# A Ubiquitin Response to Gpuccio

From: HAZEN ROBERT M, GRIFFIN PATRICK L, CAROTHERS JAMES M, et al. Functional Information and the Emergence of Biocomplexity. In: National Academy of Sciences; Avise JC, Ayala FJ, editors. In the Light of Evolution: Volume I: Adaptation and Complex Design. Washington (DC): National Academies Press (US); 2007. 2. https://www.ncbi.nlm.nih.gov/books/NBK254300/

Accordingly, we define “functional information,” I(Ex), as a measure of system complexity. For a given system and function, x (e.g., a folded RNA sequence that binds to GTP), and degree of function, Ex (e.g., the RNA–GTP binding energy), I(Ex) = −log2[F(Ex)], where F(Ex) is the fraction of all possible configurations of the system that possess a degree of function > Ex.

It’s not about the replacement of single amino acids at a time in the peptide.

For a peptide with a.a. length of ‘n’

1. “Et” = total number of all possible peptides = n^20
2. “Ex” = Number of all possible 'n-length peptides capable of performing the specified function ‘x’
3. “Q” = Number of all possible single a.a. replacements for a single peptide capable of performing the specified function ‘x’

Note that:
FI = -log2(Ex/Et)

‘Ex’ is not ‘Q’. Nobody has calculated Ex. They are only searching 20n sequences for activity when actually, there are n^20 sequences possible. They only calculated ‘Q’, which only considers a microscopic fraction of Et. To be specific they’re off by a factor of (1/20)n^19 total sequences…

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And this is merely a single error in their calculation. There are so many more.

This is the estimate that both gpuccio and Durston are making.

With incorrect assumptions and mathematical errors, yes they are attempting to declare their number shuffling = FI. I agree that is their attempt. The errors however, make it an erroneous attempt.

It is an estimate for sure but both would agree to that. Do you have a better method?

Yes. I’ve partly explained it here. Computing the Functional Information in Cancer - #15 by swamidass. They have to take into account (1) the ability of common decent to create MI, and (2) alternate ways of arriving at the same function, and (3) the effect of purifying selection, and so on. Everyone of these factors biases their results upwards.

Yes. I’ve partly explained it here. Swamidass: Computing the Functional Information in Cancer . They have to take into account (1) the ability of common decent to create MI, and (2) alternate ways of arriving at the same function, and (3) the effect of purifying selection, and so on. Everyone of these factors biases their results upwards.

How do you establish functional information is increasing in cancer or is it assumed?

How do you establish that common descent can create FI or are you assuming this?

Exacly!!!

No, Bill. This is exactly the calculation they aren’t making and cannot make.

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Bear in mind that Joshua, like Joe Felsenstein, is being generous. They are accepting the concept of “Functional Information” at face value. The allowed assumption is that genotype maps to phenotype. This is too generous for me.

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If this is false then what good does evolutionary claim have.

Evolution is not a claim about abstract sequences of symbols. That is to say, it is not a claim about information.

That the ID folk continue to construe evolution as a claim about information, only demonstrates the weakness of their program.

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The good of being a reasonable working hypothesis based on available evidence.

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To elaborate a little, mathematics is a modelling tool. But to come to some conclusion about reality, your mathematical model has to match reality to some extent, otherwise you need to modify or discard your model. You can’t expect reality to conform to your model.

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[quote=“nwrickert, post:111, topic:1608”]

That the ID folk continue to construe evolution as a claim about information, only demonstrates the weakness of their program.

And if you take a number and change it to “negative log to the base 2” it tells you something more than the original number.

If you want to make a criticism of a mathematical model you have to directly access that model and make specific comments. This is what Josh is doing with Eric H. Both models have strengths and weaknesses and are the beginning of measuring functional information.

I’ve done that on Durston’s work @colewd. Did you not know?

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Yet to see any strengths of Eric H. model. Josh’s simulations crush the hypothesis.

Apples and oranges. Duston’s and Puccio’s models are based on empirical measurement and not just a math proof. It is also too early to tell on Eric’s model.

No. Can you cite?