CRISPR is a great example. Please see my other post on that topic in this thread, and if you are interested I will go find the refs that led me to that position.
On the other hand, I find your skepticism and independent thinking refreshing.
I see now I had an earlier discussion with tstactplsignore, back then they were seemingly incapable of understanding what I was saying (they kept thinking I denied the endonuclease activity).
Interestingly, from table S12 and my (selection for pre-existing mutants) model we can also explain a mysterious result they observed:
"Silent co-mutations in the repair oligonucleotides were introduced into >99% of D54H mutant alleles and ~3% of the F482S allele (Fig. 2c). Carryover of these silent SNPs indicates that the alleles are the product of HR and not de novo mutagenesis. The lower rate of coappearance of silent SNPs in F482S is presumably due to the larger distance between the two SNPs in the oligonucleotide and is rather common to see with single-stranded oligonucleotide donors19."
Rather than that ad hoc explanation, it is simply that the D54H cells had more silent only mutations to begin with (0.1% vs 0.0%). Regarding that 0.0%, an annoying thing is that they only report these percentages to one decimal place.
I'm not exactly clear on the number of cells present before the CRISPR-Cas9 treatment, but it sounds like 10^8, and then they let them grow for 72 hr + 7-12 days (total of 10-15 days) after the treatment. They also don't tell us how many cells were left at the end... but anyway if we assume these cells divide once a day, and 0.1% are preexisting mutants we could calculate the possible number of mutants thus:
Nt = 10^8
p = 0.001
d = 0:15
Nt*p*2^d
So I find those results to favor the "selection for pre-existing mutants" explanation over the "gene modification" one.
Because in cells that contain the target sequence (the complement to the guide RNA), Cas9 is damaging the DNA, leading to cell death and growth arrest. The small percentage of cells that already contained indels (thus reducing affinity for the guide RNA) are "immune", so they preferentially survive and divide to take over the population.
Also, it is 100% possible to create a testable model of something at the level of toxicity without knowing the details of the toxicity. I mean, here would be a simple one (written in R) where the wild type cells divide at 1/10th the rate of the mutants for some reason, so the mutants take over the population:
# Basic parameters of the cell culture
Ntotal = 10^8 # Initial total number of cells
Pmut0 = 0.001 # Propotion pre-existing mutants
Rdiv_wt = 0.1 # Divisions/day
Rdiv_mut = 1.0 # Divisions/day
# Calculate initial numbers of WT and mutant cells
Nwt0 = Ntotal*(1-Pmut0)
Nmut0 = Ntotal*Pmut0
# Convert between days and divisions
t = 0:15
Dwt = Rdiv_wt*t
Dmut = Rdiv_mut*t
# Calculate number of cells at each timepoint
Nt_wt = Nwt0*2^Dwt
Nt_mut = Nmut0*2^Dmut
# Calculate proportion of mutant cells in population at each timepoint
Pt_mut = Nt_mut/(Nt_wt + Nt_mut)
# Plot proportion of mutants vs time
plot(t, Pt_mut, type = "b", panel.first = grid(),
xlab = "Days Since Treatment",
ylab = "Proportion of Mutant Cells")
This was explained earlier I believe, so I am not sure where the confusion lies.
1) You start with a mixed population of cells. From the literature it looks like about 99-99.9% will lack indels at the target site, the rest have them.
2) The Cas9 will cut the DNA of cells lacking indels at that locus (ie the wt cells containing a sequence complementary to the guide RNA), thus killing and/or growth arresting those cells.
3) Meanwhile the cells with indels will continue living and proliferating since they lack the target sequence. These are "immune" to the CRISPR-induced damage.
Thus the proportion of WT cells will decrease, while the "mutants" will increase. It may help to play with the code of the simple model I shared earlier.
So you're stipulating the consensus understanding of Cas9's initial action, but arguing that this results in cell death rather than nonhomologous end joining repair?
I don't see how it is even possible for you to gather that from what I have said? All you could possibly have is a rough estimate of the ratio between the priors for NHEJ vs selection. Selection is a far more common and well studied process...
There also exist simple quantitative models of that process, which allow precise predictions, something lacking in the case of NHEJ models afaik, which must remain vague. So the likelihoods will also be narrower in the selection case.
Anyway, it was productive to discuss the specific paper and model, but is now getting philosophical and pointless. I only mentioned the scientific thought process because you asked why I would be skeptical.
Quantitative reasoning can give you a lot of leverage in systematizing and inferring from a body of knowledge, but if your reasoning doesn't start from that knowledge it will lead you nowhere. In this case, the necessary knowledge is the structure and mechanism of DNA repair.
When Dirichlet computed the probability that the Sun wouldn't rise tomorrow, given a flat two-event Dirichlet prior and the observation that it had risen every day for the last 6,000 years, he added that of course, for people who understood the workings of the solar system, the probability is far, far, lower. Your arguments here are like that. They just don't take into account the relevant facts of molecular biology.
2) But please, let us skip the philosophy and talk directly about this topic, because that argument is not even necessary. It sounds to me like you do not believe that double strand breaks can lead to cell death and growth arrest? You find this implausible? I am really surprised that this is an objection.
Pr(T[0]|O) = Pr(O|T[0])Pr(T[0])/sum(Pr(O|T[i:n])Pr(T[i:n]))
1) How well the observations fit a theory (you can substitute hypothesis/model/etc) and how plausible a theory would be without the observations in question.
2) The above for all other theories
In other words, the default for the scientist is to be skeptical of any explanation until the others have been rendered implausible (ie ruled out). There is a bit more to it (eg the Pr(O|T[i]) terms depend on the precision of the predictions, which remain vague in the case of NHEJ despite generous funding), but that is pretty much it.