The number depends on the organism and the function. There are ways to estimate these numbers. There is no reasonable assumption that supports the LUCA hypothesis.
This is new to me. Can you show me where this description is specifically stated as you have. The way you are describing it is more common biological features than common descent or ancestry. Do I understand you correctly?
Wait a minute, so you didn’t know this all along! Goodness gracious! You are indeed clueless. Anyway, its good you clarified that for us.
The nested hierarchy is basically all you need to know.
The logic is really simple:
Common or vertical descent produces a nested hierarchy.
If all cellular (unicellular and multicellular) lifeforms fit into a nested hierarchy (or groups within groups), then it means there was universal common descent.
All lifeforms do indeed fit into a nested hierarchy and so we can conclude they all shared a common ancestor.
~3.5bya, that universal common ancestor was a population of “cells”. LUCA had contemporaries too and they might have contributed to her gene pool, but she was the one from whom we directly descended from.
The question is difficult to parse. State it clearly.
I have always understood LUCA the way that @Chris_Falter described. I’m puzzled that you had some different idea.
What do you take “traced back” to mean, other than ancestry, or the line of descent?
“Traced back” does not directly imply a ancestral relationship the way I read it. It means that there is common components that are observed. Its not directly implying a mechanistic (reproduction) relationship.
Is it possible there is more than one starting point? If so how many? I recently read a paper by Eugene Koonin that implied a FOL or forest of life.
Rigorous means given methodological naturalism as a working method.
Rigorous means testing hypotheses as best as possible. Methodological naturalism has nothing to do with whether we rigorously or don’t rigorously test hypotheses.
The rigor depends on your alternative hypothesis. If you limit your method to methodological naturalism the testing is less rigorous as you can ignore alternatives like @Winston_Ewert proposal or having your de novo gene models deal with sequential searches.
You then can also compare your alternative of LUCA to multiple origins using random change prior to selection to produce P values. Based on this method LUCA will always be statistically more likely than multiple origins.
This is why I think for biology at this point the design detection process (Behe) is valuable because it is the only method that can land you on the possibility of multiple points of origins of living organisms.
I have no idea what you mean by “multiple origins using random change prior to selection,” or how one would build a model that can be tested using genomic sequence data. Could you explain how this alternative model would work mathematically, and how it could be tested using genomic sequence data?
No one has ignored Ewert’s proposal, on this forum at least. I find it incredible that you would make this claim, given the very robust discussion of Ewert’s model. Were you unaware of the very detailed attention given to Ewert’s model?
In addition, were you aware of the fact that Ewert conceded, at the outset of his paper, that his model could not deal with the vast majority of the evidential record in biology–namely, sequence data?
Do you think a scientific community should trade a model with strong explanatory capabilities for 100% of the data for one with no capability of explaining the vast majority of the data?
This is why biologists can get very frustrated when they deal with your claims, Bill. Your claims seem detached from the reality of what has transpired before your very eyes (robust debates with Ewert on this forum, Ewert’s concession of the great explanatory weakness of his model).
They in fact deal with sequential searches, Bill. They incorporate sequential search implicitly by using empirical observations of distribution of functional effects, empirical observations of selection gradients, and empirical observations of mutation rates. These all incorporate, at least implicitly, search/optimization “algorithms” in the domain of biology.
The alternative model simply starts at different points. It eliminates having to mathematically explain the origin of new genes and complex biological structures generated by gene families. In reality population genetics starts from existing populations.
Who has rigorously tested Winston’s model as Michael Lynch proposed an alternative to Behe’s model?
The important data in biology for testing design vs common descent however is not historical. The gene data base is increasing dramatically. Genes are stronger representations of function than raw sequences.
My comment was about methodological naturalism and not Winstons engagement on this forum.
What is the mechanism in biology that does this? Is the claim that simply the sequence change will be directed by selection? Selection does not happen until some beneficial function arises. There is no empirical basis for believing that SNP’s from reproduction are directional on their own. Especially given the current paradigm is neutral theory.
I agree with you here.
The problem is the cause of the pattern implied by the model is outside MN.
How do you make the math work with the current empirical data?
An average of 75 mutations were fixed per population in the Lenski experiment. This is less than one per gene over 30 years and is after 50000 generations. The search length you quote is of a single gene/protein of below average residue counts and it still requires 10^8 searches for a single function gene.
If we want to show this is a general mechanism and not specific to bacteria the problem gets much worse with slower generation times and more complex protein functions.
If you think fixation is relevant to search, you don’t understand what those words mean.
Each flask could support 5x10^8 cells. With ~75k generations so far at ~7 generations per flask, that’s 11k flasks. 1.1x10^4 * 5x10^8 = 5.5x10^12 cells. At 2x10^-4 mutations per replication, that’s 1.1x10^9 mutations per lineage. In a fairly small bacterial population.
Do you think a search algorithm that has active information inside it is relevant to what happens in biological reproduction?
Do you think that mutations due to reproduction happening in large populations are relevant to mutating a single DNA sequence repeatably until some function is arrived at and that is strong enough for a selective sweep?
What we are observing in the Lenski experiment is billions of mutations happening in a large population but only a tiny fraction preserved? Whats going on?
I don’t know what you are trying to say here, and I suspect you don’t know either.
Redundant, just say mutations.
Small population for bacteria.
Since the mutations are/should be evenly distributed throughout the genome, the rate at which mutations in a particular gene should be proportionate to the rate at which mutations occur and the size of the gene. So… yes, the total number of mutations is relevant to the total number of mutations in a particular gene.
New function, variant function, modulated function, whatever. If there is a mutation that ‘can’ produce some function of whatever description, then it will have happened. If that function is beneficial, it will be selected for.
Selection and drift.
OK, but you cannot know the cause is outside of MN until you look for it using MN. In all the history of science, we have zero examples of causes that are not natural.
Is the scientific method looking for causes that are not natural?
That’s an empty question: What should non-natural causes look like? If you found one, how would you know?
Methods do not “look”, people do. There are historical examples of causes that were not previously known to be natural (magnetism, St. Elmo’s Fire, etc.), though natural cause may be suspected.
If these not-natural hypotheses are testable and can make precise, verifiable predictions, then the answer to your question is yes.