You reference Lenski’s paper for this figure, and even specify exactly where you’re getting the figure from in the paper in a later comment:
The problem is that Lenski gives no such ratio. As you later quoted yourself, the paper reads:
one can infer that the proportion of mutations that are beneficial is roughly one in a million.
This “one in a million” figure is not a ratio of “one million harmful mutations for everyone one beneficial mutations” as you originally claimed (twice).
I[quote=“Chris_Falter, post:77, topic:6085”]
Can you give an alternative explanation ?
Yes, the ages have symbolic meanings. For example, the age at death of Mesopotamian nobility from the same time period was often proclaimed to be thousands of years. One prominent king, Alilum of Eridug, was deemed to be 28,800 years old at the time of his unfortunate demise. Clearly, Alilum’s putative age was not expected to be interpreted in a literal fashion.
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Ok, lets say for the sake of the argument that you are right, that the ages of the patriarchs have symbolic meaning. How in the world would this explain the fact
that when you plot the age of the patriarchs, symbolic or not, you see a pattern that reveals a biological decay curve?
The rarity of mutations isn’t a challenge when you consider the vastness of time and the number of organisms that exist. The way you have formulated your argument, one would expect antibiotic resistance to beta-lactams isn’t possible or would be very rare! In fact, constructive mutations for enzymes (and thus organism function) are very easy to find in populations.
Regardless, (here is my main point) the reason why these reductive beneficial mutations do not limit evolution is because they are themselves self-limiting. Breaking or blunting genes is common (your statement #1), and as a result there is constant pressure on organisms to have only their essential genes. If a gene is not needed, it will be weeded out eventually. However, this process of “devolution” is self-limited. Eventually, no reductive beneficial mutations are possible because an organism is living with all required functional genes in its current environment as a result of past selection pressure (i.e. breaking anything will be worse). In its current environment, the only possible beneficial mutations are constructive.
This is where constructive evolution happens. “Devolution” or “reductive beneficial” mutations are the sideshow to constructive evolution. New constructive mutations are also essential so they cannot be reduced either. This is how complexity builds. Devolution only happens when the organism enters a new environment or the environment changes to where a previously essential gene is no longer needed. The non-essential gene is lost and then constructive mutations begin again.
In summary, yes devolution happens, but devolution only acts after constructive mutations have happened and in specific environments where previous constructive mutations are no longer needed. When viewed this way, you see how small of an effect reductive beneficial mutations have and how they are actually much less common than constructive beneficial mutations. You can only reduce things so much, but there are immeasurably more ways to obtain a constructive beneficial mutation than a reductive beneficial mutation.
This is why I strongly disagree with your assertion that:
If your statement above was actually true, think about what this would mean for the real world. Wouldn’t life as a whole basically grind to a stop over the course of a few hundred generations if life life constantly trended toward reduction? Consider the alternative hypothesis, that life rarely trends toward reduction and only based on specific circumstances (like white fur in the arctic) but life most commonly adapts in beneficial constructive ways. I believe the latter is what we observe around us.
(facepalm) Correlation does not imply causation.
You’re never going to explain why mice haven’t gone extinct due to genomic degradation, are you?
You’re right. I should have say that according to Lenski, there is one beneficial mutation for a million mutations, be them harmful or neutral. Thank you for the correction.
It is simply not the case that Mendel’s Accountant has zero connection to biological reality. Indeed, Mendel’ Accountant was used by Brewer et al to model mutation accumulation within a hypothetical RNA virus similar to the influenza virus. These initial numerical simulation experiments strongly indicated that RNA viruses should rapidly accumulate deleterious mutations at a steady rate, causing the virus to run down and go extinct in a reasonably short timeframe. On the other hand, Sanford and Carter have performed a comprehensive historical analysis of mutational changes within H1N1 by examining over 4100 fully-sequences H1N1 genomes. This has allowed them to examine the genetic changes arising within H1N1 from 1918 to the present. And guess what, what they observed in this real biological population agrees perfectly well with the aforementioned predictions of Mendel’s Accountant. IOW, these remarkable results simultaneously validate the reality of genetic Entropy in a real biological population, and validate the reliability of Mendel’s Accountant to accurately predict genetic degeneration of a real biological population.
See below the 2 corresponding publications:
https://www.worldscientific.com/doi/pdf/10.1142/9789814508728_0015
Can you explain exactly what the “degeneration” that was demonstrated in the H1N1 paper was?
Can you explain exactly how this is supposed to “agree perfectly well” with predictions from Mendel’s Accountant?
Just throwing 2 papers in our faces isn’t good enough. Be more specific.
In the H1N1 paper, the authors observed that since 1918, the H1N1 genome has accumulated mutations at a very rapid and remarkably constant constant rate and that the increase in mutations correlated tightly with continuously declining « fitness » (pathogenicity). And it happens that these observation were predicted by the numerical simulations described in the first paper. As I said, this strongly supports the reliability of Mendel’s Accountant to accurately predict genetic degeneration of a real biological population.
They documented declining fitness in H1N1? Where? You mention pathogenicity, but it’s hardly mentioned in the paper. Did you mean virulence?
Tell us precisely how this matched the numerical predictions from MA. Like I said before, I’m interested in the specifics.
Ill agree if you can tell me how you MEASURE complexity.
Yes, in this situation fitness decline is the same that reduction of pathogenicity or reduction of virulence or genetic atténuation.
I don’t see them in a search.
That’s a pretty huge difference, don’t you think?
Those aren’t the same thing. Fitness in viruses, as with organisms, is defined as reproductive success. That is not a function of pathogenicity or virulence AT ALL.
I suggest that you do some reading on defective interfering particles.
Exactly, but Sanford didn’t even quantify pathogenicity in that paper, or do any original analysis regarding virulence - he just cites Simonsen et al. 1998 for that, showing that mortality from H1N1 decreased over time.
We don’t. I don’t see that pattern, I see some dots, and someone has put a curve on that doesn’t even seem to fit all that well. What model is that curve based on, and how well do those dots fit it? Wasn’t the curve essentially just retrofitted to the dots, as opposed to being a prediction from a model? That’s not a lot of dots by the way, is that enough dots to have any sort of statistical significance?
Of course, it’s not even clear we are looking at real data, as opposed to just made up or allegorical numbers. How was the age of these individuals determined? Someone said so? Well then I am twenty million years old, and I have now disproven the curve.
An exponential decay curve
Mendel’s accountant has zero connection to any reality. It’s a completely worthless piece of software Sanford wrote just to support his idiotic claims. It’s classic GIGO to prop up his YEC beliefs.
Have you ever actually downloaded and run MA? I have. Like I said before no matter what starting parameters you give it your original population will “degrade” and die . The parameters only affect how quickly the population goes extinct.
Try it. Start with a population of 8 humans, show me the parameters which will allow that population to expand to 7.7 billion.
Oh, and all the mice on the planet say you’re full of it too. 
Not necessarily, for it could be argue that most neutral mutations are not perfectly neutral but nearly-neutral with an extremely weak negative effect on fitness.
Did you notice that Sanford’s curve only starts 4 centuries after Noah, who supposedly lived to 950 years old? Sanford’s “exponential decay curve” would predict Noah should have been capable of living practically forever. People born even just 1 century after Noah should have been living more than 5000 years. People born 1 year after Noah could have lived for 3.7 million years. See a problem?
Here’s the world’s population for the last 1000 years.
Please explain how that fits your “genetic entropy” exponential decay model.
Decreasing pathogenicity might actually be beneficial. If pathogenicity is too high, the virus eventually runs out of hosts.
