perhaps it will come to you.

Kleinman:

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The problem with your calculation is that you are assuming neutral evolution.

Taq:

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Where? All I am calculating is the number of mutations that have happened over that time period. There have been enough mutations over the last 5 million years to hit every base in the human genome 166.7 times over, and that is just with a constant population of 100,000. If we shift that to a constant population of 1 million then we get enough mutations to hit every base 1,666.7 times over.

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That's right, all you are doing is calculating the total number of mutations. Do the same calculation for the Desai experiment where selection occurs and after 100 million replications you have 44 de novo mutations and that is for a single selection pressure environment. And that selection pressure must have been pretty intense based on how few generations it takes to amplify these mutations.Also, you don't have 100 billion replications to work with, only 1 billion. Most people would say that 10,000 years ago, modern humans existed. Systematic agriculture was occurring and industry was starting.

Kleinman:

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Adaptive evolution requires that the particular mutation occur at the correct site to give an adaptive allele.

Taq:

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That's target thinking. You are getting your probabilities backwards. There is not "correct" mutation. There are only the mutations that occur and the adaptations that are found.

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You still don't get it. When a population does 1/(mutation rate) replications, it is shooting at and hitting every target (mutation) possible.

Kleinman:

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That takes a lot of random trials (replications) and most of the human replications have occurred in the last 10,000 years (99%) according to the data:

Taq:

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Where is my math wrong? I have about 20 billion births prior to the present population boom. Again, where is it wrong?

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Look at Table 1. in the following link.
How Many People Have Ever Lived on Earth?
99% of the people that have ever lived have lived in the last 10,000 years. That is when the rapid increase in human population starts. That only leaves about 1 billion people before then for any kind of adaptive evolutionary process. And these 1 billion people are not all on the same evolutionary trajectory.

Kleinman:

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The Desai experiment only shows 44 de novo mutations after 100 million replications and that's in a single selection pressure constant environment. The real environment that we live in has many more selection conditions making for much more complex evolutionary landscapes and trajectories. But don't give up on doing the math.

Taq:

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You don't model these either.

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Sure I do. When I population is subject to multiple simultaneous selection pressures, the ability of a variant to accumulate adaptive mutations to each selection pressure is subject to multiple instances of the multiplication rule. Here's how you do the math:
The mathematics of random mutation and natural selection for multiple simultaneous selection pressures and the evolution of antimicrobial drug resistance
This is the math that explains why combination therapy works for the treatment of HIV despite the fact that HIV does recombination. Apparently, single adaptive mutations to one drug or another don't give any selective advantage to those variants to increase the frequency of those variants to improve the probability of an adaptive recombination event occurring.Study the math, it might come to you.

As a mathematician, you will no doubt be able to give me the odds that every single one of the 216,000 instances of child abuse in the Catholic church in France, which were determined by a 30 month enquiry to have occurred since the 1950s did not, in fact, occur.

vimesey:

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What?????? That's another one you are wrong on.

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As a mathematician, you will no doubt be able to give me the odds that every single one of the 216,000 instances of child abuse in the Catholic church in France, which were determined by a 30 month enquiry to have occurred since the 1950s did not, in fact, occur.

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French Catholic clergy sexually abused 216,000 children, report finds – POLITICO

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This happened to you, didn't it? You are mistaken if you think this great evil is limited to the Catholic Church. You have your Prince Andrew and I have my President Clinton. Why aren't you angry with the secular justice system that doesn't bring justice for this kind of evil? Are you aware of the political battle that is going on in the United States over the secular/state education system that wants to normalize pedophilia? Our leaders in our school system want to groom and sexualize young children. There is no room in their lesson plan for modesty, self-control, and faithfulness. They would rather spread disease to everyone in society.

Total bullshit. More bullshit.You are falling for all of the propaganda and slogans.

A good description of christian schools in the U.S. Yeah, Trump the serial rapist is more your kind of guy.

No, I wasn't.I'm aware of far right-wing conspiracy nuts who claim this, not only without evidence but in the face of contrary evidence.You are trying to excuse your priests of their evil by showing other religion's priests do the same evil. Evangelical christian pastors especially who seem to be a somewhat distant second to catholic priests in the child fiddling arena. Which ones? Specifically, by name.So show us this pedophila syllabus you claim is being taught in our schools.You lie. You spread your lies just for the thrill of watching the world burn. You are the right-wing religious poison that infests society.Show us this pedophila syllabus you claim is being taught in our schools. And leave out the math. You've shown you're no good at it.

nwr:

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Are you aware of the political battle that is going on in the United States over the secular/state education system that wants to normalize pedophilia?

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Total bullshit.

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Our leaders in our school system want to groom and sexualize young children.

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More bullshit.

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You are falling for all of the propaganda and slogans.

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Let's not forget the drag queen shows sponsored by nwr's church.

And let's not forget the drag queen show specifically for children at nwr's church.
https://www.youtube.com/watch?v=XFEYz_IvTc4&ab_channel=Ch...