Don’t know.
Looking at the alignments it does not look very different. The alignments were not as precise with troponin. The sequence lengths also had more variation. All these proteins appear to have lots of functional information.
What @gpuccio says is that sequence conservation over long period of time indicates functional constraint and that functional constraint signifies FI. He also acknowledges that the FI that is measured from sequence conservation is not necessarily the whole FI. IOW, the FI derived from conservation may be an underestimation of the total FI. That doesn’t at all undermined the design inference, quite the contrary.
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And it doesn’t. That’s my point.
There’s not even the slightest correlation, Gil.
It has no correlation with FI, as muscle proteins show. It’s an overestimation in some cases, an underestimation in others. It’s not even close to correlating.
It completely undermines it. @gpuccio’s most fundamental assumption is objectively false.
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Then you have no basis for accepting @gpuccio’s assumption.
In fact, they have orders of magnitude less, despite being much better conserved. That’s why there’s no correlation between sequence conservation and functional information. These proteins go the opposite way.
A good analogy is that @gpuccio’s assumption is like saying that a segment of a railroad track has more functional information than a locomotive, or that a bit of road has more than a car.
The sequence conservation of the more functionally complex proteins is far less than that of the simpler ones. That’s why there’s no correlation.
Yup, despite the fact that it is far more functionally complex than tropomyosin and actin. That means that there is no correlation between sequence conservation and functional information.
BTW, there are 3 troponins, not one. All three contradict the assumption.
You haven’t done a thing to measure functional information. For @gpuccio’s assumption to be valid, myosin should have the highest sequence conservation, followed by troponin, actin, and tropomyosin.
We see the opposite, therefore the assumption is completely wrong.
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Isn’t the whole basis for how the dependency graph explains the gene presence/absense data, that the different genes go together because they depend on each other? But I’m pretty sure many, many of these genes in fact don’t depend on each other. The fact that we find they exist together in many species is more a product of history, than of any actual mutual dependency.
So you claim is functional complexity is the same as functional information?
How is functional complexity measured?
But it doesn’t. Any degree of conservation is readily explained by the sequence having got stuck on some on some local optimum, with steep sides. So it is entirely possible that many, many such other peaks exist.
You can’t get any appreciable estimation of the frequency with which other such peaks exist when you have only sampled approximately 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000561% of it.
Creationists like to assert that evolution could at most have sampled roughly 10^40 novel sequences in the entire history of life.
But the total sequence space for even a 100 amino acid long protein is 1.267650600228229401496703205376×10^130
So we subtract 10^40 from that, and we get that there are still 1.2676506002282294014967032053759999999999999999999999×10^130 sequences left.
In other words you want to claim we can have high confidence about how many functional sequences there are when we have sampled 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000079% of that space.
If you can convince yourself of that, then we can’t have a conversation because your position is obviously irrational.
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No, but it’s a much better estimate.
It’s incredibly obvious in this case, and you haven’t even looked at the INTRAspecies variations of cardiac myosin in humans.
Would YOU say that a segment of railroad track has more functional information than a locomotive?
Not by sequence conservation.
I. Am saying that a complex functional sequence can have less preservation then a less complex function. Preservation is based on failure of the entire system not a single protein.
I would be interested in the human to human variation if you have it handy
Tubulin is also highly preserved and it is also a railroad track or sorts. Why are we getting catastrophic system failures when we get a single deviation of the sequence that builds a small piece of the track?
I am saying that “complex functional sequence” is word salad. There are sequences and there are proteins. Proteins have functions.
OMIM for a partial picture that is proportional to the much larger known variants for each. The bottom line is that gpuccio’s assumption is simply wrong.
Why aren’t you addressing the assumption that sequence conservation is an estimate of functional information? This is completely independent of evolution vs. design, but you keep trying to divert.
If the railroad track fails it’s catastrophic. That doesn’t mean that it has more functional information than the locomotive.
First rule of holes…
You need to review what functional information is because your misusing it. The train does not function without the track. It’s a dependent system. Once the track fails the FI of the train is 0.
You also need to understand all the cellular uses of actin. It may have multiple functions. This is a current hypothesis why it is so well preserved. Relative to muscle movement the track may be less relative FI but relative to total animal function much higher aFI than myosin.
FI is number of functional sequences divided total sequence space. Some actin appears that the numerator is close to 1 relative to the animal…amazing.
On a cell phone sorry for the cryptic message.
No. As far as I know, Ewert makes no attempt to determine dependency other than by co-occurrence. The nodes are just connections among taxa that share gene families; in other words, they reflect distribution, nothing else.
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Which is another reason why @gpuccio’s method, which only deals with individual proteins, is absurd. I’m glad you finally saw that problem with it.
Bill, that’s beyond the pale. I’ve worked on myosins and muscle for decades. I’m aware of them, you aren’t.
But having a use for something does not constitute information.
It does. That’s irrelevant to the fact that there is no correlation between sequence conservation and functional information.
No, that’s not why. But I’m glad that you’re beginning to understand the absurdity of assuming that sequence conservation is correlated with functional information.
That’s word salad. That’s not what “information” means.
And you are afraid to measure the denominator.
That’s some amazing word salad.
It’s no more cryptic than your other messages.
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Moreover, if I understand it correctly, Axe’s sample was decidedly not random. Thus his study offers no ability to generalize to the combinatorial space from which he did not sample at all.
Would that be an accurate assessment?
Thanks,
Chris
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Yes, because he started with a structurally unstable protein–a temperature-sensitive mutant.
I know he doesn’t attempt to establish dependence, but afaicg that is his basis for invoking a dependency graph in the first place to explain the pattern. That’s how he introduces the concept of a dependency graph:
DEPENDENCY GRAPHS
It is common for one thing to require or depend on another. It is impossible to learn calculus unless you first know algebra: calculus depends on algebra. A modern kitchen cannot be added to a building that lacks plumbing: kitchens depend on plumbing. A society cannot invent the internet without first inventing computers: the internet depends on computers. These dependency relationships can be found in many spheres.
While many spheres of life implicitly involve dependency relationships, in software development they are made explicit. Each collection of code, termed a module, will depend on other modules in order to perform its task. For example, if a software developer needs their program to download a file from the internet, they will not write the large amount of code necessary to perform this task. Instead, they will add a dependency on a module which already has the necessary code and reuse that code.
The structure that results from considering all the modules and the dependencies between them is called a dependency graph. The dependency graph of two JavaScript software modules, jsdom and node-gyp, is depicted in Figure 1. These modules perform two very different tasks, jsdom simulates part of a web browser and node-gyp compiles, or builds, software. Nevertheless, both modules depend on the request module, which downloads files from the internet, and consequently share both the request module and many modules depended on by the request module.
Common descent postulates that life is related by a tree, such as the one depicted for a selection of mammalian species in Figure 2. In contrast, the dependency graph hypothesis postulates that life is based on a dependency graph as depicted in Figure 3. Every species is a top-level module; nothing else depends on a species. A species depends on a variety of other modules, each providing some of the genes necessary for the final genome of the species. A module may contribute a single gene, or a large collection of genes. The arrows are reversed between the tree and the graph because in the tree an ancestral species splits into child species, but in the graph the species depend on modules, reversing the direction of the relationship.
Crucially, every module may depend on other modules. For example, the Carnivora module depends on the Laurasiatheria module. As a consequence, the genes contained in the Laurasiatheria module are also inherited by all species that depend on the Carnivora module. So all Carnivora species are Laurasiatheria species by virtue of that dependency relation. Thus we provide an alternative explanation for the nesting pattern observed in biology.
Both theories have important similarities. Taxonomic categories appear in both: in common descent they are represented by their most recent common ancestral species. In the dependency graph, they are modules. Both can explain the nested relationship of taxonomic categories. In common descent, this is because one species descended from another. In the dependency graph, this is because one module depends on another.
But many of these genes, what Ewert here calls modules, generally don’t depend on each other. In so far as such a dependency relationship is not the case for any of these genes, it seems to me they can’t constitute evidence for a dependency graph because his model doesn’t actually explain their distribution when the dependency relationship isn’t actually there.
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Yes. What he calls “modules” are just sets of genes that have the same distribution among taxa. We would of course expect to see some of this sort of thing given random gain and loss. In other words, his “dependency graph” is just a demonstration that homoplasy in gene loss exists.
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