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CRISPR guide RNA design and primer synthesis
I am trying to knockout huntingtin gene from HeLa cells (human epithelial cells). I used CRISPR explorer and benchling.com to determine the best guide RNA sequence that would bind to the target DNA sequence and cas9 could then make the cut. The gRNA I have is as follows:
GGAGGCCTCGGGCCGACTCG
Strand (-)
The next step for me is to design the primers with the following sticky ends
Forward primer: 5'-TTGTTGN(19)-3'
Backward primer: 3'-ACN(19)AAA-5'
The problem I'm having is writing the oligonucleotide sequence of the primers. Since the gRNA strand is negative, I'm assuming it's a 3'-5' sequence, hence the reverse of it will be the 5'-3' sequence, which I can then just use as the forward primer. Sequentially, I can take the reverse and complement of this forward primer sequence to then determine the backward primer. Is that correct? I appreciate all your help.
Thank you!

Is GGAGGCCTCGGGCCGACTCG the output of the gRNA finding software? If so this sequence is most likely 5'→3'. Can you share the output file of the software. If it is an online tool then can you provide the link?

As CRISPR is quite a popular topic, it would be nice to have some basic explanatories for newbies, like:  Backward primer is the term equivalent to reverse primer   in pcr? More blunty: why do you need a backward primer in CRISPR, at all?

A quick check with BLAST returned the following result:
Homo sapiens huntingtin (HTT), mRNA
Sequence ID: NM_002111.7Length: 13669Number of Matches: 1
Related Information
Gene-associated gene details
GEO Profiles-microarray expression data
PubChem BioAssay-bioactivity screening
Range 1: 267 to 286GenBankGraphicsNext MatchPrevious Match
Alignment statistics for match #1

Score           Expect  Identities  Gaps        Strand
40.1 bits(20)   0.017   20/20(100%) 0/20(0%)    Plus/Minus
Query  1    GGAGGCCTCGGGCCGACTCG  20
            ||||||||||||||||||||
Sbjct  286  GGAGGCCTCGGGCCGACTCG  267

The sequence you have is 5'-3' and it matches the Minus strand on the human genome. You don't need to reverse the sequence!
Here is a screenshot from IGV showing the gRNA sequence matching the minus strand of the HTT gene.

For those unknowing it would be nice to have some more information. The question is right in assuming the sequence is negative according to your answer (how can you tell by that sheet?).  The question then says "hence the reverse of it will be the 5'-3' sequence, which I can then just use as the forward primer." That conclusion, implication is not correct. That's what you mean? For the forward primer, what you need is the 5'-3' sequence? "You must not" reverse?

@PeterBernhard the gRNA sequence is written in 5'->3'. There is no need to reverse it. If you see the BLAST output, the Query sequence matches perfectly a minus-strand. Note that the minus strand coordinates are from 286 to 267, indicating that the Sbjct sequence is already reversed in the output. The gRNA sequence is already in the 5'-3' order, so there is no need to reverse it. I have added an IGV screenshot showing the gRNA sequence matching on the genome, I hope that will help clarify.


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What are the appendeges behind a crane fly's wings?
I noticed that crane flies have strange appendages behind their wings. The appendages look like a pair of antenna or a pair of vestigial wings. In the following picture I marked these appendages with arrows.

The specimen in the picture sat on my window this morning. I looked through the genera on Wikipedia, that is, on the images if there were any. All crane fly species seem to have these appendages.
While the appendages are clearly visible in most images I couldn't find a description of them. I've never seen anything like them and would like to know:

What's the name of these appendages?
What are they good for? Are they vestigial wings, antenna, or some sort of counter weight to stabilize the flight?

They are the halteres. These structures help the flies to perceive body rotation and maintain balance.

source: Stack Exchange

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What are the microscopic mechanisms of plant branching?
For a long time I've been idly interested in how the shape of a complex organism gets determined during development at a microscopic level. Recently I've realized that plants could be a good place to start trying to understand that, because the structure of even a quite complex-looking plant can sometimes be described by a few simple behaviors. Here are some examples.

My radish plants have a single base where all foliage grows from. After it's first two seed leaves, it grows some number of large rough leaves which all descend to the same point. No other branching ever takes place.
My pea plants have only one long stalk, which grows symmetrical pairs of leaves at regular intervals. Occasionally, the base of a leaf pair spawns a mini-stalk, never more than 3 or 4 leaf-pairs long, ending in a "claw" of three tendrils. Most of the time, one of the three tendrils will split again in three (always the one aligned with the "top" of the stem, as determined by leaf orientation)  .
My fennel plant has thick bases to its stems which each extend a few inches before branching starts. At that point, it branches at regular intervals in symmetric pairs of fronds, which in branch the same way. Those third-layer fronds branch at regular intervals but alternate sides, and there are always exactly four layers of branching no more and no less.
My basil plants seem roughly able to grow branches in any advantageous spot, with no rigid rules or geometric behaviors like the other examples.

My background is in mathematics, and all my knowledge of biology is dated to high school. I've always been confused by how DNA is presented as a "list of proteins", when it is clearly so dynamical and not static: each cell type only produces some of those proteins, and those proteins can in turn determine what proteins it's children produce, I guess, and this can create a rich (non-linear) hierarchy of tissue differentiation out of a linear list.
For a really complex organism like a mammal I guess that our understanding of how DNA gets actualized is phenomenological (i.e. we can tell that gene X correlates with macroscopic trait Y, even if we have no idea what the signalling mechanisms are behind it and which cell types produce a relevant protein and when.)
But is this true also for plants, or do we have a deeper understanding of what proteins differentiate, for instance, the four stalk types of a fennel plant, and why each type can only produce the next one? I think simple animals like sponges and nematodes and cnidaria probably would be as helpful as (or more than) plants, but as a gardener I see these plant geometries every day and am fascinated by them.
Does anyone know of a good reference, book or online, that discusses these topics? I'm okay with something a little out of my technical depth - I'd prefer a difficult answer to an incomplete one.
EDIT: Leaf architecture is also interesting, and probably inseparable from branch architecture on some level, but I focused on stalks and branches probably because I don't have the terminology to meaningfully describe leaf architectures.

It sounds like you are asking for an explanation of how genetic regulation works, which may be too long for the site. for just branching this will help https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1630745/ for gene regulation try this https://ghr.nlm.nih.gov/primer/howgeneswork/geneonoff

Oh this video is a good starting point as well https://www.youtube.com/watch?v=3S3ZOmleAj0 Hint we actually know how many gene lead to macroscopic features. You should also be looking for embryonic development as well.

Thanks a lot John! That video was a really great foundation, but it doesn't go into how/why gene regulation would change in a cell line, which I think is more what I'm interested in. I'm really looking forward to looking at that first paper, and then once I have some context, diving into the second one.

It doesn't change in a cell line, which ones are switched on or off change. There are dozens of reasons this can occur, cells signaling each other, Internal cell pathways,  external environmental factors, ect. https://en.wikipedia.org/wiki/Cell_fate_determination

By the way, this was a refernence request and not a request for a direct answer, so if you want to repost that as an answer it will be accepted!

I may have used the wrong terms, but I think we're saying the same things. In the fennel plant, for instance, cells in a layer-one frond at some point start giving rise to cells that form a branch, which have somewhat different gene expressions, right? Cell line may not have been the right term. (There are also other reasons gene expressions change, like the presence of lactose in the video, but this one narrow mechanism or pile of mechanisms is what I'm most interested in)

I'm not being vague on purpose but there are literally dozens to hundreds of signalling pathways involved in branching across all plants.

And of course even in the branching case, I suppose those other mechanism must be at play, since nutrition and weather surely affect how and where a plant branches, and this paper points out that long-distance signalling does as well. So I'm maybe I'm mistaken to differentiate between them.

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Ammonium sulphate precipitation assay pH dependence
In general does the pH effect the precipitation, e.g. would a pH of 6 cause less precipitation than pH 7.5. Or are they unrelated?

I think it would affect some proteins, but ammonium sulfate precipitation usually requires such large amounts of salt, that you would have to add a lot of base to adjust the pH.
When I have done ammonium sulfate precipitation, its to take a rough cut of proteins out of a whole cell/whole organ lysate - its more like a hammer than a pair of tweezers as far as protein purification is concerned.  I would rather use a charged plastic resin or a sizing column if I were to care about the isoelectric purification.

Thanks. I'm actually trying to stop precipitation when incubating a protein in ammonium acetate, and I wondered if an acidic pH would reduce this. Do you have any suggestions

Do you know the pI of your protein? Proteins are least soluble at pH=pI.

also consider detergent - might the protein be a bit hydrophilic?

@Anake, I would look up http://en.wikipedia.org/wiki/Hofmeister_series

the protein isn't hydrophobic, it is the ammonium's precipitating tendency as in the Hoffmeister series. I HAVE to use an ammonium salt, so the hoffmeister series cannot help me in that respect

So how did the pH change work?  Some proteins are just plain insoluable... is the solution quite dilute?  There are many possible things to try - its hard for us to guess.  maybe you can add a more complete description of your situation in the question...?

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How do ribosomes contribute to their own synthesis?
In other words, what products synthesized by ribosomes are actual parts of ribosomes (if any)?   How are they involved in their own synthesis otherwise? What is the cycle/chain of products starting with the proteins synthesized by ribosomes and actually ending with the synthesis of a ribosome?

The most important parts of the ribosome are not made by other ribosomes - 5 rRNA (ribosomal RNA) of the ribosome actually do most of the direct work of creating the protein and are made by RNA polymerase ( a protein, but not the ribosome).
Then there are 92 ribosomal proteins, which as a rule bind to ribosomal RNA to support their structure and keep everything going.  These are all made by ribosomes.  They are thought to have appeared later in the evolution of the ribosome though I imagine that it would not be possible to constitute a working ribosome without each one of them.
these numbers are for the eukaryotic ribosome, the prokaryotic ribosome has 3 rRNA and 52 ribosomal protein components.
http://en.wikipedia.org/wiki/Ribosome

Any hints to articles describing the RNA-P synthesis? ... or is RNA-P synthesized by ribosomes?

leonardo that was the most amazing result of the first ribosome structures - the chemical assembly of the peptide and codon recognition was done almost entirely by the rRNA.

pointernil - RNA polymerase?  its totally made of protein.  since it uses DNA as a substrate, it is a much more recent development.  the original RNA syntesis was probably done by RNA, but to my knowledge all those genes seem to have been replaced by protein mediated functions (hope i got your question right).

@leonardo that is the prevailing hypothesis.  This just out - another view of the ribosome challenging 'RNA World'.  I'm not a believer here, but its an interesting idea. http://www.sciencedaily.com/releases/2012/03/120312192839.htm


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What kind of technology is required for plant seed DNA printing?
I'm a programmer so I have little knowledge in the field of biology. However I have an interest in plants, GMOs and DNA (in terms of programming).
So, I want to know what kind of technology is necessary and what limitations there are involved in creating a DNA printer (that could print out a cell to be grown in a pot).
First, how do we print the DNA of a plant? Can we print it all from the first pair of chromosomes? I have heard we are already able to print bacteria's DNA. Is this something we would be able to achieve outside of a lab? Could it be done on commercial scale?
Second, what conditions do we need to prepare the DNA if it is not that of a bacteria but rather of a plant that would grow into a multi-cellular organism? Can we really print a plant DNA, inject it into a cell and let it grow?
Third, what is the current understanding of DNA ? Can we analyze DNA strand with digital software yet? Can we simulate how the organism will do with the DNA? And how can I start working in this field? Are there any scientific papers I can learn from?
P.S. My first goal is to make a Nepenthes Vine. I want to have a vine plant like Pothos creeping on my fence with a jugs to eat insects.

We cannot print DNA. We can synthesize relatively long fragments of it, but this is costly and relatively slow. Additionally a cell is much more than its DNA. It is a bunch of proteins, I suggest you start by reading the Wikipedia article on it.

This is far too broad for a single question. You should read up on the basics and then post several more focused questions instead.

Before I have create this post I already seen things about DNA laser printer long times ago. So I want to ask you people how this field progress. Seem like not so much. And, somehow, I have seen most of tech blog calling "DNA Synthesize Machine" as "Printer" in so I think it is standard vocab

The closest we have come to achieving this was probably when the J. Craig Venter institute made a synthetic bacteria. They produced the genome for the smallest simplest bacteria they could find and it still took a large team of researchers several years and a LOT of money. Building a plant would be exponentially more difficult.
What complicates this is that you need more than DNA. You need the proteins that read the DNA and make RNA and more proteins. JCVI not only had to synthesize the DNA for the bacteria they wanted to make, but they had to take cells from another species of bacteria, remove all its DNA, then insert the new DNA. The leftover proteins from the previous species were able to read the new DNA, and make the proteins that built the new species. This only worked because the 2 species used were close enough that the same promoters and regulatory elements on the DNA could interact with the pre-existing protein. If you could make a plant genome and insert it into a bacteria, some plant proteins might be produced, but it would not be orchestrated well enough to actually produce a plant.

As Chris mentioned in his comment, 'printing' DNA from scratch (i.e. synthesizing a long strand de novo) is expensive and difficult. Unfortunately, the process of GMO creation is not as simple as assembling a beautiful DNA sequence on the computer, printing it, and then inserting it into a cell. Here's a related question about de novo sequencing. Plant biologists, please correct me if I'm wrong but I don't think it's a trivial matter to grow a plant from a single cultured cell.
Your third question reminds me of DNA programming, which is an amazingly cool, developing field. Many companies (including Microsoft) are funding research into DNA circuitry and biological computing, making computers out of biomolecules. Here's an interesting paper that will tell you more. What you may be most interested in is the branch of DNA programming that seeks to identify discrete protein-coding and otherwise functional DNA that can be utilized as a sort of "programming language" to build a new organism. One of my past supervisors recently joined this field and I'm a little jealous. Overall, this is a very new field so there is much to be accomplished and very little is technically possible, although the principle of GMOs is really just an optimization of an existing organism.
In summary, the answer to all three of your questions is: No, not very well. But we're trying.
Most likely this isn't something you'll be able to pick up in your spare time in order to have a cool plant to grow on your fence. It's very expensive and very difficult (in some cases, maybe even impossible). However, there are so many unknowns in this area of research and everything you propose is reasonable in theory. Let me know if you figure it out. I would love to code my own garden.


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What is the closest relative to odd-toed ungulate?
I have seen about 3 variation of phylogenetic tree about odd-toed ungulate and I don't know which one is really updated or outdated or which one have more backed up theory

First is odd-toed ungulate are closest to even-toed ungulate

https://en.wikipedia.org/wiki/Odd-toed_ungulate#Outer_taxonomy

Second is odd-toed ungulate are closest to carnivore

http://www.ultimateungulate.com/FamilyTree.html

Third is odd-toed ungulate are closest to Hyrax and Elephant

http://tolweb.org/Eutheria/15997
http://www.newworldencyclopedia.org/entry/Eutheria

Second or Third

https://research.amnh.org/paleontology/perissodactyl/node/55

Could anyone please ordering the actual relativity of each branch in the most reliable theory in present. And also could anyone please explain why there was such a very confusion in evolution of odd-toed ungulate specifically?

It would be great to add the sources of the three phylogenetic hypotheses.

The best would be to see results based on the TOL tree of life file, which is about 80 mb open source updated by researchers relative to science papers... TOL is the resource that is most accurate here, but I am surprised that it is not more recently updated.

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120-year-old gene regulation problem independently solved by a computer. How?
My Background:
I'm a mathematics graduate student with a physics background.  I have a very little biology knowledge and a little knowledge of machine learning and statistics.
Topic:
I recently found out about the following computational biology breakthrough: A computer managed to independently develop an explanatory theory for a 120 year old problem in biology. It is detailed in the popular media here and here and in great detail in this paper.
Questions:
I lack domain expertise but I'm very curious as to how this was possible and why it happened now. For example:

Were these methods novel (from a machine learning perspective), or does the novelty lie in using old tools/techniques and adapting them to a
workable problem?
Mathematically speaking, what kind of models were produced by the computer? Are these standard in this field of genetics/biology? Is this area particularly mathematically tractable?
What about this particular biological problem made it amenable to this machine learning approach?
What other kinds of low hanging fruit (in biology or science in general) could we expect to see tackled with these techniques?

Popscience blogs have a flair for exaggeration. This is not the first application of GA and machine learning has been used to solve a lot of problems in biology. I am sure that those who have written the blogs have not even read the article.

Agreed. However, my first exposure to the topic was through the popsci articles via social media so I figured I'd throw those in to give a picture of how I came to understand the topic.

I find this question to be of low quality. Details are hidden in links, and premise is speculative at best.

The fruit, sadly, does not hang so low.
Short version
Lobo et al (the work you refer to) is a nice and not especially novel application of basic Systems Biology modeling approaches to the wound healing system in flat worms. The main barrier to the wider application of such work is the lack of the necessary experimental data. Lobo et al themselves don't really have enough data to justify their models.
Long version
Lobo et al  can be thought of as consisting of two separate parts: the models themselves, and their genetic algorithm based search of modelspace.
The Models
The models aren't novel. Their basic form is extremely common in Systems Biology work. Take a look at the equations in the methods section of their paper and in their Supporting Information. For example:

This is a very standard way of representing a set of coupled biological "species". Some of those species correspond directly to the concentration of some specific protein (ie βcat), whereas other species are more vaguely constructed (ie head) and presumably correspond to some generalized set of biological elements (eg proteins, DNA, lipids, etc).
The first term in each of the equations is a production term. It represents how the given species is increasing with time. Each of the fractions takes the form of something called a Hill Function. These come up all over the place in biological modeling. The important thing to know about Hill Functions is that they vary either from 0->1 (for activators) or from 1->0 (for inhibitors) as the concentration of the relevant species varies from 0->infinity, with a smooth transition region in between.
The min and max operators are meant to stand in for the Boolean operators AND and OR, respectively. In other words, if two of the Hill Functions are related by a min operator, they both have to be in an ON state in order for the overall term to be in an ON state. If the two Hill Functions were instead related by a max operator, only one of them would need to be in the ON state for the term to be in the ON state overall.
The second term is a degradation term. It represents how the given species is decreasing with time. The particular form of the degradation term (directly proportional to species concentration) derives from the fact that the main sink for generic biomolecules is dilution via cellular growth and division.
The third term (the thing with ∇² in it) is a diffusion term. This describes how the species moves around the in the worm, assuming that it is free to do so (not all proteins/species can exit the cell). Not all of the species have this diffusion term.
The Search of Modelspace
Their modelspace search isn't novel either, although some very specific aspects of it may be.
the basics of modelspace search
The basic problem of modelspace search works like this: imagine a generalized form of your model in which every species interacts with every other species in every possible way. Each of the equations will have a (large) set of free parameters associated with it. You then take a set of experimental observations and-using any one of a number of methods-find a set of parameter values such that your model will reproduce those observations. This is called parameter fitting. In order to find the unique set of parameters that describes the "real" system that you're trying to model, in general you need at least as many independent experimental observations as you have independent parameters in your model. If you have more parameters than you have data, then you will be able to find infinitely many different parameter sets that reproduce your experimental data.
Here's where things start to get complicated, and a bit fuzzy. What separates modelspace search from standard parameter fitting is that you usually employ some version of the Principle of Parsimony in modelspace search. The Principle of Parsimony can be thought of as equivalent to Occam's Razor: the simplest model is usually best. In practice, what this means is that when you don't have enough experimental data to uniquely parameterize your model, you should favor versions of your model with as few species-species interactions as possible.
The Principle of Parsimony is one specific example of a broad category of modelspace constraints known as "prior knowledge". This refers to everything you know in advance about what your final model "should" look like. Prior knowledge tends to be specific to the particular modelsearch at hand, and it can be incorporated into the search algorithms in many different ways.
With enough prior knowledge the modelsearch can be constrained to an extent such that, in theory, it should become possible to identify the one true parameter set, even when you only have a relatively small set of experimental observations. Good luck constructing a formal proof for that, though. In fact, for a complex system it's usually impossible to quantify the extent to which a given piece of prior knowledge reduces the number of experimental observations required to uniquely fit the parameter set.
the modelspace search in Lobo
For the type of model used in Lobo, a rough calculation says there's going to be
$3n^2 + 4n$
independent parameters (where n is the number of species). Further, there's going to be a set of parameters that describes whether each species-species interaction is activating or inhibiting, and another set of parameters that describes the Boolean logic of those interactions.
The "comprehensive" model in Lobo has 14 species, so there are at least
$3\cdot14^2 + 4\cdot14 = 644$
644 free parameters to fit during the modelspace search. Against those 644 parameters they have exactly 16 independent experimental observations.

The paper by Lobo and Levin is an attempt to learn a model that represents the inner workings of a biological system by fitting parameters to data. This is a common topic in "systems biology", a model-based approach to studying biology that is popular in some fields.
Even for small systems, this is a phenomenally hard problem. Unlike most machine learning problems, achieving good accuracy (on independent test sets) is not enough; the model structure itself must be correct, meaning that it should reflect the actual mechanisms operating in the cell or organism. It's like trying to identify the actual structure of an integrated circuit by only observing inputs and outputs --- not only identifying the logical rule that the system exhibits, but the actual wiring diagram that implements that rule. It is of course quite possible that alternative models exist that perfectly captures the behavior, and yet is completely different "on the inside". In other words, the system may not be identifiable from the data --- two different models may produce exactly the same behavior. So there's no way to know beforehand if these problems are even theoretically solvable (let alone practically feasible).
Lobo and Lewin claim to have accomplished this task for a small system of gene products. As far as I can tell, the central piece of evidence is that the model they come up with does capture some mechanisms previously known to exist. Whether this is just plain luck, overfitting, or a special case where (parts of) the system is actually identifiable from data is hard to tell. But it's notable that Lobo and Levin fall back on demonstrating predictive accuracy in the end, which is not enough to support their thesis. The critical test is to take a new prediction from their model and actually test it experimentally, to prove their (strong!) claim that their method can actually infer new mechanisms. Doing so is expensive and time-consuming, and much more difficult to get right than tweaking models to fit mechanisms you already knew beforehand --- which is exactly why I want to see that evidence. If the authors really believe that their method is a breaktrough, those experiments should have been done.
Finally, these type of algorithms are applied to models containing only a handful of components, and typically to systems that have been studied for decades and are very well-characterized to begin with. In most of biology, the problems are orders of magnitude more complex, and often we don't even know what the components are. So I thik there are a limited number of cases where this approach is feasible.

Christian, great idea to ask this question here before taking important decisions.
Are those media articles a hype?
Yes. Over the last 10 years I constantly see those hype stories in media about "revolutionary" large-scale-study/big data projects with mind-blowing numbers (gigabases, teraflops, terabytes, thousands of papers and hundreds of genes).
Journalists are competing for readers with pompous titles as well as scientists need to promote their results. I recall that ~7 years ago Russian national TV came up with nothing less than "Scientists unravel the mystery of Russian soul", when one of our scientific institutions, close to government, finally managed to actually launch (not buy and put on hold as usual) a next-generation sequencing machine and sequence a full genome. Curiously "Russian soul" belongs to half-jewish, half-armenian guy. :)
Though it's nice that in media papers journalists and scientists managed to explicitly articulate the problem we can't solve: how to predict morphology using molecular biology data. This work is not even close to doing that. It essentially predicts a binary trait, "heads or tails".
Is this a new breakthrough approach in science, connecting areas that noone could imagine to connect?
No. This approach is called Systems Biology. It's been getting popular, I'd say, since late 90s-2000s. This paper is not like "look, I've invented a DNA computer/quantum computer" etc. It's an ordinary systems biology study, its quality is a matter of discussion.
Is this a good systems biology paper?
I don't claim that it's bad. But some things sound strange to me:

I don't think that de-novo predicting the network of interactions should be done by systems biology, when some facts are known from literature and could be enriched by Omics. If you're modelling a molbiol/biochem network, too much freedom might render your network totally different from reality.
Robustness test is unconvincing to me. Ok, "We dropped 3 crucial papers" and still network holds. Ok, network predicted some results from papers, not taken into account upon its construction. But this is not a systematic test. Split the data into two parts, optimize the network on one part and test it on another one.
Small number of genes involved in the described process and binary trait in consideration make the authors do overambitious claims, IMHO.

Can a smart and diligent person as yourself make a better career, doing systems biology than something else? Will your higher IQ and/or mathematical background give you an edge?
I think, no. I think, this area downplays the difference in intellect between people, while you might prefer a more discriminating area, even if you allow the assumption that you're not the single brightest person to ever live on Earth. :)
You should have worked out (or have from birth) some mental habits (e.g. habit to consider simple examples with small numbers of elements; use modular thinking; not to get stuck with a single approach - switch tricks; etc. etc.) that less smart people lack. Systems biology often works with too complex objects to imagine, thus your edge might be lost.
Complexity:
In reality cascades of biological pathways include huge numbers of players involved. Moreover often a single protein has several seemingly unrelated functions. I was a bit shocked at first, when aconitase, a Crebbs-cycle protein turned out to be at the same time an iron-responsive element and regulator of mRNA turnover. What a mad engineer could've created this?! Later I heard that many Crebbs-cycle proteins have seemingly unrelated "part-time jobs".
Stability of solution:
Look at any pathway in KEGG. Or this one: a famous cascade of cell signal transfer: . Although, it's not that complex, when you know all those proteins by heart, it might be hard to work with it quantitatively. Solutions might be unstable. Lack of experimental data may lead your simulation in a wrong direction. For instance, famous p53 protein (in this cascade it has 3 arrows or so) in reality has more than a hundred counter-agents, loss of interaction with one of them might lead your simulation to a state, critically different from reality.

Publish-ability:
Another problem for  systems biology papers is lack of comprehensible results. People are usually interested in a paper, when they can build something on top of it or at least when it's an interesting special case to know about. To be honest, I can't make myself read them.
What's the scientific takeaway of this paper for instance? It doesn't exist. I can't use it as a scientist.
Systems biology guys often speak of holism approach as opposed to reductionism. To me it's an empty word - the only comprehensible (and thus publishable) way to approach complex entities for human being is modular thinking - you just replace a complex cascade with a black box with a set of inputs/outputs and rates and treat it as a single object, reducing the complexity that way.
I feel that systems biology approach is better suited for commercial use in industry than for small-lab science. You need tools to mine data, to visualize results etc. Companies can afford building them for themselves.
As for the results, in industry you don't need them to be publishable. In agricultural industry you can optimize producing organism's metabolism with systems biology means to make it output more product of interest e.g. in cows or chemicals-producing organisms (though same goal can often be attained with pure genetic engineering easier). May be pharmacology can be interested in this to optimize their drugs to find best way to modify particular coefficients in particular reactions.
Practical achievements based on fundamental biological science still make use of reductionist works. They stem from little facts that cervical carcinoma is caused by a specific virus and vaccination against it seems (silver bullet warning) to prevent it. Or the fact that CCR5 mutants are seemingly immune to AIDS or similar results for causative agents for stomach cancer or hepatitis. Or useful discoveries of tools, such as siRNAs, Crispr/CAS9 or Tal effectors.

If you still decide to give biology a try, it won't take you too long to grasp the molecular biology. You can mostly work with it without knowledge of biology or chemistry. In a month you'll grasp the basic concepts such as replication-transcription-translation, in a year will be familiar with most key processes, in ~3 years will be a good specialist in some area.


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What is the trigger of pollen tube formation when the pollen is on the stigma?
What is the mechanism of pollen tube formation when the pollen is close to the stigma ?
Is it because of chemotaxis? In other words, is the growth triggered by some chemical substance that is released by the stigma?
We have learnt that on a stigma of a flower, you can sometimes find pollen of other plant species. Would these pollen grains also form tubes in response to a possible signal from the stigma of a different species?
EDIT :

So in this experiment we put pollen grains in 2 bottles which contained all chemical substance required for the formation of pollen tube.
In bottle 2 we observe that all pollen tubes are oriented to the stigma! If it really is some kind of chemotaxis then what is the chemical substance that causes this?

Germination of the pollen tube begins when the pollen grain becomes hydrated on the tip of the stigma. The pollen grain absorbs water and also exchanges signals with the stigma. Microchannels form between the pollen grain and the tip of the stigma papillae and water is transferred from the stigma to the pollen grain (Taylor 1997). cAMP, which is produced by the protein PSiP, and cytosolic free calcium can be used as signals to orient the pollen tube to grow in the proper direction (Moutinho et al 2001).
Pollen tubes can also germinate in vitro even without contacting a stigma. If you place a pollen grain in germination medium, which contains calcium and boron, among other things, the tube will germinate but it will not grow to its full length.
Often when pollen of a different species lands on the stigma, the pollen will germinate but the pollen tube growth will stop before it reaches the ovary (Ascher and Peloquin 1968). Mating barriers are very complicated, however, and there are many different mechanisms for species recognition and arrest of pollen tube growth. In some cases, the tube is halted in the style while in other cases the tube grows, but slowly and inefficiently (Swanson et al. 2004). Species recognition in some species (for example, tobacco) is related to self-incompatibility and the S-locus. Species recognition can also be unilateral: that is, the pollen of species A can fertilize the ovule of species B, but the pollen of species B cannot fertilize the ovule of species A.
I would recommend that Swanson paper if you want to read more.

Thank you so much ! So the chimiotaxis is just for animals ?! Or calcium is a chimiotaxis ?

@user233658 According to wikipedia, chemotaxis is the movement of an organism in response to chemical stimulus. So yes, this is an example of chemotaxis

But here my teacher told me that the experience ( see edit ) is even not true !

@user233658 Among other things cAMP and cytosolic tree calcium act as the chemicals involved in the chemotaxis


text:
How does CO₂ concentration affect photosynthesis?
I have heard the theory that with the increase of CO2 in the air, the speed of the photosynthesis would increase, thereby limiting the increase of CO2 levels.
What is currently the rate limiting factor of photosynthetic reactions in plants? Is it really CO2, or rather the limited energy that a plant can absorb from sunlight?

Do you have a source for that theory?

@MattDMo Unfortunately, no. I think I've heard it back in highschool in connection with the Gaia theorem, as an example of a homeostatic process of Earth (along with the theory of increased oxygen increasing the chances of forest fires,thereby regulating itself ) I wonder if these have scientific basis.

@WYSIWYG - how on earth did you get the subscript in the title?

@AliceD It is UTF. You can either get it from the character map or enable compose key in your keyboard and then use [hold compose] : _ → [number] for subscripts and [hold compose]:  ^ → [number] for superscripts. It doesn't work with alphabets (no alphabet subscripts/superscripts in UTF yet)

The rate-limiting step of photosynthesis is the CO2 assimilating enzyme Rubisco (short for ribulose-1,5-bisphosphate carboxylase/oxygenase) (Jensen, 2000). It uses ribulose-1,5-bisphosphate and CO2 as substrates to generate glucose.
Given that Rubisco is the rate limiting step in photosynthesis, an increase in its substrate CO2 would expectedly lead to an increase in photosynthesis. However, the regulation of Rubisco is complex and is influenced not only by CO2, but also by O2 (which competes with CO2 for the active site), Mg2+ and a regulating enzyme called Rubisco activase (Jensen, 2000). Hence, the effects of an increase in atmospheric CO2 may be more complex than simply enhancing photosynthesis by increasing Rubisco activity.
Indeed, a review by Poorter (1993) showed that a doubling of CO2 lead to an average increase in photosynthesis of only 37% across more than 150 plant species. He describes various factors that determine photosynthesis rates other than CO2:

One factor that limited photosynthesis under high CO2 was nitrogen (N). Since Rubisco is an enzyme, it has to be synthesized from amino acids. As Rubisco constitutes about 30% of the total protein in a plant leaf, Rubisco is probably the most abundant protein on earth and a major sink for plant nitrogen (Jensen, 2000). For example, C3 species capable of symbiosis with N2-fixing organisms had higher growth increases compared to other C3 species under high CO2;
Certain plants utilize CO2 better than others. CAM species were the least responsive, followed by C4 plants, while C3 plants increased their photosynthesis rates the most under high CO2. Examples of C3 plants are herbaceous crop plants (Poorter, 1993);
Plants with a large intrinsic growth rate may benefit more from high CO2. These plants are said to have a high sink strength, as they rapidly convert additional photosynthesis products into outgrowth.

References
- Jensen, PNAS (2000); 97(24): 12937–38
- Poorter, Veg (1993);104/105: 77-97

Isn't the available sunlight the primary rate limitation?  After all, a plant with access to abundant CO2, water, and other nutrients still can convert only as much as the incoming solar energy allows.

@jamesqf -The question is on the rate limiting step under high CO2. CO2 fixation is part of the Calvin cycle and hence independent of sunlight. However, you are right that, quite obviously, the Calvin cycle cannot run without sufficient sunlight for the Krebs cycle. The studies cited revealed limiting factors other than sunlight or CO2 when sunlight was abundant. In other words, with enough sunlight and increased CO2, other factors become rate limiting preventing the fixation of more CO2.


text:
Are thunder and lightning necessary for any organisms?
Comparatively irregular events such as forest fires are necessary for some plants to grow (see here).
But do any plant or animal species have similar dependence on thunder and/or lightning?

Lightning is more or less the only non-human cause of forest fires

@C_Z_ Ah, yes. That was not an intended connection, even though it is obvious now that you point it out. I was more thinking of direct effects of the immense light and sound…

do you mean for reproductive purposes? Or just in general?

@C_Z_ +1 for trees

@C_Z_ What about dry fires in summer/dry season? They happen very frequently. Are they always started by people (genuinely curious)?

@James The only way for those fires to start in the dry season is with a spark. As far as I know, lightning is the only non-human source of a spark

@C_Z I disagree, volcanic action is also known to cause natural fires. Other sources of literature even suggest rubbing of some reactive rock materia can cause these sparks

I think it is higly unlikely that there are species that directly depend upon lighting. Lighting strikes occur to infrequently and random to be of any use to an organism from energetic point of view.

@VanceLAlbaugh Generally, was my thought. I realise this easily becomes open ended. But every necessary aspect of an organism's life cycle is reproductive in some way, maybe?

Lightning results in nitrogen fixation (Draphco et al., 1967; Hill et al., 1980). It is one of the main abiotic nitrogen fixation mechanisms. As you would know very well, nitrogen fixation is important for all organisms.

This is very good, but if there are other sources of nitrogen fixation, organisms are not really dependent on this source of it?

@trmdttr there are some organisms (mostly micro-organisms) that can fix nitrogen on their own.

So basically, all other organisms that cannot fix nitrogen themselves are dependent on lightning? Or do they use the fixed nitrogen from those microorganisms?

@trmdttr they can depend on the other organisms that fix nitrogen as well as use the nitrogen fixed by the lightning. I believe initial lifeforms consumed the abiotically fixed nitrogen; subsequently some evolved to fix nitrogen themselves.

So, this answer doesn't really catch a necessary reliance on thunder or lightning for any specific organism? More a need for fixated nitrogen of which the majority comes from that source (but also from others)?

@trmdttr the necessity will depend on many bio-geographical factors.


text:
How is it determined in a zygote state of foetus, that which side is mouth and which side is for anus?
After the fertilization when the zygote  is in the growing state to a foetus, the main canal is introduced first, from mouth to anus. But my question is when the canal is created first how does it determine which side is for mouth and which side is for anus?

This question reminded me of the fact that humans are assholes

From my understanding, the location of the initial fold (blastopore) during gastrulation always becomes the anus (in deuterostomes, e.g. humans). I don't know how the polarity of the primitive streak is formed though.

You have enough rep now to  upvote mate.

The gut tube is formed from endoderm that migrates through the primitive streak. Before this, however, the anterior-posterior axis of the embryo is defined by the formation of the streak at the beginning of gastrulation. In mice, which are phylotypic with humans at this stage, the streak forms posteriorly and then extends to the anterior. I'm not sure if you'll be able to find much research on humans, but mice and chicks are quite well studied. That said, I don't know the molecular and cellular mechanisms which define where the primitive streak forms. I don't even know if it is known. How much detail are you looking for? If I find the time, I can try and elaborate on this further. In the mean time, this review looks promising:
http://m.dev.biologists.org/content/136/5/701.long

Thank you so much for the answer. Actually as much research paper and books I studied so far didn't get any particular answer for my question. I will will statisfied if I get to know the determination of position of anterior and posterior axis in which basis. I mean if an anterior and posterior axis is chosen for the purpose then can the place where anterior is present could be for posterior position?  I heard from my teacher that it's determined by a particular protein condrin. Though couldnt understand the answer properly.

And the link is good and sorry couldn't vote up the answer as dont have enough reputation, just joined today.

apparently polarity is already present in the gametes. I tried to formulate an answer but gave up because of the complexity.

@AliceD Is that true of amniotes? Maternal effect is quite contentious in this class.

This wiki-section should be relevant to your answer: Wikipedia: Gastrulation - Formation of the primitive streak, and it contains references to a couple of signalling pathways and protein involved.


text:
Is NMDA produced in the body?
On the wiki page for NMDA it says that NMDA is a synthetic substance that mimics glutamate. So why does the body not use glutamate instead of NMDA?
Also how is it possible that our body can produce something that is synthetic? I thought synthetic was man-made chemicals. Does that mean the NMDA receptors it binds to are also synthetic? How did this come to be?

(my comment reiterating the answer seemed useful, so I've reproduced it here)
There are "NMDA receptors" in our body. There is not NMDA naturally in our body*. "NMDA receptor" is just a name people gave to one of the receptors that normally binds glutamate. They could have called it something else, like the "slow glu receptor", or "Glutamate Receptor A", but they called it the NMDA receptor because they discovered it was different from other glutamate receptors in that it was activated by a chemical called NMDA in their experiments.

(original answer)
NMDA receptors are in fact glutamate receptors natively: there is no endogenous NMDA*. They are named NMDA receptors because experimentally they were differentiated from other glutamate receptors using the compound NMDA. At first glutamate receptors were known as "NMDA" and "non-NMDA" glutamate receptors, and later the non-NMDA sort were found to be sensitive to AMPA or kainate and named thusly.
Many other receptors are named this way: the nicotinic acetylcholine receptor is the receptor that nicotine is an agonist at, but the endogenous ligand is acetylcholine; muscarine is an agonist to the muscarinic acetylcholine receptor but the endogenous ligand is acetylcholine.

Watkins, J. C., & Jane, D. E. (2006). The glutamate story. British journal of pharmacology, 147(S1), S100-S108.

*In the comments, @gilleain pointed out some research suggesting that NMDA does exist endogenously, although in fairly specific areas and not disputing that glutamate is the primary endogenous agonist for NMDA receptors. This work was long after the NMDA-sensitive glutamate receptor was discovered and named, but is some interesting trivia nonetheless.

So does that mean the so called "NMDA" in our bodies is just a chemical scientists found that is similar to glutamate, and thus named it that way? I still don't understand why there is no endogenous NMDA, where does it come from in our bodies?

@user3665690 There are "NMDA receptors" in our body. There is not NMDA naturally in our body. "NMDA receptor" is just a name people gave to one of the receptors that normally binds glutamate. They could have called it something else, like the "slow glu receptor", or "Glutamate Receptor A", but they called it the NMDA receptor because they discovered it was different from other glutamate receptors in that it was activated by a chemical called NMDA in their experiments.

I see! ok that makes a lot of sense now, so basically NMDA is a synthetic chemical that mimics glutamate induce NMDA receptor activity. Is that the right interpretation?

@user3665690 Yes that's correct

It might be useful to mention that there is essentially no difference between a 'synthetic' chemical and one produced by the body. If they have the same atoms, arranged in the same way, then they are the same thing

@gilleain True, but NMDA is, to my knowledge, not found in any natural biological system, so it's appropriate to call it synthetic. There is no biologically produced molecule for it to be the same thing as.

https://www.ncbi.nlm.nih.gov/pubmed/11014243 : " we show that N-methyl-D-aspartate (NMDA) is present at nanomolar levels in rat nervous system and endocrine glands as a natural compound, and it is biosynthesized in vivo and in vitro. " but anyway that's beside the point - the question includes "Also how is it possible that our body can produce something that is synthetic?"

Perhaps that's just a different question entirely (probably already answered elsewhere) of the false split of natural/synthetic molecules

@gilleain Thanks for the reference! Quite interesting, I'll have to look into related work and possibly change my worldview a bit.

There is no clear-cut line between "synthetic" and "natural" substances.  They are made from the same kinds of atoms using the same kinds of covalent, ionic, and other types of bonds.  The fact that a substance MAY be produced by some organism by biological processes does not make it any different from the same substance made in a laboratory by a human chemist from non-living reactants.  What is relevant is the precise structure of the substance and the impurities that may be present in a particular sample of it.  Sometimes, because of the details of the kinetics and thermodynamics of biosynthetic and human laboratory synthetic processes, the relative isotopic composition of one or more of the elements may be different, but that can also be true of different "natural" processes and different "synthetic" processes.


text:
is left brachiochephalic vein and left pulmonary artery is same?
I am reading about ligamentum arteriosum which connects the left pulmonary artery and descending aorta. But I am seeing that if any figure shows left brachiochephalic vein it does not show left pulmonary artery (and vice versa). Are they both the same?

Veins are not the same as arteries. A non-capillary blood vessel is either one or the other, not both.

Please include references for those figures or add those figures to your question. Plus, as MattDMo said, veins cannot be simultaneously labelled as arteries. I guess the figures are intended to show veins and arteries separately (and not in the same picture). In any case that cannot be verified unless you show us the figures.

They are structurally and functionally distinct. The left brachiocephalic vein joins the superior vena cava which returns deoxygenated blood to the right side of the heart. Deoxygenated blood is pumped OUT of the right heart and travels to the left lung via the left pulmonary artery.
The two don't always appear in the same image because many anatomical images remove structures that aren't the area of focus for clarity. However a quick google image search reveals several pictures that illustrate the distinct positions of both.

No, they are not the same.  The left brachiocephalic vein receives de-oxygenated blood from the left subclavian vein (draining the left arm) and the left internal jugular vein (draining the brain and parts of the face).  The brachiocephalic vein then joins with the superior vena cava which flows into the right atrium.
The left pulmonary artery carries the deoxygenated blood that has just passed through the right atrium and ventricle, through the pulmonary trunk to the left lung for oxygenation.
In the foetal circulation, the ductus arteroisus (before it closes into the ligamentum arteriosum) allows blood to bypass the lungs, which are not oxygenated in the uterus!
Please see this diagram (which shows the foetal circulation including PDA for you).  The left brachiocephalic vein is circled in yellow, whilst the left pulmonary artery is circled in green.

Image from public domain edition of Gray's.


text:
Did Plant Cells Evolve from Animal, Protist, or Fungal Cells?
I know protists and animals preceded plants but I am unaware of when fungi arose in relation to plants. At the moment, I cannot find a resource stating how plants evolved from existing kingdoms, or whether they evolved independently from prokaryotes. Thanks for the help, sorry if this is common knowledge.

cladistically speaking plants, fungi, and animals are all protists.

@John protist is not even a clade, actually. It was a group of all unclassified unicellular eukaryotes.

Plants were almost always considered a separate group from fungi and animals.
As per the latest classification both animals and plants are included in the clade Opisthokonta: animals and related unicellular organisms (holozoa) form a sister clade with holomycota (contains fungi and Cristidiscoidea).
Plants (archaeplastida), aveolates and rhizaria are now grouped under Diaphoretickes (previously they were grouped under Bikonta).
To cut short the plant and animal lineages merge only at the level of the last eukaryotic common ancestor. This means that plants are as unrelated to fungi as they are to animals.
Protista is no longer considered a proper clade. It used to be a group where all unclassified unicellular eukaryotes were dumped. Now these organisms have been reclassified; some are closer to animals (for e.g. Amoeba), some are closer to plants (e.g. Plasmodium) whereas some are part of a group that is distantly related to both plants and animals (for e.g. Euglena).
Have a look at this WikiSpecies site for a detailed classification (latest and historical). It also cites proper references.


text:
Is this modification of Livak's 2-delta delta CT method valid?
I have done a modification to include qPCR efficiency in the calculations:

In a given gene I calculate the fold changes relative to an arbitrary sample. That sample will be one and the rest will vary according to 2-dCT (equation 13 in Livaks paper). The efficiency comes into play when 2 becomes 1+efficiency (e.g. 1.9 when efficiency is 90%).
The gene of interest will be normalized by the reference gene:
$$\frac{(1+\text{efficiency})^{-\Delta CT_\text{GOI}}}{(1+\text{efficiency})^{-\Delta CT_\text{ref.}}}$$

This makes sense to me more because it includes PCR efficiency and keeps the all groups in the calculations. But I can't tell if I am missing somethng, since no one has descirbed this approach before.
What do you think?

PCR efficiency is not usually expressed as percentage (or per-unit). Its bounds are [0 2] i.e the maximum efficiency is 2 and the minimum is 0. (Ruijter et al. 2009; also see this post for calculation of efficiency.)
However, I just noticed that some people do express efficiency as percentage or per-unit. In that case your formula would be correct. I realize that there may be a confusion within the community; so you should know what you are calculating. For avoiding confusion lets just call this measure as amplification factor (denoted by E), which would be 1 + per-unit efficiency (as you mentioned).
When the primers have different efficiencies then you can't really use the $\Delta\Delta CT$ method. You'll end up with this formula (pretty much similar to what you have mentioned):
$$\large\text{Fold Change}=\frac{\left[E_\text{GOI}^{-CT^\text{condition}_\text{GOI}}\right]\bigg/\left[E_\text{Ref}^{-CT^\text{condition}_\text{Ref}}\right]} {\left[E_\text{GOI}^{-CT^\text{control}_\text{GOI}}\right]\bigg/\left[E_\text{Ref}^{-CT^\text{control}_\text{Ref}}\right]}=\frac{E_\text{GOI}^{-\Delta CT}}{E_\text{Ref}^{-\Delta CT}}$$
Where:
$CT^\text{condition}_\text{GOI}$ means CT value of the gene of interest (GOI) in a given condition (other than control), and $E_\text{GOI}$ means the efficiency of the primers for your GOI.  Similarly for the reference (Ref).
See this post too.


text:
How does anaerobic burst-flight performance scale with mass?
Quails employ anaerobic burst flight to escape predators and the Quetzalcoatlus northropi have most likely used it during launch and climb out, then transition to soaring.
Now, I have read these papers, from Marden, Witton and  C.J. Pennycuick
I won't play pretend I really don't know that much about bird flight mechanics, so I easily got confused reading them.
How does anaerobic flight performance scale with mass?


text:
Bootstrapping (symmetry breaking) in evolution
Many fascinating phenomena in nature show different behaviours on the micro and the macro level. Here I am especially referring to phenomena that are symmetric on the micro and asymmetric on the macro level. Let me give an examples from physics:

Arrow of time: Physical processes at the micro level are
  completely time-symmetric, if you reversed time, all laws would remain
  the same. Yet on a macro level they are not time-symmetric, we have an
  obvious direction (flow) of time.

It seems to me that we have a similar situation with evolution:
On the micro level it is really only about adaption (and not about improvement): You put some beings into an environment and they adapt, you change this environment and they adapt. No improvement, just blind "back and forth".
Yet looking at the whole tree of life on the macro level we have a different picture, we see all kinds of emergent phenomena:

explosion in diversity
emergence of complex interconnected ecosystems with completely new dynamics of their own
true innovations in functionality (sensors, actuators, information processing etc.)
and finally of course higher order functions like abstract intelligence (and  even qualia)

To make the difference more concrete I will give a gedankenexperiment of two planet simulators. In both cases you accelerate time so that one hour translates into one billion years. Both simulators function differently in a subtle way but you don't know how. You throw some simple sims onto both planets and let the evolution simulation run. You don't switch your computer off and go to bed, the next morning you decelerate time and take a look:

Planet 1: The simple sims quickly adapted to the environment as expected (but they stayed simple because evolution is not about improvement but about adaption).
Planet 2: Some strange looking sims wish you a good morning and want to discuss the technicalities of the Riemann hypothesis with you.

My question
What are the minimal conditions that have to be met to get this kind of bootstrapping (symmetry breaking from micro to macro level) started. And what is going on at the core of this transition from micro to macro.
Edit
My specific point is about the symmetry breaking process in the sense of system dynamics. To give an example of a possible answer it could be something like Parrondo's paradox, a fascinating phenomenon from game theory where you combine two symmetric (losing) processes to get a (winning) asymmetric process. More can be found here: Parrondo's paradox.

This would require an entire book. There are several books on biochemical as well as mathematical explanation to how diversification could have happened. Most of our understanding is also incomplete. You have to ask a precise question or it is highly likely that this question would be closed for being too broad. Also, it is really essential for you to know some basic concepts of molecular biology, biochemistry and evolution.

I wish to reiterate that we are not being hostile towards you. Any good answer on this topic would require a good knowledge of biology. The issue with general evolution questions is that anyone who has read a popscience book comes up with a handwaving answer. So, please focus on a specific point instead of asking how and why the world is as it is.

@vonjd just because you produced an answer doesn't mean that the question itself isn't too broad, and I'm not even sure that it answers the question in a self-contained manner. You also need to stop ascribing motivations to people that may not have them, as many of your previous comments have done. This just makes other users less and less likely to answer your questions. No one on this site has a vendetta against you, but we all care about the quality and focus of this site, and so vote accordingly.

@WYSIWYG It is why SE has a voting system to promote good answers. Closing down questions for fear that they may attract bad answers seems to put the cart before the horse. I don't understand this kind of censorship. I won't even try to use the argument that anyone who doesn't like broad questions has no obligation to read this one.

Good well written questions would attract clear good quality answers - yours don't, they're consistently attracting junk. Anyone can turn up and vote on SE - they might know nothing about evolution, or (like me) might be an active researcher in the evolutionary biology community with a phd and masters degree in the subject... you should be more selective about whose voices you listen to if you want to learn.

@AleksandrDubinsky We are not censoring anything. I am just asking the OP to be precise. Broad questions are off-topic in any SE. Btw, I personally just pointed out to the OP that they should refine the question. I didn't close the question as you can see. Please go through the help center to know wat "broad questions" are.

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

@WYSIWYG: Yes, you asked me to be precise and I therefore edited my question accordingly and asked for your feedback. I got no feedback but this comment was deleted. Then the question was over-hastily closed (not by you). That this question is not too broad can be seen by the fact that I gave a short and precise answer. Additionally people are giving precise answers in the comments. Now those answers have been moved to chat. I think there is something fundamentally going wrong in this community. I am active in many different SE communities and I have never encountered something like this :-(

@vonjd 1. Comments are not used for answering 2. Your question is broad even after the edit and if you think your own answer was what you were expecting then I don't think that is clear from the question. Please see MattDMo's comment. There is nothing wrong with this community; many people here also participate in other SEs. Vague and broad questions are not accepted in any SE. Perhaps you are better aware of other subjects so you don't end up asking vague questions in other SEs. None of your "supporters" are active here and probably even bio graduates. Your question is broad. Period.

Comments are moved to chat on other SEs all the time. They are also deleted as soon as they are not useful anymore all the time.

@vonjd THIS IS YELLING. This is strong emphasis. This is emphasis. WYSIWYG (who happens to be a site moderator, if you hadn't noticed the ♦ next to his username), was merely emphasizing SE rules, which are common across all sites, and which you appear to be selectively ignoring. That is all.

I found a paper which gives four conditions that have to be met:

Condition 1: A rule should be enforced that individuals
must meet some minimal criterion (MC) before they
can reproduce, and that criterion must be nontrivial
Condition 2: The evolution of new individuals should
create novel opportunities for satisfying the MC
Condition 3: Decisions about how and where individuals
interact with the world should be made by the individuals
themselves
Condition 4: The potential size and complexity of
the individuals’ phenotypes should be (in principle) unbounded

Source: Soros, Stanley (2014): Identifying Necessary Conditions for Open-Ended Evolution through the Artificial Life World of Chromaria
Chromaria is "an experimental platform that allows the exploration of evolutionary theories in an intuitive and visually engaging way" from EPLEX, the Evolutionary Complexity Research Group at the University of Central Florida (UCF)

Condition 2 does not have to be the case. Prokaryotes, single celled Eukaryotes, and some organism with fertile hermaphrodites more often than not create exact clones of themselves. Condition 3 does not happen in life. Even bacteria communicate chemically at a minimum creating quorum sensing behavior within the colony. Condition 4 doesn't hold for life as most organisms will operate at some optimal level. They will lose traits that are not used, especially those that are not required for survival.

@vonjd "Later I found that it is still a huge research topic to find out what we are missing (keyword: "open-ended evolution")" You are still misstating this - it's not that we are missing big chunks of information about how evolution works, algorithms simulating evolution are missing a lot. That's what people have been trying to tell you.

@YviDe: I get that but how can you be so sure without even be willing to think about it? It is as though you thought you knew everything about aerodynamics and when you build a plane it instantly stalls in the air and crashes. You did everything according to the theory of aerodynamics. Then you ask the aerodynamics experts and they tell you: "No, no, we know everything. We don't know why your plane crashed but our theory is complete. It must be your fault and we don't want to think nor talk about it - case closed." Would you accept that? I don't - and I don't think it is good science either.

@vonjd you seem to be completely comfortable just taking the other view, though, that Biologists must be missing something that's "breaking symmetry". As for why I am comfortable saying that the fault is in the models and not in our understanding - because I actually used to study Evolutionary Bioinformatics (MSc) and we acknowledged that we lacked computer power to model everything when doing simulations.

@YviDe: NB: I am talking about the first plane ever built. If you built a plane today and it crashes it is of course an engineering problem not a problem with the theory. But in evolution despite decades of research we are still far from producing anything that faintly resembles real evolution. So, yes, you could be right but the case is far from closed. The evidence that evolution happened on this planet is overwhelming but the argument that we understand the whole process sufficiently well is far from conclusive.

It's called Occam's razor. We know our simulations are lacking, so if they don't produce the desired result we need to first look at our models and computations to find the fault, and only if we can exclude that (which we can't) should we look at the theory behind the simulations.

@YviDe: "we lacked computer power to model everything when doing simulations" - this could be true too. But it can also be the case that we miss something fundamentally different. That we do the abstractions at the wrong places. I mean we are able to simulate the creation of galaxies in the early universe because we know how to simplify the model enough to get to the bottom of things. If you wanted to simulate that with every single atom involved you would also lack the computing power. We have not yet understood what a good simplification/abstraction of evolution could look like.

@YviDe: and e.g. one of my master students is working on a multi-agent simulation of the stock market at the moment. This model is able to reproduce many important stylized facts of markets. And, believe me, markets are also very, very complicated beasts. People could tell you that you had to simulate millions of brains that are involved in the process - but that of course misses the point. All models are wrong, some are useful: We have not yet found the right abstract model (from an algorithmics point of view) of real evolution that does just the simplifications at the right places.

@vonjd If you want to ask about a right model to simulate the evolutionary process then your question should have these: 1. Kind of outcomes do you expect (i.e what exactly you are looking for) 2. The scale of the model 3. Assumptions you can allow 4. Kind of model (deterministic or stochastic) 5. Computational resource available (optional).
You should explain what you have thought of and then ask. You should also go through the existing algorithms. If you are unaware of those then your question should be a reference request asking for papers on evolutionary simulations.

@vonjd "This model is able to reproduce many important stylized facts of markets." And if it didn't, would your first question be "what are we missing about the workings of the stock market" or would it be "let me look at that model and see how good your model is"?

As it was, your first question was "What are we missing about the real workings of the evolutionary process?" which is still about what we are missing about the actual process, not the simulations - turns out, even from your own research into the topic, that that question was based on wrong assumptions. See the paper you referenced in that other question and how they talk about porevious work done on the subject. "If we cannot simulate a process we have not understood it properly" is wrong - we might actually be simulating what we know badly.

@vonjd As for why you got such a strong response, I am guessing it's because our inability to simulate evolution well has led to it being used as an argument against evolution again and again. See, for example http://biology.stackexchange.com/questions/40845/do-the-claims-in-this-paper-have-any-degree-of-validity

I've removed the comments that focused on persons, and not on the post and the content. Criticism is welcome, but please keep it constructive.


text:
Can scientists create totally synthetic life?
This particular question has been of a great deal of interest to me, especially since it dives at the heart of abiogenesis.

This is a great question, and Larian LeQuella answered pretty thoroughly to it. I just wanted to add that it is exciting time to be a synthetic biologist. Scientists have now the tools to build anything they have ever dreamed about.

In 2010, Dr. Craig Venter actually used a bacterial shell and wrote DNA for it.

Scientists have created the world's first synthetic life form in a landmark experiment that paves the way for designer organisms that are built rather than evolved.
(Snip)
The new organism is based on an existing bacterium that causes mastitis in goats, but at its core is an entirely synthetic genome that was constructed from chemicals in the laboratory.

Keep in mind, this is only a synthetic genome, not a truly unique organism created from scratch.  Although I am confident that the technology will become available in the future.  As has been pointed out, the entire genome wasn't built de novo, but rather most of it was copied from a baseline which was built up from the base chemicals with no biological processes, and then the watermarks were added (still damn impressive since they took inorganic matter and made a living cell function with it). But they are working at building a totally unique genome from scratch (PDF).
This is actually quite an emerging field, so much so that the MIT press has set up an entire series of journalsfor this.  As far as to the purpose of these artificial organisms, most research funded by companies are meant to be for specific purposes that biology hasn't solved yet (such as a bacteria that eats a toxic waste or something).  Although, a lot of people are concerned about scientists venturing into the domain of theology.
In terms of abiogenesis, there are many resources to learn more about this.  Here is a list of 88 papers that discuss the natural mechanisms of abiogenesis (this list is a little old, so I am sure that there are many, many more papers at this time).
I also found this list of links and resources for artificial life.  I cannot verify the usefulness of this since the field is a bit outside my area of expertise.  However, it does seem quite extensive.
EDIT TO ADD:  Now we have "XNA" (a totally synthetic genome) on the way.

Note that the answer was originally posted at ScepticsSE (Can scientists create a life-form yet?).

@fileunderwater Yep, and I thought it was something that belonged here as well (from the purely biological perspective).  This is two years old now, so I wonder what advances in this field have been made (I haven't kept up)?

In principle it is possible. Life doesn’t contain some divine or intrinsically spiritual element that we would have to add to our artificial organism potion to breathe life into it. At this moment we are limited by gaps in our knowledge and by the current state of technology.
We first have to better understand fundamental principles of life on a multi-level scale: from quantum mechanics, through biochemistry, structural biology, molecular evolution, to macroscopic function and behavior on the organism level. This, together with development of enabling technologies, will require decades of research but some steps have already been taken.
One of the promising approaches is re-writing, as exemplified in this work:

We redesign the genome of a natural biological system, bacteriophage T7, in order to specify an engineered surrogate that, if viable, would be easier to study and extend. (...) The resulting chimeric genome encodes a viable bacteriophage that appears to maintain key features of the original while being simpler to model and easier to manipulate. The viability of our initial design suggests that the genomes encoding natural biological systems can be systematically redesigned and built anew in service of scientific understanding or human intention.

or a minimal cell synthesis project:

Construction of a chemical system capable of replication and evolution, fed only by small molecule nutrients, is now conceivable. This could be achieved by stepwise integration of decades of work on the reconstitution of DNA, RNA and protein syntheses from pure components. (...) Completion would yield a functionally and structurally understood self-replicating biosystem. (...) Our proposed minimal genome is 113 kbp long and contains 151 genes. We detail building blocks already in place and major hurdles to overcome for completion.

So, technically it’s very difficult but definitely can be done, which is really exciting.

I would like to add a lecture on Synthetic Biology by Andrew Hessel, who introduces the open source synthetic biology field, compares it to computing, and gives an overview of what are its applications, like the creation of the first synthetic organism.
I also stumbled upon a talk from the famous physicist Michio Kaku, who shares his vision about the world in 2030, when synthetic biology will change every-day-life.
Enjoy!


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Why would stablising selection ever happen?
If the aim of evolution is to allow an organism to better compete against rivals, why would stabilizing selection ever happen? If you're not selecting the most highly adapted competitors at either end of the spectrum then how would a species progress?

Evolution has no aim. Surviving of the fittest is only the way how it works.

As @MartaCz-C says, evolution is not purposeful or goal-orientated, it's a logical phenomenon that just occurs.

It occurs when a beneficial characteristic has been developed over time and it would be harmful to stray from it.  In these cases it is not the individuals at the fringe (as you put it the most adapted) who are the best adapted.
I think it may help if I answer with an example that is widely promoted by AQA in their A2 Biology syllabus.
Stabilising Selection in human birth weight
It is harmful for an infant to be born with a very low birth weight. They are much more vulnerable to heat loss due to their high surface area to volume ratio and consequently their respiratory demands are very high.  Pre-term babies (which account for 67% of low-birthweight infants(1)) are particularly susceptible to respiratory problems (lack of surfactant in the lungs), cardiac problems (Patent ductus arteriosus - the lungs are still bypassed when the umbilical cord has been cut) and dangerous intestinal problems (Necrotizing enterocolitis) amongst many other conditions can all be fatal (further information on mentioned conditions) and are reflected in high mortality rates at these low birth rates.  It is therefore not beneficial to be on the extremes of birth weight.
Similarly, delivering a child of too high birth weight can cause complications with delivery if the head and shoulders are too wide to pass through the mother's hips.  Therefore the other extreme of high birth rate is also not beneficial and will not be selected towards.
This leads to selective pressures in both directions, stabilising towards a mean birth weight as shown below:

This is an example of evolution not pushing a species forward but ensuring that individuals have the best chance of getting to reproductive age themselves.
(1) Martin, J.A., et al. (2007). Births: Final Data for 2005. National Vital Statistics Reports, 56(6).

this a great answer Rory.

Evolution has no aim, you seem to be repeating a caricature of what the theory of evolution really says.  Evolution works by a mechanism where pressures on a population select for the traits that are best suited for that environment.  If there are no pressures on a population, then the selection of specific traits will be slowed down, as all members of the population have an equal chance at reproducing.
Now to wax a bit philosophical on the issue.
As John Rennie said in Scientific American:

"Survival of the fittest" is a conversational way to describe natural selection, but a more technical description speaks of differential rates of survival and reproduction. That is, rather than labeling species as more or less fit, one can describe how many offspring they are likely to leave under given circumstances. Drop a fast-breeding pair of small-beaked finches and a slower-breeding pair of large-beaked finches onto an island full of food seeds. Within a few generations the fast breeders may control more of the food resources. Yet if large beaks more easily crush seeds, the advantage may tip to the slow breeders. In a pioneering study of finches on the Gal pagos Islands, Peter R. Grant of Princeton University observed these kinds of population shifts in the wild [see his article "Natural Selection and Darwin's Finches"; Scientific American, October 1991].
The key is that adaptive fitness can be defined without reference to survival: large beaks are better adapted for crushing seeds, irrespective of whether that trait has survival value under the circumstances.

Michael Shermer states:

Living fossils (organisms that have not changed for millions of years) simply means that they evolved an adequate structure for a relatively static and unchanging environment, good enough to maintain a niche.

Or as a particularly brilliant writer that goes by the name of Calilasseia said:

The static species fallacy.
This is a particularly stupid canard, which the above discourse on inheritance, and variation brought about by meiosis, should flush down the toilet at a stroke. But, in order to reinforce how stupid this canard is, it is necessary to cover rigorously what a species is.
A species is a population entity, and as a corollary thereof, a dynamic entity. A species is defined in rigorous biological work, as a population of living organisms, whose members can produce viable offspring with each other, but whose members can not produce viable offspring with a separate, distinct population. Actually, this is only one extant definition, but it is the one that matters with respect to evolution, because once again, it points to the central role of inheritance.
Of course, part of the problem arises because of taxonomy. Because scientists need a reference point from which to launch further investigation, they have alighted, courtesy of our old friend Linnaeus, upon the process of cataloguing organisms and providing them with a unique, unambiguous identity. This, of course, has been most helpful in furthering our understanding of the biosphere, and indeed, Linnaeus himself, on the basis of comparative anatomy alone, alighted upon the idea that organisms were related to each other a hundred years before Darwin, which is why he constructed his taxonomic scheme in the manner he did. Yes, that's right, a creationist (though he was only a creationist because no other option existed in 1758) alighted upon the idea of biological interrelatedness, as a result of paying attention to reality. But the very same taxonomic practices that have been useful to science, have also led to a popular misconception. This is because taxonomists base their classification upon individually sampled organisms, one of which is chosen as a 'type specimen' that is henceforth declared to be the reference standard against which all others are compared. Other specimens are maintained in order to provide a record of likely variation in characteristics from that reference standard. The trouble is, of course, basing the entire classification system upon such reference standards promotes the illusion that those standards remain in place for all time. Scientists, of course, recognise that this is not the case, but it takes diligent intellectual effort to recognise that the taxonomic standards are merely particular snapshots of the state of the species at a given point in its history, which scientists then choose as their reference benchmark for current work. The species itself, however, courtesy of all that dissemination of variation across generations, does not stay still. It is NOT static.
I cannot reinforce this strongly enough. A taxonomic classification is merely a historical snapshot of the state of a species, used as a reference point for further work, and does NOT constitute "the species" itself. The species itself, is the sum total of all the living organisms comprising that interfertile population, and with each new generation, that population undergoes change, because in the new generation, each of the organisms comprising that population are genetically different from those in the previous generation.
So, if anyone wishes to erect the ridiculous idea that a species is a static entity, the simple retort is this. Look at your family album. Are you identical to either of your parents? No? There's your evidence for the dynamic nature of a species. Now replicate that evidence across millions of humans, and picture what happens with each new generation, remembering that across generations, inheritance is a dynamic process. There goes the static species fallacy.

I hope that clears it up for you.


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Spatially Encoded GPCRs?
I'm reading this paper, and I'm already lost in terms of what they mean by GPCR signaling is spatially encoded.

The trafficking of G protein coupled‐receptors (GPCRs) is one of the
  most exciting areas in cell biology because of recent advances
  demonstrating that GPCR signaling is spatially encoded.
   …
  …
  Some GPCRs appear to regulate their own endocytic rate through
  modulation of clathrin‐coated pit (CCP) maturation.12-14 This
  regulation, which can differ between ligands acting at the same GPCR,
  is an additional method by which GPCR signaling can be spatially
  encoded.

How can signaling be spatially encoded?

Looks like they're just using a fancy way of saying that the signaling pathways and outcomes depend on where the GPCR is located in the cell: "GPCRs, acting in a diverse array of physiological systems, can have differential signaling consequences depending on their subcellular localization."

How can (GPCR) signaling be spatially encoded?

Terse explanation: During endocytosis the G protein-coupled receptor can be spatially constrained.
Your paper refers to: "Fulfilling the promise of “biased” G protein-coupled receptor agonism", by Luttrell LM, Maudsley S, and Bohn LM, in Mol Pharmacol. 2015;88(3): 579-588. https://doi.org/10.1124/mol.115.099630.

Abstract: The fact that over 30% of current pharmaceuticals target heptahelical G protein–coupled receptors (GPCRs) attests to their tractability as drug targets. Although GPCR drug development has traditionally focused on conventional agonists and antagonists, the growing appreciation that GPCRs mediate physiologically relevant effects via both G protein and non–G protein effectors has prompted the search for ligands that can "bias" downstream signaling in favor of one or the other process. Biased ligands are novel entities with distinct signaling profiles dictated by ligand structure, and the potential prospect of biased ligands as better drugs has been pleonastically proclaimed. Indeed, preclinical proof-of-concept studies have demonstrated that both G protein and arrestin pathway-selective ligands can promote beneficial effects in vivo while simultaneously antagonizing deleterious ones. But along with opportunity comes added complexity and new challenges for drug discovery. If ligands can be biased, then ligand classification becomes assay dependent, and more nuanced screening approaches are needed to capture ligand efficacy across several dimensions of signaling. Moreover, because the signaling repertoire of biased ligands differs from that of the native agonist, unpredicted responses may arise in vivo as these unbalanced signals propagate.".

A relatively simple explanation is offered in another paper: "Temporal Bias: Time-Encoded Dynamic GPCR Signaling", by Manuel Grundmann and Evi Kostenis DOI: https://doi.org/10.1016/j.tips.2017.09.004:

"The notion that information is not only encoded by the chemistry of the signaling components but also by their spatial and temporal occurrence, brought about the term of ‘spatiotemporal signaling’ $^{[3]}$. Signal transduction can thus be categorized into at least three dimensions, (i) quality (which substances take part), (ii) space (where the event takes place) and (iii) time (when the event takes place). The interplay between these dimensions will hereinafter be designated as ‘dynamic signaling’ or ‘signaling dynamics’. Dynamic signaling is deeply entrenched in GPCR signal transduction as an increasing number of reports provide us with a glimpse into spatiotemporal aspects of receptor function $^{[4, 5, 6, 7, 8, 9, 10]}$.".
[3] Kholodenko B.N. et al. Signalling ballet in space and time. Nat. Rev. Mol. Cell Biol. 2010; 11: 414-426
[4] Irannejad R. Functional selectivity of GPCR-directed drug action through location bias. FASEB J. 2017; 31 (664.2)
[5] Mullershausen F. et al. Persistent signaling induced by FTY720-phosphate is mediated by internalized S1P1 receptors. Nat. Chem. Biol. 2009; 5: 428-434
[6] Irannejad R. et al. Conformational biosensors reveal GPCR signalling from endosomes. Nature. 2013; 495: 534-538
[7] Eichel K. et al. β-Arrestin drives MAP kinase signalling from clathrin-coated structures after GPCR dissociation. Nat. Cell Biol. 2016; 18: 303-310.
[8] Thomsen A.R. et al. GPCR-G protein-β-arrestin super-complex mediates sustained G protein signaling. Cell. 2016; 166: 907-919
[9]  Jean-Alphonse F.G. et al. β2-Adrenergic receptor control of endosomal PTH receptor signaling via Gβγ. Nat. Chem. Biol. 2016; 13: 259-261
[10] Plasma membrane localization of the μ-opioid receptor controls spatiotemporal signaling. Sci. Signal. 2016; 9: ra16

See: "Spatial encoding of GPCR signaling in the nervous system", by Zara Y Weinberg, Stephanie E Crilly and Manojkumar A Puthenveedu, DOI: https://doi.org/10.1016/j.ceb.2018.12.006:

"Several GPCRs, including receptors previously thought to signal primarily from the cell surface, have been recently shown to signal from many intracellular compartments. This raises the idea that signaling by any given receptor is spatially encoded in the cell, with distinct sites of signal origin dictating distinct downstream consequences.".

During endocytosis the G protein-coupled receptor can be spatially constrained.
See: "Spatial resolution of cAMP signaling by soluble adenylyl cyclase", by Giusi Caldieri and Sara Sigismund
DOI: https://doi.org/10.1083/jcb.201606123 which offers a easy to understand image:

Figure 1. Spatial and temporal control of GPCR signaling by ACs. In hippocampal neurons, CRHR1, upon binding to agonist CRH, activates the trimeric G protein complex, composed of α, β, and γ subunits. Stimulatory Gα subunit is released and activates tmAC and cAMP production (Irannejad and von Zastrow, 2014). In parallel, activated GPCR can also induce local Ca$^{2+}$ release that activates sAC, followed by an increase in cAMP levels (Inda et al., 2016). (1) These two sources of cAMP at the PM contribute to the early phase of ERK activation via the effectors protein kinase A (PKA) and EPACs (Inda et al., 2016). Activated GPCRs once released from the G proteins are phosphorylated and recruit β-arrestin (β-ARR) to be internalized via clathrin-coated pits (CCPs; Irannejad and von Zastrow, 2014). (2) At the endosomal station, GPCRs activate sAC to trigger a second wave of cAMP production and to sustain Erk signaling in an EPAC-dependent fashion. sAC activation at the PM and at endosomes requires Ca$^{2+}$ and bicarbonate (not depicted for simplicity; Inda et al., 2016). The signaling cascade leading to Ca$^{2+}$ and bicarbonate release (particularly at the endosomal station) and the exact mechanism of action of sAC are not completely characterized. The different forms of ACs, and the corresponding cAMP gradients generated by them, are depicted in different colors to highlight their different roles. Whether this specificity reflects different isoforms and/or regulation remains to be clarified.

"The work reported by Inda et al. in this issue integrates and expands our emerging knowledge of localized GPCR signaling at the endosomal level. In a previous work, these authors showed that Erk activation by corticotropin-releasing hormone receptor 1 (CRHR1) signaling is biphasic, with an early response originating from the PM and a late response that is endocytosis dependent (Bonfiglio et al., 2013). In their new work, Inda et al. (2016) reinforce these findings, showing that distinct cAMP sources are differentially involved in the two phases of Erk signaling. Whereas tmAC and sAC both contribute to the acute Erk signaling response, sAC is specifically involved in the sustained “endocytic” phase of Erk signaling in hippocampal neuronal cells (Fig. 1).".

Note phase 1 and 2, targeting one phase or another, rather than continuous action, is spatially encoded.

In the paper (same as link 1 in the my original question), the authors state:
"there a multitudes of known GPCR interacting proteins, but only a subset of these have been shown to participate directly in basal GPCR organization"
what does basal GPCR organization even mean?


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Why are bees attracted to Stag Hides in Lime bath
A friend has some stag hides in lime-water solution. He noticed the stag hides were covered in bees of several different species. He also noticed that the nearby sheep hides in a similar bath have no bees. What attracts the bees to the stag hides? Alternatively, what repels them from the sheep?
There are a lot more bees when the sun shines. The bees principle seemed interested in the hairy side of the hides. There is a small stream and a dried up pond nearby, so probably other places if they just wanted a drink. There is no noticeable scum on the water of either container.

Photos of the stag hides:

Photo of the sheep hides:

Short answer: They are probably looking for minerals from the lime
Explanation:
Insects from time to time need minerals (salt) they can't get from there floral diet. Because they can't chew these directly of rocks, they suck these up from puddles or occasionally from human sweat. This behaviour is well known from butterflies, but also from honeybees
Notice BTW that there are indeed several bee species (Probably Apis mellifera, Bombus terrestris agg. and Bombus lapidarius), but also several flies including Eristalis cf. tenax, a hoverfly.
Why not from the sheep hides? I'm not sure, but the water around the sheep hides looks awfully brown, suggesting there are a lot of humic acids un the water. That might be less attractive to the insects.

So a combination of attracted to both and repelled from the sheep. I shared this post and answer with my friend and he is pleased with the answer and will be letting the other people he was discussing with know.