text:
If I squash an insect and it produces red "juice", does it always mean it is a blood-sucking type?
If I squash an insect and it produces red "juice", does it always mean it is a blood-sucking type of insect? Or do some insects have red "juice" themselves, so the color is red on its own and not caused by sucking higher animal's blood?
I squashed a small fly-like insect recently, it produced a red stain, so I am wondering if the stain come from the insect itself or if it has to be some other animal's blood.

No - invertebrates don't have "blood" though they do have hemolymph. Hemolymph flows around the body cavity, rather than through vessels such as veins and capillaries, and comes in to direct contact with tissues and generally it is not red.

Hemolymph fills all of the interior (the hemocoel) of the animal's
body and surrounds all cells. It contains hemocyanin, a copper-based
protein that turns blue in color when oxygenated, instead of the
iron-based hemoglobin in red blood cells found in vertebrates, thus
giving hemolymph a blue-green color rather than the red color of
vertebrate blood. When not oxygenated, hemolymph quickly loses its
color and appears grey. The hemolymph of lower arthropods, including
most insects, is not used for oxygen transport because these animals
respirate directly from their body surfaces (internal and external) to
air, but it does contain nutrients such as proteins and sugars.
~ http://en.wikipedia.org/wiki/Hemolymph

The red you see from squashing, depending on the insect, could come from blood of other animals or (eye) pigments produced by the insect. For example, in my lab we have some Drosophila melanogaster with red eyes and some with white eyes (they carry a genetic mutation which represses the production of pigments in the eye) - if I squash the red eyed fly red "juice" is left on the desk, this doesn't happen with the white eyed flies.

I edited my question slightly. I knew insects did not have blood, I just did not know how to name the "juice" which they have inside.

Other examples of strongly red non-blood pigments: clover mites, which are abundant, red, and easily squishable; and cochineal bugs, which have been an important source of red dye for hundreds of years.

Some insects do have "blood pigments", namely hemoglobins, e.g. larvae of many chironomid species.

@har-wradim yeah..chironomous larvae are beautiful ! Especially when they undulate. Amazing to watch them under stereo microscope. ..

Very interesting. Does the red pigment from fruit flies look exactly like human blood? Or does it have a slightly different colour/hue?

Insects do not have blood as we know it from the higher animals. They have a kind of, which is called hemolymph and is, compared to human a mixture of blood and the lymphatic fluid. The most important difference is that hemolymph doesn't transport oxygen and thus has no red blood cells. The "breathing" of the insects takes mostly place through passive oxygen diffusion through the "skin" and small openings in it.
The hemolymph is mostly made of water and contains things like amino acids, carbohydrates, lipids and so on and is very important for the distribution of nutrients in the insect. It also contains hemocytes which are part of the insects immune system and play a role similiar to human phagocytes.
The color of the hemolymph comes from pigments and varies quite a lot between insects from green to yellow to red.

I edited my question slightly. I knew insects did not have blood, I just did not know how to name the "juice" which they have inside.

The term beetle juice does not come from the blood of a beetle or bug. There is a type of red dye, carmine, that is derived from the outer shell of a type of beetle. Carmine is a common food dye used in things like punch, soda, Popsicles, and so forth.
See Cochineal.

Hi and Welcome. Would you be able to provide references to the answer you have provided so that if others want to read further they can. Thank you.

@Julianne - I've added a reference. Feel free to edit in your own if you prefer. FWIW, in our family, we called all juices colored with cochineal "bug juice". :)


text:
How efficient is the human body at metabolizing food?
My friend and I were having a discussion over how "efficient" human digestion is. If a human ate a 1000 calorie hamburger, how many of those calories (how much energy) does the body process into usable energy (i.e. fat or other stores). I've heard things like "it's better to workout on an empty stomach because you will burn fat instead of energy from the food you just ate" - so from this I presume that storing energy as fat is less efficient than using the energy from digestion immediately (though in all honesty I'm not sure if that even makes sense.)
So, to summarize: how many calories of food does your body turn into usable calories via digestions? Percentages would be nice.
Bonus question: If the body does not process at 100%, does the FDA take this into account when labeling food products on their calorie content? If my Basal Metabolic Rate is 1800 calories a day, do I need to eat more than that since my body won't process all of the calories into usable energy?

The calories listed on nutrition labels are calculated so that they are representative of the amount of "digestible energy" - that is the amount of energy that actually enters the blood stream.

Different foods are diested very differently, too.  Richard Wrangham's "Catching Fire" goes into some of the research on the topic.

Do you have a source for "digestible energy"? I thought they used bomb calorimeters to establish the number of kilocalories in an item, which would figure a raw amount. Do they calibrate the calorimeters for a certain % of utilization for a human? If so, that would be the % I would be looking for for "efficiency."

On nutrition labels, they determine the constituents (protein, fat, etc) by mass in the food and use a standardized factor to convert these masses into energy. They do this to address the concerns you raised in your question: that not all energy in food is available for metabolism.

For example, see this: http://www.legislation.gov.uk/uksi/1996/1499/schedule/7/made

The FDA's base metabolic rate is based on how much food you actually have to eat, it was originally established experimentally not through calculation.

A year and a half later, I stumbled upon this article by Ars Technica.
To summarize, research/experts acknowledge that the calorie system is a poor way to measure human metabolic performance. Gut fauna, calorie measuring device differences, and caloric differences produced by different levels of cooking all contribute to this system being unreliable.
To address the original question specifically: there isn't a clear answer or "rule of thumb", because it depends on far too many variables, many of which are directly attributable to the "calorie" being an inappropriate way to gauge food consumption and weight loss.

I suspect that what you are actually looking for is the following:
- 1 gram of fat = 9 kcal
- 1 gram of protein = 4 kcal
- 1 gram of sugar = 4 kcal
- 1 gram of alcohol = 7 kcal

Those are general and inexact values. They're just often used to give a rough idea of the amount of energy we get from different types of food.

I was under the impression that sorbitol was able to be reduced to fructose through the action of sorbitol dehydrogenase and aldose reductase.  I believe that is part of the reason sugar alcohols are not *great sugar replacements for those who have T2DM.

Also, you might look up enhanced nutrient shuttling in the gut from enterobacteria. Depending on the microbiome that's been developed some bacteria will shuttle more calories from the chyme(found typically in the chronically obese).  The question the OP asks is multifaceted.  A good answer (in my opinion) should touch on host nutritional factors, gut microbiome colonies, impact of nutrient cycling, and type of nutrients being metabolized.

@LittleWhiteLithe Hmmm... actually, my biochem is too far behind for me to confirm or infirm your statement. You are probably right, now that I think about it. So I'll edit in consequence. The first part of the answer still holds, though. It was destined to be extremely simplified, beacause I was under the impression that the OP was looking for such a "rule of thumb". However, if you prefer to go into details, please do. It will be instructive for all of us.

Unfortunately, I was looking for more than a rule of thumb. The details are what interest me. This answer is certainly closer, but to use your example: how many Kcals does 1 gram of fat/protein/sugar/alcohol yield in a blast calorimeter? I can't believe it's the same number as the body produces from absorbing the food (and indeed, the fact that we produce physical waste proves that we do not turn 100% of it into energy). In a blast calorimeter (what I am lead to believe gives us the calorie counts we so rely on), all of the matter is burnt to find this number.

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Single stranded DNA in gel electrophoresis
From http://www.nfstc.org/pdi/Subject05/pdi_s05_m01_01.htm :

... The separation medium contains a denaturant in order that the
  electrophoresis is conducted on single-stranded DNA fragments.
  Single-stranded DNA fragments are more similar to each other than
  double-stranded DNA fragments are to each other. Double-stranded DNA
  produces more electrophoretic diversity due to its three-dimensional
  structure.

Questions :

What is the denaturant they are talking about?
It's mentioned that double-stranded DNA
produces more electrophoretic diversity due to its three-dimensional
structure. Why should dsDNA have more 3-D structures than ssDNA? ssDNA can form hairpins and other structures. Which structures can dsDNA form?

As far as I see, they are talking about standard gel electrophoresis. I have done this hundrets of times for different purposes, but I have never used denaturants. I think the chance of producing unwanted 3D-structures is much bigger when it is single-stranded. This is different for RNA (which is usually not double-stranded). Here denaturating gels, as well as denaturating agent like urea or formamide in the loading buffers are used.

As already mentioned formamide can be used. However formamide has to be incubated with the nucleic acid sample and heated. Urea can be included in the gel (8%). DNA can also be denatured using NaOH. NaOH, however, cannot be used for RNA because it will cause alkaline hydrolysis.
I don't much agree on this point. Though dsDNA can produce diverse structures, as buzrw mentioned, so can ssDNA (in fact more diverse). Moreover, linearization solves the issue of supercoiling. We never compare circular plasmids with each other on the gel. Long linear dsDNAs can form structures but then in such cases gel electrophoresis is not a very suitable method for comparison. PFGE might be a better option in such cases.

Formamide and/or urea most likely. They both lower the melting point
of the ssDNA. This allows the structures to unravel at lower
temperatures. Typically you want to reach for  melting temperatures
around 50° C, and then run the gels at that temperature. RNA is
often denatured with harsher agents, like formaldehyde because RNA
form stronger structures.
ds DNA can exhibit supercoiling which is a very elegant method of
    packaging DNA. This is a huge topic and mathematically derived. It's
    important is mostly due to it's role in gene expression via the
    interaction dsDNA has with proteins it wraps around-his tones.

Note that ds-supercoiled-DNA forms two structures; a plectoneme or a toroid, and can also have combinations of these two. It's interesting stuff. Google "DNA supercoiling" with an image search  and look for a figure that shows the different levels of winding and wrapping:
I'm not very familiar with this source but the figure looks to be on the up-and-up.
Good question

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Why does bacillus thuringiensis produce bt toxin?
Background : B.thuringiensis produces an inactive crystalline toxin during sporulation which when ingested by an insect, gets activated and causes pore formation in gut , subsequently leading to death of the insect.
Question : What is the  role of the toxin in bacteria's own metabolism ? Does it "unintentionally" kill the insects or is there an evolutionary advantage gained by the bacteria ?

Apparently the bacterium is an opportunistic pathogen. See here.


text:
Which statement about phenotypic plasticity is false?
This was a test question, and I'm not too sure about the answer.
Which statement about phenotypic plasticity is false?
a. Because the plasticity of a phenotypic trait may be costly to maintain, it will not be favoured in stable environments.
b. A trait that is an adaptation will not vary with environmental conditions within one generation.
c. Phenotypic plasticity can itself be an adaptation to varying environmental conditions.
d. Variation in phenotype among populations of a given species across different environments is only caused by phenotypic plasticity.
If you could explain the answer to me, that would be great.

When you say phenotypic plasticity, do you mean developmental phenotypic plasticity? Do you exclude phenotypic flexibility (lifetime REVERSIBLE and adaptive change in phenotype in response to environment)?

What did you answer and why?

what level was this question set at? (what is the group being examined.. undergrads, post grads, high school??). I think B would be false if it read "A trait that is plastic will not vary with environmental conditions within one generation."

Horrible phrasing of options.

@fileunderwater right, whoever wrote the exam should be ashamed.

If you can change teachers, do. That is a horribly worded question and very unclear. You can make arguments that everything is false depending on how you interpret the terms used. IN any case, D is 100% false with all interpretations so if the "teacher" only wants one answer, choose that.

I'll try to answer but I'll encounter some difficulties: first because I don't know what you mean by "plasticity". Does it mean non-reversible developmental plasticity or reversible flexibity. Then, because, I don't really like the word adaptation! Anyway'll try...
a: Plasticity  is some kind of mechanism allowing organisms to adapt to various environment. If the environment is stable and under the assumption the plasticity is associated with a intrinsic (physiological for example) cost, then a population would tend to evolve so that they always display the most optimal phenotype. I would argue that this is under the assumption that we gave enough time for the right mutations happen and under the assumptions that such mutation might exist!
b I don't really understand this statement. Does it mean that between two generations (which mean in one generation time) an adaptation cannot vary.…in what terms? in frequency? Through time any trait vary because of selection, environment, genetic drift and mutations. And to what environment is the trait under consideration adapted for? Can we accept that the environment is changing from one generation to the other in this question? What does this question has to do with plasticity? I guess that when one says that a trait is an adaptation it means (among other things) that the population has no more variance for this trait. Therefore, only mutations would bring variations if there were no environmental change making the trait not adaptive anymore.
c: Well, yes. Plasticity is an adaptation to fluctuating environment. It allows to display a rather optimal phenotype in multiple different environments.
Answer d is wrong for sure. There is variation within a population due to genetic variance and non-adaptive environmental variance (and their interactions). Variance due to plasticity is just some kind of subset of the variance du to the genetic-environnment interaction
Hope this helps a bit!

Agree with you there @Remi.b - A (with caveats) and C are true, D is definitely false (Phenotype = Genotype + Environment + GE... Plasticity is just one component of GE) and B doesn't make much sense (especially given the question is about plasticity).

I think the answer should be (d) as variation in phenotype among populations of species living in different environments are not caused only by phenotypic plasticity, they may also be caused by genetic differences which arose due to different selective pressures.


text:
Advice on Cell Biology texts by Alberts
I am currently reading "The Chemistry of Life" by Rose. It's a great book (to me as a lay reader at least) and an interesting topic so I am interested in pursuing some of the further reading he suggests.
The first book he mentions in the bibliography is Alberts' "Molecular Biology of the Cell". Looking at the description and the reviews it seems that this is a pretty advanced bio-sciences graduate level text, and I am very much not that. I see that Alberts has also written "Essential Cell Biology" which looks more accessible. Before I buy this I wondered if anyone can explain the difference, which would be a better read for an enthusiastic amateur?
If it helps at all I am a science grad and I typically read authors such as Dawkins and Matt Ridley, but I'm looking for something a little bit more rigorous to expand my knowledge in cell biology.

Don't know about "Essential Cell Biology" but "Molecular Biology of the Cell" is the classic textbook for Biology undergraduates. If you start at the beginning you should be all right :).

Ah, right. I think undergrad level is what I'm after. Thanks!

Might as well make it into an answer. As I said in the comments, Albert's  "Molecular Biology of the Cell" is the classic textbook for Biology undergraduates. It has been so for decades now.
It starts from the basics and builds up. Anyone with a high-school science education and willingness to learn should be able to understand it. Just work your way through it :).

I'd agree - it seems like a graduate text, but there isn't really a simple way to break down Mol Bio.

I've actually got a copy of Essential Cell Biology on my desk currently, it's one of our core textbooks.
When comparing with the "look inside" feature on Amazon for Molecular Biology of the Cell (remembering I only have access to the first few pages) I would suggest that Essential Cell Biology certainly takes a slower pace, but I wouldn't say that it was less detailed.
It goes into great efforts to explain how we know things experimentally, which I personally find quite helpful.  The pictures in Molecular Biology of the Cell look almost identical, however I would say they are more copiously used in essential cell biology.  Essential Cell Biology also comes with a DVD including very useful animations of cell processes.
Comparing the glossaries, I would still be inclined to say they are covering very similar levels of material, however Essential Cell Biology is using more 'entry level' language.
The key difference between the two in my opinion is the copious use of images in Essential Cell Biology - it's almost half a picture/diagram book.  In your situation (pretty much identical to mine) I'd probably recommend Essential Cell Biology just as a start then move on to Molecular Biology of the Cell as everyone else rates it so highly.
Finally, Essential Cell Biology certainly has some kind of sense of humour:

Geek Spoiler:

 The bottom line reads C A G T whilst the top line reads G T C A in semaphore, two complementary strands of DNA bases acted out for your amazement...

Nice answer. Unrelated to the content of the answer, I was wondering how you created that spoiler that only showed text when one rolls the mouse over it.

Hi @Pete - just add a <! before the paragraph, similarly as for a quote :)


text:
Science experiments at home with Minimal Cost
What is a good recipe for making agar at home for a minimal cost? Are there materials that could be found in the kitchen cabinet?

why not just buy dehydrated agar powder and add water and sterilise through autoclave (which is basically oven or steam baking)? Thats how they make it in media kitchens!

Dried agar (Agar agar) is used in food recipes. You can add potato/dextrose or whatever you think your bugs will like and it's already very cheap. It's often in the oriental food aisle or baking supplies aisle of grocery stores. Surely you don't want to grow the algae?

Instructions
1
Place a coffee filter in a strainer and hang the strainer over the opening of the clear bowl.
2
Slowly pour the broth into the filter, making sure that the broth doesn't overflow from the sides of the filter. If the filter becomes clogged with particles, remove the used filter, replace with a clean filter and continue pouring the broth.
3
Remove the strainer and filter from the top of the bowl and observe whether the broth appears clear (i.e., there are no suspended particles in the liquid). If particles are floating in the liquid, repeat Steps 1 and 2 using a second clear bowl and a fresh coffee filter.
4
Place the pot on the stove and pour the clear broth into the pot.
5
Add 16 ounces of distilled water to the pot containing the broth.
6
Turn on the stove and adjust the heat to medium-low. Heat the broth to a simmer (i.e., small bubbles appear near the side of the pot).
7
Add 1 teaspoon of the agar-agar powder to the simmering broth (approximately 2.5 grams) and stir the mixture until the powder is dissolved. Make sure to stir the powder to dissolve and prevent the gelatin from sinking to the bottom of the pot (where it can burn).
8
Repeat Step 7 until all agar-agar powder has been dissolved in the broth.
9
Continue stirring the broth for about 1 minute to ensure that all powder is completely dissolved.
10
Turn off the stove, place the lid on the pot and let the liquid cool for at least 15 minutes.
11
Arrange the sterile petri dishes in a row for easy assembly, but do not remove the lids.If desired, place a trivet to hold the warm pot. If you're using lidded custard cups instead of petri dishes, sterilize the custard cups by placing the cups and lids inverted (upside down) on a foil-covered cookie sheet and bake in the oven for at least one hour at 375 degrees Fahrenheit. Make sure the sterilized cups and lids are completely cooled before use.
12
Carefully touch the outside of the pot to check that it is warm to the touch and place the pot on the trivet. If the pot is too hot to touch, wait at least 10 minutes to let the pot cool.
13
Remove the lid and set aside. Place the ladle in the pot.
14
Remove the lid from one petri dish and dispense nutrient broth to fill the dish halfway. Cover the dish immediately with its cover.
15
Repeat Step 14 for each of the petri dishes.
16
Allow the nutrient broth to solidify in the petri dishes for at least 1 hour, undisturbed.
17
Gently shake the petri dishes side to side to check that the nutrient broth has completely solidified. If the broth is not completely gelled, wait another 20 minutes before checking again.
18
Cover a table or flat surface with a roll of paper towels to create a sterile work area. Tear off a sheet of paper towel and fold it four times to create a thick, rectangular wad. Set this wad on one end of the work area.
19
Transfer one petri dish to the sterile work area and remove the lid. Carefully turn the dish over and place one end on the side of the folded wad, so that it lies inverted and slightly angled (with a slight gap by the paper towel wad).
20
Turn the lid over and place it above the petri dish on the sterile work surface. If water droplets appear on the lid, gently shake this moisture off before placing on the covered surface, but do not touch the inside of the lid.
21
Repeat Steps 19 and 20 for each petri dish, positioning one side of the inverted dish on the side of the previous dish rather than the paper towel wad. The dishes will appear as a staggered set, with gaps on one side so that excess moisture can evaporate.
22
Look at the inverted petri dishes to check whether the moisture on the sides of the plates evaporated. Once all moisture has cleared, turn the plates right-side up and immediately cover with the lids for use.
23
Inoculate the nutrient agar on the plate by wiping a swab containing the desired culture on the nutrient agar surface before covering with the lid. Turn the petri dish over so that the solid is on top. Store the inoculated petri dish with the solid on top for culturing and the lid on the bottom to catch any moisture, loose particles or growth.

What type of broth is involved?


text:
What organism most efficiently converts a given quantity of mass into heat?
Question I am trying to answer:
In the popular film The Matrix, heat given off by humans is harvested as an energy source. I wondered, if this were possible, would humans be a good organism to choose? Would any other organism be better?
Question I want to ask:
Many warm-blooded organisms produce heat by metabolism. Do different organisms produce different levels of heat for the same mass of input (food)? Is there any organism that directs energy flux into heat production more than any other organism?

This question appears to be off-topic because it not focusing on a biological aspect. SciFi-SE may be a better site for this question.

I've made some changes and think it's both interesting and (now) on-topic!

Although, one extant issue would be heat varies enormously depending on what the organism is doing: hibernation, vigorous activity, using its body heat to melt snow (skunk cabbage), etc.

Matrix doesn't work either way because even rats need to be fed. If there's no sunlight, there's no autotrophic assimilation and thus no food for any warm-blooded organism. Still a nice movie.

A small hardy warm blooded omnivore that is easily tamed.  I suggest rats.
Small:  You don't want a lot of metabolic effort going into building bone and structure to support structure.
Hardy:  it needs to be survivable with little care.
Warm blooded:  You want to harvest energy from it.
Easily tamed:  The animal shouldn't try to escape its captivity.

Warm blooded, good point. I was thinking it'd be some kind of bacteria.


text:
Modeling intracellular interactions between sender and receiver bacteria
I am involved in a modelling task of a biological process described as follows:
--
"Objective: to model behavior of a sender receiver type system
The process goes as follows

Pre-signal [PS] is in media
Pre-signal diffuses into bacteria
Pre-signal binds to activator as a co-activator
Co-activator activator complex binds to DNA and starts transcription of enzyme mRNA
Enzyme mRNA is transcribed then translated into enzyme
Enzyme begins converting Pre-signal to signal [S]
Signal diffuses out of the cell into media
Signal diffuses into receiver cell from media
Signal binds to repressor inducing activation of reporter gene

Questions for model:
What concentrations of signal will be required to get a response from receiver cell?"
--
My questions are: what kind of mathematical/computational model or simulation tool is more suitable for this task? is it neccesary to model every step, or can some of these be ignored? I know the basics of ODEs, so I wonder if these are enough or something else would be required. Any other advice about how to go through the modelling is welcome.

You can start with deterministic model using ODEs for reaction rates.
However, if you are considering diffusion then you would have to use Partial differential equations for Fick's diffusion law.
You can, however, assume that there is no "diffusion" but just transport of the signal molecule from a well mixed system (medium) to another well mixed system (cytosol). In this case you can use an ODE for "conversion" of extracellular signal to intracellular signal.
The model becomes simple and easy to analyze if you reduce the number of steps. Certain steps can be removed by making certain assumptions such as Equilibrium approximation (EA) or Quasi-Steady-State approximation (QSSA). If a reversible reaction is extremely fast compared to all other reactions (such as binding-unbinding of the activator to DNA), you can assume that reaction to be in equilibrium and represent the concentration of some species using equilibrium constants.
In Quasi-Steady-State approximation, you assume that certain species are in steady state with respect to others i.e their concentrations do not change over time. In the model you just set $\frac{d[X]}{dt}=0$ for that species. Then obtain the concentration relationships as a function of reaction rate parameters. I'll explain QSSA and EA using the Michaelis-Menten (MM) kinetics.
MM considers following steps:

Enzyme-Substrate binding (Reversible): $ E + S \rightleftharpoons ES $ with forward rate constant k1 and reverse rate constant k2
Enzyme-Substate complex converting to Product and Enzyme (Irreversible): $ ES \to P + E$ with rate constant k3

EA (The actual MM model):
Assuming step 1 is in equilibrium. $k_1[E][S]=k_2[ES]$
 Get [ES] in terms of [E] and [S]
QSSA (The revised MM mddel):
Assuming [ES] is in steady state. ${\Large \frac{d[ES]}{dt}}=k_1[E][S]-k_2[ES]-k_3[ES] = 0$  which means $[ES] = \Large \frac{k_1[E][S]}{k_2+k_3}$
This way you have removed certain reactions from the model.
You can do stochastic modeling as well.
For simulations you can use Matlab or Fortran.


text:
Optimization of E. coli growth in D₂O (heavy water)
I would like to find a method of increasing the biomass of my D2O cultures because my current method is not yielding enough protein. I would like to also minimize the amount of H2O in my culture.
My current methodology is a small scale growth of 3mL with 75% D2O and LB grown for approximately 9hrs. This culture is then used to seed a 20mL 100% D2O and M9 culture overnight and this entire culture is used to seed a 1L D2O and M9 culture.
Thanks for your time.

It looks like you're very stringently avoiding $^1\mathrm{H}$. You may want to consider replica plating your transformations onto $\mathrm{D}_2\mathrm{O}$ plates (selecting for $\mathrm{D}_2\mathrm{O}$ tolerance earlier.)
I assume you're doing protein NMR and want "triple labeling." Depending on how specific your carbon labelling is, you may want to grow your starter cultures on triple labeled agal hydrolyzate (something like "Bioexpress.") You can buy some unlabeled stuff for fairly cheap (\$55 USD at CIL for 10 mL of 10X stock) and if your starters grow well you can spring for the same quantity of $^2\mathrm{H}$,$^{13}\mathrm{C}$,$^{15}\mathrm{N}$ for around \$405.

A guy I talked to who was doing triple labeling said he had to give his bacteria several generations to adjust to increasing amounts of non-normal isotopes. I can't remember any other details, but you might not just be able dump the bacteria in a labeled media and make it work well.

@Ryan Thanks for the reply but it seems like this would not be a financially viable method for our lab. We recycle as much D2O as we possible can and to make agar plates with D2O would be wasteful for us. Additionally, we do use BioExpress and the labeling is not an issue. It just seems like our issue is with the amount of E. coli biomass.

@user137 Do you by any chance know how many generations he grew his cultures?

@gravityassist if you're already using Bioexpress that contradicts your stated use of M9. So I'm confused what you're trying.

@gravityassist I'm sorry, I don't. It was a 5 minute talk at a poster presentation over a year ago, so I don't remember much.

@Ryan No, my statement of using M9 and Bioexpress together is correct. I use both in my D2O labeled and unlabeled cultures. M9 is just my media base, if I need to label my carbons I use the appropriate glucose and Bioexpress.

@Ryan As a clarification, I use the Bioexpress as an additive to the media not as the main media.


text:
Is dinoprost PGF2-alpha or PGE2?
In many places it says PGF2-alpha.
Now, I am thinking PGE2 is a substype of PGF2-alpha.
However, I am not sure.
Is dinoprost PGE2?

Prostaglandins A,B,C,D,E,F represent different non-inclusive classes of prostaglandins and all have different chemical structure. Hence (by classification), PgE cannot be subtype of PgF:
PgE have an oxo group at C9, while PgF have hydroxyl group at C9.
Back to Dinoprost, it is PgF2alpha and not PgE2 which is Dinoprostone.


text:
Why is herpes virus neurotropic?
A neurotropic virus is a virus which capable of infecting nerve cells.
Herpes simplex virus (HSV) has low neuroinvasivesess and high neurovirulence. HSV is transmitted in vesicle fluid, saliva and vaginal secretions. So the site of infection determines the disease - "mixing and matching of mucous membranes".
Source: Murray 2013, Medical Microbiology.
Why is herpes virus neurotropic?

What's the difference between neurovirulence and neurovasiveness

1/How Herpes does it:
Quote: << HSV... can enter the Peripheral Nervous System by binding to receptors on axon termini of sensory and autonomic neurons, which respectively convey sensory and visceral information. Most alpha herpesviruses use this route to enter the PNS and establish a life-long persistent infection [3].
Human alpha herpesviruses include herpes simplex type-1 (HSV-1), HSV-2. Well-studied animal alpha herpesviruses include pseudorabies virus (PRV) and bovine herpes virus (BHV). The cellular adhesion molecule, Nectin-1, is a major neuronal receptor for these viruses. Alpha herpesvirus particles enter sensory nerve endings by membrane fusion, and engage dynein motors for retrograde transport to the neuronal cell body or soma [4]. The capsids then dock at the nuclear pore and the viral DNA is instilled in the nucleus where an acute or a quiescent, latent infection is established. These viruses are unique in maintaining quiescent infections in the PNS neurons of their hosts. This quiescent state can be reversed by certain stress stimuli, which results in the production of large numbers of progeny in a relatively short period of time (1–2 days) [5]. Despite the direct synaptic connection of PNS neurons to the CNS, spread of alpha herpesvirus infection to the CNS is rare, but debilitating, in natural hosts [3]. Alpha herpesvirus trafficking, latency and reactivation is further reviewed in the following sections. Quite complex paper that outlines the nerve invasion strategies of majore nervous system virii, even rabies, polio, smallpox: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3647473/ >> end quote
2/Why Herpes has a particular advantage:
HSV has a statistically efficient infection and dormancy habit(near 100% of adults, 60% of them have HSV-1) It has the particular ability to achieve suspended animation, blocking the nerve immune response with special RNA strands, even if is detected there, inside nerves, it stays very benign/docile, it can stay latent in a host for very many years...
The last thing a virus needs is a dead end host, it's main objective is to spread and maintan as widespread a host population as it can. When we’re healthy, HSV-1 snoozes inside our neurons, secluded from drugs and the immune system. the latency is controlled by dissemination RNA strands that control the host nerve cells ability at apoptosis, or programmed cell death due to infection, so it can live as a reservoir of virii inside a cell for many years.
Herpesviruses have been infecting and codiverging with their vertebrate hosts for hundreds of millions of years. The primate simplex viruses exemplify this pattern of virus-host codivergence, at a minimum, as far back as the most recent common ancestor of New World monkeys, Old World monkeys, and apes. Humans are the only primate species known to be infected with two distinct herpes simplex viruses: HSV-1 and HSV-2. https://www.ncbi.nlm.nih.gov/pubmed/24916030
Red blood cells and other cells don't live as long as nerve cells: Most neurons are formed before birth and survive for the lifetime of the individual, so their lifespan is the same as that of the body. (for peripheral nervous cells the figure is also impressive, they can even grow back when damaged).
When it breaks out, it has an efficient vector of transmission which more controlled and less alarming than smallpox or more virulent virii, and enable the hosts to stay associated with is group during and outbreak, rather than causing the host to become an outcast. Animals instinctively take fright of pathogen symptoms and can distance themselves from shared foods, objects, and periphery of a symptomatic host. Herpes only causes very minor symptoms.
The transfer of viral particles between individuals, as the production of viral particles does not necessarily require any noticeable damage to the host.
HSV achieves this using a very fine tuned balance of latency or lyse/cell outbreak, which is when it explodes host cells and causes an immune response that sheds the virii at the surface.
The RNA latency control mechanism is highly evolved and fine tuned to control the nerve cell, there has been recent research about it, it's here: https://en.wikipedia.org/wiki/HHV_Latency_Associated_Transcript
this means that nearly 100% of the adult population is infected with the herpesviridae family of virii: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809354/
Other virii have different survival strategies, like cold and flue virii, which flee to the epithelial cells of the host in order to not decimate the host population, as if to flee the host by going to the surface of the host, because they don't have much control over their own virulence in through other methods, they don't know if they are being virulent or not.
...
Other than that, HSV automatically explodes epithelial cells, and it's ability to enter and hide inside nerve cells is it's strength. it's very complex how it does that, HSV is a very adapted, sleek, efficient virus.
I can't prove this theory, but it's possible that this HSV can confer added robustness to the host population, by keeping it's nerve immune response tip-top through the generations, and make it more able to combat other nerve virii like H1N1. Sometimes it's difficult to think that a virus that has been with use for millions of years is not part of of the body itself.

Simply because it has neurotropic receptors. Why did acquiring these receptors seem evolutionarily advantageous? Well the immune system doesn't target nerve cells for destruction as well. There are few if any white blood cells in the CSF within which the neurones are bathed. The immune system is about protecting against damage, it doesn't care as much if a virus doesn't cause damage as it isn't geared to respond as well if that's the case.

Excellent answer and clear logic! I like it!


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Does biofuel production by microalgae need an extra source of CO₂?
It is necessary to have extra source of CO2 i.e. from a nuclear reactor or factory chimney to produce bio-fuel by microalgae or is it able to do it with the normal CO2 density in the atmosphere?

I think your question needs some clarification to be answerable.  Algae need $CO_2$ as a carbon source for photosynthesis but the source is irrelevant.  Are you asking if industrial scale biofuel production can be sustained at atmospheric $CO_2$ concentrations?

@KennyPeanuts: Exactly. Is it possible to produce bio-fuel from  micro-algae at industrial scale at atmospheric Co2 concentrations?

That is definitely an interesting question but I am not sure it is a biological question as much as an engineering question.

I asked this because it is claimed to change the world by providing green fuel, but if it needs high concentration of Co2 then it cannot exist in a complete green world (needs pollution for existance)

OK so that sounds like a slightly different question... it sounds more like you are asking if algal biofuel production could lower atmospheric $CO_2$ concentrations to the point where it is no longer possible.  I think the issue here is that the industrial process that "farms" the algae could concentrate the $CO_2$ in the bioreactor so that the atmospheric $CO_2$ concentration isn't that important.  There was certainly lots of algal production when atmospheric $CO_2$ was 280 ppm, so that is plenty to get them to grow.

@KennyPeanuts: So it is possible at industrial scale with normal Co2 atmospheric concentrations. If it is true, write it as answer and I'll accept, thanks for your time.

Maybe this is easiest understood comparing to our current rate of CO2 consumption to the possible industrial CO2 absorption rate.
Human non biological CO2 production is 29 Gtons.  A little more than four tons of carbon per human being each year.  The natural carbon cycle on the planet through CO2 is otherwise about 788 Gtons.

Is it possible to grow enough algae to balance our current rate of industrial production? I think so - it would be an additional 2% of the current carbon cycle.  In a first pass, that implies turning 2% of the non-productive earth surface into an average patch of ocean or forest/jungle.
This is possible, but it would be expensive.  we're talking about flooding a desert basin for algae production. (numbers are from google).
2% * (area of earth surface = 197million miles^2) = 3.9 M miles^2.
Sounds doable, but remember that 70% of the earth's surface is already water and producing algae at a pretty good clip.  We'd be using ~6% of all the land for the algae farm:
For comparison,  3.9M miles^2 is larger than the continent of Australia.  (2.97 Million miles ^2), and bigger than the Sahara desert (3.6 M miles^2).
just to get into the questions raised in the comments.... To DEPLETE the CO2 level in the atmosphere, we'd have to produce more fuel from the algae than we use - which seems to be impractical from a market standpoint - the fuel would be sitting around in warehouses while the plants die.  I can't imagine anyone making any money from this, and it would be expensive... so it'd be a strange set of circumstances that would deplete the atmosphere from carbon dioxide.  Also more oxygen probably would encourage more combustion and more animals too... so hard to see this happening.

Amazing answer. This is not my field of specialty but listening at a conference, the bio-fuel plants were planned to be near factories and nuclear reactors to use their extra Co2, so I was curious to know, if we produce enough bio-fuel then there would be no need for reactors/factories and is it possible to continue producing bio-fuel without these source of pollutions or bio-fuel plants get shutdown.

nice answer....

Thanks, both! I think we'd still need power plants to convert the biofuel to energy (unless its bioelectricity or biohydrogen). With a projected population of 12 billion, demand for fuel is just going to go up. I think they were saying that from the plants/factories you can pump the CO2 directly into the pools of algae. I'd like to see numbers on how this might prevent the CO2 from getting into the atmosphere. Could help... we used to bubble CO2 into the algae in the lab to help it grow. I would be surprised if it changed the efficiency of an effort this size by more than a few %..?


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How to find miRNA binding sites on a specific gene?
I am trying to find miRNAs that bind to the 3'UTR of a specific gene. What is the best way of doing that (that is, with a good scoring analysis that is most commonly used by researchers in this area)? I would also like to know the other possible methods if there are multiple ways of doing this.

Good question here

Did you get the answer that you wanted ?

Bioinformatic part of your answer may be better that is why i did not give full credit. For those who also would like to know more about tools for finding miRNA target site they could check this article. It is really good: http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00023/full
Thanks for reminding me of the question by the way.

@user1445 I don't understand what you mean? Do you want to have details of these bioinformatics tools? If you are asking that then I would consider the question as broad. As you can see that it takes an entire article to explain these things.

There are some tools for predicting the binding:

TargetScan (based on seed match [primary], extra pairing, sequence context 1 — nucleotide composition around the site etc [secondary])
miRanda (based on hybridization stability and seed match[primary] and sequence context [secondary])
PicTar (adds a layer of evolutionary conservation criteria)

1 Context means the position of the target site in the 3'UTR and the surrounding nucleotide composition (which is also considered an indirect metric of secondary structure)
miRanda and TargetScan also have classes called conserved and non-conserved targets. miRanda reports miRSVR scores and TargetScan reports context scores but these measures are based on few experimental results and may not always be meaningful. miRSVR is based on change in target expression upon miRNA overexpression/knockdown. It also uses metrics for target site context. Context+ score of TargetScan includes many metrics which include target abundance and conservation along with the regular context scoring. Some of these metrics may be more useful than the others. But scores based just on miRNA OE/KD experiments can be misleading- especially if target expression is quantified just at the RNA level. Target abundance also varies between different cell types. For more details on these metrics refer to the papers corresponding to these tools.
There are some experimental procedures for target determination which mainly involve Protein-RNA crosslinking followed by immunoprecipitation of Ago and subsequent quantification by RNA sequencing. See HITS-CLIP and PAR-CLIP. These techniques do not really find the targets. All they do is to correlate the levels of miRNA and their predicted targets attached to Ago (you won't exactly know if the ternary mRNA-miRNA-Ago complex really formed or not).
CLASH is a more recent technique which tries to address this issue by ligating mRNA with miRNA. This way you can capture the miRNA-mRNA interaction. However, I am a little skeptical of CLASH; I myself was working on this principle some time back and faced this one challenge. CLASH roughly involves the following steps:

Crosslink protein-RNA
Immunoprecipitate Ago
Use RNAse to digest unbound regions of mRNA (and make it small enough for a short read sequencing experiment)
Ligate miRNA and mRNA bound to Ago using RNA ligase
Degrade Ago using protease
Sequence the miRNA-mRNA ligated pair

My doubt was how would miRNA and mRNA be ligated when the footprint of Ago is bigger than an miRNA (reported ~50-60 nt in HITS-CLIP and PAR-CLIP). When the RNA is nuclease protected then how can it be accessible to ligase. When I was thinking about it then I thought that a partial protein degradation was necessary (after RNAse step and before ligase step) to give some space for ligase to act. Eventually I did not work on it further. Three years later CLASH paper was published and I was happy that someone made it work. But the ligase issue was not addressed (It seems to have worked but I don't know how!!).
To test the predictions you can use a reporter assay (such as GFP or Luciferase) with the 3'UTR cloned downstream of the reporter. These can have artifacts too. Overexpressing the miRNA should be avoided and seed mutations should be done to ascertain the targetability.
Another technique to determine a target mRNA is to mask its predicted miRNA binding site and see the effect on the expression. This has been done in zebrafish using morpholinos complementary to target sites but not on other models AFAIK. This seems to me as an elegant assay- no overexpression and you can precisely determine the target site.

I would thoroughly check the literature on the UTR you are interested in, a lot of this has already been done for many  genomic regions since nextgen seq began.
You will want to first off use computational prediction algorithms to help guide you in getting a good Candidate list of miRNAs
The interaction between a miRNA and its target mRNAs is usually studied by co-transfection of a reporter expression vector containing the 3'-UTR region of the mRNA and an inhibitory or precursor molecule for the miRNA.
But, this does not measure the direct and physical interaction between a miRNA and a specific mRNA. To more specifically measure binding you will need to do a ElectroMobility Shift Assay.
Here's a commercial tool which will work well if your just trying to identify it.
http://www.activemotif.com/catalog/905/lightswitch-synthetic-mirna-target-reporter-collection
I don't think there are any other very effective/easy methods.
I guess you could do something like hybridize it, run the hyb on a gel, pop out the band and seq it

Thank you for the answer but my question was actually a lot more simpler. For the time being i am just looking for a good candidate list of miRNAs. I have found a website (http://www.microrna.org/microrna/home.do) for this and it is based on mirSVR score. Is this a good way of picking candidate miRNA's? Are there other ways of finding candidate miRNA's that are based on different scoring algorithm?

I thought you wrote specific gene?

yes "For the time being i am just looking for a good candidate list of miRNAs for a specific gene (the gene of my choice)". So, i want to choose a specific gene in the database and i want that database to show me a list of mirna's that can bind to that gene's 3'UTR . Also, I want to see the accountability of mirna binding via a scoring method such as mirSVR. I have so far found only mirSVR and the mentioned website but are there other ways and what is the best way to search for that in the literature? sorry if i am not maintaining correct terminology, i am relatively new in this.


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Cell energy consumption and food intake
I am wondering if each cell in human body starts consuming ten percent more energy, how much food one has to consume compared to normal intake?

The answer is relatively simple: About 10% more (assuming that there is not much loss in the uptake and conversion).

Does efficiency of digestive system play a part? If it is 10% efficient, then consumption should be doubled.

Compared to IV injection, consumption via the digestive tract gets about 80% of administered drug. So it may be that you would need to multiply that by 1.2 so you get 12% more food intake. But this question really is not physiologically going to ever happen like you phrase it.


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Concentration of degenerate primers should you dilute to?
I'm a little embarrassed to ask but when you have for example four degenerate primers and the end protocol says that the final primer concentration should be 10 µM working stock, should you make the primers 10µM total (2.5 µM each) or 10 µM for each degenerate primer?

You can just add 2µl from each working stock (10µM) to your PCR mix of 20µl.
You can check this protocol.
Caution: The author has used $l$ instead of $\mu l$
He uses 5µl of each primer (20µM) in a 50µl reaction. This is double of what I mentioned. IMO it is best to try with a lower concentration and then increase it if necessary. Excess primer concentration can give rise to some nonspecific binding and amplification if the primers are not perfectly designed.
From this site:

Using degenerate primers
Because a degenerate primer is really a mix of different primers, only
  a fraction of the degenerate 'primer' will actually work in the PCR.
  One would therefore need to increase the primer concentration in PCR,
  sequencing reactions, etc. If a primer is 8-fold degenerate, the
  concentration should be raised 4-8-fold, and so on. However, for
  higher degeneracies, e.g., 64-fold, one cannot increase the primer
  concentration 64-fold and would have to try their luck with a
  5-10-fold increase only.


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3 fates of this matter
When an animal eats something, it obtains matter and energy right? But, what are the 3 possible fates of the matter and 3 possible fates energy. I don't understand what they are even asking. What would be the 3 possible fates of each.

Please tag this as homework question.

I think the poster wants only a simple answer, so I am going to answer in simple terms. The fates can be -
1 Used in cellular respiration as fuel and some amount can be converted to useful energy which can be used to build other substances and some energy can be lost as heat.
2.The animal can become prey for other animal.
3.The animal throws away some matter as feces.

I understand the matter portion but, the energy can be lost as heat? I get that it can be used to create substances in the body. And that is only 2 fates of the energy.

@John it can be passed on from the animal to the prey.

Maybe this video might help you:
Matter and Energy in Organisms


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Resource for finding Repressive/Inhibitory factors for a given gene?
I have a list of genes for each of which I'd like to find:

A list of transcription factors that up-regulate the gene
A list of inhibitory factors that down-regulate it.

I used this tool on Qiagen's website to find transcription factors (and will be thrilled if someone can share a better tool). Its less than ideal for my purposes because I need to search each gene individually and there doesn't seem to be anyway of downloading or copy/pasting the transcription factors it returns; but it works.
What I really need now though, is a way to find inhibitory factors by gene. Does anyone know how I can do this?
I have already tried using  wikipathways and found it to show almost none of the transcription factors I found on Qiagen's website leading me to conclude that their data is far from comprehensive. I have applied for access to Ingenuity only to discover that I will not be granted access soon enough to make use of it.

I don't think such a tool exists simply because there are too many interactions possible. They also depend on the cell type, state of the cell and so on. I have been working with a transcription factor for quite a while and I would have loved such a tool. We endet up analyzing new targets ourself.

There is no tool and there is no direct way. There are databases that have predicted transcription factor binding sites (TFBS). There are experimental data (ChIP-seq) for TFBS too; however it is difficult to ascertain if the TF is activator or repressor without further experimentation. Moreover, same TF can act as either repressor or activator in different circumstances.


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Scale up of Bacterial growth from 250mL to 1-5L?
I am currently growing up a specific strain of E.Coli with a knockout in 40mL of growth medium (LB) in a 250mL shaker flask... My ultimate goal is to scale up this process to a 1-5L large scale setup for the lab. But for now I'd like to quantitatively characterize this 250mL scale using Monod Kinetics so that the mathematical model can be used to build the aforementioned bio-reactor.
Currently I am making two growth curves of the E.Coli in the current 250mL flask. One is OD vs. time and the other g.Dry mass vs. time. The idea of doing so is to ascertain the $\mu_g^{max}$ (maximum specific growth rate). However, I am stuck as to how to find the saturation constant (Ks). I am also stuck on how to measure the sugar concentration ([S]) at each time-point that I would measure the OD. The idea behind these two variables is to use the equation(s):
$D = \mu_g^{max} . \frac{[S]}{Ks + [S]}\\
D^{\ max} = \mu_g^{max} . \frac{[S^{\ max}]}{Ks + [S^{\ max}]}$
Am I looking for the right variables and/or using the right equations?
Thanks again for your help!

Are you aware of Lineweaver-Burk plot for enzyme kinetics??

No I am not... only Michaelis menten and monod

Michaelis-Menten:
$V=V_{max}.\frac{[S]}{Ks+[S]}$
Lineweaver-Burk:
$\frac{1}{V}=\frac{Ks}{V_{max}[S]}+\frac{1}{V_{max}}$
Plot $\frac{1}{V}$ vs $\frac{1}{[S]}$; find the slope and intercept of this plot.
Depending on what you consider as primary substrate (glucose??) measure the concentrations using appropriate assays. Glucose sensors (based on glucose oxidase) are available or you can do conventional biochemical assays (DNSA) for glucose and other reducing sugars. To find the growth rate, measure OD at different time-points and get the differences of OD in a time interval. A better method would be to make a scatter-plot of OD and time and fit a polynomial function. Get rate by taking the tangent of the curve at that time-point.


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Sequence of ribosomal RNA
Is it possible to sequence rRNA directly, that is, using the ribosome rather than the DNA from the nucleus? For example, this paper, Complete nucleotide sequence of a 16s rRNA gene from E. coli,  suggests that it uses DNA to find the sequence. Would it be possible to isolate the rRNA and derive the sequence from that?

Unequivocally, YES.
Before DNA sequencing was used to deduce the sequence of cloned or reverse-transcribed RNA molecules, slower and more cumbersome direct sequencing of RNA was performed using the method of Sanger et al. (Sanger, F., Brownlee, G. G. and Barrel B. G. (1965) J. Mol. Biol. 13, pp. 373–398). This used ribonucleases in a manner somewhat similar to pre-Edmann protein sequencing.
Because of its abundance and ease of purification ribosomal RNA was a suitable species for this approach, and Carbon et al reported the complete sequence of E. coli 16S rRNA in 1978 (Carbon P., Ehresmann C., Ehresmann, B. and Ebel J.P. (1978) FEBS Letters 94, pp. 152-156).
Sigh! How little do modern molecular biologists know about the history of their subject.

In addition to that most people doing (m)RNA/transcriptome sequencing nowadays actually worry about sequencing rRNA - there is so much more rRNA in cells that it needs to be thoroughly removed before you can even sequence any other RNA species.


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Find how much times blood is filtered in the kidney?
The blood that circulates in our body is about 1/12 of our mass. If the kidneys filter 7.5 liter of blood a hour, then how many times does it filter in the kidney the whole blood of a person will mass 60kg?
Here's what my teacher did:
1/12*60=5 liter blood
0.55*5=2.75 liter plasma of blood.
The plasma is filtered 60 times is 24 hours. (how did she find that?)
24 hours ->60 times the plasma filters
1 hour -> x
x=2.5 times
Do you understand her explanation? What should I do next?

Could you please explain a bit better what the question of the exercise is?

This is what is written in my book. I don't understand either if it says find how many times the blood filters in a day or hour.

@prishila, If you feel that my answer was correct or helpful, please vote up or accept it (click on the hollow check mark you see below the voting buttons). Thanks :)

I'll try to dissect your teachers answer for you:

1/12*60=5 liter blood

no explanation required here

0.55*5=2.75 liter plasma of blood.

only the plasma is filtered in the kidney (the 45% cell content remains in the blood stream), so out of the 5 l blood, only 2.75 l need to be filtered.

The plasma is filtered 60 times is 24 hours. (how did she find that?)

the kidney filteres 7.5 l per hour so if the whole blood plasma has 2.75 l, then in one hour the whole blood is filtered 7.5/2.75=2.73 times. In one day the whole blood is filtered 2.73*24=65.5 times (the numbers are slightly rounded).


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Which diagram correctly describe an effect of tar entering lungs?
Which flow diagram correctly describes the effect of tar entering lungs?

Tar is a cause of Chronic obstructive pulmonary disease (COPD) which includes both chronic bronchitis and emphysema and it is also a carcinogen, too. So, I think that A, B, and C would be correct answers, but the solution is A. Why B and C are incorrect?
Thank you very much!

COPD is not caused by an infection (which would require a pathogen) but by the inflammation reaction the body triggers because of the tar particles. Thus the statement "mucus accumulated causing infection" does not correctly describe the effect of tar on the lung. Consequently, B and C are incorrect.


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What physics knowledge can be applied to biology of organisms and ecosystems?
In the wiki page of Biophysics:

Biophysics spans all scales of biological organization, from the molecular scale to whole organisms and ecosystems.

But after searching on the internet; the dominant application of physics in biology that I see is at the molecular scale. In the wiki page Mathematical biophysics, there is a lot of interesting information, but it is only about mathematical knowledge applied to biophysics, not physics itself.
What, if any, physics knowledge (based on principles and laws of physics) can be applied to biology in the sense of organisms and ecosystems?

For a non-molecular example, see biomechanics.

This question should provide one example: http://biology.stackexchange.com/questions/11044/how-do-trees-lift-water-higher-than-10-meters/

Thanks, that's organism scale. What's about ecosystem? @fileunderwater

You might be interested in reading about biomimetics.

Brownian motion.. It is used for migrations and dispersal

This question is really asking for examples, and the list of ways that knowledge of physics can be used in biology could be very long. However, here are a couple of examples:

Systems ecology, especially with regard to energy and nutrient flow.
This type of ecology can be strongly influenced by physics. For one example see the book Theoretical Ecosystem Ecology: Understanding Element Cycles by Ågren & Bosatta (Ågren was originally a physicist)
Physical limitations to growth and transport
This can include for instance mechanical contraints on plant growth (see e.g. the book Plant Physics by Nicklas & Spatz), water transport in trees (see e.g. this BioSE question) or the biomechanics of movement (see e.g. Hudson et al (2012) on the speed and movement of cheetahs or Wikipedia: Biomechanics).
Allometric relationships between organisms, e.g. with regard to metabolism
To explain these types of relationships knowledge in physics is useful. See e.g. Kleiber's law for more.
MAXENT as a general approach to ecological patterns or to model species distributions
This is basically a tool lifted from physics that can be applied to ecological problems. There are many papers to look at, but Harte & Newman (2014) (Harte is another previous physicist) and Elith et al (2010) are two good starting points.
Dynamical modelling of populations and communities
This field use many of the same tools for analysis as physics, e.g. systems of differential equations. One of the pioneers in this field (among many) were Robert May (also started with a PhD in physics), and his classical book Theoretical Ecology: Principles and Applications is still a good starting point.
Energy harnessing and conversion by organisms
This can refer both to how organsims convert prey to energy (e.g. conversion efficiencies) and the physics of photosynthesis (which is an interesting intersection between physics and molecular biology). See Jang et al (2004) and O'Reilly & Olaya-Castro (2013) for examples of the how quantum mechanics can inform us about photosynthesis.

Hopefully this will give you a sense of some different ways that knowledge in physics can be useful for biology.

Thank you so much for taking your time writing this wonderful answer :D

I am currently interested in evolution. Does any of your suggestions have it? Does any field in evolution have physics in there?


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Low copy numbers of plasmids
I have a plasmid with the P15A origin which apparently has a low copy number (see here). This would explain why my purification yeilds for subsequent digestion are low (gel shows the plasmid after digestion and clean-up with 5µl being loaded). I am currently growing my E. coli containing plasmid up in 5mls overnight before doing a miniprep. My question is should I grow my 5ml cultures up for longer (2 days) or just give it up and do a larger amount for Midiprep.

I am also curious as to what actually controls the plasmid copy number inside cells. An explanation of the regulation of plasmid copy number would be much appreciated.

What is the basic name of the plasmid? How much plasmid did you actually use for the digest? What are your yields from a miniprep?

@Chris It's called pACYCHO so I am guessing it is a derivative of the pACYC plasmid. Oh and thanks WYSIWYG, I didn't know that was how you do links.

I am sure you have done it; just a note to others: do a PCR screen to
ascertain that the plasmid is there.
For minipreps you generally do not need a starter culture.
If you have screened your clones and now just want to amplify and keep the plasmid for cloning purposes then go for a maxiprep with 200-400ml cultures.
Do not overgrow the cells, your extraction becomes difficult and you would get even worse yields. Just set up a huge culture and pool the cells before extraction. Always seed and harvest cells that are in exponential growth phase.
If you need to screen the plasmid for clones or something then do a colony PCR, keep the colony inoculated in a small culture (make a temporary glycerol stock if your screening is taking time). If screen is positive then incoulate the starter culture for maxiprep/gigaprep culture with this stock.


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How do download a list of the gene length of all genes?
I would like to get the gene length information of all the human genes. Is there a quick way to get this information? Thank you!

Which genome are you looking for?

The Ensembl 2005 data is here: http://www.zoology.ubc.ca/~whitlock/ABD/teaching/datasets/04/datasets04.html

Some basic information like length can be obtained in the GTF file (you can get the latest ENCODE GTF from the UCSC genome browser).
Other information are also available and obtainable from the UCSC genome browser → table browser. Select the group and tracks that you want from the corresponding dropdown boxes. You can export the data as a text file or forward it to the galaxy interface for analysis.
If you are familiar with some programming then you can pull out the lengths (it is not explicitly given in the output or in the GTF). Also, the gene length may be different from the mRNA length because of the presence of introns in the former. The third field in the GTF file denotes the feature (gene, transcript, exon, etc); you can parse the GTF for picking up only desired features, lets say the mRNAs.
The fourth and fifth fields denote start and end positions respectively; all you have to do is end - start to obtain the length.
A small scriptlet such as this would work in linux/cygwin terminal (I wont go in detail because this is not the scope of this forum):
awk -F "\t" '$3=="transcript"{print $9, $5-$4}' file.gtf > output.txt

The $9 refers to the ninth field which holds information about that feature including the gene name, gene id etc.
The first link that I mentioned also shows some basic methods of extracting information from GTF.

I'd use Ensembl's BioMart.  It's not connecting for me right this second, but it should have that information.
http://www.ensembl.org/index.html


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How to model species interactions on population size dynamic?
Here are two classical discrete time models of population size dynamic. The exponential growth model
$$N_{t+1} = r\cdot N_t \qquad\qquad(1)$$
and the logistic growth model
$$N_{t+1} = r\cdot N_t \left(1 - \frac{N_t}{K}\right) \qquad\qquad(2)$$
, where $r$ is the growth rate, $N_t$ is the population size at time $t$ and $K$ is the carrying capacity.
I am wondering how are species interaction generally modelled into this framework. Let's consider three interacting species called "a", "b" and "c" with populations sizes at time $t$, $Na_t$, $Nb_t$, $Nc_t$, respecitively. The effect of these three species on the focal species are $\alpha_a$, $\alpha_b$ and $\alpha_c$.
How do we typically compute, for both a logistic and an exponential discrete time model, the recursive equation of the focal species' population size?
For the exponential model, is it
$$N_{t+1} = r\cdot N_t \cdot (Na_t \alpha_a + Nb_t \alpha_b + Nc_t \alpha_c) \qquad\qquad(3)$$
or
$$N_{t+1} = r\cdot N_t + Na_t \alpha_a + Nb_t \alpha_b + Nc_t \alpha_c \qquad\qquad(4)$$
or something else? And for the logistic model, is it
$$N_{t+1} = r\cdot N_t \left(1 - \frac{N_t}{K}\right) \cdot (Na_t \alpha_a + Nb_t \alpha_b + Nc_t \alpha_c) \qquad\qquad(5)$$
or
$$N_{t+1} = r\cdot N_t \left(1 - \frac{N_t \cdot (Na_t \alpha_a + Nb_t \alpha_b + Nc_t \alpha_c)}{K}\right) \qquad\qquad(6)$$
or something else?
It is possible that we typically define more parameters that just the $\alpha$ values I define above to describe a species interaction even in the simplest models. Ideally, I would love an answer that draws from a source (a textbook in theoretical ecology, a peer-reviewed article or whatever).

You can write these equations in the form of ODEs but it is not really essential.
This is my analysis (just the model formulation and not literature):
The effects of interactor species may or may not be dependent on the population of the species that is being analysed (recipient). Now you have to model based on how you would realistically expect the interactions to have an effect on a species. A simple assumption would be that the interaction improves or reduces fitness (growth rate), additively. So you can model the effects as:
$$N_{t+1} = (r_0 + \beta_a + \beta_b + \beta_c) N_t$$
where $\beta_x$ denotes the effect of species $x$ which in turn you can model as dependent ($N_tN_{x_t}\alpha_x$) or independent ($N_{x_t}\alpha_x$) of the recipient population. Now, you can also impose a condition that the growth rate cannot increase beyond a certain value and so you can model it as a saturating function. A logistic or a Michaelis-Menten like function would work. For e.g:
$$\begin{align} r &= \frac{r'}{K+r'}\\[1em]
 r' &= r_0 +  \beta_a + \beta_b + \beta_c\end{align}$$
You can likewise adapt model using the logistic equation.

What you are looking for can easily found in any undergrad text on ecology and it goes by the name (competitive) Lotka-Volterra Equations. This is a system Ordinary Differential Equations and in my opinion, can easily be converted to recursive or difference equations. Your guess with logistic growth is fairly close to these equations. You can remove the term corresponding to intra-specific competition $\alpha_{ii}$ from those equations and you get the exponential model.
Please let me know if you'd like me to elaborate on my answer.
Lotka-Volterra Equations
Competitive Lotka-Volterra Equations
The book I know (Ecology: From Individuals to Ecosystems) has the 2 species interaction model.

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


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Sequencing from PCR
As far as I understand it, PCR can be used to make many copies of one gene. My question is, is it possible to sequence DNA after PCR and is it easier than sequencing it via other methods. If it is possible, how would you do it?

All sequencing methods, be it classical Sanger sequencing or next-generation sequencing (or even third generation) need a certain amount of DNA to work with.
You either need to extract DNA from a large-ish tissue sample or you need to amplify DNA content from a smaller sample. The first approach is often impractical, or downright impossible (when you want to work with single cells or very small organisms).
So PCR is used routinely to amplify DNA before sequencing. The sequencing itself isn’t influenced by this – you use whatever technique is appropriate. However, the PCR may introduce biases into the data because not all DNA fragments amplify equally efficiently. This needs to be taken care of in the downstream analysis of the sequence data if quantitative information is required (as in RNA-seq or ChIP-seq).
Coming back to your question, PCR isn’t a sequencing method in itself. It just helps getting the necessary amount of raw material in order for sequencing to work. Furthermore, most sequencing methods actually include PCR steps as well, since they rely on the generation of of overlapping DNA fragments to be stitched together.

We do it all the time. You can use one of your end/flanking primers and use that as a primer for sequencing. Companies will typically have a setup where they can take a "premixed" sample.
Since we tend to use sequetech, here are their details: http://www.sequetech.com/requirements.php?premixed=1
For a second opinion look at Elimbio's: http://www.elimbio.com/Sample_Preparation.htm

Could you expand your answer with more of the "how"?

Yes, I'm actually more interested in how they sequence the DNA.

It should work exactly the same way as sequencing a plasmid. Traditional sanger sequencing read by capillary electrophoresis.


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Intrinsic apoptosis in erythrocytes
With a lack of mitochondria, can red blood cells perform intrinsic apoptosis and do they have another way of generating cytochrome c to attach to a CARD domain and assemble the apoptosome?
Or are they dependent on killer lymphocytes to bind via the fas ligand and activate procaspase 8 or 10 in extrinsic apoptosis? Do red blood cells even have fas ligand receptors for this process?
Or is there an entirely different method for red blood cells?

No expert, but I thought that red blood cells were relatively inert, and were essentially killed/recycled by the immune system after their useful lifespan. How this is determined I don't know, but I am interested to hear the answer to this question.

Senescent red blood cells are cleared by macrophages in a process called eryptosis or is better known as erythrocyte programmed cell death or apoptosis. The mechanism is excellently reviewed here. In summary it is mediated by caspases, phospholipase A and prostaglandin E.

Red blood cells (RBCs) don't have nuclei in mammals, so they are usually considered to be pretty inert compared to other cells.
They do have some biochemical activity, and evidently there is some mechanism for cell death  in RBCs when they are induced to degrade or infected by pathogens.  I found this reference that shows caspases -8 and -3 are evident in mature RBCs.  Several other components of the cell death pathway are not evident.
At this time the authors speculate that they don't activate to induce cell death.
It seems that over the broad field of disease research 'apoptosis' and 'cell death' are used equivalently even when the well known apoptosis pathway is not involved.

Apoptosis occurs by both external stimuli and internal stimuli.As RBC do not have nucleus apoptosis may occur by external stimuli by activation of caspase8

Can you add references/links?


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How to validate the regulatory interactions inferred from gene expression data?
My algorithm learns regulatory interaction between genes using Bayesian Network approach from gene expression data. After the algorithm has converged to a network of interacting genes, how to validate that the interactions are correct? I have used Lung Cancer dataset from NCBI GEO with ID: GDS2771. Also, how to get the set of genes responsible for a disease (in this case lung cancer) , and how to quantify their expression levels as over or under expressed?

this may need some more detail of the model you use, for example, the model could have just randomly picked 10 genes and said they are interacting - though I assume it's doing something a bit more complex :)

And yes I concur with GriffinEvo that you should first make sure the algorithm was used correctly;the parameters rightly adjusted etc.

Some important parameters of the algorithm are the discretization thresholds for classifying genes expression values as over, under or normal expressed. I am also quiet unsure about what should be the threshold values, and the other important parameter is atmost how many regulators can be there for a gene? Any idea about how to sort that out?

I have tagged your post as bioinformatics too. If your query is related to model optimization then clarify that in the question.

You can validate the interactions by knockding down (KD) or overexpressing (OE) a gene and checking the change in expression levels of the downstream nodes. You can do this in a high throughput fashion using microarray or RNAseq. For protein you can do an LC-MS. However this method cannot help you in:

Differentiating direct vs indirect interactions
Finding regulation in case of loops and other non-linear interactions in the network

Loops are tricky but sampling at multiple time intervals can let you know if oscillations exist or not. For most usual cases this approach works out.
Usually this is followed by another round of validation using a relatively low throughput but sensitive technique such as

Real-time PCR (KD vs OE)
Western blots (KD vs OE)
Reporter assays (KD vs OE) would detect direct interactions. For e.g. possible promoter for a (Transcription Factor) TF (upstream of gene-Y) is used to express GFP to see if GFP responds to the TF; thereby validating the effect of TF on Gene-Y via the promoter.

In some cases you may have to do ChIP-seq to find out if a gene has binding sites for a TF in its promoter/enhancer. You may also use predictions for TF binding sites. For finding regulation by miRNAs you can see this post.
For finding complex dynamics such as pulses and oscillations you have to collect time-course data.

how to quantify their expression levels as over or under expressed?

For that you first need to define your control (up/down regulated wrt what?). Having done that you can compare the expression and use right statistical tests to check differential regulation. If you have just one sample then most tests won't work. For RNAseq,  EM algorithms are used which use a bayesian model to obtain likelihoods and p-value (I have used cufflinks and eXpress). Then a FDR correction can be performed when comparing the test vs control. I am not very sure about algorithms used for comparing LC-MS data for proteins.

well i am not doing any wet lab experiments, i am using machine learning techniques on expression data matrix. Then how would i knock down or over express a gene's expression pattern??Is it by setting low values for knock down and high values for over expression and then check how the regulatory interactions change for that gene?

@Aparajita You said "validate".. You have to do wet-lab experiments to validate. Your model can only do predictions from a given data. You would need to test if it predicts correctly, a control experiment. If not you should update your model parameters.


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Is there any evolutionary advantage of selection of L-amino acid over D-amino acid?
After listening to a scientific talk, I had this question that why in the natural selection process, are the L-amino acids selected over the D- form. However, we still we produce D-amino acids; specifically in the brain there is a higher concentration of D- amino acids. One possible reason is that they act as neurotransmitters.

It would be nice if you can add a reference for brain having D- amino acids.

http://www.ncbi.nlm.nih.gov/m/pubmed/18564180/

Just in case the situation changes.

As you say yourself, biological molecules are usually available in both chiralic possibilities, yet nature uses only one of the two possibilities. At some point in our molecular evolution (and at a very early one) L-amino acids were stochastically "chosen" over their D-equivalents (I think that the choices would have been equiprobable). There is no reason why D-amino acids shouldn't work here as well.
There is some discussion on why L-amino acids are used, see the references for some ideas. It might be that the proteins made from it where slightly more stable or because they were more abundant. D-amino acids are in use today as a protective step - most proteases for example cannot hydrolyse the D-amino acids in the peptidoglycan wall of bacteria. Using this "unusual" amino acids gives some protection here.
References:

Mirror symmetry breaking in biochemical evolution
Mirror symmetry breaking at the molecular level
Origin of homochirality in biological systems

I added a small comment in the bracket if you don't mind.

You mean that organisms use the D-variant in their cell wall? Interesting.

@ChrisStronks yes.. the peptidoglycan of gram positives have D-amino acids

The current thinking amongst biophysicists is that if we all woke up tomorrow to find that someone had edited the book of life so as to exchange all of the L-'s and D-'s (and made similar mirroring changes to all of the molecules that any protein interacts with), everything would be exactly the same.
Milton, et al. (Science, 1992) lent supporting empirical evidence to this view by carrying out the total chemical synthesis of a D-enzyme, D-HIV-1 protease. They showed that the L- and D- versions of the enzyme had identical and reciprocal activity, meaning that L-enzyme cut the L-substrate well and the D-substrate poorly, and vice-versa for the D-enzyme:

They also showed that the structures the L-enzyme and D-enzyme were mirror images of one another:

The preference for L- forms over D- emerged sometime early in evolutionary history, and there's a lot of speculation as to how the preference arose (it could have been cosmic rays!), but the evidence is thin.

Interesting! But do you think their results (mirror structure and swapped substrate preference) extrapolates to all enzymatic reactions or is this a lucky shot?

@ChrisStronks At least for enzyme activity, I think that the invariance of L- and D- follows from the fact that electromagnetic interactions don't change for a parity transformation (reflection is a type of parity transformation). So it would probably be the same for any enzyme.

@ChrisStronks And the mirror structure result would almost certainly be the same for any protein. A protein is the same as any other molecule in that if you flip all of the chiral centers you end up with its mirror image.

This question is a matter of discussions, but I'd like to mention that there's basically two viewpoints. The first viewpoint is that the symmetry breaking is global (in other words, there's a reason why exactly L-amino acids; if there are other planets with Life, L-amino acids should be "used" on all of them or at least on the major number of them). The second viewpoint is that the symmetry breaking is local (or spontaneous; and hence Life outside Earth can "use" aminoacids of arbitrary sign).
The only global reason for global symmetry breaking known in physics is weak interaction. There were some efforts to connect weak interaction and symmetry breaking in biomolecules, starting from Vester-Ulbricht hypothesis, but it seems that there's no confirmed mechanism of this connection. You may read this review which may be outdated (2000) but at least it can give you an idea about what kinds of mechanisms were tested.
The science around the local symmetry breaking is much more rich but works like Sutherland's (Powner, Gerland, Sutherland, «Nature», 2009, 459, 239—242, doi:10.1038/nature08013) are about the mechanism of possible local symmetry breaking, and the question "why L?" is meaningless in frames of this view (or in other words answer is "it was just a random choise"; in fact, it is not even concluded if this "choise" was made on a chemical level or during biological evolution).
PS By the way, more recent papers (1, 2) suggest there's some progress in the field.

There were some efforts to connect weak interaction and symmetry breaking in biomolecules. That is the most crazy-yet-still-vaguely-plausible thing I have ever heard. Well done

@tel yeap, that's because it's a very attractive approach for physicists. Actually it's not that vague if you read that review, but the point is -- there's no reasonable success in proving that point..


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Why does hair grow after trimming but remains at a constant length after a while?
Some hair especially body hair regrows after trimming but stops growing after a while. What is the mechanism behind control of hair growth and how is the length limit determined?

Hair grows from the follicle; there is no way for the follicle to determine if the hair tip is trimmed or not. This is unlike plant shoot/root growth that happens at the tip. Also there is no signaling from tip to follicle.
Hair growth has four stages: Anagen (Initiation), Catagen, Telogen (resting) and Exogen (falling off). Initially the primary follicle is formed during development of the skin, after that the hair growth continues in a cycle. In other words the old hair shaft is replaced by a new one. The period of the cycle (or how fast it happens determines the final hair length); the faster is the cycle the shorter will be the hair (you may note that certain people have a very short head hair even when they do not trim). This is because the old shaft doesn't get time to grow before it is shed and replaced by the new one.

From: Stenn & Paus (2001)
From the same source

Because there is a limit to the time a follicle stays in anagen, there
  is also a limit to the length of its product, the hair shaft. The
  anagen phase has been divided into six subphases (64, 359). Except for
  the last subphase, anagen VI (the duration of which dictates the shaft
  length), the length of the anagen subphases I–V does not differ
  substantially between follicles from different regions (490, 565).
  […]
   Because the actual length of a hair follicle does not appear to
  dictate the length of its shaft (rather the duration of anagen
  determines hair shaft length), it is tempting to speculate that the
  subcutis offers optimal growth conditions…
  […]Although influenced by environment (light, temperature, and
  nutrition) and systemic (endocrinological factors) parameters, it is
  generally believed that there is considerable innate local control of
  hair shedding; each fiber grows to a specific length, for a specific
  period of time, before being shed

Interesting. I looked at the source, and it seemed to imply that hair length is determined only by the length of time in which the hair has grown, which would assume constant growth speed. This would be a very interesting fact, I'd intuitively assume that hair (within the same animal) grows with different speed depending on body part and the current state of the body chemistry. Do you know if such difference exists, or is it a constant speed growth process? I don't remember an explicit statement from the paper, it was just an impression I got, maybe I overlooked something.

@rumtscho Yes that happens indeed. It is mentioned in the paper. For example head hair and body hair. I also cannot find an explicit statement but it is indicated implicitly. for eg: "Except for the last subphase, anagen VI (the duration of which dictates the shaft length), the length of the anagen subphases I–V does not differ substantially between follicles from different regions"

To be clear, you are implying that the only reason your hair has a limited length is that the skin itself regenerates, causing the hair to fall out?

To be clear, you are implying that the only reason your hair has a limited length is that the tip of the hair "sheds" (breaks and falls off)?

@cowlinator not the tip but the root. It doesn't break. It is displaced by a new incoming hair tip.


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Why does Thiamine pyrophosphate have phosphates?
The reactions that Thiamine Pyrophosphate(TPP) catalyses are associated with the thiazolium ring. Why is pyrophosphate a part of the structure of TPP then? What benefit do the additional phosphates give TPP? Is there some reaction with TPP that does not center on the thiazolium ring?

The diphosphate* part of TPP does not serve any catalytic function but, rather, helps bind the coenzyme to the enzyme. In this respect it is similar to the non-nicotinamide portions of NAD/NADP (think, why the P?), and represents a general theme, rather than a unique instance.
A pertinent paper that I found from an internet search unfortunately requires access to an academic library. This is P. Arjunan et al. J. Mol. Biol. (1996) 256, 590–600, and describes the crystal structure of yeast pyruvate dehydrogenase. I quote the following from it:

The roles of Asp444 and Asn471 in binding the cofactor to the enzyme, observed in PDC and in their counterparts in transketolase (Lindqvist et al., 1992) and pyruvate oxidase (Muller & Schulz, 1993), conclusively establish the importance of a conserved sequence roughly 30 residues long (Hawkins et al., 1989) starting with GDG and terminating with NN (Figure 8). These residues are among the few highly conserved in all ThDP-dependent enzymes and form the Mg2+ binding site which assists in anchoring the diphosphate to the protein matrix.

And the Figure 8 referred to is a stereo-pair diagram (squint at the centre — with your specs off if you wear them — to see it in 3D):

￼
This also has a Pfam designation. The structure was deposited in the Protein Data Bank as 1PVD, where there is a useful diagram showing the interaction of the protein and magnesium ion with the phosphates:

*The paper quoted refers to the pyrophosphate part as “diphosphate”, which I assume is the standard chemical nomenclature.


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IU Unit in Virology
In a paper recently I saw "Cell are infected with 50,000 IU of virus per 100ul well". I can not seem to find a defintion of this unit? Is it a functional unit or just a count? If someone is familiar with this definition please let me know.
It is a retrovirus. The thesis is here (page 18):
https://tspace.library.utoronto.ca/bitstream/1807/98170/3/Mazzanti_Andrew_D_201911_MSc_thesis.pdf

IU usually stands for "International Units". It can be functional of just a count. You'd have to search specifically for the meaning of IU for viruses to know what number of viruses per 100ul tat specifies.


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Questions in Regards to Isolating Promotor Region for Recombinant DNA Techniques
For a while now I have been trying to isolate the promotor region of this Protein.
Using sticky end PCR, I need to isolate this promotor region so it has paticular sticky ends for the purpose of placing it into a plasmid. However, I have a couple of questions on how to do this:
A. For inserting it into a plasmid, how precise does the sequence have to be? It is nearly impossible to find a restriction enzyme which lines up exactly to the start codon. How far can I deviate away, while still allowing the protein to be transcribed properly (on the plasmid)?
Thanks.

If you are performing sticky end PCR you do not need any restriction enzymes at all.
Design primers for your acceptor plasmid to 'linearize' it at the ATG and add a unique 4 nucleotide on the full length forward and another unique on on the full reverse primer. Design your promoter fragment primers to have compatible overhangs to that of the vector. And to answer your question: you will have very very poor yields if both are not perfectly complementary.
You will need to phosphorylate the full length primers for the promoter because primers are usually not phosphorylated at the 5' end. Do your PCR, anneal and ligation as per normal.
But if I were you I would switch over to Golden Gate cloning, our lab did a year ago and I have not regretted it one moment.
Edit:
I have added a diagram below. There was one thing which I overlooked in my original answer: You can get a direct repeat of the overhangs when the two longest products of each annealing reaction combine in a blunt end ligation but that should not affect your expression too much. However I still think that sticky end PCR is not the best way out of this. Blunt end ligation, gibson or my favourite: Golden Gate are all much more powerful techniques

Sorry, I may not completly understand how these primers work: This is how I currently think they work:

Sorry, I may not completly understand how these primers work: This is how I currently think they work: Lets say you have a 5' to 3' fragment GGATTCGATCGAAG. And I have I want to make it have sticky ends GGA. I design a primer that binds to TTC, and another that binds onto the 3' end. When the PCR cycle goes though, I will obtain the stick ends?

Or can I design my primers to contain the sticky ends, so I do not need to worry about finding the correct overhang regions


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how can a pseudo coelomate like ascaris lay 200k eggs in a single day
as per this site the dimensions of eggs of ascaris lumbricoides are 78-105 microns and as it is shown in the pic down here only half of the body cavity would be used for storage of all the stages of different sizes of eggs

and as we all know pseudo coelomates have very minute width of their body cavity
and rest of the body cavity is filled with mesoglea
so my question is how can a round worm accomodate $2$x$10^6$ eggs in its womb before its release

First of all, the statement that "pseudo coelomates have very minute width of their body cavity and rest of the body cavity is filled with mesoglea" is not correct at least for (large) nematodes and priapulids: they have quiet voluminous body cavity and no mesoglea. In small nematodes (to which Ascaris doesn't belong) the body cavity is indeed vestigial, but the "space" between the organs is filled with extracellular matrix.
Another point is that the eggs in nematodes develop not in the body cavity directly, but in the ovaries and uteri (as correctly shown in the picture you provided), which indeed occupy very large part of the body.
Now to the question about the counts: individual capacity of a single female 29 cm long was once (see also) estimated to be ca. 27 mln eggs contained in its ovaries and uteri, which is impressive, but reasonable given the extremely small size of the eggs and extremely large size of the worm.
If we very coarsely approximate the volume occupied by a single egg with a cube 70x70x70 µm3, we get ca. 9 ml for 27 mln of them, and not much imagination is required to see, how a female ascarid can find room for this volume of relatively loosely packed eggs in its genital system.
EDIT:
To check my statement about "quiet voluminous body cavity and no mesoglea" one can open any modern textbook on invertebrates (or attend a practical course of invertebrate zoology). This is how the nemotode body cavity is described in the monograph "Nematoda" by Vladimir Malakhov (the original I cite is in Russian, but there is also a translation of the book):

"The presence of a spacious body cavity is considered to be the most characteristic property of the whole class of nematodes".
"Schizocoel [= primary body cavity] doesn't have its own walls made of cellular material and is represented by spaces between organs filled with liquid".
"Nevertheless, it should be stressed, that the opinion of the schizocoel being a voluminous body cavity, appeared as a result of investigations on large parasitic nematodes."

Please add a reference backing up your statement: 'they have quiet voluminous body cavity and no mesoglea.'

in summary - very small eggs

authors: فيوتشر أكاديمي