text:
What are we missing about the real workings of the evolutionary process?
As a scientist (and a computer scientist at that) my view is that if we cannot simulate a process we have not understood it properly. I have been following the interesting field of Artificial Life for quite some time and the results are sobering - let me just quote two paragraphs from current overview articles:

One thing that always seems to happen with such projects is that,
  after they achieve their intended aim, if the ‘evolutionary’ program
  is allowed to run further it produces no further improvements. This is
  exactly what would happen if all the knowledge in the successful robot
  had actually come from the programmer [...]
That is why I doubt that any ‘artificial evolution’ has ever created
  knowledge. I have the same view, for the same reasons, about the
  slightly different kind of ‘artificial evolution’ that tries to evolve
  simulated organisms in a virtual environment, and the kind that pits
  different virtual species against each other.

Source: David Deutsch (2011): The Beginning of Infinity

One of the earliest networked artificial life experiments was based on
  the well-known A-Life system, Tierra. This was created in the early
  1990s by the ecologist Tom Ray to simulate in silico the basic
  processes of evolutionary and ecological dynamics. After Ray began his
  work, he soon recognized the potential of the Web to create a large
  complex environment in which digital organisms could freely evolve. So
  he set up a project called Network Tierra to exploit this potential
The results of this experiment were mixed. One goal of Network Tierra
  was to reproduce the Cambrian explosion in which single-celled
  organisms on Earth evolved rapidly into multicellular ones and then
  into more complex animals.
The in silico experiment began with a human-designed multicellular
  organism consisting of two different cell types. This survived under
  natural selection, a significant success in itself, but the number of
  cell types never increased beyond two.

Source: MIT Technology Review (2014): The Curious Evolution of Artificial Life
The point is that I have myself successfully worked a lot with genetic algorithms and genetic programming (I am also teaching this stuff) but what bothers me is that we are still not able to create some abstract form of (co-)evolution inside a computer where some real dynamics take place to produce ever and ever more sophisticated "species".
My question
Are there hints from the biological sciences what this mysterious ingredient could be which we still seem to be missing? Is it physics? Is it chemistry? Is it something else?
EDIT
Obviously the question is not clear as it stands, so I try a clarification: I refer to complexity of the resulting "species" in artificial life simulations. For example their behavioural or structural complexity. Why do these simulations always get stuck at some very low level (e.g. following food) and never ever even create something as complex as a bacterium? The computing power should be more than sufficient by now - and still, nothing... It seems that only what has been put into the simulation comes out but real evolution produces something really new (this is what the renowned scientist and polymath David Deutsch (University of Oxford) means by "I doubt that any 'artificial evolution' has ever created knowledge.")
EDIT2
Nathaniel gave me a decisive hint in the comments that this problem is called "open-ended evolution (OEE)" in the Alife community and it is one of the biggest research challenges there - unsolved yet! As a starting point see here: https://www.google.de/search?q=%22open-ended+evolution%22&artificial&life
Very interesting that it doesn't seem to bother the biological community and is met even with hostility here (some even lecturing me that the evidence for evolution is overwhelming and thereby implying that I might be some kind of crackpot creationist - unbelievable...)
...and no, the answer is not a matter of opinion (why this question was closed) but a valid research question (hopefully with some good answers someday)!
EDIT3
Last year there was even a big conference on this topic with many interesting results (although the problem itself is still unsolved):
http://www.tim-taylor.com/oee1/

See also my follow-up question here:
If evolution is not about increased complexity, why does so much complexity evolve?

@Downvoter: It is good practice to state your reasons. How can I improve the question? Thank you.

I am voting to close as this question will only generate answers based on personal opinion. I will also be the second downvoter, as we understand and have 150 years worth of scientific evidence to support Evolution and the process of Natural Selection. Furthermore you have cherry-picked two articles that back up your point, and it is not the case that "if we cannot simulate a process we have not understood it properly." Galileo could not simulate the solar system, yet he could prove heliocentrism. Newton and Leibniz could not simulate the infinitesimal, but they could understand it.

@AMR: What does it have to do with it that we have evidence of evolution with my statement that we don't really understand the process? We have evidence of qualia and yet don't understand it scientifically. We have evidence of quantum mechanics and of the general theory of relativity and are still not able to combine them - so what is your point? And I would argue that Galileo, Newton and Leibniz didn't know all there is to know - and today we are able to simulate those processes!

@AMR:  If you think I cherrypicked something give me one reference (only one) where artificial simulation really created something sophisticated.

http://jgs.lyellcollection.org/content/147/2/223.short

@AMR: Are you kidding me? I had a quick look at the paper and I have done much more sophisticated stuff with genetic algorithms myself. The author only writes about some simple simulation on a simple morphological structure - which is not surprising considering the paper is over a quarter of a century old. Seriously: You should acquaint yourself with the area of artificial life which is much richer and deeper. The wikipedia link I gave above is a good starting point.... and yet it does not fly! (and no, it is not a matter of opinion but a hard scientific question!)

@AMR: See my edit at the bottom of the question - Thank you

You've answered the question in your first lines: .. my view is that if we cannot simulate a process we have not understood it properly. How can you make a model whilst it is unknown how the first cells appeared and why and how multicellular organisms arose? It ain't magic; if you don't put the parameters in, a model won't generate magic

There is an inherent problem to Deutsch's argument, " if the ‘evolutionary’ program is allowed to run further it produces no further improvements." He is falling for one of the biggest misconceptions about evolution that you can, that evolution is about "improvement." Evolution has simply only ever been about change. Think of sickle cell anemia. Without Plasmodium driving selection, sca is a debilitating disease, and the wild-type population thrives, while scan mutants are less fit. No throw in the parasite, and people with the mutant allele survive while WT organisms die.

Under one set of conditions one group is more fit, under the other, the other group is. Neither is an improvement or better, they are what they are in the context of the environment that they find themselves in.

@AMR: Your last two comments were quite helpful - Thank you.

@AMR: Please see my follow-up question: http://biology.stackexchange.com/questions/42050/if-evolution-is-not-about-improvement-why-is-there-so-much-improvement

@Christiaan: Please see my follow-up question: http://biology.stackexchange.com/questions/42050/if-evolution-is-not-about-improvement-why-is-there-so-much-improvement

@shigeta: You write in your profile: "I believe that biology and other stackexchange sites should welcome newbies and encourage dialog about science, evolution, technology and etc." Just to let you know I don't feel very welcome here (just look at the comments) and I know how stackexchange works... see e.g. my profile here: http://quant.stackexchange.com/users - I know it is not your fault but it is a shame for the biology.SE site. Sorry to say...

This is known in Artificial Life circles as "the problem of open-ended evolution." It is (still) an area of active research (e.g. there was a workshop on it at the last ALife conference in York, UK last year) but nobody believes with certainty that they know the answer at this stage.

In my opinion it's mostly due to the constraints of computing power, which was very limited throughout most of the field's history, and is even now not quite enough. Because of this, we tend to use the smallest population we can get away with, and very strong selection pressure, because otherwise we would be waiting too long for anything to happen at all. But with small populations and strong selection you should not really expect anything other than strong convergence to a local optimum.

Deutsch's "doubt that any 'artificial evolution' has ever created knowledge" seems unfounded to me, since there are a moderate number of well-documented examples of exactly that, in the form of electric circuits that work in ways no human ever designed, and that sort of thing. (But I have not read Deutsch's paper and perhaps he addresses those.)

@Nathaniel: "the problem of open-ended evolution." - this is a very good hint from where I can get started. Interesting though that the biological community seems unaware of it...

@AMR: See my new edit2 - perhaps you would like to reconsider your downvote... Thank you

No. Nothing has changed. I do not need a computer simulation to be able to look at genetic homology between species and understand how evolution worked. I can look at actual data and interpret it. It tells me why humans have 46 chromosomes per diploid cell and not 48 as our ape relatives have. I can look at fossil records and see how species evolve. The Earth has provided all of the simulation and data for evolution and natural selection that one would ever need to understand it and prove it.

You are also missing the point of the close votes. None of the answers on your other post are referenced and they are most all based on opinion, even if some of them are valid opinions. The guiding principle of the site is not to allow for questions that lead to that kind of answer. It is for questions that will lead to fact based, referenced answers. Your question is more appropriate for a lab meeting or a working group to brainstorm ideas and not for a site that does not look to be an open opinion forum. There are plenty of those on the Web and that is not what we are trying to be,

@AMR: You don't get the difference between "seeing that something works" and "understanding how something works", right? For you it is enough to stand in front of a miraculous machine in awe and just acknowledge that it works. We agree on that one. What seems to be the difference between us: I really want to understand how it works - up to the tiniest detail of the process. This is why I became a scientist and this is why I asked this question: "What are we missing about the real workings of the evolutionary process?"

Again, you are pretty much completely wrong and that is likely because you haven't truly invested the time and effort to learn biology. It isn't mysterious or miraculous. It is predictable and behaves in a logical way. That is why it is the Theory of Evolution and not the hypothesis of evolution. We understand Evolution so well that we can use it to make predictions about nature and natural phenomena that hold up and can be proven to the greatest rigor of science. The only things we are missing is actual proof of the very beginnings up to the Last Universal Common Ancestor.

@AMR: Oh, and by the way I am fully aware of how SE sites work - I am No. 1 by far on one of them: http://quant.stackexchange.com/users - and to tell you the truth: The climate is much more welcoming there than here!

To clarify my close vote - you write ...and no, the answer is not a matter of opinion (why this question was closed) but a valid research question (hopefully with some good answers someday)!... It may be a valid question, but the answers will be primarily opinion-based, making the question off-topic here.

@AMR: "The only things we are missing is actual proof of the very beginnings up to the Last Universal Common Ancestor." You forgot the point that we are not able to reproduce open-ended evolution in a computer simulation... So there are at least two things...

@Christiaan: How do you know that before you have even encountered the right term under which the research results are to be found ("problem of open-ended evolution OEE")???

A computer cannot reproduce Graham's Number, even though it is finite, and has been used in a rigorous mathematical proof. So I am not going to lose any sleep over the inability of computer "scientists" to simulate evolution in silico. Computers are a great tool, but just because something cannot be done with them doesn't mean there is a problem with the actual event they are trying to mimic.

@AMR: Fair enough, yet I think the problem is not that we are not able to reproduce it but that we do not even know what is missing (we know that in the case of Ramsey theory!) - and this is all I asked for.

I may be entirely wrong, but I don't hear Evolutionary or Paleo Biologist saying that we need computer simulations to understand the subject, in the same way that astronomers ask for computer models that simulate cosmic expansion or galaxy formation. And that is because the model we are given from the Theory is fairly complete. We may have gaps, but we have no unexplained life forms to date. I can plate a dish of yeast, mutagenize them, and put them under selection, and I will, in some small probability find adapted mutants not present in the control. And we understand the mechanism for that.

""What are we missing about the real workings of the evolutionary process?" - "Are there hints from the biological sciences what this mysterious ingredient could be which we still seem to be missing? Is it physics? Is it chemistry? Is it something else?" - Different people see "different hints", and there is no definitive answer. Anybody could be correct as to what direction we should head in. This is the very definition of opinion based. That doesn't make it a bad question - it just means there cannot be "one true answer", which is what the SE sites aim for, (as you know)

@AMR "I may be entirely wrong, but I don't hear Evolutionary or Paleo Biologist saying that we need computer simulations to understand the subject" - actually there are quite a number of very senior and influential evolutionary theorists who use simulation extensively. Szathmáry and Nowak both come to mind straight away, though there are many others.

@Nathaniel Szathmáry is using simulation to test specific hypothesis about molecular machines and also multicellular development, based on the assumption that evolution does occur in a predictable and understandable way. This question is saying that we cannot understand evolution as a mechanism if we cannot simulate it in a computer, there is a difference. "if we cannot simulate a process we have not understood it properly." That is very different from using a computer algorithm to test a hypothesis that would have developed over millions or billions of years.

@AMR: Imagine physicists observe the solar system and collect evidence beyond a reasonable doubt that the planets are attracted to each other and that "gravity" is responsible for their movements. They even summarize "gravity" into some nice laws. Now you put these laws into a computer and see how different objects are affected. Unfortunately instead of ellipses the objects move in rectangles. Getting back to the physicists they tell you: "Never mind, we fully understand gravity and we don't have to simulate it because there is evidence that the planets move in ellipses" How would you react?

The sim is wrong, not the evidence or the theory. Take a look at the code you wrote and find the error. You are enamored with computers and for some reason cannot seem to divorce yourself from the idea that the data that they generate is perfect and that everyone else is wrong. If you want to deal in hyperbole, if a simulation has sheep giving birth to monkeys, I'll find the grad student that wrote the code and tell them to kindly stop being sophomoric. If however, an entire flock of sheep start giving birth to monkeys, then I would say that maybe we have to look at something in evolution.

@AMR: Very good answer indeed: many scientists double checked the code over decades... still rectangles (see my edits and the links given there). I am not saying that the theory is wrong but that it may be incomplete (you do recognize the word "missing" in my question, right? And you do understand that this is different from "wrong"?) And one clarification: Declaring oneself to be beyond any criticism whatsoever is not only bad style but is not science either. The majority of this community agrees with me now so you seem to be the one saying "everyone else is wrong" ;-)

"The majority of this community agrees with me now...." Because you have a question that got 21 upvotes? @Remi.b got 39 upvotes for an answer about Morning Wood

@AMR: So obviously this is also a great contribution - but I did this in one (!) day and that answer is nearly two years old. I am very happy about this development :-)

Does it not phase you that almost ever single answer tells you your premise is basically wrong? And he got 78 for this answer, even though he didn't bother to provide references and the original, unedited question got 26 upvotes, even though its premise was entirely incorrect. http://biology.stackexchange.com/a/40581/16651

I'm also a CS guy, so maybe my opinion shouldn't have that much weight, but I did do biology research in college and I work at a company that produces biology research instruments. That said: I find it pretty sad that this question was closed, and it's absolutely ridiculous that it seems to have been interpreted as "in your opinion, why is the theory of evolution flawed/lacking." (Not to mention the bizarre comparison to "simulating" infinitesimals, whatever that means.) Have an upvote.

@KyleStrand the close is not about the question. "Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise."

@AMR: My premise is that we are not able to successfully reproduce important stylized facts of evolution and this is correct - many people agree with me. But I understand that you think that you are the one in this community who decides which premises are correct or incorrect, you are not to be bothered by dozens of votes from the community. I think you should just relax and find a good answer to my question - Thank you.

@AMR I see that. I don't understand why the question as stated is necessarily opinion-based, though.

@KyleStrand It does not matter if the question is opinion-based or not, what matters is the answers that are generated. And as can be seen from the follow up to this question one out of the ten answers had a reference. The majority of posters opened accounts yesterday to answer, and as such, that goes against the stated goals of the site.

@AMR: So questions that are able to grow this site are against the stated goals.. I think you might have misunderstood something here ;-)

It is actually the stated goal of the entire network. http://blog.stackoverflow.com/2010/09/good-subjective-bad-subjective/

@AMR The frick?? No. No no no no no no no no no. You do not close questions because bad answers show up; you delete the answers. You do close questions that have an inherent tendency to attract opion-based answers, i.e., subjective questions.

@AMR In case the above gets flagged as rude (sorry, but it shocks me that someone with a relatively high rep on any SE site would so badly misinterpret the subjective-questions policy), I'll restate the point more calmly: the blog post you cite clearly states that it is about questions. The fact that a question draws some opinion-based answers may be an indication that the question itself is opinion-based, but it is not in itself sufficient reason to close the question as "primarily opinion-based."

@AMR: Please show us the part where it says that it is "against the stated goals of the network" when "the majority of posters opens new accounts to answer". I think this is getting more and more ridiculous and we can all see that you are just not prepared to admit a mistake.

@vonjd Extensive discussion in the answers or below in the comments is indeed discouraged. stackexchange is set up as a question and answer site, not as a discussion site. This is something you can regret in terms of scientific discussions, but these are the rules. You could look into this stackoverflow post for example. Opinion-based questions are seen as unscientifically and not welcomed here on biology. So please, respect the rules of the community. And please stop discussing in the comments and move this over to chat.

If the discussion goes on here, I will move it.

@Chris: I fully agree and upvoted your comments. Thank you for your service to the community.

I don't really understand @Chris's objections to the discussion here; AFAIK there is no problem with using the comments to discuss whether a question is appropriate for a site, though at this point perhaps someone should just open up a Meta question to resolve the issue.

But OP, I think am beginning to understand the claim that your question is subjective, and I think it could use a pretty substantial edit (not just another addendum, but a rewrite of what's already there) to clarify who "we" are when you say "we are missing" things in the theory of evolution. As far as I can tell, there are two possible interpretations, and both are actually fairly valid as (non-subjective) questions:

first, "what parts of evolutionary biology are missing from CS evolutionary simulations?" (which would completely evade the "evolution is not in doubt" argument from @AMR et al), and second, "what open questions in evolutionary biology might also be 'missing factors' in CS simulations of evolution"? The second question is obviously more open to the charge of subjectivity, though I personally wouldn't consider it overly subjective (though it might be too broad; every field, including reasonably well-established ones like evolutionary biology, have lots of open questions!).

@KyleStrand: I agree but I am pondering the possibility to ask a new and clearer question. New because closed questions tend not to be reopened on SE sites and I want to give everybody a chance to answer this question.

Editing is nevertheless the "standard" way to go. Note also that editing a closed question automatically places it in a "reopen" review queue, so there is actually a good chance it will be re-opened if it has improved. (There may be some exceptions to the automatic-placement-in-the-reopen-queue rule, but I'm not sure.)

@KyleStrand The comments are not meant for discussion here on stackexchange. Like it or not, but respect it.

@Chris When the automatic "continue this discussion in chat" link is created, I almost always use it, but there apparently has not yet been enough back and forth to generate one. Since you have the mod-powers necessary to move the discussion without the auto-gen link, it would be fine if you did so (your earlier comment to that effect sounded a bit like a threat, though, which I found odd).

Based on prior experience, I don't think your attitude is actually all that standard for StackExchange; between this and OP's follow-up question (where non-discussion comments were deleted), it seems Bio.SE is generally less welcoming toward long sequences of comments on a question. This is fine, but I'd appreciate it if you stopped accusing the rest of us of not understanding network-wide policies.

@KyleStrand: I asked the new question and it is again on the brink of being closed although this time I really tried to make the point explicit: http://biology.stackexchange.com/questions/42151/bootstrapping-symmetry-breaking-in-evolution

@AMR : Galileo is a bad example, because he didn't prove heliocentrism, he was right for the wrong reason. He argued that the tides are caused by the motion of the Earth around the Sun, while his opponents correctly argued that the tides are caused by the Moon. Also, the theory of gravity was not invented yet, so as strange as it seems, with the instruments available at that time, heliocentrism didn't have any more solid scientific proof than geocentrism.

@vonjd If you are interested, I recommend looking into Basener's ceiling.  It's a mathematical proof that all evolutionary algorithms hit the complexity limit that you see in practice.  It is from the Intelligent Design literature, which I know is disliked around here, but the proof is sound.

The question appears interesting and made me think but I might not fully understand it. Let me know if I am answering your question.
Genetic algorithm vs simulation of evolutionary processes
I think that the whole issue comes from a confusion between the concept of simulating evolutionary processes and the use of genetic algorithm (type of optimization algorithm) for various purposes.
Genetic algorithm
Genetic algorithm is a type of optimization algorithm (and the OP knows much more than I do in this field) aiming to find solutions to search problems. The accuracy of the analogy between a genetic algorithm and the biological reality that inspired such algorithm is completely irrelevant to the usefulness of the algorithm at doing a specific task (such as the NP-hard travelling salesman problem for example).
Numerical simulations in science
I think your question is not specific to evolutionary biology but rather to science as a whole (this leads me to think that Philosophy.SE would be a good place to ask such question).
In natural sciences (Physics, Chemistry, Biology and others), we model things! We abstract the essentials from a complicated world and model it. When we model, we assume a number of properties of the system of interest. These assumptions might be extremely well documented and verified or not. When the assumptions of a model are not well documented, it is of course essential to study a posteriori the robustness of the model to violation of the assumptions and to consider the results of the model with a pinch of salt. A model can be purely verbal or most often expressed in mathematical formulations. However, many complex systems cannot be modelled mathematically (even for the most brilliant mathematicians). This is where numerical simulations come into play. Note that once a process has been modelled, we empirically investigate the accuracy of our model by formulating predictions and testing them.
You say:

if we cannot simulate a process, we have not understood it properly

If we already understood a process, there is no point spending time and money to simulate it anyway! So again, this sentence suggests that numerical simulations is worthless in science. It is true though that we can only simulate the processes for which we know the basic components (but we might not understand the dynamic of a system of interest).
Simulations in Evolutionary biology
You cite one work (which I am not familiar with) which fail to reproduce the observed pattern. In other words, the predictions of the model are not met/observed in reality.
As I said above, one needs to understand the basic components of a system in order to be able to simulate it. We happen to already know a faire amount of stuff! Of course, it is impossible to address the question "what do we know in Biology" as it would be way too broad. There are thousands of studies that have used numerical simulations (and also mathematical simulations) to study evolutionary processes.
Example
Imagine for example, you are interested to know the probability for a given new neutral mutation to rise in frequency in a diploid population to reach "fixation" (that is a frequency of 1; everybody then carry this mutant allele). There exists a number of mathematical models (Wright-Fisher (binomial) model of genetic drift, Moran (Birth-death) model and Coalescence (branching process) model) to calculate this probability but let's assume we fail to develop such mathematical/analytical model and and we need to simulate it. We could simulate this process a lot of time (using a ABC kind of approach) and calculate the expected probability of such mutant allele to get fixed. Btw, this probability is $\frac{1}{2N}$, where $N$ is the effective population size.
Want to know more?
I am not a philosopher of science (but a PhD student using numerical tools to model evolutionary processes) and I think the question is not specific to evolutionary biology. I would recommend to ask the question What is usefulness of numerical modelling in science? or Are numerical modeling worth as much as analytical modelling in science? on Philosophy.SE.
If you do so, can you please link to your posts here, I would love reading the answers. If you don't ask these questions on Philosophy.SE, I probably do it at some point and will add the links here.

Thank you, I think your answer goes definitely into the right direction (and I upvoted it). My main question is why has no artificial simulation ever been able to really create something sophisticated but seems always getting stuck after reaching some low level of complexity?

[...] reaching some level complexity. Do you refer to the complexity of the model?

I refer to complexity of the resulting "species" in those artificial life simulations. For example their behavioural complexity. I mean why do these simulations always get stuck at some very low level and never ever even create something like a bacterium? The computing power should be more than sufficient by now - and still, nothing...

See also my Edit at the bottom of the question. Thank you again.

Oh, so your question is less general as I thought. You are talking about a few quite specific type of simulations apparently. You should clarify the definitions of these simulations. Are the authors of those simulations particularly interested in the evolution of cell cooperation in a multicellular organism or maybe they are interested in the evolution of a complex genetic network... or maybe something else.

I would suggest that you investigate a bit more about what they are trying to build (and from which basic mechanisms) and then ask a more specific question about why these simulations fail to produce what is observed. For the moment, it is unclear what category of simulations you are referring to.

If the term artificial life experiments refers to a specific type of simulation, then your question relatively clear I guess. But I would guess that most people don't know what is meant exactly by artificial life experiments (at least I don't)

Please see my follow-up question: http://biology.stackexchange.com/questions/42050/if-evolution-is-not-about-improvement-why-is-there-so-much-improvement

Agree with the previous answer.

Are there hints from the biological sciences what this mysterious ingredient could be which we still seem to be missing? Is it physics? Is it chemistry? Is it something else?

The OP already seems to support evolution theory, as anyone with basic biology knowledge would do.
Since he is asking about possible "mysterious ingredient", the question is very likely to be regarding the stimulation of the evolutionary process rather than generic algorithm.
Even more specifically, he wants to stimulate the evolution to know if "Probablity theory" will support evolution theory without any need for the "mysterious ingredient".
As answered above, without fully understanding all the components of the system, it may be difficult to stimulate an evolutionary process.  There is not even need for that.
But if you want to test if complex characteristic can be achieved by chance you can stimulate it easily by some other method.
Develop a program which has "face detection" (from the image)function, and add some other functions such as self replication, forced "mutation", and an enviroment which will select the fittest.  Try super computers where your software will  self-replicate "unlimited" times at a second, and consider yourself successfull when your program gain a newer function such as "sex" detection from the or image after several years (assuming sex detection feature will make the program "fitter" at your enviroment)

"The OP already seems to support evolution theory" - why would anyone doubt it? I just want to fully understand it. Anyway, could you please clarify your last paragraph, how exactly would you go about?

I have read some comments under your question,  and thought that some  people may think you do not support evolution because of lack of stimulation.  I am sorry if i misunderstood this.    Anyway i found your question very interesting and already upvoted it.  I agree with you.

Please see my follow-up question: http://biology.stackexchange.com/questions/42050/if-evolution-is-not-about-improvement-why-is-there-so-much-improvement

@vonjd  nice question. i will comment.  I have some computer programming background i generally share your feelings about improvments

Algorithmic depth and processing power are miniscule compared to the algorithms of life. The 70 common chemical elements have Van-Der-Valls force, flow, dissolution, 70^70 simple combinations, a google number of proteins, The best mathematicicans can't even model a single tree... it takes them months to program diatoms, leaves, and they always fail. trillions of gygabytes database and algorythms that cover many cd's are used in biology, and for the moment we rival them to about a billionth of their numerical depth.


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How do proteins perform their function
I have asked a question on physics stackexchange, but was redirected here. I copy the entire question word for word. The original is here.

Let's, for example, take a ribosome. It is an enzyme that is in turn just a molecule that must follow the laws of physics.
Correct me if I'm wrong, but it can be looked upon as a molecular machine made up of several pieces. What exactly makes those pieces work together?
Why does the ribosome bind to a strand of RNA? Is it just the shape and electric charge or is it something more? Once the ribosome is bound to a piece of RNA, how does it move?
In a way, I'm looking for the "ghost in the machine". I'm interested in molecules in general, not just ribosomes. What is it that, on a level of single atoms, makes molecules "alive" - move, assemble a protein, etc. ?

PS. I'm having trouble phrasing my question, so if anything is not clear, please leave a comment and I will address it.

Edit 1: This is most definitely a physics question. I'm looking at the scale of several atoms.
Let me give a hypothetical example:
Imagine a strand of carbons. Also imagine that there exists a molecule that can move along this strand. How would it do it? What forces would move it along this strand? Is it electromagnetism? Would gravity be involved to a significant degree? What effect would shape have?
Does this make the question clearer?

Looking at the scale of atoms will not help understanding a molecular machine made of thousands of them.

@Chris How should I try to understand molecular machinery? What topics, books, resources should I look for?

I agree with Chris, a lot of how enzymes process things is related to their structure and the charge or lack of charge in the active site.  You also have to consider that ATP is used to catalyze many reactions, meaning you have to take a more macro look.  I'd start with Biophysics.

@Nathan do you have a suggestion for a resource to start? Maybe a textbook?

http://www.amazon.com/Molecular-Modeling-Simulation-Interdisciplinary-Mathematics/dp/038795404X/ref=sr_1_1?ie=UTF8&qid=1452024349&sr=8-1&keywords=038795404X

@Chris Looking at the scale of atoms will not help understanding a molecular machine made of thousands of them. It does help, it's just really, really difficult.

@tel I'd say its like looking at a screw in a robot that puts doors on cars and trying to understand how the robot puts doors on cars.  In the reference of what a particular screw does in light of the whole, you may gain something.  But in just looking at the screw you can't understand the bigger picture.  And that's a decent analogy since the atoms will make up the amino acids, most of which (in a high number of proteins anyways) are structural and have little function in the actuation of the protein.

@tel I would say it's lloking at the atoms of a car when you want to know how it works. From the viewpoint you can only see very small parts of the machine and this will not help you with overall understanding.

@Chris Your analogy is off by about 8 orders of magnitude in terms of the relative scale. Rather, it's like looking at all of the hundreds of gears, shafts, pistons, etc that make up a car's drivetrain in order to fully understand how it works. You don't just look at each of those parts in isolation, rather you try to characterize the network of their interactions.

@Nathan Yes, the level of detail can be overwhelming, but modern science is capable of productively dealing with much of that detail. Remember that one of the ultimate goals of this type of work has always been to produce and control our own custom-made proteins. It's fiddly work. The lesson of the last 40 years of progress in this field is that the details matter, often in surprising and significant ways.

@tel So is the functioning Ribosome an extremely complex machinery. I would start by reading a standard biochemistry book, which usually has a good overview and then start going more specific.

This question is waaaay too broad, but I'll give some short and simplified answers to the hypothetical you asked at the end. First, let's reformulate your example a little bit:
How does the ribosome move along a strand of RNA?

How would it do it?

By hydrolyzing GTP and somehow coupling the free energy associated with that reaction to forward motion. Keep in mind that both ribosome and RNA are constantly undergoing random-walk Brownian motion (ie jiggling), so it's more a matter of biasing the ribosome's motion in a particular direction than it is of moving the ribosome in the first place.

What forces would move it along this strand?

The current understanding of molecular biophysics, which is by no means complete, says that the primary driver of this sort of protein activity is electrostatic forces.

Is it electromagnetism?

There are no electric currents, per se, so magnetism does not play a role.

Would gravity be involved to a significant degree?

At these scales gravity does not have a significant effect.

What effect would shape have?

Shape has an enormous effect. One of the fundamental axioms of molecular biophysics is that function follows form. The 3D configuration of those electrostatic forces I mentioned is determined in turn by the 3D configuration of the involved macromolecules.

If you want to know (a lot) more about this specific question, and if you have access to journals, check out this review: How Should We Think About the Ribosome?

You write "By hydrolyzing GTP and somehow coupling the free energy". When you say somehow, do you mean to say that it is very complex process or that humanity does not know?

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text:
Binomial nomenclature: Why am I seeing different genera with the same species name?
I have looked online but still do not understand how two organisms can have the the same species names but be in different genera?
Do all genera share common species names?
Also which would be more closely related, two organisms with the same genus name but different species name or two organisms with different genus names but the same species name?
According to a textbook, the two with the same genus are more closely related, but I do not understand why?

For a very simple analogy, consider the Genus name to be like a person's last name and the Species name to be like their first name. John Smith is more closely related to his brother Bob Smith than he is to his neighbor, John Brown.

@CactusWoman, good analogy, unless of course John Brown and John Smith's mom, well, you know... John Jr....

I assume that you are talking about the second part of the binomial name when you write 'species name', which is in fact called 'species epithet' (or 'specific epithet'). 'Species name' is referring to the full binomial name in binomial nomenclature.

Two different species can have the same species epithet if they belong to different genera ('species name' is referring to the full binomial name). Consider for example Pinus glabra and Ilex glabra
P. glabra

I. glabra

Two species can be have the same genus name (meaning they belong to the same genus) and will therefore necessarily have different species epithets (otherwise we would name them exactly the same way which would be very confusing). Consider for example Pinus glabra (again) and Pinus resionosa
P.glabra

P. resinosa

As a consequence, if two species share the same species epithet then it tells you nothing about how closely related they are. If they share the same genus name, then they belong to the same genus (except exceptions probably!).
For the above examples, the term glabra comes from "glaber" (latin) which means "Hairless". So the only thing you can tell from P. glabra and I. glabra is that they are probably both hairless when you compare them with their respective closely related species.

Could the analogy be used for two organisms with different family and same genus? (I am aware that this is no longer binomial nomenclature). So, they would be less closely related compared to two organisms with same family, different genus? In simple terms, the category that comes first should be prioritised when deciding how closely related they are? (if that makes sense).

Yes indeed. Extending on @CactusWoman nice analogy (which is almost more than just an analogy): If the species is a first name, then genus is the family name and family is the ethnical origin and so on... A case could exist where two individuals of different ethnic groups would have the same family name but that would not be due to common descent but only to "chance".

I corrected your use of 'species name' to 'species epithet', since this is the term for the second part of the binomial name (also 'specific epithet' or 'specific name').  'Species name' is referring to the full binomial name.

And then there are cases when completely different organisms have the exact same genus and specific epithet, such as Orestias elegans Ridl. and Orestias elegans Garman, 1895.

This is a widespread issue amongst binomial names. Only the full binomial name has to be unique in nomenclature, and therefore many genera contain different organisms with the same specific epithet. As Remi.b has already mentioned, two organisms with binomial names that have the same species but different genus are very likely more different from each other than two organisms that share a genus but have a different specific epithet.
For example, the Wikipedia page on the specific epithet "Officinalis" lists dozens of organisms with a variant of that specific epithet conforming to the Latin declension of its genus.
This list consists of organisms which are completely unrelated to each other, containing fungi, plants and animals.
Althaea officinalis (marshmallow)
Anchusa officinalis (bugloss)
Archangelica officinalis (angelica)
Asparagus officinalis (asparagus)
Avicennia officinalis (mangrove)
Bistorta officinalis (European bistort)
Borago officinalis (borage)
Buddleja officinalis (pole butterflybush)
Calendula officinalis (pot marigold)
Cinchona officinalis (quinine)
Cochlearia officinalis (scurvygrass)
Corallina officinalis (a seaweed)
Cornus officinalis (cornelian cherry)
Cyathula officinalis (ox knee)
Cynoglossum officinale (houndstongue)
Euphrasia officinalis (eyebright)
Fumaria officinalis (fumitory)
Galega officinalis (goat's rue)
Gratiola officinalis (hedge hyssop)
Guaiacum officinale (lignum vitae)
Hyssopus officinalis (hyssop)
Jasminum officinale (jasmine)
Laricifomes officinalis (a wood fungus)
Lavandula officinalis (lavender)
Levisticum officinale (lovage)
Lithospermum officinale (gromwell)
Magnolia officinalis (magnolia)
Melilotus officinalis (ribbed melilot)
Melissa officinalis (lemon balm)
Morinda officinalis (Indian mulberry)
Nasturtium officinale (watercress)
Paeonia officinalis (common paeony)
Parietaria officinalis (upright pellitory)
Primula officinalis (cowslip)
Pulmonaria officinalis (lungwort)
Rheum officinale (a rhubarb)
Rosa gallica 'Officinalis' (apothecary rose)
Rosmarinus officinalis (rosemary)
Salvia officinalis (sage)
Sanguisorba officinalis (great burnet)
Saponaria officinalis (soapwort)
Sepia officinalis (cuttlefish)
Sisymbrium officinale (hedge mustard)
Spongia officinalis (bath sponge)
Stachys officinalis (betony)
Styrax officinalis (drug snowbell)
Symphytum officinale (comfrey)
Taraxacum officinale (dandelion)
Tormentilla officinalis (tormentil)
Valeriana officinalis (valerian)
Verbena officinalis (vervain)
Veronica officinalis (speedwell)
Zingiber officinale (ginger)

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text:
What is the general term for tree-dwelling animals?
Many different types of animals live primarily in trees, from birds to monkeys and beyond.
Is there a general term that I can use to refer to all of them?
The closest I've been able to come is "arboreal", and I'm not sure that that particular term is appropriate for the broad range of animals encompassed by the casual usage of "tree-dwelling."

What do you mean by "the casual usage"? Dictionary.com seems to make the meanings rather explicit and clear. An animal that lives in or amongst trees is arboreal.

Arboreal is indeed the correct term to use.


text:
What are some examples of small-scale physical phenomena that snakes fear?
What are some ways to use technology to repel snakes?
One idea I was researching is low-frequency sound, which I read snakes could "feel" even though they are naturally deaf. Would this be a viable option? What sound ranges would need to be used?
What are some other ways? Could targeted light be another option?


text:
Can the retina emit light?
The retina is light sensor, which converts light to an electrical signal.  LEDs can both emit and receive light. Similarly, is it possible to apply electricity to retina to generate light?
Simply put, in theory, can humans emit electromagnetic waves from their eyes?

No. More importantly, if the retina was emitting light you'd almost certainly saturate the photoreceptors with the emitted light, making it impossible to see anything, so what's the point?

@user137 I just think if retina could emit em wave in the same manner as LED, It would emit longer wavelength, maybe infrared, so not the same wavelength it could absorb. And it could be possible if we could control and practice it tobe useful with electronics device. Or have some hypotheses about superpower like mind reading or telepathy

@user137 but The main point is I want to know mecanic of absorption and energy conversion of retina that is it the same as LED or not. If it is then we could grow organic LED with bioengineering

Close voter and skeptics: this is a great question. Ever heard of otoacoustic emissions?  Ever thought about the physics between photon absorption and relaxation generating fluorescence?

It is very interesting but it's making no attempt to understand the biology of the problem, I'm going to close vote but would retract/reopen if the user makes it more biologically focused.

I'm voting to close this question as off-topic because it needs to explicity seek understanding of the biological considerations.

Even if it were possible to make the photoreceptors emit light at some wavelength, I imagine the efficiency would be so low that getting any appreciable energy out would require a huge amount of energy in. Of course, everything emits photons when you get it hot enough.

LEDs do not absorb light; a photodiode does.

@user137 - first you guess any photo emittance would swamp the retina and then you guess the efficiency would be too low to occur anyway? Criticizing questions is OK, shooting at them randomly with unfounded arguments is not.

The two close reasons so far are Primarily Opinion Based and "off-topic because it needs to explicity seek understanding of the biological considerations." Addressing the first, how is it POB? It either happens or it doesn't. Addressing the second, it might be addressed in this meta question. It seems that @user137 might benefit from seeking to understand biological considerations.

@anongoodnurse I retracted, but based on the way the question is written, it is asking if humans can emit light if electricity is applied to the retina. As I  don't think that there has been an experiment expressly that has been conducted to apply electricity to the human retina to the point at which it emits light, then an answer to that question would be opinion based, even if you are drawing on information based on the research that you have referenced; that the retina can absorb blue light and emit yellow light. You provided a really good answer, but is an answer to a different question.

I think either you or @Christiaan should consider an edit to the question to bring it in line with the answer. Given that autofluorescences is definitely a plausible phenomena for which there is evidence for.

Yes, you can grow organic LEDs @Thaina, that is entirely possible. You might as well call human fabrication of LEDs 'growing '  LEDs, but I think I see the actual question you were after...;) Cool thought.

@AMR - "You provided... an answer to a different question." Yes, I think you're right. Some is due to not knowing anything about LEDs, another to misinterpretation of electricity (I was thinking of electromagnetic waves). But the titular question remains, and that's what I answered. :-/ But I agree; it's not an answer that addresses the spirit of the question.

I still maintain that there is no attempt to illicit a biological answer, there is no evidence that the user has even considered the physical properties of the retina

The retina is already constantly emitting electromagnetic waves. It emits infrared light.

Please allow me to start by saying I know nothing about LEDs, so my answer isn't going to address your analogy. I'm also going to keep it more simple.

Can the retina emit light?

Yes. The retina is able to give off light in the form of fluorescence. Since fluorescence is the emission of light by a substance (granted that substance must first absorbed light or other electromagnetic radiation of a different wavelength.)

“We can use autofluorescence emission to image the retina and see certain features of it,” said Dr. Sparrow, the Anthony Donn Professor of Ophthalmic Science in the departments of Ophthalmology, and Pathology & Cell Biology. ...Most objects in the environment are visible because they reflect, as opposed to emit, light. However, the retina—which exists within the eye’s back wall, known as the fundus—can generate its own fluorescence.

The cells of the deepest layer of the retina, called the retinal pigment epithelium (RPE), contain fluorescent compounds, which when excited by blue light emit a yellow fluorescence.
This ability changes with age, disease, etc.

Excessive accumulation of lipofuscin granules in the lysosomal compartment of retinal pigment epithelium cells represents a common downstream pathogenetic pathway in various hereditary and complex retinal diseases, including age-related macular degeneration.

Lipofuscin is one of several fluorophores in the RPE. Different fluorophores may be related to different damage to the eye.

A major hydrophobic component of RPE lipofuscin is the fluorophore A2E... Given that A2E strongly absorbs in the blue region of the spectrum, together with the known susceptibility of RPE to blue light damage, we undertook to investigate A2E as a fluorophore involved in blue light toxicity.

Science Insight: Using the Retina’s Natural Fluorescent Light to Measure and Treat Disease
FUNDUS AUTOFLUORESCENCE IMAGING: Review and Perspectives
The Lipofuscin Fluorophore A2E Mediates Blue Light–Induced Damage to Retinal Pigmented Epithelial Cells

Thanks for your answer. But the main point I mention LED is because it is photovoltaic, conversion between electricity and electromagnetic wave. The point is photovoltaic will absorb wave to generate electric current. And it is reversible material that if apply electric current it will emit light. So I wonder if the mechanic of light absorbtion to convert light energy to electric signal of retina is photovoltaic then it could reversible by retina material emit light when apply cirrent

Still your answer amaze me that retina is also fluorescence emitting yellow light. So maybe human always see blue light with a bit of yellow, never see true blue, or even any true color if other cell fluorescing other light too

@Thaina To specifically address the idea of using electricity to make photoreceptors "work in reverse" and emit light, we have to look at how photoreceptors work. A photon hits a chemical in the receptor, causing it to absorb energy and change shape. This triggers a series of proteins to signal each other and eventually close sodium and calcium channels. With those channels closed, the cell becomes hyperpolarized. While this is described in terms of electric charge, it is not the same as semiconductors like LEDs.

I think is an simple yet very applicable answer here.
Anything with a temperature will emit photons via thermal radiation.

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text:
If evolution is not about increased complexity, why does so much complexity evolve?
In my last question I asked why we don't see increased complexity in artificial life simulations of evolution. It seems I had fallen for a common misconception, that evolution was about improvement by increasing complexity. One comment discussing that post read

"... he [David Deutsch] is falling for one of the biggest
  misconceptions about evolution that you can, that evolution is about
  improvement. Evolution has simply only ever been about change..."

However, when you look at the history of life you see increases in complexity. You see this increasing complexity evolving over billions of years, suggesting that it requires an explanation.
My question
If evolution is not about increasing complexity then how does so much complexity evolve?

The word improve can mean many things. There are many ways to "improve" - developing intelligence, flying, surviving extreme temperatures, good eyesight, good sense of smell, fast running, echolocation, etc. We just happened to develop some of those. I think it's better to call it complexity than improvement.

Comments are not for extended discussion; this conversation has been moved to chat.

@KyleStrand The question had just too many long comments which nobody would care to read. Moreover, as I can discern, comments and answers are getting repetitive. The usual SE practice is that when the OP addresses the point raised in the comment, you delete the comment. We are not hostile towards this question; unfortunately this topic is such that everyone wants to give their opinion whether or not they have a proper understanding of the subject. Moreover when a question hits HNQ, you have to be really careful. If we were really hostile then we would have closed the question.

You need to clarify what you mean with 'improvement', as the answers show. This is a vague term, and it is unclear if you are referring to improvement as in higher fitness, higher complexity (which is in itself also rather vague) or something else.

Change happens for the "benefit" or "detriment" of an individual. If it is detrimental, that individual stands less of a chance of contributing to the species' "gene-pool". Therefore beneficial changes are overrepresented in "gene-pools". Am I missing something here?

Not all changes are beneficial or detrimental @James - selection is often very weak, few mutations have large fitness effects.

@rg255 I see your point. A better way of talking about this change would be avoiding "molecular changes". Or rather saying that many molecular changes that contribute to net beneficial or detrimental changes in characteristics over large timescales.

I think possibly the problem here is the way you're approaching the issue.
You're considering improvement as anything that increases the abilities or complexity of the organism—that isn't necessarily what an improvement is though. The outcome of natural selection is that the organism best equipped to survive/reproduce in a certain environment is the most successful. So, for example, thermophillic archaea do much better in 60°C-plus pools of water than humans do. Our capacity to process information, use tools, etc. doesn't actually confer much advantage in that situation. And there can be downsides to that kind of complexity as well, requiring more energy and longer developmental periods. So, natural selection in 60°C-plus pools of water gives you archaea, and in (presumably) the plains of East Africa, it gives you humans.
The comment you quote mentions sickle-cell anaemia, which is a different example. While there is little benefit to having the sickle-cell anaemia allele in a temperate region, in those regions where malaria is endemic, heterozygosity can provide a survival advantage, and so the allele is maintained in the population. If you're someone living in a malaria-endemic region, and you don't have access to antimalarials, heterozygosity for the sickle-cell anaemia allele is arguably an improvement. It depends entirely on how you define the word.
The fundamental principal of natural selection is that it favours the organism most suited to a particular environment. But, that isn't always the most complex organism. It's important not to confuse human-like with better. It isn't the universal endpoint of evolution to produce an organism similar to us, just the organism most suited to the environment in question.
Also, to briefly address the previous question you asked—you asserted that we must be missing something from the process of evolution because we were unable to simulate it. You also pointed out that (in your opinion) we have sufficient computing power to simulate the kinds of organisms you're referring to. But natural selection is intrinsically linked to the environment it occurs in, so the simulation wouldn't just have to accurately simulate the biological processes of the organism, but also all of the external pressures the organism faces. I'd imagine that, in simulating evolution, that would be the real obstacle.

Comments are not for extended discussion; this conversation has been moved to chat.

Evolution is simply a process of change.  It is a change in trait values of populations over time. It results from four mechanisms: mutation, migration, drift, and selection. The first three lead to random change from one generation to the next, which may increase or decrease fitness, while selection will generally lead to adaptation (relatively increased fitness in subsequent generations).

"Evolution means change, change in the form and behaviour of organisms
  between generations. ... When members of a population breed and
  produce the next generation we can imagine a lineage of populations,
  made up of a series of populations through time. Each population is
  ancestral to the descendant population in the next generation: a
  lineage is an ancestor-descendent series of populations. Evolution is
  then change between generations within a population lineage." - Ridley,
  Evolution, Page 4.

This is what Darwin termed "descent with modification". Later in Ridley's book he goes on to say something which is important to for evolutionary biology; why is there so much adaptation?

".. not every detail of an organism's form and behaviour is
  necessarily adaptive. Adaptations are, however, so common that they
  have to be explained. Darwin regarded adaptation as the key problem
  that any theory of evolution had to solve. In Darwin's theory - as in
  modern evolutionary biology - the problem is solved by natural
  selection." - Ridley

Another good clue as to what evolution really is comes from the Charlesworth & Charlesworth book:

"Evolution means cumulative change over time in the characteristics of
  a population of living organisms. ... All evolutionary changes require
  initially rare genetic variants to spread among the members of a
  population, rising to high frequency..." Charlesworth & Charlesworth,
  Elements of Evolutionary Genetics, page XXV

Basically the random mechanisms of evolution (mutation, migration, drift) are not as good at making rare beneficial alleles spread through a population as selection is. Selection is the major mechanism that should, as a general rule, fix beneficial alleles in a population. Drift, mutation, and migration will rarely cause the beneficial (adaptive) alleles to fix. Furthermore, mutation will generally have deleterious (maladaptive) effects according to Fisher's geometric model of adaptation.
You can read more about the process of adaptation and why selection doesn't guarantee adaptive evolution in my answer here. Briefly, selection will lead to adaptation if there is sufficient genetic variance in fitness, selection is a constant from one generation to the next, and genetic correlations do not impede the response to selection. Furthermore, the other evolutionary mechanisms can counteract selection, preventing adaptation. These are some of the reasons that simulating evolution accurately is so difficult.
The reason that we can't say complexity increases by evolution is that none of these mechanisms give a consistent increase in complexity. While mutation, migration, and drift will have random effects on organismal complexity, fitness (thus selection) may have some relation to complexity. To evolve, some degree of complexity is required such that the minimum conditions for evolution can be met. However, selection should favour the most fit genes over time, which depends on the niche/adaptive landscape and genetic variation available. Selection in the real world (as opposed to alife* world) would, as an approximate rule of thumb, favour an intermediate level of complexity where fitness is optimised (individuals are good at producing offspring in their niche) with minimal wasteful complexity (complex structures that do not increase fitness).
In summary, to answer your question, we see so much improvement because of selection, which leads to the process of adaptation, but adaptation does not equate to increasing complexity. The key to understanding your problem is an understanding of the difference between the process of evolution (change) and the process of adaptation (improvement), and the difference between optimality and complexity. In the world of alife simulation complexity $\equiv$ adaptedness, in the real word complexity $\neq$ adaptedness.

Good reading can be found in a link that AMR posted in a comment to another answer.

* Artificial life (alife) simulations of evolution generally use complexity as a proxy for fitness such that selection will be directional for increased complexity

Just as a response to a comment you made under your question, as to why simulations don't produce "stylized facts found in real evolution": Scientists understand quite well how evolution works (as explained in my answer, is a result of selection, genetic (co)variation, and population demographics), however, simulation to produce "stylized facts found in real evolution" would require a complete and precise history of the selection, genetic (co)variation, and population demographics that have existed since the origins of life. That is why simulation does not work like you think it should.

Concerning your edit: No, no, I am not talking about an exact reproduction of how evolution worked out on earth but about stylized facts, that is e.g. emergence of real innovations. And it is not about me but the whole Alife community - please see also the links given in the new edits here: http://biology.stackexchange.com/questions/42033/what-are-we-missing-about-the-real-workings-of-the-evolutionary-process

"I must explain why I have a general feeling of unease when contemplating complex systems dynamics. Its devotees are practicing fact-free science. A fact for them is, at best, the output of a computer simulation: it is rarely a fact about the world... My difficulty, then, is that I do not know what observations complex systems dynamics is trying to explain. It is a theory looking for a question to answer." - John Maynard Smith.

Could you explain what you mean by "stylized facts found in real evolution" if you don't mean similarity between simulated and real evolution? @vonjd

@AMR: something for you to read: appeal to authority fallacy: https://en.wikipedia.org/wiki/Argument_from_authority

"stylized facts" is a term mainly used in QuantFinance and I'll come back to you to clarify what I mean by that. I have to run now to deliver a lecture... till then...

@AMR... by the way, the quote continues: "the only time a fact was mentioned was when I mentioned it, and that was considered to be in rather bad taste." - so he was the smartest guy in the room surrounded by idiots... by his own account... why am I not impressed?

@vonjd John Maynard Smith was often the smartest guy in the room...

I am now reading through your answer again. So you are saying the evolution and adaption are two separate things and adaption is the process by which improvement comes into the world?

@Vonjd In a sense... Separate, but not separate... Adaptation is kind of a "sub-process" of evolution... It's important to the question - you want to know why evolution is not about improvement - therefore you need to know what evolution is about (change). Its much the same way that all humans are mammals, but not all mammals are humans. Adaptation is an evolutionary process but evolution is not only adaptive. It is because of selection that we get adaptation on such a grand scale.

Ok, so that would mean that we are missing something in this sub-process of adaption. That there are some details that we have not understood well enough yet to be able to simulate them.

No, you could simulate adaptation icluding the effecs of mutation, migration, drift, and genetic covariance - we understand the effects they have on adaptation very well - you'd just get something that is not realistic because those processes are complex, variable (spatially and temporally) and, most importantly, quite random

It might help to not think about evolution as a process at all - it tends to imply some sort of planning or goals or something like that. That's not what evolution is - evolution is simply a fact. When we talk about "the evolution of humans", we're describing the history of various human precursors. Evolution is basically a historical record of things that worked in the past in a given environment.
Most people tend to antropomorphize evolution, give it goals. There's no such thing, and it just makes you even more confused. There's nothing paradoxical about "evolving to extinction" - evolution is not a path from a base organism to an improved organism. It's simply a history of the changes that survived and thrived in a population. Sometimes that's because those changes gave the individuals and populations a better chance of surviving in their environment, so those traits became more and more prevalent in a population - for example, skin turning to hardened skin, turning to armour plates or weapons, or a better beak allowing it to reach into a food source that isn't available to others. Sometimes, it's simply dumb luck - don't forget that there was a point where the whole (pre-)human population was reduced to a ridiculously low number (I think it was something like 10 000 individuals or maybe even less). It would only take one local catastrophe to kill off the whole human species, no matter how "improved" and "advanced" we might consider ourselves to be.
Another rather brutal example would be the evolution of photosynthesis - when the atmosphere started filling up with free oxygen, it killed off almost all life on the Earth. Sounds like an improvement? Getting rid of your competiton? Well, it also fueled a massive growth of new species that were not only adapted to an oxygen atmosphere, they used it as a source of energy! Not only would they thrive on the "waste products" of the photosynthesers, they even consumed them.
Even if you wanted to describe evolution as a process that improves fitness, you must not forget that a change that improves your reproduction rates in one kind of environment can hinder (or kill) you in another.
When pre-Koala bears drifted to be exclusive Eucalyptus-vores, it gave them an advantage - they had a food source noöne else can use. But it also made them 100% dependent on Eucalyptus. When Eucalyptus dies, they will as well. Something that was arguably an improvement can easily be the thing that kills of your entire species. It only "improved" their ability to survive and thrive in one specific environment - it also entirely locked them in their niche.
In summary:

Evolution doesn't have goals, so it's weird to say "evolution is about improvement". Random changes have a tiny chance of becoming (locally) useful traits, and useful traits have a tiny chance of becoming entrenched in the population, and thus forming a new species over time. It's a history of changes, not a prediction of the future. The great thing about Darwin's Theory of Evolution is that it predicts what kinds of changes are possible (and which are impossible!) - for example, that complex systems can't arise out of the blue, or that different branches of history ("evolutionary tree") cannot exchange traits.
Almost all changes also have their drawbacks - it's a balancing act. There's some great examples of changes that are almost universally good - sexual reproduction and human-level intelligence are a great example of something that works in almost any environment. But even so, there's still examples of where they didn't "win" yet. There's still asexual reproduction on Earth, and most of Earthly life doesn't have human-level intelligence yet. Lions do not rule the world, even though they're apex predators in some environments.

Thank you - are there also instances where species "devolved" into less and less complex ones over long periods of time? Because it seems that they normally evolve into ever and ever more complex ones or they stay the stable or they die out (like in big consulting firms: "Up or out" ;-)

@vonjd It happens all the time. The Koalas are one example. Cave fish evolved from normal fish, but they lost their pigmentation and sight - "became less complex". Species change their diets all the time (e.g. omnivore -> herbivore -> carnivore -> ...). That's why "devolve" doesn't make any sense - there's simply changes. Are there examples of a multi-cellular organism becoming able to work as a single-cell organism again? In modern history, probably not (though you might count "colony" cells). What do you really mean by "complexity"? Are rats more complex than fish?

@vonjd If you look at genetics, for example, humans are one example - on of our chromosomes is damaged, and we "lost" others. All Mammals have two-colour vision by default, because some common mamallian precursor lost two colours - once you go far away, tetrachromancy was the standard. Some mammals got one colour "back" (including humans). We no longer have a tail, and our nails are almost nonexistent. Our intestines got comparatively shorter over time. Does this mean we're "less complex" than primates or other mammals? Is a plant with 100 chromosomes more complex?

I gave an example of complexity in my question: "e.g. in the capability to process information and the sophistication of the internal representation of the world." - Are there examples where species got "dumber and dumber" over long periods of time? I am not talking about skin colour more or less dark.

@vonjd I just gave you one - cave fish (and other cave dwellers) lost sight. The thing is, "in the capability to process information and the sophistication of the internal representation of the world" is a gigantic topic we can't really quantify well. If we could, we'd already have (or be on the road to) "real" artificial intelligence - we don't know what makes intelligence "tick", even, say, dog-level intelligence. We know tiny subsets (mostly the kinds where there's a direct A -> B relationship, like reflex movement).

@vonjd That said, complexity generally tends to increase. It's hard to keep complexity low, not to increase - something every programmer can tell you from experience. Hell, an accountant can tell you the same thing! Keeping things simple while still having them work is a huge challenge. It's actually far more impressive that evolution can simplify things - complexity is the natural thing. And if you look at human DNA, a huge portion of it is just pure garbage, like remnants of old, mis-transcribed viruses and such.

"It's hard to keep complexity low" - and yet it happens in Alife simulations all the time...there it seems to be impossible to increase complexity over a certain level! Why?

@vonjd Because most of evolution simulations have very big fitness penalties for code size. This is mostly due to two things - one, the larger the code and complexity, the slower the simulation, and two, you usually use evolutionary algorithms to try to find a cheap solution to a problem. If you remove the complexity penalty, complexity is going to explode all over the place. In nature, there are still costs to complexity (it takes energy, resources, ...), so it isn't unbounded either, but it's a lot less strict, since it's usually relatively small compared to e.g. the whole body.

So you are saying that if we were to allow for larger code sizes in those simulations (by reducing the associated costs) we would see more improvements (in the sense of e.g. higher intelligence) in those creatures?

@vonjd No, just a higher complexity. The thing is, to get intelligence, you also need some pruning mechanism that gets rid of the things that don't work. Now, evolution is such a mechanism - but that's where it gets absurdly computationally expensive. If we want to have a chance of "evolving intelligence" artificially, we must guide the evolution far more than the natural evolution does. It's a problem of big numbers, basically - you're trying to simulate something that took billions of years running on the whole planet in a few days on a single computer. That's not easy.

@vonjd So the real answer is more like "Yes, eventually. But don't hold your breath - if you're lucky, it might happen in a few billion years. Or a few hundred billion. Or the universe might end before it happens." Your computer is going to die long before you get anything useful.

Ok, so you are saying the biggest obstacle is still computing power (and storage size)?

@vonjd I'm not saying waiting for more computing power and storage size is a good strategy. We need to figure out how intelligence works and find a way to reproduce it - I don't think we necessarily need more computing power and storage than we already have to run an AI; it just isn't enough to semi-randomly create it. But yes, if you had unbounded storage space and computing power, you'd get to intelligence eventually. The universe is fundamentally very simple, and it produced intelligence anyway - it just took a huge amount of work, and lot of time.

Why have the answers either side of this been tagged for improvement by adding references, and this not? It makes similar sorts of claims without offering citations.

I'm going to chime in here. As both a scientist and a software engineer.
Firstly, evolution is not about improvement at all. It is about survival and random change. There are as many if not more mutations that are disadvantageous. But they tend not to survive.
On the other hand, genetic algorithms are an attempt to use a similar process of mutation and survival of the fittest.
But the first step in a genetic algorithm is to define a fitness function. This function will cull the weakest algorithms, just like an environment kills life in the real world.
A good primer on Genetic Algo can be found on https://www.youtube.com/watch?v=qv6UVOQ0F44
However that fitness function will only optimise for certain goals. For example, a badly tuned fitness function will end life on earth either by the paperclip apocalypse, or giving rise to skynet.
In these cases the algo is not improving towards the goals you want. But never the less it improves.
Another complexity is that, genetics is a very greedy optimisation strategy. Mutations tend to be small, because large mutations tend to more often move away from optimal solutions. This means that evolution can only find local maximas and will often miss the global maxima.
Hence improvements can only occur when there is a small tunneling cost to the new maxima.
An example of this can be found in mammalian eyes. Our optic nerve passes through retina and connects to the front of our retina, and physically blocking the retina from doing an optimal job. If evolution were able to find a global maxima, then mammals would have been able to evolve to have squid like eyes, which route from behind.
Moreover, had evolution been about pure improvement, then we should have evolved away our blind spot many many generations ago.
However, human ancestors have rarely been attacked by circles and crosses that are precisely spaced apart in the African continent.
Saying that evolution is about improvements is like setting up a school where there is no teaching, and every year you expell the bottom 10% of the students.

If your answer contains no scientific claims/statements how can it possibly serve as an answer on a biology Q&A site?

@rg255 Logic. The first half of the post merely reiterates definitions. The later half saree the consequences. I'd admit that I have no basis for the statement of no quantum leap mutations a la x men.

You cannot compare GA with actual process of evolution because in reality the fitness function keeps changing. In reality fitness cannot be assessed unless selection happens.

Sorry, You are saying that inter-species competition does not cause for simpler organisms to become prey and be vulnerable to extinction. Evolution caused complex functions of motility and defense to be added to every species on earth, even plants all have biochemical and physical defense, and simpler organisms that exist in extreme environments use the environment as a defense, without which their simplicity would cause them to be decimated.

@comprehensible Correlation is not causation. Complexity is only one of many solutions to the ever changing problem.

Evolution produces branching trees, and branching is multiplicity.
Fitness is associated with complexity, and, with radiation into more or less challenging environments. Fitness increases with versatility and added functions, in equilibrium within one niche, and for change across niches. Are cold blooded animals simpler than warm blooded ones? the consensus is that they are simpler, less apt and globally superseded. Even if a lizard has as many genes as a human (40,000) it is less complex than a human.
Take Motility for example. Locomotion is complex, compared to passive or sedentary displacement. Most prokariotes have evolved some kind of motility, cilia and flagella. The simpler procariotes were eaten out of existence, as the motile ones grew to dominate and pervade. There is a predator prey issue which has resulted in the extinction of simpler, slower species and in the promotion of more complex ones. The ones that survived did so by adding defense functions.
Life is thought to have started in simpler environments with less biochemical and physical fluctuations than it later evolved into. Eukaryotes have not evolved back into prokaryotes, even though they could, and eukaryotes have more scope for complexity, same as lego blocks in multiple numbers are not as simple as single blocks.
Evolution is also about the blind use of an initially small but potentially much larger memory bank of many gigabytes."Gene Duplication is believed to play a major role in Evolution."
Unless life began in greater quantity than it now exists, evolution requires that natural processes have, over time, increased the total quantity of genetic material (DNA) present on our planet.
I'm going out on a pirate's plank of logic here. sorry about that.
Survival fitness is about increased complexity when the environment is increasingly complex. Evolution Causes complexity to occur...
Change is an additive process, and the more change is provoked, the more added functions tend to result along.
The more complex the path has been to arrive at the current stage of the species, the additive complexity rises. (Also the DNA keeps on record the genes from old environments to not lose many precious years spent finding useful genes/biochemistries, while adding new ones). However evolution can be about the conquest of less complex environments:
Put a fish into a cave with no lights, constant temperature, and simple tasks, it will lose some of it's complex genes and may, over time become genetically simpler, than a fish living in a river. It requires less senses, less thermal adaptations, less locomotion pressure and less species competition. It is rare for species to retrograde generally, they tend to extend their range, but in deep sea and caves, locomotive and biochemical retrograde can happen.
Increased size gives increased fitness in most settings: larger metabolic reserves, less sensitivity to change, bigger predator pray advantage... and bigger size means more cells, different locomotion pressure, which means different metabilic resource distribution(lymph, digestion, blood), and That is that's the essence of your complex topic: Do environments encourage complexity? if so, how "complex" are environments in the universes land/sea biomes? Geology, climate and hydrology are of incredible complexity... So... can we say evolution is not about the conquest of new environments? It requires a good philospher to shed light on this question.
The pressure is more often on increased performance in a complex environment using highly complex foods and locomotions.
Increased complexity is an inevitable ramification of the evolutionary process through time and space, rather than a direct and inevitable requirement of it.
Because species evolve into new niches, The most logical and efficient way to do that, is to keep the genes for old niches, in the DNA library, and to add new ones next to it. If the organism did not keep genes from old niches, and use them for a proportion of it's mutations, it would be less apt. Useful genes are costly, they can cost millions of years to find them, for example The more of a toolbox of biochemistry and morphology.
For Biochemistry, life "discovers" new materials and proteins, and puts them to use, and it keeps a record of those materials after they are not needed.
A sea slug can evolve into a vertebrate fish, but a fish can't evolve back to a slug, because complexity enhances fitness, so perhaps we can say that fitness and complexity are not disassociable.
Change is complex thing, and evolution is about changing, so for me, evolution adds complexity every time it changes.

You need some citations here, especially because your answer is somewhat at odds with existing answers. It's also quite difficult to read.

Except life very often evolves to be simpler, vestigial and lost structures are a great example. complexity can enhance fitness it can also reduce fitness.

The simplest way to look at it is there is a near infinite number of ways to be more complex but a very limited number of ways to be simpler. There is even fewer ways of being simple. So even with just pure random variation, over time all things being equal you will end up with more complex organisms.
This becomes even more true when you take competitor into account, competition, go too simple and you lose the ability to do things that you really need to compete with the rest of life around you, go too simple and you can't reproduce fast enough to keep up with all the things eating you. While on the other hand the cost of complexity can be offset by better capabilities. On top of this imagine a ruler with as simple as life can be and still function on one side and as complex as life can get and still function on the other. the first life is going to be pretty close to the simplest possible life, so most of the ways of being alive that are possible are going to be more complex, so again even if you ignore selective pressures either way, just random variation is going to create more complex life than simple life. There is more complex phase space to occupy than simple phase space.
imagine I stand with my back to a cliff and throw a ball randomly in the air, now after I throw a thousand balls the vast number of balls I find are going to be in front of me, not becasue I am actively trying to throw them there but because most of the balls that fall behind be get lost, (go extinct for our analogy)

"The simplest way to look at it is there is a near infinite number of ways to be more complex but a very limited number of ways to be simpler. " I think this argument is flawed: it would mean that in artificial life simulations you would see the same tendency towards enhanced complexitiy... which is false. The agents in those simulations tend to stay on the same level of complexity.

@vonjd I will admit simulating life is not my forte, but simulations of evolution do often show an increase in complexity, that is not to say only more complex life evolves but that on average there are more complex entities than your starting condition. Of course the actual history of life also shows this.  I would argue not seeing increasing complexity in a simulation is evidence of the limitations of the simulation since it is not matching the real world system it is trying to simulate.


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How to choose secondary Antibody?
when isotype of the Primary antibody is IgG2a (from Mouse) and IgG2b (from Rat), how to choose secondary Antibody against these Primary isotypes?
Does secondary Antibody is that much specific to detect IgG2a/2b? What is the logic behind choosing secondary antibodies? A detailed note in this regard will highly be appreciated.

Secondary antibodies go against species, so you would use something like goat anti-mouse. If you are using rat to label a different protein along with mouse, then you will need to use an anti-rat antibody with a different marker than the anti-mouse so that you can actually make the distinction.

You are not telling, what you do, but I think itis western blot - and for this application it is not important to know which isotype your antibody has. The secondary antibody (which is supposed to bind the primary) has to be directed against the isotype (IgG for example) and the species in which the primary antibody was made, in your case this would be this would be anti-mouse or anti-rat IgG. Since most commercially antibodies in the lab are IgG people usually don't think about it.
The reason is that the secondary antibody is directed against the Fc part of the primary antibody (which has a species specific sequence) and even with the differences in the sub-classes due to different assembly of the light and heavy chains this will not be detected by the secondaries. See the figure (from here) about the schematic differences:

With a careful selection of the peptides used for immunisation it is possible to generate secondary antibodies which can be used to distinguish between sub-classes.

Part of my job is developing ELISAs, and I use isotype- and subtype-specific secondaries all the time.

@MattDMo Adapted the answer. Maybe you want to write a more specific answer?


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Determine which seed will germinate first
I recently studied about plant physiology for a test. Strangely, there was this question which I had no idea how to approach.
The question is,
Which seed will germinate first?
a) Castor
b) Wheat
c) Mung( Vigna radiata )
The answer is (c). I want to know how do we determine if which seed will germinate first? Which factors does it depend upon?

Mung germinates over night, doesn't it ? Think of sprouts..

It does. But that wouldn't be the correct way to answer. Right? I mean, It'll be pure hunch and no scientific reason.

Yes that's true. But maybe the question is just targetting that general knowledge. Anyway, it isn't a very good question. Typical of exams that just test your cramming capacity :(

It was asked in a model test for a medical institution ug admission. There was a separate gk section. This question was in the bio section :P

Catcha ! AIPMT/AIIMS treachery ;)

Ah! You know!!!

Well, I am giving them this year. Best of luck to you too ! Hope we survive the craziest exams on the planet :P

The odds.  Such low is the probability.  Yeah,  hope we do survive it! Hope to see you in aiims Delhi xD

You'd have to know the individual germination times for each family.  I'm linking a table for vegetables, but it doesn't include the three plants you're looking at.  A google search for each one individually shows that yes Mung does have the shortest germination time.  It's a memorization question.
To add to that here is a paper where they review variation times between over 600 seeds and find that the variation in time is mainly constrained within families.

I'm wondering if there's a more general answer, like if it's a general property of that subfamily of Fabaceae to have fast germination times, so they wouldn't expect students to know the germination times for those three exact plants but rather general patterns of germination across grasses, Euphorbs, and legumes

@CactusWoman excellent point, I found a paper that shows you are correct and edited appropriately.


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Chromosomomal walking
I'm having a great deal of trouble understanding chromosomal walking, especially in regards to this article:
Control of male sexual behavior and sexual orientation in Drosophila by the fruitless gene.
I can get the fact that you can use recombination frequency analysis with chromosomal markers to assess the relative position of a gene, but I'm just stumped as to what it exactly it means when they say that they cloned the gene using DNA segments flanking the two insertions (fru3 and fru4) as a basis for the chromosomal walk.

How to perform a chromosomal walk
I believe that the first publication to describe this approach was Bender et al., Science, 1983.  In that paper they explain how they "walked" along the chromosome to reach the Bithorax Complex (BX-C).
This approach requires two reagents:

a cloned DNA fragment that you have mapped to a defined genetic interval, and possibly to a cytogenetic interval as well (this is only possible if the organism in question has cytogenetic tools).
a genomic library of larger cloned DNA fragments.

Step 1. Use the DNA fragment to screen the library and isolate genomic clones.
Step 2. Purify each genomic clone and prepare enough DNA to construct a restriction map of the foreign insert (N.B., Bender et al., actually used heteroduplex mapping--an EM-based technique--as well) in each clone.
Step 3. Isolate a suitable DNA fragment, devoid of repetitive DNA, from one end of the clone that extends the farthest along the genome in the direction in which you wish to "walk".
Step 4. Congratulations, you have taken one "step." Now use the new DNA fragment as a probe and screen the genomic library again.  Each time you screen the library you will recover all of the genomic clones that hybridize, or overlap, with the fragment you are using as a probe.
Step 5. --> go back to Step 2. above.
Each time you take another "step" you are isolating cloned DNA that is taking you closer to your target (the gene of interest).  For the very first step you will not know which clone is extending in the desired direction (typically) so you need some type of complementary assay to decide which end of the overlapping clones' restriction map is the direction you want to pursue. In other words, you need to align the physical map to the genetic map. In Drosophila one can use  in situ hybridization to polytene chromosome squashes to visualize where the genomic clones are derived from in the genome, and this lets you pick the correct clone at that first step.
Periodically during the walk you will need to test the new genomic clones by in situ to find out when you are getting close to your target (or if you have even walked past your destination).
Ideally you will have some chromosomal rearrangements that alter the physical and genetic maps, and let you unambiguously determine when the chromosomal walk has reached, or entered, the locus.  Alternatively, a strong loss-of-function mutation in your target gene might be a deletion or deficiency that causes a RFLP, and isolating genomic DNA from such a mutant will let you use a Southern Blot to confirm that you have reached the gene.

Thanks for your answer, but can you also explain what it means when they write that they used DNA flanking the fru4 and fru3 insertion alleles as starting points for the walk?
Does it mean that these DNA segments had already been isolated and sequenced by others beforehand?

I have not read the entire methods section of the paper you cited, but presumably further specific details are in there. The authors state that fru4 and fru3 are independent insertions of the P-element transposon. As described by Gerry Rubin (I think it is Bingham et al., Cell, 1982), if you have a cloned DNA fragment that hybridizes to a transposable element, then all you need to do is construct a genomic library from a strain carrying the insert and probe the library to isolate clones containing P-elements. One of those clones contains the fragment(s) used to start the walk.

Okay, a more detailed scan reveals several more germane facts. Figure 1 is your friend. When coupled with the methods section "Molecular Cloning" you have all of the facts at hand. Their task was even simpler than I described because all four of the P-element insertions they describe, fru1, fru2, fru3, and fru4 are loss-of-function alleles of the fruitless gene, so they knew they had knock-outs. They were not walking to they gene, they were walking to recover all of the gene--it is huge (compared to an average fly gene).

Thanks! Now it all starts to make sense. I have one more question though, if you don't mind.
In the "Molecular Cloning" section they say that "The positions of the fru mutants were determined by Southern analysis and in situ hybridization to salivary gland chromosomes in the case of the fru1 inversion and the Df(3R)P14 and Df(3R)ChaM5 deficiency breakpoints." - I don't quite understand how they did this and how they mapped it. Did they make a genomic library of the particular mutant and probe it or something?


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Small mottled beetle identification
I live in Chapel Hill, North Carolina, and recently started seeing a bunch of these little guys in my house. They're about 1/8" long (estimate), with brown/black zigzag stripes; it's possible they're babies, but over the last two weeks their size has remained consistent so this seems unlikely.

Related: What's this black, white and orange beetle?

It's probably an adult varied carpet beetle (Anthrenus verbasci). They are quite small and commonly found in homes. Here's one of a number of images available on Wikimedia:

You'll likely come across more of their larvae than the adults themselves. Their larvae are often found munching on organic matter throughout the home (e.g., bedding, clothing, organic crafts, hair or crumbs in cracks, dead mice in walls, etc.). See the following images showing relative size, actual size, and a zoomed-in view of larvae.

You can contact SE for help fixing this here: http://biology.stackexchange.com/contact


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Why would a python go after such large prey?
I read that most Pythons would go for smaller prey, around the size of a house cat, but that larger species enjoy crocodiles, deer and even a bit of antelope for dinner. But why?
Now, I realise that evolution is ongoing, and that even in the year infinity evolution can't be expected to result in any subjective measure of "perfection". But, even so, it seems odd to me that this behaviour could have survived the eons.
Perhaps I'm misunderstanding the animal's physiology and it's more mobile in this state than I expect, but isn't this animal utterly defenceless for the next couple of weeks while it digests its tasty snack? Couldn't something else now come along, rip open the snake, steal its crocodile then leave the snake for dead, all without encountering much in the way of effective resistance?
One possible answer I thought of was that other animals may not much like the taste of half-digested crocodile. Conceivably, one could stalk the snake waiting for it to start swallowing the croc, then grab it while the food is still relatively fresh. But I doubt any animal that's not already substantially stronger than the snake would feel too comfortable stalking it in the first place. Is that enough to explain why Pythons have gotten into the habit of apparently making themselves sitting ducks for protracted periods of time rather than simply eating less more often?

I dunno, maybe it is a behaviour liable to be selected out, but it just hasn't happened yet.

Also I may have overestimated the digestion period ("Witnesses said it took this snake about five hours to swallow the crocodile. During that time the snake is vulnerable to attack from a dingo.") based on something I read elsewhere. Anyway, looking forward to your expert thoughtz.

@DonJulian: Not really the sort of deep thought I'm looking for in an answer...