MortenS

You may want to request a copy of the following article at your library: The origins of insect metamorphosis. [Nature. 1999] - PubMed Result Managed to find a copy of this available as pdf online: http://www.biology.usu.edu/courses/biol5530/Truman99.pdf A good read...

One of the main ways genetic information can increase over time in organisms (over generations, not in a single individual), is via gene duplication and genome duplication. Once genes are duplicated they have an independent history in terms of what mutations they will get, and via genetic drift or natural selection, they will over time diverge in function (or one of the duplicates may loose its function and become what is known as a pseudogene). PS: Good to be back...

See here: http://www.madison.k12.wi.us/west/science/biotech/tissueculture.htm for example. The callus initiation medium recipe can be found here: http://www.rowan.edu/biology/faculty/obrien/IB%20Carrot%20Cloning.pdf This medium can be bought from biological supply stores.

I am sort of surprised that nobody has brought up sexual selection as a potential selection mechanism for the evolution of hairlessness in humans.

I found this introduction to evolutionary algorithms for a MatLab toolbox...not sure if you have read it. http://www.geatbx.com/docu/algindex.html As long as computer scientists use vocabulary taken from evolutionary theory, I can follow it :steering:

I just tried to attach to them, and I got connected to them...no work thoughl.

hmm...and you can log in on their website, but not via the the manager?

Have you set up an account at their website? Or set it up from the project manager? http://lhcathome.cern.ch/create_account_form.php

Server status says: Up, out of work... They need to put out new work units, I guess.

I'll see if I can grab a picture...

I'll use this post as a repository for some of the articles and website I found during my research of this question. You might read some of them for amusement, as they contain a few ideas that might reduce your probabilities a bit. Evolution of alternative splicing http://www.fdhope.org/FamilialDysautonomia/Articles/alternativesplicing.pdf Evolution of protein kinases from yeast to man http://kinase.com/evolution/TiBS_Kinase_Evolution.pdf Protein and proteome evolution http://www.la-press.com/EBO-1-Gabaldon(Pr).pdf Evolution of the protein repertoire http://supfam.org/gough/chothia_science_repertoire.pdf The Evolution of Controlled Multitasked Gene Networks: The Role of Introns and Other Noncoding RNAs in the Development of Complex Organisms http://mbe.oxfordjournals.org/cgi/content/full/18/9/1611 Identification of Intron and Exon Sequences Involved in Alternative Splicing of Insulin Receptor Pre-mRNA http://www.jbc.org/cgi/content/full/273/17/10331 Treefam database: http://www.treefam.org/ This post will be updated...

Interesting...I would have to study this in some detail, its been a while since I looked at insulin. Is the following a good representation of how insulin works together with other enzymes? I have kept glucagon out for now, haven't started on researching that yet. When glucose level is high, insulin (INS) attaches to the alpha subunits on the insulin receptor (INSR) in the cell membrane of some cells. (e.g.muscle cells and fat cells) As insulin attaches, the beta subunits of the insulin receptor phosporylates (via ATP), thus activating the catalytic activity of INSR. This in turn, phosphorylates proteins such as insulin receptor substrate 1 (IRS-1), which lets phosphoinositide 3-kinase (PI3-kinase) attach to IRS-1. PI3-kinase then phosphorylates the cell membrane lipids. The phosphorylated cell membrane lipids acts as docking sites for the Ser/Thr protein kinase B(=PKB-AKT-1) and 3-phosphoinositide-dependent protein kinase (PDK-1). When these two are close together, PDK-1, phosphorylates PKB-ATK-1. Activated PKB-ATK-1 then moves into the cell, and activates other enzymes via phosphorylisation. Eventually, an enzyme (not sure which one) attaches to vesicles in the cytoplasm that has glucose transporters (GLUT4) in its membrane, and stimulates these to move to the cell membrane and fuse with it (fusion involving VAMP, syntaxin, Munc 18C and NSF). GLUT4 can now move glucose into the cell. I have not found a description of the mechanism where GLUT4 is removed from the cellmembrane, but some form of endocytosis must happen. I hope this is a reasonably detailed and accurate representation of what is known about the insulin pathway. Heh, and now the job starts...it is going to take me some time to look into the phylogenetic history of all these enzymes. Here is the list I am going to try and look at INS INSR IRS-1 PI3-kinase PKB-ATK-1 PDK-1 GLUT4 VAMP Syntaxin Munc 18c NSF as well as glucagon and the glucagon-like peptides. We'll see where this all will take us. It would be interesting to find an organism that LACK either insulin or glucagon, but not both, as that would put a nail in your hypothesis, as far as I can see. I do think that the maintenance of homeostasis of blood glucose is important in mammals, at least, I just do not hold to the idea that the homeostasis of glucose levels have allways been important. Anyway, I will be collecting some studies done on both glucagon, and the various enzymes in the insulin signal pathway, to see where this leads us. I do suspect an independent origin of glucagon and insulin, and that they have gained new functions during evolution, and that they are now working as antagonistic pairs in vertebrates. NCBI is down at the moment for me, so I cannot search for literature or download sequences to my phylogeny programs.

I like the Rosetta screen saver. I hope I get a really low RSMD or Low energy folding, and that they can show my best prediction graphically on my result page one day.

We know have a Stat page at DC-Vault: http://www.dc-vault.com/showteam.php?team=350

Thanks... I'd like to know one or two examples of such an enzyme systems...so that I can research them. Only in the fossil record. Not in the genomic record. Hint: check out Hox genes and genetic switches, and how they are turned on and off in the various phyla that have been investigates so far: It is probably a topic worthy of a separate discussion, so I will let it pass for now. Probably not relevant, I agree. I just got the notion that lysosomes mysteriously managed to separate wanted from unwanted proteins, and spared the wanted ones. I hold that there is a tagging that tags both wanted and unwanted proteins, but that the tagging efficiency is increased when proteins show certain "signals" such as misfoldings, or specific sequences. Untagging mechanisms may work to "rescue" proteins that are tagged, but not misfolded. Oh, take a look at the phylogenies of protein families at: http://www.treefam.org/cgi-bin/misc_page.pl?home Just enter a name of a a couple of protein families,and check out the most similar sequences. The Treefam traces genes back to metazoa. I would like to know the name of one of these hundred gene families that appeared to have arrived de novo around the cambrian explosion. My claim is that there most likely will be a precursor found in archaea, bacteria or one- celled eukaryotes, or a distant relative gene in plants. But then we are not discussing vertebrates anymore. Since globin genes are found in plants, fungi, protists and bacteria as well as animals, it has probably been present since the dawn of life (or at least back to about 3-2.5 bya). Read: http://www.findarticles.com/p/articles/mi_m1200/is_15_161/ai_85175717 But then we mainly need to discuss morfogenesis, not the evolution of all sorts of functional proteins. Basically, regulatory genes and genetic switches can explain differences in body plans pretty well. There is still much to understand about regulatory genes and genetic switches for a lot of organisms, but the big picture is starting to get clear. Yeah, that is an unfortunate fact of the fossil record, I am afraid. I do contend that the fossil record is not only incomplete, but that it is biased towards easily fossilized specimens (bones, teeth, shells), occuring abundantly in certain environments, for a long enough time period, so that fossilization was favorable. Organisms that did not have hard parts, lived in environments where fossilization was not favorable, or had a short geological life time did not make it to the fossil record. That this is so, is evidence by the many softbodied phyla we have today, that have not left a single fossil at all. Yet phylogenetic studies based on DNA place their divergence back several 100 million years. The fossil record is very incomplete, even if we find all fossils that have been formed (which is the reason why I am much more interested in phylogenetic studies utilizing DNA and proteins than doing phylogeny of fossils). In reconstructing the history of life, comparative morphology, genomics and biochemistry is needed in addition to the fossil history. Fossils are snapshots of what life looked like, and they are useful for establishing minimum age estimates for when organisms appeared on earth for the first time. They are, unfortunately, not very useful for establish maximum times, since the finding of one fossil earlier will push the boundary further back. First of all, I am not sure if new enzymes are required for the evolution of new body plans (unless you consider DNA-binding proteins attaching to genetic switches as enzymes) The evolution of new genetic switches, or turning off genetic switches, during development of an organism should be enough for morphological change. I agree. Evolution builds on code that is already present. Descent with modification. The modifications are due to recombination and mutation. It's late, and I cannot either... We have no fossil evidence for it, but we do have some genetic evidence for how body plans are built, during embryogenesis. Trace the phylogeny of the regulatory genes involved in morfogenesis and maybe you find some clues :)

Time for me to enter the discussion... I will start with Biochemist's initial points, and state which I agree with, which I disagree with, and which I would like to add some more information to. 1: Agreed 2: Agreed, although it is worth mentioning the redundancy in the genetic code, particulary in codon position 3. 3: Agreed, one theory is that natural selection acts (or acted) on the genetic code to reduce the effects of genetic errors (see point 2), and thus favored redundancy. 4: Agreed, although the functions of RNA is a bit glossed over here. RNA has a couple of important functions: m-RNA, r-RNA and t-RNA are the three best known, but RNA-molecules are also important components in processing of m-RNA via alternative splicing and polyadenylation, which is the main reason that only 25-30000 genes can code for the roughly 90-100000 proteins we find in mammals (not sure where you get the 300k number you have put forward earlier, I have not yet seen a single organism with an estimated 300k different proteins). Each protein coding gene make on average 3 alternatively spliced mRNA's. 5: Agreed 6: See comments in point 4. 7: Agreed 8: Not completely agreeing. Many proteins fold up pretty well in isolation from the rest of the cell. Chaperones are necessary to keep the folding protein from interacting with other proteins, since the cell is stuffed full of various proteins that may interact with the folding protein. There are examples of proteins that do not require chaperones at all, there are examples of proteins that use chaperones, but that can fold correctly in absence of chaperones when in a diluted solution, and there are proteins that cannot fold correctly without the presence of chaperones at all. Current estimates I have seen is that about 30% of human proteins require chaperones, but this is a relatively new field, so I guess this number can change. 9: Agreed 10: Agreed 11: Agreed 12: Wrong: Ribosomes do not exist inside the nucleus in the cell, mRNA has to be transported out of the nucleus, to the ribosomes that is freely in the cytoplasm, or in the endoplasmatic reticulum. Now to the math section: 1. You are assuming a de novo formation of a specific protein. Evolution does not usually work like that. Usually, existing structures are modified. Only in successful frameshift mutations will you get de novo proteins that may look like something completely different. Also, you need to consider alternative splicing as a method of generating variations of proteins, 2. Not sure if this holds, but I am not going to debate this at this point. 3. Again you are calculating as if everything sprung into existence at once 4. Same as 3. 5. Same as 4. 6. same as 6 7. Autophagocytotis is stimulated by the lack of amino acids in the cell. Proteins destined for destructions get tagged with a tag (Ubiquitin) for a vesicle, the vesicle is then transported to the lysosome and the content of it digested. The lysosome itself does not have any means of recognising proteins as foreign or non-foreign. The signals on proteins that determine them to be tagged are still largely unknown, although some clues have been gained: A. certain proteins with specific N-terminal amino acids degrade faster than others (Asp as N-terminal has a half-life of 3 minutes, while Ser as N-terminal has a half life of 20 hours), B. proteins containing certain sequences of amino acids (e.g a socalled PEST sequence which is 8 amino acids long) have a half-life of 5 minutes, but if these 8 amino acids gets removed, the half life increase to 50 minutes. C. Signals may be hidden in the hydrophobic core of the protein, an abnormal protein with parts of the core exposed will therefore expose the signals, while a normal protein will hide the signals. In addition, cells have mechanisms for removing Ub from the proteins, thus rescuing them from being destroyed. Ok, so for the answer to your question: I do have an explanation for how one ancestral globin protein became two gene families on two different chromosomes in mammals. Look at the figure in my attachment (shows human alfa globin and human beta globin phylogeny) Start with one globin gene...then one chromosome duplication (or genome duplication). We know have two globin genes in two different chromosomes. Let the two different globin genes collect mutations over time, as speciation etc happen in various lineages. Unequal crossing over will cause two copies of the same gene on the same chromosome. If this chromosome then gets fixated in the population, all descendants will have the two copies of the gene on the same chromosome. In one chromosome, the ancestral globin gene duplicated to (Epsilon + Gamma) and (Delta + Beta), later (Epsilon + Gamma) duplicated to Epsilon and Gamma, and (Delta + Beta) duplicated to Delta and Beta. A similar story can be told for the alfa globin family. We can also add myoglobin to this phylogeny, which would then be a sister group to both the alfa and the beta globin family, indicating that myoglobins split off from the alfa and beta globin lineage very early. If you add neuroglobins to the phylogeny, these split off even earlier than myoglobin. This is a phylogeny done on human proteins only. It gets more interesting once you put the globins of many organisms into the same phylogeny. Then you get much better support for when the various duplications in the globin gene families occurred. As more and more genomes gets sequenced, I am sure we will get a more detailed history on the evolution of all kinds of proteins. In closing: if you want to calculate the probability of new enzyme system arising, you need to take into account: - gene duplication - genome duplication - that it is more important what class the amino acid belongs to rather than what amino acid it is. - cumulative selection, as provided by natural selection, and even by genetic drift, not single step creation ex nihilo as your calculations attempt to. The power of cumulative selection over single step selection has been shown many times. A short program that displays the difference between single step selection and cumulative selection, is a little program called MONKEY (http://members.aol.com/dwise1/cre_ev/monkey.html) (named after the infinite monkeys typing randomly and eventually producing Shakespeare's collected works). I used the following sentence as the target sentence: "EVOLUTION OF ENZYMES" which is a 20 letter large sentence. Now, consider the follwing model: Start with a random 20 letter string. Create two offsprings (copies) of the string. Let one (picked at random) of the strings receive ONE random mutation The offspring that is closest to the target sequence get to replicate, if none are closest, one is randomly picked out. Let the selected one again have two offspring, and select randomly one of these offsprings to receive a random mutation, and so on. Stop the iterations when one of the descendants is similar to the target sequence. When I ran this several times, I arrived at the target sequence within 1000-3000 iterations. Try to calculate the probability of picking the letters out at random in the correct sequence. The probability should be (1/28)^20 or so, much different from 1/2000. I made a sentence with 80 letters, and arrived at the correct sequence in around 9-11000 iterations. If I increase the number of offspring, it will go even faster. If I increase the model from 2 offspring to 10 offspring, the 80 letter sentence is found in about 800-2000 iterations. The program is not a model of evolution (since evolution does not have a goal, or rather, the goal is allways in the previous generation), but it does demonstrate the power of cumulative selection over single step random assembly of whole sentences. Puh...this post is getting so long that I loose sight of what I have written, so I guess I will take a break here...(maybe focusing on one topic at a time would be an idea for me :))

Move to strike...I have already posted in this thread :)

Thanks for the sticky.

I noticed we had a Einstein@Home team, but could not find any other Hypography teams in the other distributed computing projects that exists, so I took the liberty of starting quite a few teams in the name of Hypography. This first post will be updated to reflect the current projects we are members of. Current Hypography Team statistics (click image for details) Project Presentations: Einstein@Home: http://einstein.phys.uwm.edu// Rosetta@Home: http://boinc.bakerlab.org/rosetta// Predictor@Home: http://predictor.scripps.edu// BBC Climate Change Experiment: http://bbc.cpdn.org// SIMAP@Home: http://boinc.bio.wzw.tum.de/boincsimap// SETI@Home: http://setiathome.berkeley.edu// ClimatePrediction.Net: http://www.climateprediction.net/ LHC@Home: http://lhcathome.cern.ch/ Online status of the various project servers: What is distributed computing? Read the Wikipedia entry for distributed computing: http://en.wikipedia.org/wiki/Distributed_computing How can I contribute? Instructions can be found here: http://boinc.berkeley.edu/participate.php[/url] Latest development version of Boinc (beta version, may be unstable): http://boinc.berkeley.edu/download.php?min_version=5.0&dev=1 How can I join the Hypography team? First, attach to a project, as described in the link in the "How can I contribute" section. When you are a member of a project, go to the Hypography join page for the project listed under. Hypography Join pages: Einstein@Home: http://einstein.phys.uwm.edu/team_join_form.php?id=1691 Rosetta@Home: http://boinc.bakerlab.org/rosetta/team_join_form.php?id=2084 Predictor@Home: http://predictor.scripps.edu/team_join_form.php?id=2880 BBC Climate Change Experiment: http://bbc.cpdn.org//team_join_form.php?id=372 SIMAP: http://boinc.bio.wzw.tum.de/boincsimap/team_join_form.php?id=417 Seti@Home: http://setiathome.berkeley.edu/team_join_form.php?id=123937 Climateprediction.net: http://climateapps2.oucs.ox.ac.uk/cpdnboinc/team_display.php?teamid=4539 What is in it for me? - The knowledge that your idle computing time is used for research that can be beneficial for mankind - Friendly competition between other distributed computing users/teams about ranks and credits, based on how much work your computer(s) do. - opportunity to be credited in scientific publications (for some of the projects at least) - learning more about the science behind the projects How can I see my statistics? There are many servers that provide combined statistics for BOINC projects. One of the best in my opinion, is Boincstats. Here you can search for team names or user names and get their individual statistics both combined, and on individual projects. Come and join! It's crunchin' time...

Current statistics for Hypography:

Now we have a few Hypography teams up and going... Here are the URL's Predictor@Home: http://predictor.scripps.edu/team_display.php?teamid=2880 Einstein@Home: http://einstein.phys.uwm.edu/team_display.php?teamid=1691 Rosetta@Home: http://boinc.bakerlab.org/rosetta/team_display.php?teamid=2084 Simap: http://boinc.bio.wzw.tum.de/boincsimap/team_display.php?teamid=417 BBC Climate Change Experiment: http://bbc.cpdn.org/team_display.php?teamid=372 All run using the BOINC Manager All the teams lack now is members...

I will! Here are the projects I have put my computers to work at, and created a Hypography team on. Rosetta@Home: http://boinc.bakerlab.org/rosetta/team_display.php?teamid=2084 Predictor@Home: http://predictor.scripps.edu/team_display.php?teamid=2880 Simap: http://boinc.bio.wzw.tum.de/boincsimap/team_display.php?teamid=417 BBC CCE: http://bbc.cpdn.org//team_display.php?teamid=372 Team statistics:

Is it ok for me to start a Hypography team on those projects I am joining that lack a Hypography team? I will set the URL to www.hypography.com

I just found out about this Hypography team... I just started doing some protein folding and, donating cycles to climate change prediction, and will try to join Hypography if I can.

Do we have a Hypography distributed computing team? There are many science projects being done with distributed computing, and I would love to run my computer under a Hypography Science Forums banner if one existed.