I’m not sure what you you’re saying about aclocal, but do you have automake installed? (aclocal comes with automake)

I had trouble with the “xmllib2″ requirement mentioned in the README, but eventually inferred that xmllib is a synonym for libxml.

Then when running bootstrap, I got an error saying “macro `AM_PATH_XML2′ not found in library”, which I’m told (http://osdir.com/ml/finance.quickfix.user/2007-01/msg00003.html) means I need libxml2-dev.
After installing libxml2-dev, that error is gone, but I still get “./bootstrap: 119: Bad substitution”.
And there I’m stuck. I’ll try the sourceforge stylus help forum.

Seems that Stylus was updated today (June 17 2008). Must have been in the early hours since I dled it at 5:24 EST.

It seems to build and run OK on my Fedora 7 linux system. I used the:

./bootstrap
./build

which completed without incident. Then I ran the ‘test’ trial with:

./stylus -e -r — -g 52DC.gene -p simple.xml -u ./sample/,./sample/

Which produces a bunch of ‘report’ files. Some of these are in html format and can be viewed in a browser.

Pretty uninteresting to me so far since I don’t know how to really test the program and why it might even be interesting. I will have to read up on it a bit more ;-)

I am interested in your results and the results of others.

-DU-

Funny thing is, when you saw the actual images of the proteins, they were pretty unimpressive. But when the speaker showed the diagrammatic version on a slide the crowd oohed and awed.

Why didn’t they use the cross in tRNA to include all life? Oh, I guess animals are of higher “baramins”.

Commenter shcrodinger says on The Big View:

While I have no desire to disparage anyone’s beliefs, I would like to say a few things about this issue. The cross diagram is just that, a diagram, not a photograph of the molecule. Diagrams of the bond formation of many different types of molecules consist of many different types of geometrical shapes including; triangles, pyramids, hexagons, tetrahedrons, octahedrons, as well as linear shapes including crosses. In fact, crosses are very common in diagrams showing bond formation. With such a geometrical grab bag to reach into, just about anyone of any belief should be able to find something of interest. A more realistic representation is in the structural diagram which shows the true bonding angles which are usually at multiples of about sixty degrees. These are referred to as “xo” drawings because of the resemblance to those letters. As a connoisseur of good cognac, I take great comfort in that symbolism, particulary since the entire family of alcohol molecules can be diagrammed as crosses! In reality the laminin molecule in question here has a hexagonal shape with six bonding places and is more similar to water molecules than the cross you see in the structural diagram. It also seems to me that the [/font][FONT='Verdana','sans-serif']asymmetrical leg in the cross diagram is slightly exaggerated but I may be wrong about that. Anyway, I must cut short my comment as I have a tennis match to attend. Did you ever notice how the strings of the racket form crosses, wherever the strings cross? I wonder what that means? [My bold, not his.]

If laminin is actually hexagonal in structure, as is ice, does that mean it is actually evidence for that Wicca precepts works?

“Cost” function assumptions? “Fuzzification”? I haven’t looked at the code yet, but it sounds to me on the face of it like someone is setting up an argument for front-loading “evolutionary” selection.

Yes, or at least the old “there are in cases evolutionary costs, so all mutation is degrading” strawman.

Well, at least Axe et al started with a functional protein just as an organism would have, instead of Dembski et al “it is improbable that organisms would have a functional protein”.

In principle as a random protein sequence grows it will actually represent a larger proportion of the solution space of smaller proteins.

Thanks, I hadn’t figured out all the consequences of the cost function yet. Obviously this is another zinger.

the subject of, if anything, more rigorous analysis

Ca Ching!

Of those, the Mac OS X install got the furthest, displaying a fair amount of the usual autoconfiguration verbosity before erroring out about underquoted aclocal stuff. I think that the list of dependencies is not yet complete in the installation instructions.

I’ve been thinking about this paper for a couple days now and seeing this is where the sensible discussion is taking place – here is my 2-pence-worth.

Well, at least the ID folk are making a start. I’m sure we’re all itching to take the simulations they produce for a spin (Austringer – have you managed to install it yet?), correct the erroneous assumptions and then show a working evolutionary simulation (just like the Behe and Snoke paper in 2004). Bring on the next manuscript!.

The solution space they’re going to simulate is HUGE… and they’re only going to populate it with ~5000 functional solutions. This is a major problem. I think they show that the decay of ‘proficiency’ around fitness optima (a character) seems reasonably simulated. But if they only put 5000 such ‘islands of function’ in the whole solution space, there’s almost no way you could get from one island to the next easily. I think this what they’ll try and demonstrate.

We have to argue that the solution space they’re simulating is grossly under-populated with function. Because:

(a) Alleles of the same enzyme (separated by 2 or 3 key amino acids e.g. ABO glycosyltransferase) can have different catalytic abilities – suggesting that solution space either has a reasonable clustering of functional solutions.

(b) We can show reasonably short journeys in the solution space when new functions are developed (e.g. nylonase, antibiotic resistance, etc) – suggesting that the entire solution space is well populated.

(c) Simulating a solution space with a static finite distribution of function is, of course, a bit pointless as potentially any protein could be functional given the right setting.

(d) There will be certain concepts that have only one character in Chinese. In contrast while biological organisms may use certain functional solutions for historical reasons (e.g. RuBisCO in plants) this does not prove that there are not other possible solutions that could also do the job as well (or better).

e.g. Humans use haemoglobin to transport Oxygen (the ‘one-true-sequence’ of haemoglobin being the subject of previous creationist arguments from improbability), but similar functions are being carried out by hemocyanins in Octupi and hemerythrin in other marine invertebrates.

(e) The authors are being a bit sneaky by penalizing the growth of proteins. I would like to know whether people think that the penalty for growth they are imposing is fair. In principle as a random protein sequence grows it will actually represent a larger proportion of the solution space of smaller proteins.

e.g. a simple linear model –

if we imagine a functional solution needs a precise sequence of 50 amino acids, if we examine random 50 amino acid polypeptides for function – only one potential permutation will works.

However, if we examine random 1000 amino-acid proteins each have multiple (hundreds of) 50 aa sub-sequences that could be the correct permutation. This applies in 2 and 3 dimensional objects as well – and maybe even more significant at these higher dimensions.

There, I feel better.

That’s pretty much just equivocation. Biological evolution is constrained in ways that cultural evolution, such as writing systems undergo, is not.

It is possible that there are fewer constraints on the evolution of these glyphs (illegibility, confusing similarity to other glyphs in the character set) than there are biophysical constraints on functional protein conformation.

And then again, it is possible that the opposite situation applies. The way to resolve that is with research on proteomics, not speculative analogies with writing systems.

Thus any model that considers the glyphs within Chinese character sets to be discrete and unchanging is fundamentally flawed, I would think.

I see that the supply of straw is still plentiful. A model of writing system evolution that postulated discrete and fixed properties for each and every character would indeed be lacking, but nobody has suggested that, so bringing it up seems an irrelevancy.

How can one tell whether or not the program has come up with a glyph that will be perfectly understandable to a 30th-century Chinese-reader (or whatever the 30th-century equivalent of a Chinese-reader might be)?

And why would anyone care about such an assertion? I know I don’t.

This program is likely to figure in future claims that certain Chinese Han character ‘families’ are discrete and unreachable from one another, therefore one should be suspicious that the same result applies to protein families. Such a statement would simply be begging the question, as the desired conclusion is implicit in the selection of the analogy.

It is possible that there are fewer constraints on the evolution of these glyphs (illegibility, confusing similarity to other glyphs in the character set) than there are biophysical constraints on functional protein conformation. Thus any model that considers the glyphs within Chinese character sets to be discrete and unchanging is fundamentally flawed, I would think. How can one tell whether or not the program has come up with a glyph that will be perfectly understandable to a 30th-century Chinese-reader (or whatever the 30th-century equivalent of a Chinese-reader might be)?

When I stumbled across the paper in PLoS ONE, my first reaction was “WTF?”