In regards to Al Kafir’s comment, I think you actually made it more difficult to make sense of what he suggest.

I think he might have been referring to the fact that some of the patentable electronic circuits created using evolutionary algorithms have more parts that commercially available designs.

the article I linked discusses the problem that evolved designs — at the time the paper was written — were not economically competitive.

Still, I find the commercial applications of evolutionary algorithms more interesting than academic discussions of why they don’t work or can’t produce novel designs.

This is very interesting, but evolutionary design methods are unfortunately all about specification and very little about the complexity generated by evolutionary algorithms.

There is hardly any relationship between this modern design method and any unguided evolutionary proposal. Maybe it would do you good to read some of the EVOLUTIONARY INFORMATICS articles.

But in the end, if you want to take this work as some sort of analogy of evolutionary processes, then you have to admit that initial and continued specification of biological systems would be at least as well designed as these design methods.

In regards to Al Kafir’s comment, I think you actually made it more difficult to make sense of what he suggest.

I am also very interested in any form of logical outline you can give for coming to this suggestion. (You will find that you can expect a very high level of insight into complex-system theory.

This is not exactly the answer to your question, but it addresses some issues in the complexity of evolved systems.
Abstract:
http://www.demo.cs.brandeis.ed.....ssertation
Paper:
http://www.demo.cs.brandeis.ed.....tation.pdf

In the case of a 747, we are told there are six million parts, half of them being fasteners [which shows the importance of interactions and coupling to get a complex whole to work].

747′s are known artifacts, and have been plying our skies for what, forty years?

There are also now a lot of computer programs that use millions of lines of code and megabytes of associated data.

This is a gem!

Probably too complex to have been designed, since complex-system theory shows that evolved systems tend to be more complicated than designed systems for a given function.

I am also very interested in any form of logical outline you can give for coming to this suggestion. (You will find that you can expect a very high level of insight into complex-system theory. Someone in this discussion will understand you or have a very informed follow-up question.)

Personally I would like to see why this would be a parsimonious conclusion.

You are probably new here, so let’s make some order:

gpuccio ORFans are not a type of gene, such as a pseudogene. A gene can be an ORFan today, and not an ORFan next month.

I know very well what ORFans are, but in my post I was referring to a very specific subset of them, those 1000+ human orphans which were recently “eliminated” from the list of human protein coding genes by a paper which was the object of a long methodological discussion in a previous thread here. Sorry if you did not catch the reference.

As to regulatory functions, one of the significant findings of evolutionary development (Evo Devo) is that most evolutionary changes since the Cambrian have been primarily regulatory. Very few “new” genes have appeared in the past few hundred million years.

Although I agree with the general concept that recent information growth has been primarily regulatory, I disagree that “very few “new” genes have appeared in the past few hundred million years”. Indeed, even if we look simply at protein domains appearance, it can be seen that about a quarter of all known protein domains originated in metazoa. That’s certainly not “very few”.

You might want to read up on this subject, because it’s complex. (And, I think, very interesting.)

In this moment I am discussing with you, and i will take into consideration the arguments you explicitly make. Anyway, thank you for the reading advice.

For example, temperature or stress causes cells to producer “heat shock” proteins to limit damage to other proteins. Once cells have differentiated, their switches for other functions get turned off—by methylation of their DNA, e.g.

And so?

Now, please, don’t come with all the evo-devo stuff as though it were astonishing news. Evo-devo is certainly interesting, and certainly over-inflated, and certainly badly interpreted in many contexts.

Now, let’s go directly to the core of the question.

The genome is one (I mean, in a single multicellular being). It differentiates into different cell types, organs, tissues, systems, according to a specific body plan. The question is: where is the information which controls all that?

Beware, I am not askin which are the mechanisms regulating all that. I know that many mechanisms are know (at least, a few of them). I am not denying the importance of hox genes, or of DNA methylation, or of epigenetic factors. I am asking what controls the regulation, and guides it.

For instance, every single cell type in multicellular beings is characterized and determined by a specific transcriptome. A transcriptome is in turn controlled by transcription factors (and other factors), which are transcribed proteins. We can imagine a specific transcriptome as a specific subset of genes which is activated from the general pool in a specific cell. But, obviously, the information which guides the activation of those genes (and not others) must also specify:

a) the sequence of activation

b) the life span of each activation

c) the relative quantitative regulation of each gene, so that each protein is producted in the right quantity

d) the detailed transciptional and post-transcriptional regulations which can modify the mRNA and the protein

e) the post-translational modifications of the protein itself

and probably many other things.

Now, would you please explain how do you think all that is regulated in each cell type, starting form the same genomic information?

Hox genes do not contain all the info to produce a leg. They start the process, then the cascade of regulation (that I mentioned before) takes over automatically to produce the necessary detailed structures.

They obviously don’t. They are just switches.

For example, the same hox gene that produces a bvutterfly’s wing structure in its first 2 iterations has a third iteration that produces the colored spots, an entirely different structure.

And so? Hox genes are regulatory proteins, they can regulate different functions, without having the information for those functions.

PS: The set of regulation components in a cell is called its “regulome.” Those components can be genes, mRNAs, proteins, and metabolites that interact with each other and with subcellular localization, tissue, developmental stage, and pathological state.

So, now that we have a new “ome” word, would ypou explain what controls the regulome? Why has each dofferent cell type a different regulome? How do cells “know” which specific set of “genes, mRNAs, proteins, and metabolites” are to be included in its regulome, or how those components should “interact with each other and with subcellular localization, tissue, developmental stage, and pathological state”. Details or explicit models, please.

Regulomes are quite complex.

You bet!

Probably too complex to have been designed, since complex-system theory shows that evolved systems tend to be more complicated than designed systems for a given function.

I can only repeat BA’s statement: it’s “the most ludicrous statement I have ever heard”.

Granted that complex-system theory is very new, and not well known, even among scientists.

Again, please, not just reading advices. Could you please give us some concepts which justify your statements, since you certainly understand this fundamental theory so well? Be kind.

For example, Meyer never quantifies “information,” so it is impossible to argue how whatever it is might be generated.

We have quantified functional information a lot of times here, and in great detail. If you are interested, I can give you the links to some pertinent posts.

And finally, as you have named BA “an intron” (which, IMO, was not kind: I have the highest opinion of introns, but you probably don’t), and as you probably consider yourself an exon, can I be a transposon? That would make my day.

one of the significant findings of evolutionary development (Evo Devo) is that most evolutionary changes since the Cambrian have been primarily regulatory.

Don’t you mean “speculations” instead of “significant findings”. And even granting that most of the change is regulatory, it hardly means Evo Devo solve the mechanism of evolution of regulation since Evo Devo itself assumes regulation is already in place to spawn more regulation!

Evo Devo strikes me like the geek observing the regulatory mechanims on his computer known as the “on off switch”. He notices that when he presses the on switch it regulates all the power in the computer and hence all the marvelous function comes to life. He then concludes this regulatory mechanism is the explanation for all the other regulatory functions in the computer (like the logic gates and memory managment and disk drives). What passes off as “significant scientific finding” in evoltionary biology is almost beyond belief!

Functional information and the emergence of bio-complexity:
Robert M. Hazen, Patrick L. Griffin, James M. Carothers, and Jack W. Szostak:
Abstract: Complex emergent systems of many interacting components, including complex biological systems, have the potential to perform quantifiable functions. 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. Functional information, which we illustrate with letter sequences, artificial life, and biopolymers, thus represents the probability that an arbitrary configuration of a system will achieve a specific function to a specified degree. In each case we observe evidence for several distinct solutions with different maximum degrees of function, features that lead to steps in plots of information versus degree of functions.
http://genetics.mgh.harvard.ed.....S_2007.pdf

Mathematically Defining Functional Information In Molecular Biology – Kirk Durston – short video
http://www.metacafe.com/watch/3995236

further note:

“There are no detailed Darwinian accounts for the evolution of any fundamental biochemical or cellular system only a variety of wishful speculations. It is remarkable that Darwinism is accepted as a satisfactory explanation of such a vast subject.”
James Shapiro – Molecular Biologist

Michael Behe on Falsifying Intelligent Design – video
http://www.youtube.com/watch?v=N8jXXJN4o_A

For example, Meyer never quantifies “information,” so it is impossible to argue how whatever it is might be generated.