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How to find a Cancer PPI network data
I need a Protein Protein Interactions network of Cancer (any type - just I want a PPI of any type cancer), I search and see DIP PPI and ....
How to specify that is  Cancer PPI and how to get a PPI of Cancer ?

What Cancer? What species? Please [edit] your question and make it more specific.

I assume that your are looking for human data. Take a look at these papers:
Human Cancer Protein-Protein Interaction Network: A Structural Perspective
From the abstract:

Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network). The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%). We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3, a multi interface, and Raf1, a single interface hub. The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub. These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates.

Tools for protein-protein interaction network analysis in cancer research
From the abstract:

As cancer is a complex disease, the representation of a malignant cell as a protein-protein interaction network (PPIN) and its subsequent analysis can provide insight into the behaviour of cancer cells and lead to the discovery of new biomarkers. The aim of this review is to help life-science researchers without previous computer programming skills to extract meaningful biological information from such networks, taking advantage of easyto-use, public bioinformatics tools. It is structured in four parts: the first section describes the pipeline of consecutive steps from network construction to biological hypothesis generation. The second part provides a repository of public, user-friendly tools for network construction, visualisation and analysis. Two different and complementary approaches of network analysis are presented: the topological approach studies the network as a whole by means of structural graph theory, whereas the global approach divides the PPIN into sub-graphs, or modules. In section three, some concepts and tools regarding heterogeneous molecular data integration through a PPIN are described. Finally, the fourth part is an example of how to extract meaningful biological information from a colorectal cancer PPIN using some of the described tools.

Thanks, I looking for Cancer Data of Human or ... ! These are use Cancer data ?

Can you please expand your answer beyond dropping in some links? Some text about what is written in the articles would be great. Links tend to disappear sometimes which would make this answer useless.

@Digicom yes they focus on cancer data. Please take a look at http://www.doc.ic.ac.uk/~natasha/course/docs/cytoscapeTutorial.pdf to see a practical example of PPINs. Just follow the steps in the tasks and try to answer the questions. It will give you hands-on approach with what I suppose you are looking for.

@asalic Thank you. I will perform your tutorial and read your article.

@Chris All right, now the links won't disappear, unless DOI.org does. I cited the abstract from the papers, so any reader can now have an idea about the texts.

@Digicom You're welcome. Good luck and have fun.


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Do residue sequence numbers in PDB files correspond to the positions in the backbone?
In other words if two residues have sequence numbers, say, 20 and 21 then are they next to each other in the backbone? If no (or not necessarily) then is there any way to find consecutive residues from a PDB file?

The residue numbering convention in the PDB is more-or-less entirely up to the depositor of the structure. While generally speaking sequential numbers are next to each other, there is no guarantee of that fact.
For example, the PDB allows for what are called "insertion codes", which are extra residues which interrupt the regular sequence progression. For example in the PDB entry 1IGY in between residues 52 and 53 on chain B is residue "52A". Normally, these insertion codes are used because the depositors want the numbering scheme to correspond with some external numbering standard, and there are extra residues (e.g. from affinity maturation) which would throw off this numbering.
Alternatively, the depositor might choose to mix up the numbering significantly. An example of this is the PDB entry 4OO9, which is the transmembrane domain of human mGlu5 with a lysozyme insertion to help crystallization. The mGlu5 is numbered from chain A from 568 to 832, but between residues 678 and 680 are residues 1002 to 1162, representing the inserted lysozyme. (So it goes 677, 678, 1002, ...)
Ultimately, the only reliable way to tell if two residues in the PDB which are sequential in numbering are actually sequential polymerically is to actually look at the coordinates and see if they're in a bonding configuration. There may be some information about "out-of-order" residue numbering in the header lines, but that's not always clear. Usually you have to deal with such situations on a case-by-case basis.

Thanks to @R.M. for the good description. I've also found a nice, detailed description with a few interesting examples here.

source: Stack Exchange

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how do I find the number of bp in chromosome 3 by knowing number of bp in chromosome 1?
If i have a number of bp in chromosome 1 for example(298,295,559 bp) can I use this number to find the number of bp in chromosome 3.

Chromosome of what?

human chromosomes

Since chromosomes are numbered according to size, the only thing you can say is that #3 is less than 298,295,559 bp.

http://www.ncbi.nlm.nih.gov/projects/genome/guide/human/

You cannot.  BP numbers vary between the chromosomes.  Chromosomes are numbered by size, so you can always say that 2 is smaller than 1; however, there is no function to determine the exact size of one chromosome based on the exact size of another.

Except for the point raised by MattDMo. At least we can tell that chromosome 3 is shorter than chromosome 1 as they are numbered based on their length (to the exception of sexual chromosomes)

That is certainly true but knowing that two has fewer bp than one and twenty three has fewer than that doesn't really help us determine the number in either based on the number in one.  Perhaps it points at a smaller stack of hay, but doesn't predict where the needle will be.

I agree that It offers little information but it is sufficient to make one of your sentences wrong. You say There's no relation function for the chromosomal number to the number of base pairs it has., while there actually is such a relationship.

Chromosomes vary in length and there is no reason to expect a nice relationship in the relative length of chromosomes.
However, by definition chromosomes are numbered from the longest to the shortest (except for the concern of sexual chromosomes). The chromosome 1 is the longest, the chromosome 2 the second longest and the chromosome 8 is the 8th longest. Following @ping comment Also note that human chromosomes 21 and 22 are numbered the "wrong" way round in that 21 is actually smaller than 22; so even for autosomes using the numbering to determine size rankings doesn't necessarily work.
In consequence, if you know that the chromosome 1 is x bp (base pairs) long, then the only thing you can tell about chromosome 3 is that is has less than x base pairs. Note that x ≈ 250,000,000 base pairs in human.

Also note that human chromosomes 21 and 22 are numbered the "wrong" way round in that 21 is actually smaller than 22; so even for autosomes using the numbering to determine size rankings doesn't necessarily work.

I didn't know that. Thank you, I added your comment in the answer

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meaning of "usually greater than 200 base pairs"
From "Risks from GMOs due to Horizontal Gene Transfer", by Paul Keese:

Homologous recombination. All cellular organisms have molecular functions dedicated to recognizing and recombining DNA molecules that have extensive sequence similarity at the region of cross-over, usually greater than 200 base pairs. The principle molecule involved in homologous recombination belongs to the family of RecA/Rad51 DNA binding enzymes that catalyse post-replicative strand exchange during meiotic/mitotic recombination or repair of DNA double-strand breaks. These enzymes show some sequence preferences (Raja et al., 2006) and require minimally efficient processing segments of 20–30 base pairs to initiate strand exchange (Majewski and Cohan, 1999).

I'm not sure of the meaning of "greater than 200 base pairs". Does that relate to "sequence similarity"? Is similarity somehow measured in base pairs?
Or does that relate to "the region of cross-over"? Is the meaning "the region of cross-over is usually greater than 200 base pairs"?

No, similarity is not measured in base pairs. They are talking of regions of cross-over being marked by at least 200bp of highly similar sequence between the two strands involved in exchange.


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How does Tylenol relieve pain without making me drowsy?
So I have a crown that popped out and has been killing me, I've lived on a steady diet of Tylenol since it popped out. I've noticed that aside from killing most of the pain from the tooth, the tylenol hasn't made me particularly drowsy, which to my understanding was how most pain killers work. If it doesn't just put me to sleep, how does tylenol work to numb pain?

Opiate pain killers or pain killers that act on opioid receptors have a different mechanism of action than NSAIDs. They tend to act as receptor agonists which exert their effect on the CNS (oxycodone as an example). Opioid receptors are associated with analgesia, CNS depression, sedation, etc. Tylenol on the other hand is thought to exert its effect by downmodulating cyclooxygenase, and therefore inhibiting the production of prostaglandins like H2, which is a precursor to lots of pro-inflammatory compounds.


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What causes inhalation during breathing?
I have read here that the two major inhalation muscles are the (1) diaphragm and the (2) external intercostals.
Additionally, inhalation can also be caused by (1) expansion of the abdominal cavity, or (2) movement of the pelvic floor.

I thought that inhalation was caused by muscles that essentially cause the intra-thoracic pressure to drop, thus causing air to flow into the lungs to fill the absence, like a straw. This is what I thought caused us to inhale.
MY QUESTION
If my understanding is correct, then do the muscles cause this drop in pressure? Which spaces within the trunk experience this drop in pressure? If not, how exactly do these muscles facilitate inhalation?

there are two main muscle groups. The diaphragm and the rib cage assists with breathing by using a pair of muscles located between each rib known as the internal intercostals and external intercostals. which are mostly unconcious and which change depending on hormones and air intake. The upper group is for high oxygen intake and it is very mysterious because it is a passive muscle group which allows the ribcage to become flexible and expand to a higher lung volume, try hyperventilating using only the diaphragm! The diaphragm is for rest and sleeping.

Other than the branching, gas filled spaces within the lungs themselves, there are no other spaces within the trunk and above the diaphragm that contain gas - for a normal healthy lung. All the other space is filled with either tissue or fluid that serve to extend any pressure applied by the muscles you mentioned to the pleural sac that surrounds the lungs - like a hydraulic device. It's only the gas space that expands in volume and this is what causes air to move into that space through the upper airway. Exhalation is caused by elastic recoil - the springing back of this compartment.

Either the diaphragm or the rib lifting muscles dependently or together can enlarge the lungs and cause a (partial) vacuum. The vacuum is caused by enlargement of the the chamber like for a bike pump.
The less known muscles raise and lower the ribs:

https://en.wikipedia.org/wiki/Intercostal_muscle

Here's a video of diaphragm movement:
https://www.youtube.com/watch?v=hp-gCvW8PRY

The best is to research the muscle groups on google images and check out graphs of them to see lists of studies.
The pressure drop is actually an expansion of the lung cavity, with the diaphragm being very good at drawing air in like a serynge and the expiration groups being stronger for exhaling, they can both expand, contract and compress for activities like swimming and coughing. You may want to check graphs and studies of air pressure, then it gets complicated with trans-thoracic and trans-pulmonary pressure:

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How did the first life form on Earth reproduce without DNA?
How did the earliest life forms exist without DNA? The most likely scenario I can think of for life happening from nothing is that, over billions of years, with trillions of water molecules and dust particles and chemicals moving around, eventually a small clump of this murky stuff formed a pattern. Or maybe, under some lucky conditions, a whole mud pit of such a inorganic system started forming into structures which had a pattern of motion, and one of those structures became the first sub-cell, like a primitive ribosome or something. I remember learning some parts of cells were once separate entities, but there are massive gaps in that idea.
The primary gap being: How did reproduction occur in that incredibly lucky, freak incident of a tiny dust clump or whatever? How did reproduction occur without DNA? Do scientists have a theory on how the first reproduction occurred?

I find it surprising that people usually think of incredibly lucky events. I mean, 3-4 billion years is an incredible amount of time. 100 years is already a lot of time. Adding a zero makes it 1000. What happend 1000 years ago... hum. Another 0 will bring us beyond the origin of cultural development I guess. Oh hey, there are another 5 zeros to add :)

Jack Szostak's lab works on this kind of stuff. It really seems impossible the know how life first originated, but he and his colleagues have some hypotheses that can at least be shown to be plausible. Check out this video: https://m.youtube.com/watch?v=PqPGOhXoprU

Before DNA must have come RNA, as DNA is only a way of storing information. And primeval soup experiments suggest that amino acids should have played a significant role in the early stages of reproductive life, because they are one of the first molecules that form.

There are a lot of organic molecules formed naturally in the universe, I'd guess that those are a part of the pussle.

@canadianer I think Jack's claims (specially about lipid membranes) are controversial.

@cel Revisiting this question, I think luck is balanced by complexity in this case. Yes, billions of years are a long time for extraordinary things to not be so "lucky" but also the complexity of the system can have an equally massive exponential unlikeliness. And in evolution we say that millions of years will eventually produce a change that works, but what happens if the thing that works cant reproduce? Because reproduction is the main factor. So Im just curious to know exactly how we can explain this. We've made hypotheses for pretty ancient feats in biology.

@Viziionary Take into account that complexity increases over time. You don't start from a very complex system from the beginning, rather simple systems accumulate complexity over time. At the beginning, given a rich "primordial soup" you can imagine billions of molecules interacting and forming very different systems (pathways, micelles, etc..) if one of that systems is complex enough to accomplish reproduction the game is on.

So earth is estimated to be around 4.5 billion years old, and life has existed for about 3.4-3.9 billion years of that, around >75% the time. For a little perspective, Homo sapiens have been around for up to ~250,000 years, just 0.00005% of the time earth has existed. In that time the earth has changed massively, early earth was pretty hostile, but that could have made it a good place to develop early life.
What was the pre-cursors to self-replicating DNA like?
Well one popular theory is the RNA world hypothesis, which suggests that precursors to life as we know it were RNA molecules that could self replicate (reproduce). These RNA molecules supposedly evolved in to life in the DNA/RNA/Protein world via an intermediate stage of ribonucleoproteins. The reason why RNA could have preceeded DNA based life is because it has the potential to carry information and to be the catalyst of its own synthesis.

"The unique potential of RNA molecules to act both as information carrier and as catalyst forms the basis of the RNA world hypothesis.
"RNA therefore has all the properties required of a molecule that could catalyze its own synthesis (Figure 6-92). Although self-replicating systems of RNA molecules have not been found in nature, scientists are hopeful that they can be constructed in the laboratory." in Molecular Biology of the Cell

However, the RNA world probably wasn't the first step on the road to self-replicating life, but other polymers with characteristics similar to RNA that could have acted as the template and catalyst required for RNA synthesis.

"Although RNA seems well suited to form the basis for a self-replicating set of biochemical catalysts, it is unlikely that RNA was the first kind of molecule to do so. From a purely chemical standpoint, it is difficult to imagine how long RNA molecules could be formed initially by purely nonenzymatic means. For one thing, the precursors of RNA, the ribonucleotides, are difficult to form nonenzymatically." in Molecular Biology of the Cell

-

"Shapiro doesn't think it's necessary to invoke multiple universes or life-laden comets crashing into ancient Earth. Instead, he thinks life started with molecules that were smaller and less complex than RNA, which performed simple chemical reactions that eventually led to a self-sustaining system involving the formation of more complex molecules." on LiveScience.com

This means that the first self-replicating RNA molecules were preceded by other molecules that were important in the progression from simple atoms, through non-biotic molecules, to large self replicating polymers which can be considered life. Copley et al suggest a progression in detail, with monomers being an early step in the progression (see figure 4 in the paper, and section 4 for extensive detail) where monomers precede multimers, which precede micro-RNA.

"Our model describes a continuous path for emergence of this sophisticated system
  from a simpler reaction network fueled by geochemical processes. The processes involved in metabolism and replication were intertwined from the very beginning, a concept that neatly eliminates the chicken/egg problem. Further, this model suggests that many features of the RNA World, and indeed modern life, arose long before the RNA World and were retained as pre-biotic systems became more sophisticated." - Copley et al

An alternative and slightly different theory suggests that viruses were the precursors to modern life, a so called Virus world. Similarly it supposes that early replicators were RNA based, but that viruses evolved in to the earliest forms of self replicating DNA carriers.

"The Virus World Theory is closely related to the RNA World Theory, which says life first evolved as small pieces of RNA that slowly developed into complex DNA-carrying organisms. The Virus World Theory agrees that life's genetic material began as RNA. But it differs by arguing that the ancestors of viruses evolved before cells." from NationalGeographic.com.

Further good reading here, here, and here. I also recommend the book The Story of Life as an interesting related read.

I just want to add a link to this answer. This video https://youtu.be/w2lqZL153JE illustrates how simple molecules can replicate without the need of any enzymatic reaction.

Short answer
Before the RNA world, mineral surfaces may have facilitated the prebiotic containment and organization of biomolecules. Minerals are believed to have promoted the transition from a dilute chaotic prebiotic “soup” to highly ordered local domains rich in key biomolecules.
Background
As pointed out by others, the transition from a hereditary DNA world as we now know it, is believed to have evolved through an RNA dominated world. Life in this RNA world is believed to have deployed self-replicating ribonucleic acid (RNA) molecules and it is hypothesized it was the precursor to all current life on Earth. Because RNA can store hereditary information and can have enzymatic properties, it can potentially be self-replicating and catalyze critical biochemical processes. In contrast, DNA can only store information. Hence, RNAs could be used as information storage and catalysis (Alberts et al., 2002). One of the strong motivators for this hypothesis is that ribosomes contain catalytic RNA that is critically involved in protein synthesis (Orgel, 2004).
However, I think the question here is, what happened before the emergence of RNA, i.e., what led to the formation of RNA in a prebiotic world? The question of life’s origin is in essence a problem of the information storage and transfer from a prebiotic, geochemical environment to the first life forms, as aptly stated in the question. Crystalline surfaces of common rock-forming minerals are likely to have played an important roles in life’s prebiotic origins.

Metal sulfides (such as pyrite) and oxides promote a variety of organic reactions, including nitrogen reduction, hydroformylation and amination.
Fine-grained clay minerals and hydroxides facilitate lipid self-organization which could have mediated the formation of lipid membranes necessary for cell membrane formation. The cell is the building block of all life and critical to its existence is its integrity. A closed membrane allows to build up a membrane potential necessary to sequester biomolecules inside and keep toxins and waste products out.
Inorganic minerals have been shown to be able to mediate the polymerization of RNA monomers. Hence, minerals may have been the prebiotic template for RNA.
Surfaces of common rock-forming oxides, silicates, and carbonates selectively concentrate amino acids, sugars, and other molecular species essential to present-day life. These minerals are also capable of stabilizing these monomers. Cells are much like little containers with essential biomolecules. The capabilities of minerals to sequester them, accompanied by the intrinsic capability to form lipid structures could potentially make minerals hot spots for life formation.
Chiral mineral surfaces are actually able to separate left- and right-handed molecules. Chirality is central to current life. For example, in humans the major form of amino acids is the levo form, or left-hand variation. (Hazen & Sverjensky, 2010)

References
- Alberts et al., Molecular Biology of the Cell. 4th ed. New York: Garland Science (2002)
- Hazen & Sverjensky, Cold Spring Harb Perspect Biol (2010); 2(5): a002162
- Orgel, Crit Rev Biochem Mol Biol (2004);39(2):99-123

You can do without DNA, you need RNA to make proteins. Living organisms create and maintain a bubble that is very far removed from thermal equilibrium. So, you can speculate that at the origin of life, conditions existed where processes that are far from thermal equilibrium could still occur naturally without the support structures one finds inside living cells. This suggests conditions involving very low temperatures e.g. in comets.
If you consider complex molecules forming in comets then some unstable intermediary molecule can be metastable at the low temperatures there. Chemical compounds will only react with their immediate neighbor, not with molecules that are a bit farther away. This allows for intermediary unstable compounds to be created and to survive for long enough until it gets a chance to undergo further reactions making it more stable. Of course many more unstable molecules will be formed in such a process.
Imagine this happening in a comet kicked out from the Oort cloud that is periodically coming close to the Sun for a short while. When it is far away from the Sun, chemical reactions induced by cosmic rays happen in the interior which creates all sorts of strange chemical compounds. When it comes close to the Sun part of the comet melts and then some of the chemicals formed can disperse. The molecules that are more stable will have a better survival probability.
Darwinian selection of molecules may thus happen inside comets and that may have led to RNA molecules and many of the proteins and enzymes that we today find inside cells. All these enzymes and RNA together could then replicate by interacting indirectly with each other, but they are then not organized inside cells. You can speculate that cells could have evolved much later on Earth after  the organic materials from comets arrived on Earth.
Alternatively, you can consider scenarios where during the Late Heavy Bombardment processes leading to life where going on both on Earth, Mars and Venus. It is known from studies that the impacts that happened during that time were powerful enough to eject materials from one planet and dump them on other planets and that molecules essential for life could survive the entire transfer process. This means that it's possible that one part of what is necessary for life was developed on, say, Mars, another part developed on Venus (which at that time had oceans) and yet another part developed here on Earth and that it all came together during the Late Heavy Bombardment.
The results of a recent experiment on the formation of organic compounds in the interstellar environment has appeared in this article.

The molecules that are more stable will have a better survival probability. Darwinian selection of molecules may thus happen [...] and [...] may have led to RNA molecules and many of the proteins [...]* - Of all the compounds I can think of, RNA and especially proteins are ranking amongst the least stable. I like your approach, but I don't get your reasoning. Further, references would be appropriate too.

@Christiaan I'll try to dig up some refs, the basic scenario was mentioned in an old NGC documentary, they showed experiments being done involving freezing materials similar to what you find in comets and then bombarding that with radiation, partially thawing it, refreezing and then repeat this cycle.

Well another (and less popular) theory called Panspermia says that life, actually, came from space, it was not originated here in earth. So this completely change everything, because the conditions where life would have been formed could be very different from our's planet, so we can't make any predictions with this considerations in mind. But thinking in the DNA and our planet, the possibility that the DNA replicates with the help of other molecules could be possible. I don't think that it's necessary the steps of DNA to RNA and proteins or even DNA to proteins, to explain his reproduction, maybe some proteins with catalytic activity already exists and DNA with some good information in itself, it reproduce and continue evolving till have enough information to create other molecules with beneficial properties. We all know that proteins can actually be created by pure chemical reactions, i think that's the real secret of the life. We dont need a creator molecule to manufacture the entities entrusted with the job of create other molecules. The rest of the story is just the azar.


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Does the human abdominal cavity use pressure to maintain posture?
Several of my PT friends have referenced a physical therapist p who has studied breathing named Mary Massery. In her articles, she has referenced the idea of "intra-abdominal pressure"
http://www.masserypt.com/images/pdfs/Massery.%20Seating%20Symposium.%20Vancouver%202010.pdf
See page 2
If I am understanding her correctly, she is basically saying that

an unopened soda can is sealed and hard because of the pressure inside the canister
the human trunk tries to support itself upright. In this sense it is rigid. Therefore, it too must be using an internal pressure to create the rigidify necessary for uprightness. This is what she calls "intra-abdominal pressure."

My Question
Given what is known about the mechanics of human breathing, do the pressures of the abdominal cavity actually behave this way? Does the abdominal cavity rigidity itself using some sort of pressure? If so, how does this pressure mechanism relate to the mechanisms used during respiration?
If I am understanding her model correctly, I am extremely skeptical. A soda can is really not similar at all physically to the abdominal cavity.

Given our chat, I am confused whether this question is on posture (ref. question title) or on respiration (ref. body question). What exactly is your question?

I was previously trying to understand respiration in an attempt to understand how pressure is utilized in the body during breathing. My logic was if I can better understand how pressure is utilized during inhale, I would have enough info to answer this postural question myself. Nathan's answer wasn't sufficient. It didn't actually address the levels of pressure in the abdominal cavity itself and how that works with respiration. My ultimate goal was understanding this issue. But I got the sense the respiration approach seemed to not be specific enough, so I am presenting a different viewpoint

@AliceD Massery's claim is that posture is dependent on respiration in a sense. She seems to be making some claim about how pressure in the abdomen affects posture. At least, I think that is what she is saying.

Great, thanks for explaining. I took a quick look at the cited abstract and it seems that the author is simply saying that bad posture impairs breathing, but I just took a quick glance.

Haha I would argue my answer was sufficient to the question, which had no words like "intra-abdominal pressure" or even posture in it.  Hope you find what you're looking for, though intuitively a meaningful relationship between the two sounds bogus to me.

I found this review which can help to clarify this notion of intra-abdominal pressure : Intensive Care Med. 2009 Jun;35(6):969-76.
What is normal intra-abdominal pressure and how is it affected by positioning, body mass and positive end-expiratory pressure?
De Keulenaer BL1, De Waele JJ, Powell B, Malbrain ML.
The authors mention that there is indeed a positive pressure in the normal subject. This IAP is contributed by gravity, uniform compression (abdominal contraction, diaphragmatic contraction, mechanical ventilation, rib cage excursions) and shear deformation, this last one depending on the intrinsic stabilty of the tissue.
The authors conclude that "the abdomen behaves as a hydraulic system with normal IAP of about 5–7 mmHg..". Kind of a can of soda, without the bubbles...
Pathologies affecting this pressure are named "abdominal compartment syndrome) and result from multiple etiologies (obesity, trauma...,).

This addresses the IAP issue, but not the actual question about posture.

@kmm. Right, but I would say that this amount of pressure is not enough by itself to maintain a rigid posture. As a comparison a tyre normally filled at 2 bars is at 1500 mm Hg.


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Why is it easy to separate the cotton wool from the roots of young plants grown in cotton?
In my book it is said that if we grow Maize and Gram seeds in wet cotton, after the seeds are sprouted it is easy to separate the cotton wool from the roots of young plants.
My question is why is it comparatively harder to separate roots from soil while it is easy for cotton wool?


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How can hyperplasia occur in non-fetal hypokinesis?
I have read that the hypoplasia can occur in fetal hypertrophic cardiomyocytes.
However, I am interested if this can be theoretically be possible in non-fetal cardiomyocytes which are hypokinetic. I think there must be some growth factors to allow this to happen. Other proposals

lysozyme, sepsis and cardiac adrenergic neural response / NO / G protein 1
shear-wave, vortex and refolding of proteins [2 Yuan et al.]

Sources

1 Lysozyme, a mediator of sepsis, impairs the cardiac neural adrenergic response by nonendothelial release of NO and inhibitory G protein signaling.
2 Yuan. Shear-Stress-Mediated Refolding of Proteins from Aggregates and Inclusion Bodies.

How can hyperplasia be regulated in non-fetal hypokinesis?


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Artificial Adrenalin
I was watching Sherlock Holmes - A Game of Shadows yesterday. I saw that he had developed a serum that causes a  person to react like he has been injected with adrenalin.
Watsons's dog fainted, I think, and the moment Holmes injected the dog with this serum, it jumped up and ran away.
So my question is: Is it possible to extract adrenalin, or was it a steroid?

Yes, it is possible to extract adrenaline from the adrenal glands.  This was done in 1901, a patent was filed, and it was marketed under the name Adrenalin.  It is also known as epinephrine.  It is synthesized nowadays for most uses.


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How does intron retention make the alternative transcript non-coding?
I faced with a non-coding transcript that specified as one the isoform of BIN1.
It sounds that this isoform generated as a result of alternative splicing with a intron retained; am I right? However, Ensembl reports that the transcript has 5 exons, could you please let me know more about this event? How does the transcript have 5 exons and all are non-coding?
Also, I'll glad to hear why the organism or cell has retained such a transcripts? they have any function?
Thanks

An exon is just a sequence that is retained in the mature RNA. It doesn't have to be protein coding.

I could not find the sequence of this transcript but it must have become non coding most probably because retention of the intron must have caused a frameshift leading to loss of the ORF (there are no stop codons).

Some changes could also destabilize RNA and make the turnover so fast that it does not code. As an example, the intron could add a target for interfering RNA (siRNA) that both blocks translation and forces the degradation.


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Why insects are attracted towards light?
Many insects like bees, dragonflies, wasps and other small insects are attracted towards light. Sometimes they are in groups, sometimes they are in solitude. When the light source source is switched off, they do not fly, rather stay still. What causes them to be so attracted towards light?

They think that the light is the "sun", so thry go towards to the light.( insects are guided from the suns position ).

@Orphee this is likely a myth

No it isnt.Its true.

Not a myth, but is among the current theories. Except it is more likely the moon. To fly in a straight line, an insect might use the moon as a stable landmark. An artificial light however, used as a landmark in this way, would result in flying in a spiral.

Phototaxis is a common behavior to many species, including insects but not only (e.g. most fishes do phototaxis).
The ethological reason is quite simple: with light you can see, and if you see you can find food, avoid predators and increase your survival chances. Phototaxis is thus a very strong innate reflex: at the neural scale in many species it is "hardwired".
I would say that the initial "purpose" of this reflex was to avoid shade areas during daytime, the light during nightime being pretty rare before man invented artificial light. The fact that insects gather around artificial lights during nightime is, to me, a side effect of this reflex.


text:
How can a sequence be downloaded from UCSC genome browser
If I have genome coordinates is there a simple way to download the entire intervening sequence from the UCSC genome browser? I found some fancy way of using ftp but I can't figure it out.

Yes. Table browser allows you to do that.
In the dropdown box called output format select sequence and click the button named get output. You'll get the sequence.
If you don't think it works then this is the output that I am getting. (Fist few lines):
>hg19_gold_AL096773.6 range=chr1:115207623-115347729 5'pad=0 3'pad=0 strand=- repeatMasking=none
GATCTGCCAGCGTCCACCTCCCGAGGTGCCGGGATTGCAGACGGAGTCTG
GTTCACTCAGTGCTCAATGGTGCCCAGGCTGGAGTGCAGTGGCGTGATCT
CGGCTCGCTACAACCTCCACCTCCCAGCCGCCTGCCCTGGCCTCCCAAAG
TGCCGAGATTGCAGCCTCTGCCCAGCCGCCACCCCGTCTGGGAAGTGAGG
AGCATCTCTGCCTGGCCGCCCATCATCTGGGATGTGAGGAGCCCCTCTGC
CTGGCTGCCCAGTCTGGGAAGTGAGGAGCGCCTCTTCCCGGCCGCCATCC

Actually Table browser is quite useful as you can get different kinds of data for a given genomic region which include annotations, variation, transcription factor binding sites etc.

Actually this does not seem to give me intervening sequence for coordinates. (I'm trying this: chr1:115227814-115227978) It just outputs some coordinates.

@TheNightman In the dropdown box called output format select sequence and click the button named get output. You'll get the sequence.

I did find a way of doing this using UCSC das. If the pertinent info is changed in the page address here, it will output the intervening sequence between two locations:
http://genome.ucsc.edu/cgi-bin/das/hg19/dna?segment=chr1:100,200


text:
IBD-value and IBD-fraction
I have some questions about the IBD-value and IBD-fraction for two of the condensed identity states, $S_1$ and $S_7$.
My questions:
1) Is the IBD-value for the individuals i and j two (2) in $S_1$ and 2 in $S_7$ as well? (We can form the pairs aa aa in $S_1$ and the pairs aa and AA in $S_7$. The number of alleles in $S_1$ (and similarly for $S_7$) for individual i that are IBD with alleles of individual j is therefore 2)
2) Is the IBD-fraction ( or coefficient of relationship) 1 in $S_1$ and 1 in $S_7$ ? (We have the fraction 2/2 in each case since both alleles of each individual are IBD with the alleles of the other individual as described in 1) )

Picture source: http://openi.nlm.nih.gov/imgs/512/114/3579841/3579841_pone.0057003.g001.png
Edit: Comments about the figure: The dots represent alleles for one particular locus. When two alleles are IBD they are connected with a line. We look at two individuals i and j and consider the SAME locus in both individuals. There are only two persons in each of the states. In state $S_5$ individual j is homosygous and it's alleles are also IBD. One of the alleles of i is also IBD with the alleles of individual j. We therefore get the three lines between the alleles.
Definitions used:
IBD-value: "An Identical-By-Descent (IBD) value is the number of alleles which are the same because of descent from a common ancestor. Since each (diploid) individual has two alleles for a given (autosomal) gene, the number of them that are IBD with another individual can only be 0, 1, or 2."
Source:
IBD-value in pedigree with inbreeding
Coefficient of relationship: "..is the fraction of genes in two individuals that are identical by descent,..."
Source:
https://books.google.no/books?id=Q9ofvKZAmpEC&pg=PA35&lpg=PA35&dq=identicAL+BY+DESCENT+FRACTION&source=bl&ots=oOc4-PBzZg&sig=jJgJarUg36b4DbyT7sphDD6j_7c&hl=en&sa=X&redir_esc=y#v=onepage&q=identicAL%20BY%20DESCENT%20FRACTION&f=false

Can you please tell a little bit more about the figure, I don't understand it. The dots represent allele I think. Is each dot a different allele? Is each trait is an individual (there are 3 individuals in $S_5$ for example)?

Of course @Remi.b. I have added some comments. I hope the comments make the figure more understandable

If I've understood the graphs correctly, then I think you are right on both questions. The difference between 1 and 7 is just that the alleles are in LD in 1 but not in 7


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blastn: What substitution matrix is used?
I'm currently working aligning sequences, and I need to compute similarity between pairs of DNA 'words' of a particular length.
For amino acids I am able to use the substitution matrices in Biopython (Bio.SubsMat.MatrixInfo).
However, I haven't found anything similar for DNA, so I read up and found that most systems use a match/mismatch scoring system where each nucleotide match and mismatch is scored and then the scores are summed. This works fine as long as I am only dealing with A, G, C, and T, but I run into problems when I get a sequence containing N or M and the like (meaning nucleotide unknown).
Is there a standard way to handle the situation with unknowns? That is, how do I score A versus N or M versus N?
Thanks in advance.

BLASTN does not use a substitution matrix. There are scores for match, mismatch and gaps which you can also define.
There is no feature available as of now to allow scoring of matches against unknowns. They are considered mismatches (as shown below). If these unknown are in the middle of a HSP, then you can probably re-score the HSP according to your scheme using a python script. If the N stretch is disrupting the HSP, then you can try relaxing the mismatch penalties and reduce word size (basically reduce stringency). I can't think of any other solution.
Query  1    CAGCGTCCANNTCCCGAGGTGCCGGGATTGCAGACGGAGTCTGGTTCACTCAGTGCTCAA  60
            |||||||||  |||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct  8    CAGCGTCCACCTCCCGAGGTGCCGGGATTGCAGACGGAGTCTGGTTCACTCAGTGCTCAA  67

Query  61   TGGTGCCCAGGCTGGAGTGCAGTGGCGTGATCTCGGCTCGCTACANNCTCCACCTCCCAG  120
            |||||||||||||||||||||||||||||||||||||||||||||  |||||||||||||
Sbjct  68   TGGTGCCCAGGCTGGAGTGCAGTGGCGTGATCTCGGCTCGCTACAACCTCCACCTCCCAG  127

Query  121  CCGCCTGCCCTGGCCTCCCAAAGTGCCGAGATTGCAGCCTCTGCCCAGCCGCCACCCC  178
            ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct  128  CCGCCTGCCCTGGCCTCCCAAAGTGCCGAGATTGCAGCCTCTGCCCAGCCGCCACCCC  18

I understand that, I'm using the term matrix because I am handling it with one programatically, but I'm generating it with said match and mismatch values. I'll modify the question to be more specific on what I want.

@ChrisChambers Edited. But I'm afraid what you are looking for is not possible without additional scripting.

"They are considered mismatches (as shown below)" This is what I wanted to know. So basically, if I run into any nucleotide that is not specifically specified (i.e. N, M, etc), it's automatically a mismatch. It doesn't matter if I'm comparing N with M, or N with A, etc etc etc, it's a mismatch, correct? That's all I need to know as I'm working on implementing scoring to determine similar words for alignment.


text:
What is a detailed chemical explanation for describing how an enzyme may lower the activation energy of a reaction?
If you can provide some sound reasoning that touches on tertiary structures of proteins and does not use a lot of advanced chemistry jargon that might be really helpful, especially for an intro biology student.  I think my problem is that activation energy is lowered by an enzyme and I know how its like on the Gibbs Free Energy diagrams.  However I think I've been taking this as an axiom and I'm not really sure why.

Have you read the wiki article? What specifically don't you understand? How should we provide a detailed chemical explanation without using advanced chemistry jargon? You need to [edit] your question and explain exactly what you know and where exactly you're stuck. Otherwise, this question may be closed as violating our homework policy. "Homework" is interpreted to mean any academic or other assignment, test preparation, or task given in relation to a class, educational setting, or self-learning.

Take a piece of wood and set it down on a slippery surface. Now take a saw and without holding the piece of wood try sawing through the wood. Much of the energy will likely be dissipated by the board sliding around and you will expend a lot of your own energy trying to cut the wood without much success. Now take the wood and securely fasten it between two clamps, like on a work bench. Now use the saw and cut through the wood. All of the energy that you are expending is transferred to the cutting action and isn't dissipated because the wood cannot move around. The clamps are holding it in place

The clamps are like the enzyme. They provide a more efficient path to catalyze the reaction. Just as the clamps make each of your strokes with the saw more efficient than the exact same stroke when the wood was free to move on the slippery surface.

Nice description @AMR :) As an add-on - enzymes with multiple substrates often bind these substrates and bring them close to each other such that it is a near-perfect 3D confirmation to establish the bonding / breakage of chemical bonds.

I voted to close because "an enzyme" is too broad. There are many enzymes that may catalyze reactions in various ways.

I don't think this should be closed. It can be pretty easily answered, and I may even do so in the future.

@AliceD Thanks... If the OP had said that they were taking Biochemistry then I would agree that enzymes would not be specific enough, but they did say Intro Biology, which means at this point of first semester they may be around Chapter 8 of Campbell and they likely are just struggling with the basic understanding, although Campbell, goes into a little more detail, they are taking very broad brush terms. We aren't Academia SE where it is geared towards graduate students and professionals only, so I don't think it needs to be closed. You are right though that there are many types of enzymes.

@MattDMo the wikipedia article does not say how enzymes lower activation energy. Actually it is not easy to find answer to this question by a simple google search and most basic level biochem textbooks also do not explain this issue properly.

@WYSIWYG The Wikipedia page does go into it briefly in the Mechanism section, after the stuff about substrate binding.

@canadianer ahh yes.. I just noticed. But I do think that it won't be very clear to many people. It is not that trivial. So I won't consider this a question a homework.

BACKGROUND
We should first understand what activation energy really is and what does Arrhenius equation mean. For any chemical reaction to happen you need to break some existing bonds in order to form new bonds. Breaking existing bonds consumes some energy; in other words you have to convert the reactant to an active form which undergoes a reaction readily. The energy that is required to do so is called activation energy.
It is essential to note that activation energy is an empirical term because it does not actually consider the reaction mechanism. The above example of bond-breaking and re-formation can happen in many ways. Some mechanisms may require less activation energy than the others.
The Arrhenius equation denotes the relationship between reaction rate and activation energy:
$$k=Ae^{-\frac{E_a}{RT}}$$
where $k$ is the reaction rate constant, $A$ is the Arrhenius constant (an arbitrary proportionality constant), $E_a$ is the activation energy, $R$ is the universal gas constant and $T$ is the absolute temperature.
This equation tells you that the rate of reaction decreases with increase in activation energy and increases with increase in temperature.
The temperature indicates the internal energy of the system i.e. the movement of molecules. Collision between molecules is essential for chemical reaction to happen and increased temperature increases the chance of collisions (due to higher molecular movement).

How do enzymes lower activation energy of a reaction?
They either do that by increasing the chance of interaction between the reactants or diverting the reaction to follow a different mechanism that has a lower activation energy.
As you know, the enzyme binds to the reactants and forms a complex. This effectively brings the reactants closer thereby increasing the probability of their "collision" which in turn increases the reaction rate. This can be further extended to the level of molecular orientation. The analogy presented by AMR in their comment is great. It is one of the mechanisms by which the free energy of the system would increase — by reduction of entropy. Clamping the substrate in place can expose its reactive groups to the co-reactants, thereby allowing the reaction to proceed faster.
In most other mechanisms of enzyme catalysis, the functional groups in the enzyme active site react with the substrate to form intermediates and these intermediates which have lower activation energy give rise to product faster than the substrate in absence of the enzyme.
It is to be noted that this entire process of catalysis adds a few extra steps to the actual reaction (binding,  formation of intermediates etc). The catalyst would be effective only if these combined rates are still faster than that of the uncatalyzed reaction.


text:
Why is it often the case that an enzyme is favorable only towards one direction of a reaction and not both directions?
In class when we're studying enzymes like amylase or protease it only works well when you're using it to break down compounds like polysaccharides.  I'm just curious but why is it not possible for enzymes to work efficiently in a reverse reaction?  I mean a formula like
$\ce{amino\ acid + amino\ acid + energy<=>H2O + protein}$
does imply that our reaction can go both ways , right?  But why is it that the enzyme favors the catabolic reaction?

An enzyme does a specific task. Sometimes there is one that does the opposite task. For example, for the protease that cleaves a protein at a specific site, there is the ribosomal complex that builds proteins amino acid by amino acid. Enzymes are generally too reaction-specific to perform both forward and reverse reactions.

@MattDMo - But enzymes merely enhance the speed in which equilibrium is reached? the fact that the reaction keeps on going is because of the fact that the product is being taken away, or because substrates continue to being added (I hope that last part is proper English but you'll likely get my point  :-).

Enzymes only alter the rate of a reaction, not the equilibrium. Since Keq=kf/kr and Keq is constant, an increase in forward rate (kf) requires a corresponding increase in the reverse rate (kr). Therefore, enzymes do catalyze the reverse reaction.

Reactions have their set equilibrium constants, which are determined by the free energy of the reactants involved, and this cannot be changed by catalysts, including enzymes. Let's look at your example of peptide bond formation between two amino acids A and B,
A + B + energy $\leftrightharpoons$ H$_2$O + AB
This reaction might proceed in both directions; the rates of forward and reverse reactions depend on the free energy of A,B and the dipeptide AB, as well as how much "energy" we are talking about in the left-hand side. Let's ignore the energy term to start with, and just consider
A + B $\leftrightharpoons$ H$_2$O + AB
As it turns out, this reaction is not favorable: the $ \Delta G$ is about 10 kJ / mol (it depends a bit on which amino acids we're talking about). So this reaction goes in the reverse direction; dipeptides actually spontaneously decompose in water. But the rate of this reaction is very slow, because the peptide bond is quite stable. We say that there is an "energy barrier" between the substrates and products. Catalysts, including enzymes, lower this barrier, which increases the rate of reaction in both directions. It is physically impossible for enzymes to change the equilibrium by catalysis alone. There are plenty of enzymes that catalyze the above reaction, causing peptides to break down rapidly (peptidases, for example trypsin).
But enzymes don't just provide catalysis: an important role of enzymes is to couple two (or more) reactions together, so that a favorable one can drive an unfavorable one. This is how we obtain the "energy" term in the first reaction. The most common mechanism is to couple the unfavorable reaction to ATP hydrolysis,
ATP + H$_2$O $\leftrightharpoons$ ADP + P$_i$
which itself is highly favorable ($\Delta G$ around 40-50 kJ / mol). The coupled reaction is then
A + B + ATP + $\leftrightharpoons$ AB + ADP + P$_i$
which is now both favorable (due to ATP) and fast (due to catalysis). This is pretty much what ribosomes do (well, the actual mechanism is much more complex, but the principle is the same).
So, catalysts cannot change the equilibrium of a given reaction, but enzymes can couple reactions together to come up with something that is favorable. This is a central theme in biochemistry, allowing cells to drive reactions in the desired direction; but there is always a "price" to pay, often in the currency of ATP.


text:
What is the biological significance of finding palindromes in DNA sequence?
I found a function called palindromes in Matlab that finds palindromes from DNA sequence. Now what is the biological intention behind incorporating this function? What the biological significance of finding palindrome in DNA sequences?

In regards to a Matlab function, it is probably used to find Restriction Enzyme digestion sites. Many, but not all of the recognition sites of Restriction Enzymes are palindromic, and having a function that can search a sequence and find these site will give you the location where restriction enzymes will cut. Palindromes also can represent places in genomes where viral DNA has been incorporated. So the basis of Bacterial adaptive immunity are these short palindromic repeats that encode RNA template strands for an enzymes such as Cas that can identify and cleave infecting DNA.

My knowledge of biology is extremely limited, but this is what I know of palindromic sequences:
Palindromic sequences are their own reverse complements. I have seen many restriction sites be palindromic. Also, some Transcription Factor Binding sites are palindromic. The canonical E-box site, for example, can be expressed as CANNTG.
Also, these palindromes, when occurring in pairs with a sequence between the occurrences, can double back and bind to each other (when the DNA is transcribed to RNA, say), and this would result in the RNA stem and loop structure.

Are these the only biological significance? do you have the extensive list of all possible significances ?

I wish I did. In my opinion, you learn as you go. Maybe Google+dig deep would help get a longer list of potential areas of significance, but an extensive list serves no purpose as it is not constant, and because most problem solving is done with the thought process "What are the available solutions to this problem" and not "What are the problems this individual piece of a larger solution can address"

@Rishika Please take a look at my answer

@RamRS what must be the length of such palindromes??

I've encountered 6 and 8-mers, and I remember a colleague saying those are the typical length you see, but that statement may be specific to human TFBS sites.

Palindrome sequences are important in bioinformatics understanding, it helps us to extract patterns in the genomic sequences. I'll give an example in HIV (very important application).
HIV has a nasty requirement that the virus must keep the cell alive. It does that by inserting itself into the host's genome. We know from biology that cutting and pasting of DNA works best when the sequences at either end of the cut site are identical. In fact, we usually look for palindromes, sequences that read the same backward and forward. We know the virus prefer to integrate at a palindromic sequence.
Therefore we should look at those palindromic patterns for HIV. Once we find those regions, we might do an alignment with known HIV sequences.
Please Google "restriction sites" for more information.

I'd recommend you Google restriction sites, because that's exactly what you've described here. Good example though!

@StudentT what should be the size of these palindromes?

This information is mainly from my bioinfo class notes. Palindromes or inverted repeats in genome can help in

Predicting regions in RNA that are self-complementary and that, therefore, have the potential of forming secondary structure.
Identification of binding site of regulatory proteins involved in regulation of transcription. This is because sometimes the binding protein has a couple of monomers which bind to both sequences (orignal and inverted) resulting in stronger interaction than cases where only one monomer binds.
Inverted repeats of small length are also present in some transposons (though I am not really aware of their function there).

I also came across a section on function of inverted repeats (palindromes) on google books that discusses the topic in more detail.

what must be the length of such palindromes??


text:
Best method for measuring very young plant growth?
Is there a preferable method for measuring plant growth for a plant before the "sprout" stage? Preferably at the microgreen stage? I assume it would not involve some type of leaf based measurument, since they would have few, and maybe then should be based on mass or volume somehow?

I am assuming you want to monitor these traits in controlled conditions because in wild type conditions it will be real challenge to monitor in details unless you have easily accessible way (like leave surface area and related visual traits). Remember these techniques will change according to your plant/seed and your study interest. Lot of things change during development. You need to look at characters which suits your study. You can try following,

Monitor real time oxygen consumption in controlled chamber ( Generally researcher do this by monitoring loss of oxygen in
current chamber by some non invasive method. However there are
other techniques like this which can be also implemented
depending on type of plant.)
There are some automated image acquisition techniques like this or this
Use of volatile organic compound (VOCs) traces
Change is fresh and dry weight during development
Root moisture sensors ( in microgravity environment ) like this
I don't know whether you can use this kind of sensors in any way but these are very useful in monitoring particular element in vivo

This is very big list. There many other components which you can monitor like protein content, lipids etc. However such techniques are most reliable but destructive and you can use if you have large amount of samples.   You can go through papers cited in above list. Take a look at their methods and materials. You will know how to do such measurements and what are advantages/disadvantages.


text:
Do oral vaccines exist?
Do edible vaccines exist, and if yes, what is their mode of action? Are there any edible vaccines commercially available now?

In short, yes.
A good example of an orally-administered vaccine is this Polio vaccine.

Other than polio..?? Have anyone heard about that vaccine that are incorporated into food items like potato or banana?

Rabies, typhoid and many others are available as oral formulations. @Ashwin.N You are asking about a GM Food with vaccine properties?

Yes sir!! You are right!! I was asking about that only!! Can you tell me more about GM food with vaccine properties with real life examples!! Plz

Ah. My mistake. Yes, those exist. I am not aware of any large-scale deployments, but there have certainly been trials.
Examples: Hepatitis B vaccine trial in transgenic potatoes: http://www.pnas.org/content/102/9/3378.short
E. coli vaccine, also in potatoes (mouse trial): http://www.sciencedirect.com/science/article/pii/S0264410X98800200

@Ashwin.N In that case can you edit your question to add this information?


text:
How to interpret the simple sequence repeat (SSR) on the coding sequence, but not the related protein sequence?
I have predicted some SSR repeat on the gene of interest using SSRLocator program, which the result creates a question for me. Please consider the below sequence, which is part of gene sequence of interest (coding sequence):
GGTGATGAGGTAGAGGAAGAGGCTGAGGAACCCTACGAAGAAGCCACAGA
GAGAACCACCAGCATTGCCACCACCACCACCACCACCACAGAGTCTGTGG
AAGAGGTGGTTCGAGAGGTGTGCTCTGAACAAGCCGAGACGGGGCCGTGC
The program reported CCA motif as a repeat; since this repeat located on CDS, I expected to find the related amino acid track (prolin), but I observed the Threonine track in the corresponding protein sequence.Threonine is coded by ACC. Now I would like to know which motif is really a repeat, CCA or ACC. Is it possible to consider CCA as a repeat, but just on the transcript level not protein sequence or the real repeat is ACC and the software didn't work well here? Please share me any your opinion in this regard.
Thanks so much for your help and participation.

SSR searching tools have generally no idea about CDSs: they consider DNA/RNA sequences as strings and search for tandemly repeated patterns, nothing else. It is also important to remember that in many real-life cases different programs with different settings will reports differing results.
[Knowing that you always have two strands in the genomic DNA, one has to bear in mind that also SSRs can be "read" in either direction, such that for a stretch ACCACCACC the unit can be reported as ACC or GGT. Some programs by default will report SSR units sorted according to alphabet: ACC in this case. Now with coding sequences the story is a bit different: RNA has only one strand, i.e. it has a direction, which means that you cannot "flip" RNA sequences.]
mRNA besides having a direction, codes for protein with the protein sequences sometimes showing their own repeated patterns. SSRs in mRNA are mostly tri-nucleotides or with unit lengths divisible by three and coincide with the tandem aminoacid repeats. So, from the functional point it is a ACC|ACC|ACC|ACC|ACC|ACC|ACA (note that the last element is ACA) repeat, which codes for the respective T|T|T|T|T|T|T aminoacid repeat. To formalize this idea you search for SSRs in the RNA sequence and in the protein sequence and try to find the overlap.
From the point of view of the DNA-polymerase [the enzyme which changes the SSR length] it is a perfect CCACCACCACCACCACCACCA repeat: within these limits the fragment is prone to slippage. But for an actual slippage event the unit boundary does not matter. Imagine that the polymerase "skipped" a CAC unit in the middle: CCACCAC[CAC]CACCACCACCA. The outcome for the protein sequence will be the same: minus one threonine.

Thanks a lot for your nice response. As DNA polymerase is often prone to slippage on the repeat sequences, so the repeat unit can be CCA unit as the software predicated. But, how about the situations that the RNA sequence and related protein sequences are matched? the CAG repeat unit is one of famous examples that generated poly-glutamine tract in the protein sequence that resulted in some human disease like Hungtingtun. Could you please help me to figure out what happened, how polymerase slippage occurred here?

If you have a repeated region, re-annealing of the newly synthesized second strand can cause priming of the DNA-polymerase at a different location. Somewhere in the middle it is no longer possible to "tell" whether the first unit started with C, A or G. This logic is implemented for example in Phobos: it can report "alphabetical normal form given by the alphabetically minimal string among all cyclic permutations of the unit", i.e. for SSRs with the first repeat units being AGC, GCA or CAG it will report AGC.

Yes, of course. However, I'm a bit concerned about how I should reported the repeat unit on this sequence?!

As is: "such and such tool identified in this sequence an SSR with the following tri-nucleotide unit: ***, which corresponds to a stretch of seven threonine aminoacids in the translated sequence." The exact formulation for *** depends on the program: SSRLocator reports "CCA", Phobos reports "ACC". In both cases you can mention that the RNA SSR is shifted in relation to the protein repeat (if it is relevant).

By the way, Phobos identifies here (correctly) an SSR with a length of 22: CCACCACCACCACCACCACCAC.


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How can I preserve hair or saliva for future genome sequencing?
Suppose I want to preserve myself so that I can be reproduced as best as possible, in future or be simulated in future. At the moment full human genome sequencing is a bit expensive.
One could get some of my hair or saliva, but what can I do with it to best preserve it?
Would it need to be put in a freezer?
How long would the DNA of the hair or saliva be preserved for in a regular kitchen freezer?
How long would the DNA be preserved if the hair or saliva was not in a freezer?
Are there any issues of contamination?
Also, once the full genome sequencing has been done, is that all the info that could be gleaned from it genetically? can the hair/saliva be thrown out, or might it be possible that future human genome sequencing could pull more info from the sample?

Best of the two would be hair, since the saliva contains enzymes that degrade DNA. DNA is fairly stable, and the hairs could be kept at room temperature for future use. The problem with home freezers is that they defrost once in a while to lower the condensing ice that forms around it. These cycles of freeze-thaw are rather bad for everything you would want to keep in a lab. This is why we have non-defrosting freezers in labs.

@WilliamL how long would hair last at room temp? And how long in a non-defrosting freezer?

@barlop they've sequenced DNA from hair hundreds of years old.

As @MattDMo said, DNA from hair can last for very long in ambient conditions. Let's just say it could last for very very ... very long in a non-defrosting freezer. It just seems overkill much. Put your hair in a plastic bag (ziplock style) and put them in a binder/scrapbook (it will get creepy if you start a collection of those...).

Your biological information and your experiences are two entirely different things. You could make a cell for cell, protein for protein, base pair for base pair replica of yourself and that organism would be different as it will experience things differently than you have. Even identical twins are individuals. You can fool yourself into thinking that clone will be you, but they won't be, no more than a child will be you. They will carry more of your genetic code than a child or grandchild, but nurture plays a role in the person you develop into. Embrace your mortality, enjoy the ride.

Your old baby teeth might be a better-preserved source of DNA.

Full genome sequencing is only one of many experimental approaches. It's important in the field of genomics and useful in transcriptomics and proteomics.
It's best to extract DNA as soon as the sample has been removed from the living organism since all kinds of physiological processes start breaking down DNA in dead tissue. Kitchen refrigerators are not ideal for storing extracted DNA because they operate at about four degrees celsius. In the lab, minus eighty degree freezers are used. The lower the temperature, the better. Minus eighty degrees, however, is good enough.
Contamination is a big problem, so the more technical repeats you run, and the more biological repeats you have, the better your chances of getting reliable results. Biological repeats refer to, for example, taking three strands of your hair for analysis, while technical repeats refer to, for example, carrying out three PCR experiments on material from the same strand of hair. Once you've used tissue to extract DNA you don't keep remaining tissue because it gets contaminated during experimental preparation. That is why you may want to have biological repeats. It's also important to take precaution when removing the sample from the organism. This means wearing latex gloves and even using dilute ethanol to sterilize the sample before placing it in a sterile container. The problem is that ethanol can interfere with DNA extraction and be a contaminant itself. Balancing contamination prevention with quality results is important in the lab.
There are additional biological information such as DNA methylation events that occur intermittently on a genome and are inheritable. This information cannot be predicted from the sequence composition of your genome, but there are experiments meant for elucidating methylation patterns that can be carried out using uncontaminated biological samples (such as hair and saliva) stored in eppendorf tubes at minus eighty degrees celsius.

"Kitchen freezers are not ideal for storing extracted DNA because they operate at about four degrees celsius." That would be a kitchen refrigerator, as that is 4° above the freezing point for water. A kitchen freezer operates at -20°C. The issue with most consumer freezer's are that they have an automatic defrost cycle, so the temperature is not maintained constantly at -20°C, which can cause sample or reagent degradation.

@AMR - Now that is interesting...

@AliceD What? You think I am only a Biology hack? I know my way around household appliances! ;-)

@AliceD, also, I am pretty sure that -20°C is a holdover from 0°F, which is ~-18°C... so as you metric wonks like your base 10 numbers, you went with -20°C, because what is -2°C amongst friends....

@AMR - Now I'm curious - what is -2°C amongst friends? Did the F system precede °C?

@AliceD sorry, idiomatic usage. What is x, amongst friends, is an idiom for a trivial detail that can be ignored, though it can also be used as irony as well... but I will save you from that explanation. Fahrenheit was proposed in 1724, Celsius in the mid 1740s, and Kelvin in 1848, so Fahrenheit was first. But refrigerators were mainly developed in the US, and as we hold onto the Fahrenheit scale.... Interestingly, 0°F is the temperature of brine made of equal parts ice and salt (that is where the zero point baseline was defined). https://en.wikipedia.org/wiki/Fahrenheit

@AliceD it's more realistic to store your samples at minus 80 degrees celsius

If I had a substantial hair sample from my own head as a small child kept in a scrapbook made in the 1970's, sealed behind plastic, would this still contain my DNA? When I Googled it said hair DNA can remain up to 10,000 years but not sure if that's true.

As a general rule, you'd want to keep the sample as cold and dry as possible. If a sample is wet, heat/thaw cycles can damage tissue integrity due to ice crystal formation.
In the future, a small hair sample would probably only be able to recreate a small part of who you were, because it excludes epigenetic factors (methylation can be different in different parts of the body) and the entire microbiome.

In addition to -20 freezer, keeping your sample in 100% ethanol will help preserve your sample.
Cold temperatures help, because the bases (ATGC) in DNA can be damaged (deaminated) and even knocked out (depurinated) leaving abasic sites (and once that is lost no information can be recovered) and the DNA strand can be broken up into shorter and shorter pieces.
How long will a DNA sample survive in -20 freezer without other protection? Years. Decades if you are lucky. But it will probably be years.
How long will DNA in sample survive not in a freezer? Depends on ambient temperature, humidity and  and microbe activity. Anywhere from days to years,
Contamination is a huge huge problem. A saliva sample is going to be very very contaminated and is going to degrade fast. A hair sample would be okay. But there is a certain trick to pulling hair to getting the follicular cells. A biopsy, some nice human tissue is even better.
DNA extraction from the sample is destructive process. Once the extraction process is done, the sample is gone.


text:
How does Cas9 interact with CRISPR?
I read that Cas9 protein along with guided RNA binds at a specific DNA fragment of foreign organism integrated in a host organism DNA. To make the host immune to virus infection Cas9 along with gRNA which is complementary to viral DNA attaches to it host DNA by unbinding it and then cuts the DNA at this site thereby removing the viral DNA. So the host can only remove the virus that enters second time as it makes a complementary RNA from the first virus that has integrated in it and cannot completely remove virus.

Is the process that I am thinking right?
And can we use Cas9 to cut the DNA at any site of our choice?

Are you asking about CRISPR/Cas9 editing systems that you would use in the lab to recombine DNA, or are you actually interested in bacterial adaptive immunity? The Cas9 systems used in a lab setting are engineered, and behave a bit differently to what is found endogenously in bacteria.

We are currently using CRISPR in the lab to modify cells. Like @AMR said, it was modified from the In Vivo immune system to be used in the lab in DNA editing field. The Addgene website features both historical background and an application guide that are very well done. To answer your questions:

The bacteria (S. pyogenes, S. aureus, etc) integrates part of the viral DNA (protospacers) inside its genome, flanked by repeats. When transcribed the RNA derived from this newly combined sequence will direct the Cas9 protein to the viral DNA, which is complementary. Another element required is the PAM (protospacer associated motif) which is on the viral DNA just besides the protospacer. By not being on the bacterial DNA, it ensures that the Cas9 does not cut its own DNA.
The only limitations are the PAM and the off-targets. The PAM is specific for every species (EG S. pyogenes is NGG). Some sequences can be repeated in the genome of an organism, meaning that if you find a sequence that would result in a cut at a desired site, it could still cut elsewhere. That would make the guide sequence a poor choice.


text:
Role of drift in evolution of sexually dimorphic traits
Is there a model for predicting how drift can affect the evolution of a sexually dimorphic (SD) trait? I've been trying wrap my mind around this paradoxical question; sexually dimorphic traits evolve due to sex-specific selection. So how can drift affect SD traits?
I've found the quantitative genetic equations (eq 1) in Lande 1980 very helpful for thinking about the parameters and how sexual dimorphism evolves.
Can this model be applied to a population bottleneck? Or comparing the degree of SD in a trait of two divergent populations (assuming s, Ne and other parameters remains constant).
$$
\begin {pmatrix}
\Delta z\\
\Delta y\\
\end{pmatrix}
= {1 \over 2}
\begin{pmatrix}
G_m B \\
B^{T} G_f\\
\end{pmatrix}
\begin{pmatrix}
P_m^{-1} s_m\\
P_f^{-1} s_f\\
\end{pmatrix}
$$
Edit: I've added the equation from Lande 1980, which models the phenotypic response for sexually dimorphic traits. I'm wondering how aspects of drift (Ne and fixation probabilities) can be merged with an equation such as this. From my understanding drift may affect the G, B and P matrices (that is the Genotype variance covariance, intersex (B) variance covariance and Phenotype matrices. One of the assumptions of this equation is that these matrices remain constant.

I don't understand; where is your paradox?

Note that it is a good habit to always link your references. I can't check right now, but I am pretty sure that Russell Lande has many publications in 1980 making it hard to know what equations you are referring to.

I added the link to the Lande paper, I've only recently been allowed to link sources. Maybe a better way to rephrase my question is 'can drift overcome sexual selection in the evolution of sexually dimorphic traits?'

[C]an drift overcome sexual selection in the evolution of sexually dimorphic traits?

Short answer
Yes
Long answer
There are many ways to consider the effect of genetic drift. Here is one: One can approximate the probability of fixation of new mutations (using diffusion equations) with
$$P_{fix}≈\frac{1-e^{-4Ns} }{1-e^{-4s}}$$
, where $s$ is the selection coefficient and $N$ is the populations size. As you can see, a larger population size (the lowest is the genetic drift) corresponds to a higher probability of fixation of a beneficial mutation (to reach frequency of 1 in the population). If you don't have a good intuition of why genetic drift is inversely proportional to the population size, please have a look at this post. The most important point to keep in mind about the effect of drift is that when drift is high, every mutation pretty much behaves like neutral mutations.
This approximation is accurate under some assumption, for instance that the population is panmictic (=random mating). This equation holds true regardless of the reasons why a mutation affects fitness. The mutation may affect the activity level of an enzyme or it may influence how attractive an individual is to the other sex. So yes, in any case, a beneficial mutation may fix although it is deleterious (because of drift), or it may not fix although it is beneficial (because of drift).
A mutation might be beneficial in both sexes, detrimental in both sexes or beneficial in one sex and not in the other. In such cases, I suppose it is possible that the above equation must take a slightly different form. In any case where the mutation has a different impact on the fitness in each gender, I don't know how the above formulation would solve to but I'd think that you could simply replace $s$, by $\frac{s_m+s_f}{2}$, that is the average of the effect in both sexes (where $s_m$ and $s_f$ are the selection coefficients in males and females respectively). Intuitively, I think this should hold true as long as the sex-ratio is 1:1. The exact calculation of the probability of fixation doesn't matter to your question though. Note that mutations having the opposite effect on fitness in each gender often end up being on sexual chromosomes (if any) or at least being expressed in one sex but not in the other. In such case, a fixed mutation often end up being beneficial in one sex while neutral in the other. In any case, genetic drift will obviously affect the evolution of sexually dimorphic traits.
EDIT

could you clarify "As you can see the highest is the population size (the lowest is the genetic drift),"?

The impact of genetic drift can be measured in terms of how it affects probability of fixation or how it affects the rate at which heterozygosity at a neutral site is lost. An intuitive explanation for why $N$ and genetic drift are inversely proportional can be found here.

Are you referring to the positive correlation with P fixation and Ne?

Yes. I could have referred to that as to something else though. Note that the correlation between $P$ ans $N_e$ is perfect ($r^2=1$) because $N_e$ is defined on the concept of genetic drift.
Definition of $N_e$
Let's WR (stands for Wright-Fisher) be a population of size $N_{WR}$ where mating is random and there is no variance in fitness.
Consider now the population A. Let's its size be $N$. A experiences a given level of genetic drift based on its mating system, population structure, variance in fitness, etc.. the effective population size $N_e$ of A is (by definition) the size of the WR population ($N_{WR}$) that experiences the exact same level of drift, that is the same probability of fixation for a new mutation and the same rate of heterozygosity lose at a neutral site.

Thanks for the answer, I think I see where you're going but could you clarify "As you can see the highest is the population size (the lowest is the genetic drift),"? Are you referring to the positive correlation with P fixation and Ne?

Thanks. That is a good review for me on the parameters of drift. However for this question I'm trying to understand how drift (stoichastic sampling of alleles) can affect the cross-sex genetic covariances for a trait (B). Reading more of the literature this week has led me to conclude that this question is likely more complicated than I initially thought.

I wrote my question asking about the relationship of drift in the evolution of SD (sexual dimorphism), but after reading up more on the subject I've determined there are at least two parameters to consider when thinking about the evolution of SD.

Population structure and demographic history (Ne and drift outlined nicely by @Remi.b )
Intersexual correlation of the genetic architecture of the trait(s) evolving SD (how much genetic variation is shared between the homologous male and female traits?)

The intersexual genetic correlation between the sexes will constrain evolution in the sexes if selective forces are distinct for the sexes. This degree of correlation can be represented as rMF for single traits (Lynch and Walsh 1998 chapter 24) or the B matrix when examining the relationship between multiple traits (Lande 1980).
Two recent papers by Connallon and Clark (1 and 2) outline their adaptation to Fisher’s geometric fitness model which to predict mutation affects on traits given distinct phenotype-fitness spaces for the 2 sexes. The mean phenotype of each sex is a certain distance and angle away from the fitness optima (this vector represents the magnitude and direction of directional selection). The angle of these vectors represent the difference in selective forces for male and female traits. The current phenotypes are moved along the landscape by the addition of novel genetic variation (mutations).
 Mutations shift the trajectory of the current phenotype towards the optima. As in the selection vectors, the difference between the mutation vectors is quantified by the angle between the sex specific vectors, and can either move both sexes towards their respective optima or move only one sex towards their optima (sexually antagonistic selection).

While this model doesn’t directly take into account Ne for predictions on drift, it does emphasize that distinct fitness landscapes eventually cause antagonistic selection due to decoupled fitness effects for female and male traits due to random mutations.

“Evolution of sexual antagonism occurs whether or not selection favors phenotypic divergence between the sexes.” 1

This wasn't a direct answer, but it provided insight into how selective forces and pleiotropy of traits can shape the evolution of SD.


text:
What are the differences between how glucagon and cortisol work to increase blood sugar?
As I understand it, both cortisol and glucagon cause an increase in blood sugar concentrations. However, I don't understand how they work differently or why they work separately. I would be very grateful if someone could explain the separate roles and functions of these two hormones and also the different effects that they have (I believe cortisol also has something to do with promoting lypogenesis in adipose tissue at the trunk of the body and lypolysis at the extremities).

Glucagon and cortisol are VERY different types of hormones, though each of them can affect glucose metabolism and effectively can increase glucose concentrations in the blood (albeit through different mechanisms).

Glucagon, pictured above, is a 31 amino acid peptide hormone (i.e. PROTEIN) that is released from the alpha-cells within the pancreatic islets. Glucagon travels through the blood and acts predominately in the liver on glucagon receptors. The glucagon peptide binds to the glucagon receptor on the OUTSIDE of the cell and transmits a signal to the INSIDE of the cell. This occurs when the glucagon binds to the receptor, changing its conformation or "shape" on the inside of the cell, which then causes binding and activation of a G-protein inside the cell ($G_S$). This $G_S$ protein activates an enzyme within the cell (adenylyl cyclase) to increase the concentration of a substance called cyclic AMP (aka cAMP) that activates a number of enzymes that ultimately increase the amount of glucose released by the liver. These effects are IMMEDIATE effects, which is why we sometimes give glucagon injections to treat patients with hypoglycemia (i.e. dangerously low blood sugar concentration). However, sustained glucagon signaling likely has effects on the expression of the enzymes that cause the glucose to be released from the liver as well (though that is a discussion for another question).

Cortisol, pictured above, is a steroid hormone (i.e. LIPID) that is released from the adrenal glands, specifically the zona fasciulata, which is the middle of the three 'layers' of the adrenal gland. Cortisol is referred to as a glucocorticoid and exerts its major effects through changing gene transcription in a variety of cell types. The way that cortisol does this is by binding its cortisol receptor, which is generally thought to be in the cytoplasm inside the cell. Upon binding of cortisol to the cortisol receptor, that complex is then transported into the nucleus of the cell where it binds DNA and can increase or decrease the transcription or expression of target genes. In the case of cortisol, these genes are for the enzymes that increase glucose production of the liver, and thus increase the body's capacity to maintain or elevate blood glucose. There is also some evidence that cortisol has additional effects independent of gene transcription, but that is outside the scope of this question. Regardless, the effects of cortisol on glucose metabolism are not immediate, but rather they take >30 minutes to several hours to begin to take effect.
As mentioned in the question, both glucagon and cortisol affect glucose metabolism. Cortisol also affects the transcription of a number of genes involved in adipose tissue and lipid metabolism, as well as genes that are involved in protein metabolism and inflammation. Cortisol can be injected into a patient with an autoimmune "flare-up" in order to quell the inflammation, but those are different mechanisms of cortisol than its effects on glucose metabolism. A description of those effects is also outside the scope of this question, but could easily be read about in recent reviews on cortisol available on PubMed, e.g. Recent Cortisol and Fatty Liver Review.

Good job on the molecular mechanisms. Is it really established though that the effect of cortisol on immune cell activity is distinct from its effect on glucose uptake and metabolism? Since activated immune cells are generally glycolytic, I thought the immunosuppressive effect of cortisol could be mediated by suppressing immune cell glucose uptake.

Thanks @Roland - the non-metabolic actions of glucocortioids isn't my area of expertise...but it appears that many other pathways (e.g. NF-kB and others) have been established that lead to the anti-inflammatory actions of cortisol and other glucocorticoids. See http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3047790/  and  http://www.clinsci.org/content/94/6/557

Glucagon increase the blood sugar level in normal physiological pathway. It just mediate the blood sugar level when the level is too low.
On the other hand, cortisol is used when body under stress. It is used for "fight or flight" response in body, and it increases the energy production temporarily. When body is faced with stressor, the cortisol is secreted by a complex hormonal cascade, and the cortisol helps body to produce energy immediately to muscle and narrows the arteries to increase the heart rate, which force blood to pump harder and faster, and it also inhibits the activity of insulin. When body solves the problem, the hormone level return to normal.
Cortisol also can reduce the inflammation in the body, which can suppress the immune system.
Here is the paper maybe you will be interested.
Cortisol — Its Role in Stress, Inflammation, and Indications for Diet Therapy

Good article, please allow me to comment:
Both hormones cortisol and glucagon are insulin-antagonistic (=anti-insulin hormones).
Glucagon differs from cortisol because it is a peptide hormone and cannot cross cell membrane. It must be injected like insulin. Glucagon is released from α-Langerhans cells in the pancreas during the acute stage of hypoglycemia and counters insulin directly by reversing/suppressing glycogen synthesis and converting it to glycogeno-lysis (=release of glucose from glycogen). Glucagon may be used by type 1 diabetics to treat insulin shock/coma (severe hypoglycemia caused by excess insulin). Cortisol is steroid hormone. It crosses cell membrane abd binds to its nuclear receptor. Cortisol is more complex than glucagon:
1 - Cortisol regulates other glucose-regulatory hormones including glucagon and epinephrine synthesis.
The synthesis of epinephrine (adrenaline) from nor-epinephrine requires cortisol in the last step which SAME is involved.
2 - Cortisol is also involved in gluconeogenesis, the synthesis of glucose from muscle protein and other non-carbohydrates in order to sustain the brain during fasting and starvation
3 - Cortisol antagonizes insulin.
Evidence exist that excess cortisol can cause diabetes while deficiency of cortisol can lead to higher insulin sensitivity and hypoglycemic syncope as seen in Addison disease. Death may occur from adrenal crisis if Addison disease left untreated.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
Why is dsDNA nuclease activity by CRISPR/Cas9 only shown indirectly?
In Jinek et al., the authors show nuclease activity of their CRISPR/Cas9 system using the so-called Surveyor assay method. This assay recognizes small mismatches in dsDNA which are introduced by error prone non-homologuous end-joining after dsDNA breaks have been introduced by some foreign agent (such as Cas9).
The Surveyor assay then -again- breaks the DNA at those points where it recognizes mismatches. The newly broken DNA fragments can be assayed and so one can identify the position of the initial, Cas9 dependent breakage.
My question is: for direct demonstration of Cas9 activity at the programmed position, why not first silence non-homolguous end-joining repair mechanisms in the cell and then directly analyse the lysate? Is this technically not feasible? Or is it not done in order to not introduce another change in the cell for which there would be no control?

Because Double Stranded breaks will initiate apoptosis if they are not repaired, so you can't really tell if it is an effective method of gene editing if the cells die. Also you would need to see how many cells survive to determine transfection and site directed recombination efficiency. You also likely want to see how the breaks resolve in this experiment so silencing NHEJ would not give you that information.

@AMR You make good point about apoptosis. I'm not sure, but are these cells dividing after having been transfected? I dont understand what you mean by your last sentence.

Cells are dividing after transfection. A bit too long for comments but read Materials and Methods either from that paper or any of the other related papers on CRISPR Cas9. The first generation of engineered Cas9 nucleases were double strand cutters, so breaks are resolved via Non Homologous End Joining, as you pointed out. NHEJ introduces random nucleotide insertions, so to see how these were resolved. In other words you are looking at how the cell would behave if, say, you wanted to use this for site directed knock outs. You want the data that comes from the repair itself.


text:
Using Q solution with ready made MasterMix
I am exploring the possibility of using Q solution (5x) to get rid of non specific bands in PCR. I mostly use a MasterMix and not separate aliquots of dNTPs, Taq, buffer etc. In principle, adding Q solution to the reaction mixture when I use MM should be fine, but I just wanted to ask if someone has done so and had success (or failure?) before.


text:
The replicator equation vs the Lotka-Volterra equation
Background
The replicator equation with $n$ strategies is given by the differential equation:
\begin{equation}
  \dot x_{i} = x_{i} \left( \sum_{j=1}^{n} a_{ij}x_{j} - \phi \right)
  \qquad i = 1, \ldots, n
\end{equation}
where $x_i$ is the frequency of the strategy $i$, $A=[a_{ij}]$ is the payoff matrix, and $\phi$ is the average fitness of the population. Basically, what the replicator equation is saying is that the rate of increase of the strategy $i$ (i.e., $\dot x_i/ x_i$) is the difference between the fitness of $i$ (i.e., $\sum_{j=1}^{n} a_{ij}x_{j}$) and the average fitness $\phi$ of the whole population.
The Lotka-Volterra equation with $n-1$ species is:
\begin{equation}
  \dot y_{i} = y_{i} \left(r_{i} + \sum_{j=1}^{n-1} b_{ij}y_{j}\right) \qquad
  i=1, \ldots , n-1
\end{equation}
where $y_i$ is the abundance of species $i$, $r_i$ the intrinsic growth rate, and $b_{ij}$ describes how the species $i$ and $j$ interact (wiki entry on the Lotka-Volterra eqn).
According to Nowak's book Evolutionary Dynamics, these two equations are equivalent to each other (the same result appears in Hofbauer and Sigmund's Evolutionary Games and Population Dynamics). This is a neat result because it shows that results in ecology based on the Lotka-Volterra equation has a game theoretic interpretation and vice-versa.
Question
This would mean that the replicator equation for two strategies (e.g., the Hawk-Dove game) is equivalent to a single species interacting with itself under the Lotka-Volterra equation. How could that be? The replicator equation would describe the frequency of two strategies in the population, but the equivalent Lotka-Volterra equation would describe the evolution of a single and undifferentiated population. My problem is not so much with the proof of the equivalence between these two equations (Hofbauer and Sigmund give a proof of this equivalence in their book), but with the interpretation of the equivalence between the two eqns.

Can you please define all the parameters in both equations?

Sorry. I've just added a description of the parameters.

It'd be great if one could already prove this equaltiy between the two models. Did M. Nowak referenced his claim? Do you have references also for the replicator and the Lotka-Volterra equations? The L-V equation seems a little weird to me. It doesn't quite match with what I found on wiki. Also, it feels weird to talk about $n-1$ species, rather than replacing all the $n-1$, by $n$ in the equation and make it valid for $n$ species. +1

Nowak refers to this result in his "Evolutionary Dynamics" but the proof appears in Hofbauer & Sigmund's "Evolutionary Games and Population Dynamics." Regarding the L-V equation, my understanding is that Nowak is referring to the generalized version of this eqn rather than the one used to study competition between species. I could follow the proof but, like you, I still find it very weird that we are mapping a game theoretic model with $n$ strategies to a L-V equation with $n-1$ species. It's one of those cases in which you understand the math but you cannot quite understand what is going on.

The key point is that the first equation is describing frequencies, i.e., $\sum_{i=1}^n x_i = 1$, so there are only $n-1$ degrees of freedom. For instance, if $n=2$ (as in Hawk-Dove), you can totally describe the state of the system with just $x_1$, because $x_2$ is just $x_2=1-x_1$. This constraint is enforced by adjusting $\phi$.
To convert the Lotka-Volterra model to the first equation, define $x_i\equiv y_i/\sum_j y_j$.