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Why in acute phorpyria?
I am having confusing about treatment of phorpyria . I read it somewhere that for treating phopyria hemin and glucose is given IV . Please give me an easy explanation of it

Some more background and a link to the source of the "somewhere" is recommended. Until edited, I vote for closure

From book Lippincot review

Hi and welcome! As @AliceD said, this question needs a lot more detail in order for us to help you. This page in our Help Center will show you what she means. We look forward to seeing more of you!

@Sue acute phorpyria is a disease in which there is  accumulation of intermediate compounds in response to defective heme synthesis pathway .what else I can tell u .

@user60180, details like what exactly you are after, what effort you made in finding the answer (if any). You can refer this for a guide: http://biology.stackexchange.com/help/how-to-ask

Since porphyria is not one disease but many, I suppose that with "acute porphyria" you mean acute intermittent porphyria. The reason of the disease is an autosomal dominant mutation on the enzyme porphobilinogen deaminase, an enzyme that converts porphobilinogen to hydroxymethylbilane by a deamination reaction.
Now the heme is produced in many steps, and the enzyme above is important in the early steps of heme synthesis. In persons with porphyria, these heme precursors accumulate, causing toxicity. In acute porphyria the symptoms are mostly from the central nervous system. The figure under shows the steps of the biosynthesis of heme, and as you see at the upper right corner, defect in the enzyme porphobilinogen deaminase (PBG-deaminase) results in accumulation of porphobilinogen.

Glucose and other carbohydrates can help suppress disease activity, are given by vein or by mouth, and are part of initial treatment. Intravenous heme, however, is both more specific and effective than glucose and should be started if the patient’s symptoms fail to improve within 36 hours.http://www.porphyriafoundation.com/about-porphyria/types-of-porphyria/AIP [2015-05-02]
More about carbohydrates and the suppression of symptoms in porphyria http://www.porphyriafoundation.com/about-porphyria/diet-and-nutrition/the-glucose-effect-in-acute-porphyria [2015-05-02].

I like the explanation . But how glucose do this job . Does it help in removing those excessive intermediates?  Or something else

If you see at the figure above you can see at the second step that glucose and hemin (which you mentioned in your question) inhibits the Aminolevulinic acid synthase. Glucose therefore inhibits the formation of the heme precursor 5-aminolevulinic acid and thus downstream formation of porphobilinogen. http://flipper.diff.org/app/pathways/info/6362 [2015-05-02].


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Identify chromosome location from gene nucleotide or amino acid number
I'm sure this is a basic question, but I couldn't find an answer anywhere. Let's say I'm provided with the location of an amino acid or a nucleotide (for example, which lists the location as the ACADM gene, 985A>G (the nucleotide polymorphism) and 304 as the amino acid mutation (from Lys>Glu)). How would I go about now identifying the exact position of this on the chromosome? Eg: Chromosome 1, location 76123456?
Currently the only way I can think is to manually count 304 amino acids, or 985 nucleobases, from the start of the gene where the start location is listed. However this isn't practical. I downloaded some software (IGV & GenomeViewer) and can't see a way I can view the actual amino acid or nucleobase number from the start of the gene.

Have you tried the reference docs ? Eg. For IGV )https://www.broadinstitute.org/igv/Navigate) under searching

Option 1: You can use the SNP database, to see if it has (and it usually is) characterised before: The tutorial is here (http://www.sciencebuddies.org/science-fair-projects/project_ideas/BioMed_Bioinformatics_NCBIGene_SNP.shtml)
Option 2: is to use a go to character function in a few text editors. If you are on windows, You can do it in Notepad++ (https://superuser.com/questions/487507/how-can-i-find-nth-character-in-notepad); if in Linux/mac, you probably know how to do it in vim (https://stackoverflow.com/questions/22862558/go-to-a-specific-offset-in-a-document). If using other text editors in Linux, ask around that community, and they'll tell you how to go about it.
Option 3: One another labour intensive but easy way to do it is to use the tools available online. I personally use NCBI for this, and play around with the region shown. This prevents you from counting. Once you get the local region, select a  few nucleotides  to the left and right, and use that as a search term in your sequence (find text and replace text)

Option 4: And my personal favourite, get the computer to do it for you like this: https://stackoverflow.com/questions/23662748/replace-a-nucleotide-at-a-certain-position-in-a-dna-sequence-file
If you are using IGV, it is said in the documentation here (https://www.broadinstitute.org/igv/Navigate) on how to search for regions and even insert mutations. Were you not able to make this work ?

Some really helpful options there, thanks Rover. I'm going off 23andme data, and it seems it has listed the locations different to the ch38 genotype. Unfortunately it seems the SNP's in the NBI are also missing the 304 variant. Would I be correct in saying no SNP exists for the 304?

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The effect on the efficacy and potency of a non-competetive antagonist binding to the active site of the receptor (dose-response curve)
According to the book "Principles of Pharmacology: The Pathophysiologic Basis of Drug Therapy" by Golan et al, non-competetive antagonists can bind to both the allosteric site and the active site.
I know that a non-competetive antagonist that binds to the allosteric site causes a decrease in efficacy, but no decrease in potency (see figure below). Figure A shows the agonist alone and the agonist together with a competetive antagonist. This causes the dose response curve to shift to the right, thus increasing the EC50 and decreasing the potency of the agonist. Figure B shows the dose-response curve for the agonist alone and agonist together with a non-competetive antagonist that binds to an allosteric site. As you see this type of antagonist causes a decrease in efficacy, but no decrease in potency is observed.

But how does a non-competetive antagonist that binds to the active site affect the dose-response curve? Does it lead to both a decrease in efficacy and potency? Unfortunately, there is no figure in the book to answer my question.

I slightly modified your question. Please feel free to roll back. Thanks for this interesting question.

Short anwer
'Non-competitive active site–binding inhibitors' are called mixed-type inhibitors. These inhibitors exhibit features of both competitive and non-competitive inhibitors, as they increase Km (like a competitive inhibitor) and decrease Vmax (like a non-competitive inhibitor).

Background
What an interesting question!
In theory, a reversible inhibitor binding to the active site of an enzyme is per definition competitive. In a review by Blat (2010) the author mentions that active site–binding inhibitors that display non-competitive inhibition are indeed unusual. Such inhibitors are referred to as mixed-type inhibitors. In many cases the unusual behavior is observed with (1) enzymes utilizing an exosite for substrate binding, or (2) isomechanism enzymes, (3) enzymes with multiple substrates/products and⁄or (4) products and two-step binding inhibitors.
(1) Enzymes with an exosite have a substrate recognition site that is different from the active site. For example, some proteases bind their target on the exosite and then catalyze proteolysis in the active site. Mixed-type inhibitors then bind to the exosite, thereby inducing non-competitive behavior, as the active site is not bound.
(2) isomechanism enzymes are enzymes that undergo several structural transitional changes during catalysis. When one of these conformations is rate limiting and binds the mixed-type inhibitor, it may non-competitively inhibit the enzyme when another conformational form binds the substrate.
(3) Enzymes with multiple substrates or products sometimes follow a sequential binding and release of two substrates (or products). Suppose substrate A and cofactor B (e.g. NADPH). The enzyme converts substrate A into A' and cofactor B to B'. Now suppose that the enzyme obligatory has to release A' before B' can be released and that the B' bound state is unable to bind substrate A in the active site, but is able to bind the mixed-type inhibitor. In this case, again, a non-competitive inhibition pattern is observed.
(4) Two-step binding inhibitors refer to inhibitors that cause a conformational change of the enzyme after binding its inhibitor, that very slowly reverts back. In the conformationally changed state, the enzyme cannot bind the substrate and competition with substrate is lost. The most extreme case are inhibitors that covalently bind to the active site (note this is the mechanism addressed by @RoverEye).
You ask what the dose-response graphs are of mixed-type inhibitors. A more common way of displaying inhibitor behavior is using Lineweaver-Burk plots. Many enzymes follow Michaelis-Menten kinetics (Berg et al., 2002) and by plotting the enzyme kinetics as Lineweaver-Burk plots more insight is gained into the affinity (Km, you call this 'potency') and maximum rate of the enzyme (Vmax, you call this 'efficacy') compared to dose-response graphs. In Lineweaver-Burk plots the reciprocal velocity of the enzyme is plotted against the reciprocal of the substrate concentration, resulting in a straight line out of which affinity an maximum velocity can be easily obtained via linear regression.
The following graphs obtained from Illinois Institute of Technology show the three different modes of inhibition discussed, starting with the two common types:

Competitive inhibitors increase Km (i.e., decrease substrate affinity).

Non-competitive inhibitors decrease Vmax (i.e., decrease substrate turnover)

Mixed-type inhibitors increase Km and decrease Vmax

References
- Berg et al. (2002). Biochemistry, 5th edition
- Blat, Chem Biol Drug Des 2010;75(6):535-40

There is one more type of inhibition called uncompetitive inhibition where the inhibitor binds to the ES complex. y-intercept increases with increasing inhibitor but the slope remains constant. [Example].

@WYSIWYG,  I left it out for clarity as it wasn't mentioned in the question.

Also you can perhaps add that the dose response curves shown in the question have Hill kinetics and these graphs are for normal Michaelis-Menten.

@WYSIWYG - which graphs do you mean with these graphs, mine or PO's graphs?

Your graphs are for MM but OP's graphs look like Hill.

@WYSIWYG Thanks I'll look into it. Does it invalidate my answer?

No no.. Not at all.. :)

@WYSIWYG,  pheww :) I spent a lot of time on this one. Will work on it asap.

Wow thank you @AliceD for this answer. It really helped me.

@Sarin - no worries, my pleasure! It was a tough and interesting question :) It took me several days to have a satisfactory answer in place. With the comments from WYSIWYG it ended up as a decent answer. Glad I could be of help.

This is a tough question. I was reading this paper Patrono, C., et al. "Clinical pharmacology of platelet cyclooxygenase inhibition." Circulation 72.6 (1985): 1177-1184. and they seemed t mention this paragraph in the introduction.

Platelet Cycloxygenase or prostaglandin (PG) H synthase (i.e., the
  enzyme that converts arachidonate released from membrane phospholipids
  into PG endoperoxides) is the target of several antiplatelet agents
  that can reversibly or irreversibly block the activity of the enzyme
  by competing with the substrate or permanently altering the active
  site, respectively.Such drugs belong to the class of so-called
  nonsteroidal anti-inflammatory agents (e.g., indomethacin, aspirin),
  but also include a uricosuric agent, i.e., sulfinpyrazone.

So we have to look for  DRC of a NSAID to say COX. Now I was unable to find a good DRC for aspirin (If you find it, let me know in the comments below. I would love to take a look at it). But I did find DRC for other drugs that inhibit COX.
It led me to this paper Walker, M., et al. "A three-step kinetic mechanism for selective inhibition of cyclo-oxygenase-2 by diarylheterocyclic inhibitors." Biochem. J 357 (2001): 709-718. which described the inhibiton of COX2 by a NSAID called celecoxib.

The inhibition of COX isoenzymes by NSAIDs generally conforms to one of three inhibitory mechanisms: simple reversible inhibition, as demonstrated by ibuprofen ; time-dependent reversible inhibition, which includes both weaker binding inhibitors, such as naproxen , and tight binding inhibitors, such as indomethacin and meclofenamic acid ; and irreversible covalent inhibition, as demonstrated by aspirin  and o-(acetoxy-phenyl)hept-2-ynyl sulphide ('APHS') . Most of the traditional NSAIDs display similar inhibitory mechanisms against both COX-1 and COX-2 , and are relatively non-selective. However, COX-2-selective diarylheterocyclic inhibitors demonstrate distinct inhibitory mechanisms for the two isoforms . For example, celecoxib  has been reported to be a reversible competitive inhibitor of COX-1 while demonstrating time-dependent irreversible inhibition of COX-2.

Considering the highlighted statement, I think you can consider the COX1 curve as a standard competitive inhibition and COX2 as non competitive inhibition. The resulting change is essentially the curve shape itself (being shifted to the right).
The DRCs were reported here: Tacconelli, Stefania, et al. "The biochemical selectivity of novel COX-2 inhibitors in whole blood assays of COX-isozyme activity." Current Medical Research and Opinion® 18.8 (2002): 503-511.

Interestingly the second paper (By Walker) also described how (the authors proposed) the binding occurs. A very interesting read.

Thank you for the answer, I appreciate it. But it really doesn't answer my question, which was about an irreversible antagonist that binds to the active site (apparently there are non competitive antagonists that do bind on the active site) and how this affects the dose response curve. The figures in my first post (fig B) and in your post (fig B) show the dose-response curve for a non competitive (irreversible) antagonist that binds to the allosteric site. But thank you, once again, for the time and answer!

@Sarin Apologies. I corrected the answer with what I think happens. I know its not ideal, but I do hope it does this help a bit? It was a very interesting journey to find the answer I must add. I am still trying to find the DRC, but atleast I was able to find an example to start and give a general direction to go in.

Indeed, this one is a really great and interesting answer. It does help. I thank you once again for your effort. Appreciate it man!

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Evolution of dominance of alleles
Can someone tell me where can I find detailed text about mechanisms of evolution of dominance? Or, if it is possible, I would be grateful if someone will tell me about them here.

Why was it downvoted? Because it is asking for both (it is a either or format actually) a book-recommendation and a review?

And do you have any particular theory in mind? YOu might want to read this  for background knowledge ( Bourguet, Denis. "The evolution of dominance." Heredity 83.1 (1999): 1-4. )

Welcome to Biology.SE!
I am not aware of any book that talks extensively about the evolution of dominance. It is a very interesting field of research. You can probably search for papers on the subject that will go much further than my below answer. For example, you may want to read Mayo and  Burger (1996), Bourguet (1999) and Billiard and Castric (2011) I hope that my answer below can provide the ground for more searching by presenting the two main (and of historical importance) hypotheses.

There are several hypotheses about the evolution of dominance. It is important first of all, to note that empirical observations show that beneficial alleles tend to be more dominant than detrimental alleles. There are a number of hypotheses and I won't be able to make a complete review. Among the two main hypotheses to explain the evolution of dominance, one has been formulated by Ronald Fisher and one by Sewall Wright (and J.B.S Haldane).
Fisher's hypothesis
According to Fisher's hypothesis, between two equally beneficial alleles, if one is more dominant than the other than it's heterozygote carrier will have higher fitness. In consequence, beneficial alleles evolve to become more dominant while detrimental alleles evolve to be recessive (so that they can hide from selection in heterozygotes). The main criticism to this model is that selection for dominance is possible only when the locus is polymorphic. This means that the strength of selection for dominance is of the level of the mutation rate, seemingly way too weak to explain the observed pattern of dominance.
Wright's hypothesis
According to Wright's hypothesis, beneficial alleles are more dominant because of the kinetic of biochemical reactions. The rate of a biochemical reaction is a function of the concentration in the substrates of interest. The function is called "Michaelis-Menten function" after the name of the authors. The Michaelis-Menten function looks like this:

Think about a knock-out mutation. Such mutation will decrease by half the concentration of protein the gene in question produce in heterozygotes. Imagine, the concentration of proteins in the wildtype homozygote was 3 (see above graph). The rate of reaction of this homozygote is therefore about 3. The heterozygote would have a concentration of 1.5 and the rate of the reaction is therefore about 2.5-2.75. Assuming that the rate of this biochemical reaction is directly related to fitness, then a the locus of interest, benefical alleles are necessarily dominant and detrimental alleles are necessarily recessive. Selection for dominance is not involved in Wirght's model.
In addition to that, if you have a series of biochemical reactions, you will likely have one reaction that is causing a bottleneck. One reaction that is the limiting factor of the speed of some biochemical pathway. Let's call this reaction process A. If a mutation occurs on a gene coding for any other reaction process B that is not the limiting factor (process A), then this mutation will have no effect on fitness as long as it is not important enough to make process B slower than process A. This may lead to pattern of dominance. I am not quite sure by how much of this has been formulated by Wright and Haldane though.
Is Fisher's or Wright's hypothesis correct?
To my understanding, current state of the art is to consider Wright's model to be correct and Fisher's model to be wrong. In reality, the truth probably lies somewhere in between these two extremes. There exists more detailed (and more mechanistic) models of the evolution of dominance that and the above dichotomy might not be very representative of the current debate on the subject. For example, I (vaguely) remember that during my undergrad, Sylvain Billiard gave a talk at my university in which he reported that some alleles actually have some domain that are directly responsible for decreasing the expression of the other allele on the sister chromosome suggesting that Fisher's hypothesis might sometime be quite a good explanation as well.

This answer was recycled from what I wrote here. But it is a much better fit to your question than to where it was originally posted.

@Hans Thanks. Fixed

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Recommend good conversational books to learn about cell and developmental biology or biochemisty?
I'm an engineer by training and teaching myself the basics of cell and developmental biology. I'm using Scott F. Gilbert's Developmental Biology and Alberts' Essential Cell Biology right now, and they are both great resources.
Can you recommend good books on similar topics that are written in a non-textbook format?
EDIT: For example, I found books like The Greatest Show on Earth, Extended Phenotype, and Nature via Nurture really useful to learn about the basics of evolution while I was reading Watson's Molecular Biology of the Gene. Are there similarly well-written books for cell biology, developmental biology, or biochemistry? I know this sounds broad, but I'm not asking for textbook recommendations. I'd like to read something that isn't a textbook alongside my current studies.
SECOND EDIT: I'd be like to extend this question to recommendations of broad review articles in Cell and Developmental Biology too. For example 'How do cells know where they are?' is an excellent article on different strategies cells may use to assess distance in a developing embryo.

Cell Biology, Dev Bio and Bio Chem are three very vast topics. I presume you want a book separately for each one ?

Yes, that's what I meant.

The two textbooks you mentioned are some of the best. Gilbert's is quite technical and assumes you have some background in molecular biology.  It might be prudent to get a more introductory textbook in that case.

Since when are book requests too broad?

I don't find Gilbert's book hard to peruse at all since I've read Watson's Molecular Biology of the Gene. When I read Watson's textbook, books like The Greatest Show on Earth really helped put things in context.
While I think Gilbert's Dev Bio is an excellent textbook and well-written, it has a great deal of detail. I would also like to read something that distills the key concepts. This is where something written in a non-textbook format would really help.

For Biochemistry:

For Molecular Genetics:

For Developmental Biology:

That last one, Life Unfolding, is really cool and I think exactly what you're looking for.

Endless Form Most Beautiful: The New Science of Evo Devo introduces the reader to several classic embryology experiments and some key principles too.
I'll edit this answer when I find more books or reading material of this nature.

I recommend Lehninger principles of biochemistry.
It is one of the best, most read, and referred books on Biochemistry.

Please try reading 'Principles of development' by Lewi Wolpret. This book explains the general concepts in develpmental biology beautifully. Invertebrate models like dorsophilia and zebrafish are used as examples to simplify the concepts in developmental biology.

The Machinery of Life by David S Goodsell

I just picked this up and haven't actually read through it yet, but it looks amazing. Very well illustrated with drawings and molecular models. It's also not overly technical. I don't have much to say in the way of a review, but there's plenty on Amazon. It sort of seems exactly like the book you're looking for.

I own that book, definately a nice read. David Goodsell is one of my favourite authors and a superb scientific illustrator, providing the reader with abundant and accurate representations of macromolecules atom-by-atom, which is vanishingly rare in biochemistry books. Check out "Bionanotechnology: Lessons from Nature", "Atomic Evidence" and "Our Molecular Nature" as well.

Would you consider the New York Times a suitable level of detail (while admittedly not a book)? If so then you may want to peruse this collection:
http://topics.nytimes.com/top/news/science/topics/biology_and_biochemistry/index.html
You will have to be selective.
This was my first hit in Google searching for 'lay articles on biochemistry.' I would recommend my own textbook but it is aimed at 3rd year undergraduates, and would probably contravene the Biology SE rules.

Thanks a lot for the NYT link. However, those articles are based more on relaying findings than explaining key principles.


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Chromosome picture, how to interpret?
In this guide i was reading for annotations, there is a diagram of chromosomes of D.mel, I am confused as to why the chromosomes are all attached to one another, is this picture taken in a certain stage of mitosis in which they are all condensed?
Original source
 (page 2).

You need to look up polytene (giant) chromosomes.

Polytene chromosomes are over-sized chromosomes which have developed from standard chromosomes and are commonly found in the salivary glands of Drosophila melanogaster. Specialized cells undergo repeated rounds of DNA replication without cell division (endomitosis), to increase cell volume, forming a giant polytene chromosome. Polytene chromosomes form when multiple rounds of replication produce many sister chromatids that remain synapsed together.

You can read more about it at wikipedia (http://en.wikipedia.org/wiki/Polytene_chromosome)


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Using Jpred to predict secondary structure
I'm trying to use Jpred to predict secondary structure for a protein sequence. When I run J-pred, I get a bunch of hits from PDB. I've also noticed these 'hits' are the same name as the templates i use when using Swiss mod. How do i interpret these hits, do I just ignore them and force jpred to predict structure anyway?

That depends on what your are trying to do. Apparently your query sequence is similar to that of proteins with a known 3D-structure. As it says on Jpred's result page (and help), in this case it might be worth looking at these homologues with experimentally determined structures for information on secondary structure. Most likely it will be more accurate than Jpred's prediction.
How much of the secondary structure you can use from the 3D-structure also depends on which parts align between your sequence and the ones in the PDB. You can show the alignments on Jpred's result page by clicking the button below 'Alignment of PDB hits to your sequence'. However, if you are going with annotations from the 3D-structure, it would probably be better to use SWISS-MODEL directly.
If you don't want to see those hits and just force a Jpred prediction, you can tick 'Skip searching PDB before prediction' in the advanced options before submitting your prediction. The hits are shown merely as a convenience, ignoring them doesn't change anything about Jpred's prediction.

Thank you! So chou &Fasman, Gor, etc are just alternative alogrithms to Jpred? Do you think it would be worth comparing the results of multiple alogrithms or would one be sufficient?

@Ksims Yes, those are all secondary structure prediction methods, albeit of varying age: Chou-Fasman is one of the early methods from the 70s, GOR should also fall around that time, while Jpred still seems to be under development (at least until 2000). Wikipedia has a small list of secondary structure prediction methods which you can try out. I do think it is worthwhile to look at a few more results, although the consensus isn't necessarily always right.

awesome, I appreciate your help!


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What are the units of Phylogenetic Independent Contrasts (PICs)
PICs are a popular phylogenetic comparative method and I was wondering what units the actual contrasts are in. I want to say they are unitless but I am not entirely sure.

Are you scaling the data?

@Oreotrephes Yes, I'm scaling it by the branch lengths

From what I understand, the answer is; it depends. As you point out in the comment to Remi.b, contrasts are standardized by branch lengths (which are assumed to be proportional to "evolutionary time") before they are analysed (to obtain standardized independent contrasts). However, branch lengths can be come from a number of different sources (divergence times, genetic similarity etc), can be based on different evolutionary models, and can also be transformed (log etc). Therefore, you cannot say what the units of standardized independent contrasts are in general. They would probably not be dimensionless though, since branch lenghts are seldomly (if ever?) expressed in the same units as the raw contrasts. Units such as log(kg)/year or kg/log(year) are more likely. Ultimately, in the usual case, the point of standardizing is to assure that all contrasts receive equal weight in the the analysis, and the creation of suitable branch lengths can be a trial-and-error process, where you e.g. study the relationship between standardized contrasts and the standard deviation of contrasts to see if they have been adequately standardized.
I guess you could describe the process as:

calculate contrasts
calculate standardized contrasts (using branch lengths)
when the contrasts are appropriately standardized, you have obtained phylogenetically independent contrasts.

Here are a couple of relevant quotes from Garland et al., 1992:

...independent contrasts must be adequately standardized so that they will receive equal weighting in subsequent correlation or regression analyses.

also

Second, the statistical adequacy of any proposed branch lengths should be viewed as an empirical issue (cf. Grafen, 1989, 1992; Harvey and Pagel, 1991; Pagel and Harvey, 1992). Third, different transformations -
  and indeed entirely different branch
  lengths, based on different information -
  can be used for different characters. The
  use of different branch lengths for different characters would be analogous to using different transformations for the two variables in an ordinary correlation or regression.

finally

Standardized independent contrasts essentially indicate minimum average "rates" (or, in Bookstein's [1988] terminology, "reduced speeds") of character change occurring along two branches deriving from a
  common ancestor (Garland, 1992). Because
  they are standardized, they do not indicate
  absolute amounts of change. For example,
  a large contrast (absolute difference) between two species at the tips of a phylogeny may yield a small standardized contrast if the branch lengths of that contrast
  are long.

Thanks for your answer. I thought it might be (units of measurement)/(units of time), but that seemed strange to me because those are the same units as rates. Thinking of contrasts as being rates is not intuitive to me, but based on that last paragraph I guess that's what they are. How about that!

Good answer that proved me wrong. +1 I deleted my answer.


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History: Building a "Phylogenetic network" of famous evolutionary biologists
Seems like many of the famous evolutionary biologists are somehow related to each others. For example (and I might be mistaken), Ronald A. Fisher was mentored by a descendent of Charles Darwin. Joe Felsenstein did undergraduate research with James Crow and wrote his doctoral thesis under Richard Lewontin. Muller was mentored by Huxley, friend of Darwin. Brian Charlesworth was mentored by John Maynard Smith who was himself mentored by J.B.S Haldane.
Is there anyone who is crazy enough and interested in the history of evolutionary biology enough to draw a network of relationships between most famous evolutionary biologists since Charles Darwin?
An arrow between any two evolutionary biologist might represent a relationship of the kind family, PhD supervision or friendship. Of course there is no limit to the number of famous people to include and there is no good distinction of who should be included. But a network of the 20 (or 50 if you feel courageous) most famous evolutionary biologist would be awesome!

I am not totally sure this question will be accepted on this site but I think it might fall within the history tag. Also, the question has the default to not have a definitive answer and to ask for creating something rather than for pure knowledge.

I bet you could make one using cited/co-authored papers! I'm not sure you could derive specific relationships from that, though. I don't really think this question is really within the scope of this site, though.

This is a really fun question, but probably not within the scope of the site. I'm not where else on the internet would be a good place to ask it, but I sure would like to see it, though!

Thanks for your comment. Following CactusWoman comment: if the post is estimated to be off-topic here. I would love to know where you think I could ask for this question. Do you think they are going to accept such question on history.SE?

Disregarding whether or not it's on topic, I think it's too broad for any SE site.

It also falls under the six degrees theory. There are very few evolutionary biologists in the world, of course they're all related somehow or other. This goes double for the evolutionary biologists of a century ago. There were very few of them so of course, by definition, they mentored each other.

Maybe not exactly what you are searching for, but you should look at the evolutionary biology version of Academic tree. This provides family-tree type relationships between researchers, but mostly (only?) of supervisor-student relationships.
As an example, the tree for Joe Felsenstein includes links to e.g. Dobzhansky, Lewontin and Sewell Wright. You should be able to find much of the information you are looking for at this site.
Here is a small cutout of Joe Felsenstein's tree, just as an example:

Wow this is so cool! Exaclty what I was looking for. As you said almost everything is about supervisor-student relationships but this is really nice. +1

You can add yourself as well - just create a user account.


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Specific numbers of nucleotides in Okazaki fragments
Okazaki fragments are formed during replication of the lagging DNA strand. What determines the length of these fragments?

possible duplicate of formation of okazaki fragments in lagging strand during replication

@dustin page not found

The length of Okazaki fragments is not necessarily a tight distribution. The lengths are determined by the spacing between adjacent sites where DNA primase has synthesized a short RNA primer on the lagging strand at an active DNA replication fork. In E. coli, as I recall, this occurs on an average of once per 1000 nt.
DNA polymerase holoenzyme then uses that 3'-OH on the RNA primer as a substrate and synthesizes the DNA strand complementary to the Template strand UNTIL it bumps into the next RNA primer.
It is those nicks in the newly synthesized lagging strand, that have a deoxy-3'-OH, and a ribo-5'-phosphate, (that need to be removed and ligated) that mark the boundaries of individual Okazaki fragments.

In chromosomal DNA nucleosomes are spaced by 200 nucleotides.. so, this can be a reason of okazaki fragments limited to just 100-200 nucleotides in eukaryotes? I am confused...

Where did you obtain the number of 100-200 nucleotides? All eukaryotes? DNA replication in eukaryotes probably best characterized in yeast (budding yeast and fission yeast), fruit flies, rats (rat liver biochemistry?), and human cell lines, like HeLa cells.

During replication, DNA is naked: no nucleosomes there. I am not sure but nucleosome formation may be later than ligation of Okazaki fragments.


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An example for N50? Why do we need it?
I'm trying to understand N50 on the wikipedia. But I was unable to get a sense of the definition:

Given a set of contigs, each with its own length, the N50 length is
  defined as the length for which the collection of all contigs of that
  length or longer contains at least half of the sum of the lengths of
  all contigs, and for which the collection of all contigs of that
  length or shorter also contains at least half of the sum of the
  lengths of all contigs.

Is there a simple example that illustrates this definition? Furthermore, why do we need this statistic? What does it really tell me? Do we want a higher or less value?

Contig or scaffold N50 is a weighted median statistic such that 50% of the entire assembly is contained in contigs or scaffolds equal to or larger than this value.
Mathematically:

Given a set of sequences of varying lengths, the N50 length is defined
  as the length N for which 50% of all bases in the sequences are in a
  sequence of length L < N. This can be found mathematically as follows:
  Take a list L of positive integers. Create another list L' , which is
  identical to L, except that every element n in L has been replaced
  with n copies of itself. Then the median of L' is the N50 of L. For
  example: If L = {2, 2, 2, 3, 3, 4, 8, 8}, then L' consists of six 2's,
  six 3's, four 4's, and sixteen 8's; the N50 of L is the median of L' ,
  which is 6.

In simple words:

scaffold N50 is the median contig size of your genomic assembly. It's
  a metric that you can use to evaluate the quality of your assembly,
  since an overly small N50 suggests that you were unable to generate
  many contigs of biologically meaningful size (i.e. you probably have a
  lot of bogus little contigs in your assembly). You can increase your
  N50 by eliminating sequences which are bound to cause you problems,
  e.g. short repetitive stretches.
note that this metric only applies when doing de novo assembly. If you
  are aligning to a reference (i.e. for variant discovery applications)
  this metric doesn't apply

For a sample workout on an arbitrary small dataset:
http://www.r-bloggers.com/calculating-an-n50-from-velvet-output/
This answer was compiled from multiple sources:
http://www.researchgate.net/post/What_is_N50_Scaffold_in_Genome_sequencing_Technique
http://seqanswers.com/forums/showthread.php?t=2332
https://www.broad.harvard.edu/crd/wiki/index.php/N50


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Storing Taqman plates with polymerase in fridge before running
I'm going to be running some Taqman assays on cDNA generated from RNA collected from various samples, and will end up running a bunch of plates (right now the setup is 1/2 a plate per sample, including controls etc.). Since our thermocycler can only handle one 96-well plate at a time, it has been suggested that I make up the mixes all together, aliquot them into the plates, then store the plates at 4°C (well wrapped in plastic or Parafilm) until ready to run. I would imagine that the plates would be in the fridge for just one day, at max overnight.
My question is about the polymerase - can I add it ahead of time, and just take the plate out of the fridge and pop it in the thermocycler, or should I add it just before use? I've been kind of out of the DNA/RNA field for over 10 years, and I seem to remember from before that polymerases could have residual activity even at 4°C. Is this still the case, or have they improved to only be active at 60+°C or whatever your temperature is?
Alternatively, is there another ingredient I should leave out until just before amplification? cDNA? Probes? Something else? Also, does the storage time make sense? Is overnight okay, or should I run them all the same day? Is over the weekend okay? I don't want this question to extend into a too-broad "How do I do qPCR?" question, but any tips you have would be very helpful. As far as I'm aware, all the reagents are from Life Technologies/Applied Biosystems.
The reason I ask is because it seems to me that it would be much more accurate and reproducible to prepare the master mix with polymerase already included, instead of adding 1 μL of polymerase to each well just before running the assay.

I don't see why you can't just freeze all the samples, and then prepare the master mix and run the PCR in one single go. Purified cDNA is far more stable than PCR mixes.

I stored several pre-mixed (with all the components, the polymerase included) plates in a -20˚C freezer for up to 1 month and still worked beautifully. Was using Taqman reagents. Would opt for the freezer rather than the fridge. Just take out your ready-to-amplify plates a bit ahead of time to allow them thawing.

@MarchHo the cDNA has been prepared ahead of time and frozen. On the day of the qPCR, I prepare the reaction using 2 dye-labeled probes (gene of interest and internal standard), PCR master mix, template, and H2O. All this is aliquoted into 96-well PCR plates and stored at 4C. When it's time to run the next plate, it's taken out of the fridge, polymerase is added, the plate is put in the thermocycler, and run. What I'm wondering is whether I can add the polymerase when I'm setting up the plates, then just take the next plate out of the fridge, put it in the thermocycler, and run.

I would suggest freezing it at -20 rather than at 4. You will still have some activity at 4, and its best to avoid that.

It is much easier and better to set up the reaction again. After a run, the thermocycler (for Real-Time such as light-cycler) should generally be allowed some rest (1h). In this duration you can prepare your second plate. You can aliquot cDNAs in PCR tubes (or strip tubes) with your probes and keep them at -20. You can add taqman master mix with appropriate volume of water before the reaction and load the PCR plate just before running the PCR.

Not sure if this question is still valid, but it was marked as unanswered and frequently viewed, so here's a go :)
These days most polymerases (certainly for Real Time PCR) are so-called Hot Start enzymes (ask your manufacturer, because they save you a whole lot of trouble!). They are modified with a protein that prevents them from working until activated at 95 deg. Celsius. Be aware though that not all hot start enzymes require the same activation time at 95 deg. Celsius, and some enzymes lose activity when activated for 15 min. at 95*C instead of 2 min.
In my experience storing plates at 4 degrees (or room temperature) is not a problem at all, and most manufacturers advertise that the PCR mixes (including primers, probes and DNA) are stable at room temperature for 24 hours. You do however need to protect the mixtures from (sun)light because the probe might be damaged when exposed for longer times.
Personally, I would not recommend storing real-time PCR plates in the freezer, because condensate will most-likely form when you thaw your plates. This can influence the PCR, as real-time PCR uses light for the analysis of your samples.
If you want to save yourself some pipetting, but can't prepare plates in advance, you can prepare a primer-and-probe mix, which you can freeze in batches. In our lab, we keep a stock of primer-and-probe mix for every assay. Maybe this also works when you already add water and PCR mastermix, but I have never tried. I wonder how other people do this, so please leave a reply.
Hope this might help some people.


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Why are some housekeeping genes considered better?
Whenever a PCR is done, we have to use housekeeping genes like GAPDH, to self normalise the values to account for different starting cDNA/DNA. Now there are many different genes like GAPDH, ubiquitin, actin etc.
Now when dealing with cells, I can understand that due to their immortal nature and frankly shocking number of generations undergone by them, the expression of the housekeeping gene may change.
Assuming that I am not dealing with cells and instead with animal tissues, does it matter which house keeping gene I select? Or in other words is GAPDH going to give me the same consistency between samples as something like 18s Ribsomal RNA or am I missing something specific?

Yes, it does matter. Gene expression varies a lot thoughout tissues and living conditions, so you need to test a variety of genes and choose the ones with the smallest variation in the expression between sample and control.
If you don't find any, you have to test other genes for this purpose. This is important, as this can severly affect the outcome of your experiment, since you normalize to the housekeeping genes. If these are not constant, it will cause problems. Have a look into the references, this helped me a lot.
References:

Accurate normalization of real-time quantitative RT-PCR data by
geometric averaging of multiple internal control genes.
The normalization of gene expression data in melanoma: investigating
the use of glyceraldehyde 3-phosphate dehydrogenase and 18S
ribosomal RNA as internal reference genes for quantitative real-time
PCR.
Normalization of qRT-PCR data: the necessity of adopting a
systematic, experimental conditions-specific, validation of
references.

You need to watch out though. Some very used genes could be very uninformative as housekeeping genes in qPCR. ACTB and GAPDH have tons of pseudogenes (analogous genes not yielding proteins) that can interfere with the PCR. You can read more in this article (1).
To make it even worse, the PCR amplification fragments can be of the exact same length as the ACTB amplicon, making it impossible to differentiate.
In addition, you have to consider the specific cell type/application/treatment, because some mRNAs could be very constant through treatment A on cell type 1, but vary with treatment B or on cell type 2.
I think the best would be to use multiple housekeeping (3-5) and see how they behave relative to one another. You could also use the mean of these housekeeping.
(1) Sun, Yuan, et al. "Pseudogenes as weaknesses of ACTB (Actb) and GAPDH (Gapdh) used as reference genes in reverse transcription and polymerase chain reactions." PloS one 7.8 (2012): e41659.


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Why MHC class I antigen presentation exist in normal cells?
The book "Kuby Immunology" states that normal infected cells might present antigenic peptides on their surface via MHC class I, but these can't activate naive CD8 cells. Only infected professional antigen presenting cells have the necessary co=stimulatory signals to do so. This is why cross presentation happens.
But then why does the process of antigen presentation on normal infected cells happen then, if it is of no use?

"This is why" cross presentation happens - is not my understanding. This is why presentation happens, by the professionals (APCs). Your question refers to presentation  by APC in contrast to normal body cells that are not part of the immune system, right? Cross-presentation is a special form of presentation by "antigen presenting cells" - APC. It occurs to me: normal cells present antigen, however may not be called antigen presenting cells. And, please correct: normal cells do not present on MHCII, hence do not cross-present.

It is of use. They're saying that naive T cells cannot be activated by normal cells because the latter lack the appropriate co-stimulus, namely B7. However, activated CD8+ T cells can recognize antigens presented by normal cells and form a response. This is how cytotoxic T cells are able to eliminate transformed or infected cells.


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Why do teeth decay of sugar?
Sugars is a common component in diet of mammals. Why then their teeth did not develop resistence to sugar decay?

The reason for the decay are ever evolving bacteria, which happen to be very hard to overcome, even nowdays. There is a lot of research centered towards protein interactions, even mapping whole interactomes, which would help us get rid of decay, yet naturally our system wasn't able to evolve as fast as technological advances, but bacteria did.


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E. coli not growing in liquid medium
We are regularly doing bacterial transformation and subculture from plate to liquid media to extract DNA. This usually goes very well and is straightforward, but occasionally, the colonies that grew well on a plate, will not grow in liquid culture (with the same antibiotics that were in the plate). This has happened now to three persons on multiple plasmids.
For example, I was trying to ligate an ampicillin resistance gene (with its promoter) inside pENTR4 plasmid (already with Kanamycin resistance). My plate and LB both contained the two antibiotics, but the colonies only manage to get slightly turbid at a 16h timepoint.
Phage contamination is very unlikely, and the effect cannot be due to a toxicity of the inserted fragment, as both insert and vector have been grown in these same bacteria multiple times without problem.
Help?
EDIT1 - We use Carbenicillin in place of ampicillin in the plate, but not in the liquid media.
EDIT2 - After managing to grow some colonies in liquid, I managed to get a small amount of the plasmid (profile was good; concentration = 70ng/ul). The weird thing is that when I retransformed this DNA in Stbl3 bacteria, I had absolutely no trouble. Do you think the bacteria stock could be responsible?

Did you change your batch of antibiotics, or media recently?

Simplest explanation is something is culture tubes or media prep bottles that inhibits growth (detergent residue?). But lots of people have seen what you describe if the colony-bearing plates have been stored too long at 4 C. One way to test (besides comparing media +/- drugs) is to "patch" out colonies on a new gridded plate--you may do this routinely, lots of people do. So either after finding cfu patch candidates to a master grid, or, when you innoculate the tubes for mini-preps, also patch out the cfu on a fresh plate. If the patched grid plate grows but the culture doesnt, voila

In very rare cases patching shows healthy tiny lawns with pin-prick holes from phage contamination. Also, if it is phage contamination from a lytic phage, in my experience the broth will clear and accumulate a small stringy clot of lysed cell debris, that floats. What if there was a non-lytic phage like M13? Sounds like you are carefully considering the "obvious" reasons.  There definitely ARE toxic inserts that are virtually impossible to propagate in regular E. coli, but then you would only find transformants without inserts (typically), but that is fortunately a pretty rare occurrence

The antibiotics are stocks stored at -20C, and they have been used simultaneously by a colleague of mine that had the same problem on some of his vectors (2/8). Since we have used the same type of tubes successfully, I doubt it could be the reason.

did you try positive control transformation? Of some test vector, like pEGFP-type

also your incubator might not work, sometimes people switch temperature for no reason or turn shaker off (maybe your outlet has timer that goes off overnight :)

This transformation was done with my colleague; we did the thing together. In his plasmids (stuff he received as is from another lab and transformed directly in bacteria) two out of eight presented the same problem as mine. The others grew perfectly. It was done in commercial DH5a bacteria that we had just bought. The temperature and rotation were fine.

how big/numerous are the colonies on the plate?

The colonies were small to average in size. It made no difference overall.

Which strain of E.coli are you using?

And about your insert, do you have the terminator as well? Because on your post you just mentioned promoter and AmpR

I'm not sure about the terminator as it is not labeled...

I think there is more than one possibility for this.

Plates bearing colonies are stored for too long in 4C(as @mdperry suggested). This causes the colonies to lose the plasmid over-time as the antibiotic in the plate gets degraded over-time. There is a study on this and I've shared the link. (http://homepages.bw.edu/~mbumbuli/biotech/colin/index.html).
The insert is toxic to cells and promoter is not strong enough. There is a leaky expression causing cells to die.
Check the competent cells and their storage condition as it would greatly affect the efficiency of transformation. Do a mock transformation with plain plasmid without insert and spread them on an antibiotic plate.


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Infrared emitting diode eye safety
I'm thinking of creating my own laser tag system and am a bit concerned about the eye safety aspect. Most of the homebrew system I've seen use the TSAL 6100 diode(http://www.vishay.com/docs/81009/tsal6100.pdf).
From what I've gathered from hobbyist forums it should be totally eye safe. However the math doesn't add up, my math could be wrong, read probably is, but I couldn't find any conclusive data that would disprove it. Also I cannot manage to factor in what effects lenses would have, which are a must to keep the beam from having to large a spread. Here are my calculations.
First off using the inverse suqare law and a TSAL running at 1Amps (from what I've read most players run it at 1.5 Amps) at the minimum distance of 200mm we get the following result:
Ee = 1.5W/sr / (0.2)^2m = 37.5W/m^2 (the 1.5 W/sr is from the TSALs datasheet at 1A)
The irriadiance of 37.5 is a bit high, but it's still under the 100W/m^2 safety limit for the cornea.
The same equation done at a distance of 100mm however yields an Ee of 150 W/m^2 which is above safety levels. Assuming you don't point a tagger in your face and start firing of shoots you'll be safe.
What I'm curious about is how a lens affects the safety characteristics. Assuming a lens with a focal length of 100mm. Won't the irradiance be the same with the irradiance at a distance of 100mm from the IRLED.
My math and understanding of the principals could be wrong, but I was curious if anyone could put my mind at ease.
I've also made a rough calculation of the retinal hazzard.
R(lamb) = 0.33 rad
(angular subtense) alpha= 5mm/200mm = 0.025 rad
We'll use an alphaeffective = 0.011rad and that gives us an emission limit for the radiance of 545mw/mm^2/sr
and our iriadiance is Lir = 1500mw/sr * 0.33 / (5mm)^2 = 99mw/mm2/sr
that's way below the limit, but if we modify that active light source to 1mm it becomes 495, which is skirting the line.
(the tec doc doesn't define an active light source size, at least I couldn't find it so I used 5mm )
Again the above calculations are for a source without a concentrating lens. What would happen if we added a lens in the mix? Would it stay in the safe zone? How does pulsing the diode effect intensity? I'm guessing it lowers it, but if anyone could manage it, I'd like a numerical demonstration that it brings it closer to safer bounds.
The above calculations are based on the folowing OSRAM paper (http://www.osram-os.com/Graphics/XPic2/00052113_0.pdf/Application%20Note%20Eye%20Safety.pdf), which explains the principles involving

I built a NIR LED light for use with a raspberry pi NIR camera. It worked well, and I probably got it my eyes several times with 25 LEDs. I'm not blind yet. But I know you don't have a look away reflex in the NIR range, so if you accidentally flash yourself, you might not realize it until it's a problem.

Re: the pulsing issue, I can say for sure that if the LED is pulsed at the same power, the effective brightness will decrease. This is how many torches implement their "brightness switch" mode.


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Mitochondrial D-Loop
I know that the D-loop is  a DNA complex in which the strands of double helix DNA molecule are separated for a stretch and held apart by a third strand of DNA. Usually, this third strand has a base sequence, which is complementary to one of the main strands and pairs in accordance with it. The third strand then sub sequentially displaces the complementary main strand in the region. Im also aware that D-loops occur in many DNA systems, such as in DNA repair, telomere activity, and in mitochondrial circular DNA molecules.  However, my question is why does the D loop only provide information about maternal DNA ancestry?

because roughly speaking, sperm mitochrodrias do not enter the ovule, just the sperm DNA

All the mitochondria of an individual are originating from the mother. This means that if the mother is afflicted by a mitochondrial disease, the offspring will also be affected. This is why the three parent model is being considered in some cases (1).
(1) Pritchard, C. (2014, September 1). The girl with three biological parents. BBC News. From http://www.bbc.com/news/magazine-28986843


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Evolutionarily, why do male rats and horses lack nipples?
Developmentally male rats don't have nipples because (reddit)

Testosterone release in the fetal male rat happens before the stage of mammogenesis where the teat is formed whereas other species halt that same process after the teat is formed.

According to the same page, male rats and horses (and mice) are unique among non-monotreme mammals in not having nipples.
Male humans have nipples because there's no evolutionary pressure for them not to. (Why do men have nipples?) Then, why was there selection for males to lack nipples in the case of mice, rats, and horses?
(Apparently, male horses lack nipples because "female horses have their nipples between their hind legs, and in males, that real estate is used for other purposes". (Straight Dope))
Evolutionarily, why do male rats and horses lack nipples?

Your question is interesting +1. I just want to note that the post "Why do men have nipples?" currently has an accepted answer (that is pretty highly upvoted) that is wrong (to my opinion at least)! Or at least that pictures a set of work (neutral theory) into something that it is not.

For rats at least, the answer is in your quote: because developmental sequences evolve. I don't think one need to posit an evolutionary reason for losing nipples, because in all likelihood that change was associated with some other developmental difference. And there is probably no way to know what the reason for the alteration of the timing of testosterone release.

For those wanting a paper instead of reddit: Hotchkiss, Andrew K., et al. "Prenatal testosterone exposure permanently masculinizes anogenital distance, nipple development, and reproductive tract morphology in female Sprague-Dawley rats." Toxicological Sciences 96.2 (2007): 335-345.

Male mice lack nipples too. Mice are frequently used for embryonic research as they are small and reproduce quickly. It is thought that male mice do develop nipples, but that they regress during development (Wysolmerski, 1998).
In general, it is thought that mammalian organisms develop as females by default when there is no male (Y) chromosome present (Hughes, 2004). Notably, X_ individuals and XY individuals with certain Y-chromosome deletions also develop as females. Basically, it is testosterone that suppresses female features. Hence, also boys with testosterone production, but without the appropriate receptors also develop as females.
Hence, men have nipples because females do (Simons, 2003).
The development of nipples is a pretty complex process (Wysolmerski, 1998). Disruptions in the process cause regression. Indeed, in mice, the tissues typical for nipples do develop in males during embryogenesis, but degenerate within a few days, leaving no trace of nipples at birth.
Hence, given the mouse example, I think that every mammalian male species develops by default nipples somewhere in development like mice, but that development is halted at various stages across species. The later in development it halts, the more it resembles female nipples. When it is stopped very early after gestation, they regress completely, like in mice.
Why is there a difference between species as to when the nipple development halts? Because the nipple developmental sequence is complex (Wysolmerski, 1998), and because evolution typically prefers losing things over gaining them, it is likely that by happenstance one of many crucial genes was lost in males, but selective pressure was simply too small to be of any significant advantage over males with slightly better-developed nipples. But from here on, it is guessing.
References
- Hughes, N Engl J Med (2004); 351(8): 248-50
- Lawrence, Nature News, August 1999
- Simons, Sci Am, September 2003
- Wysolmerski et al., Development (1998); 125: 1285-94

So that means for some reason rats and horses may have a gene similarity at some level for exhibiting this kind of phenotype? (Anyway, best answer! Bounty rewarded.)

Thank you @DevashishDas - you have been investing quite a lot of rep into expensive bounties of late! Good work. This is my first received bounty -- Thanks! Regarding your question - they may, or may not. These why questions are all kind of guess-away questions. Given the fine bounty I tried to add some elements backed up with proper citations and make a coherent theory, but it is still a guess. My theory is they share a developmental similarity, that may, or may not be reflected at the DNA level.

Good to know. :)

That female development is the default pathway is perhaps not entirely accurate, though often stated. This is evidenced by XX sex reversal in r-spondin 1 and Foxl2 mutants. See here. Not trying to nit-pick, it's just too fascinating of a topic not to bring up. Good answer, +1.

@canadianer - I tried to be on the safe side of things by saying: "In general, it is thought that mammalian organisms develop as females by default...". There are pretty much always exceptions to a rule and I will check out your linked information for sure. Interesting! Thanks for upvoting :)

I just realized the same is likely true for Bos taurus, as well. Cows have udders and bulls and steers have, nasty bits....

Stallions/Geldings don't have nipples because a mares "udder" ,so to speak, is underneath her flank area. A stallion's/Gelding's penis is also as well underneath their flank. Their evolutionary genes keep male horses/donkeys/zebras from having these nipples.

Welcome to Biology.SE! It isn't clear that this is adding anything to the already existing answer. In addition, answers are much more likely to receive a favorable response if they include supporting references (primary literature is best). ——— You may also wish to take the [tour] and then consult the help pages on the standards for additional advice on [Answer] effectively. Thanks!


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Why don't we keep evolving until we are super-human?
Humans (homo sapiens) have been on the earth for thousands of generations, and we have kept evolving throughout that time. Why don't we just keep evolving so that, let's say, we live for an average of 300 years? Or why don't we evolve so that we don't need to sleep?
This could also apply to many other species.

This question also has an element of "why isn't evolution all-powerful?". The Understanding Evolution project at UC Berkeley addresses this nicely.

Welcome to Biology.SE!
There are a many misunderstanding of what evolutionary processes are in your post and it is therefore quite hard to answer (hence the current two close votes I guess). You might want to read some introductory textbook on evolutionary biology or other online resources such as the "understanding evolution" project.
The most obvious misunderstanding is that evolution is NOT some kind of forever "improvement" process in a static environment. Evolution is something else! But I can't make a good overview of evolutionary processes in just one post.

[why don't we live on] average of 300 years?

You may want to read about this post concerning the evolution of aging but it would probably be slightly too advanced but you would probably still get something out of the answers.

Why don't we just keep evolving [..]?

Humans, definitely keep evolving. Nothing have stopped. We evolved as we have always and it won't stop until we get extinct! This post may for example help you to understand that evolution keep occurring today in humans.
When you say Humans (homo sapiens) have been on the earth for thousands of generations it sounds a bit like human have came to existence some time ago and have evolved since. It is a terribly misleading point of view, one of the reason is that one cannot date the existence of the first humans without making a very arbitrary decision about what a human is. A more correct way of looking at life on earth is that everything have come to existence about 3.5 billions years ago and everything have kept evolving since. Any single living thing on earth have been evolving for the exact same amount of time. Humans have evolved for 3.5 billions (even if we don't call earlier forms as humans) and unicellular algea have evolved for 3.5 billions years (even if we don't call earlier forms as algea).
Hope that helped!

Natural selection selects for individuals who can leave the most progeny in the next generation. Traits such as longer lifespan do not typically affect fecundity/fertility. So males and females who have the largest families as quickly as possible are providing the raw material for natural selection.

"So males and females who have the largest families as quickly as possible are providing the raw material for natural selection" is a bit misleading. The raw material for natural selection to occur is genetic variance. The males and females having the greatest number of offspring are those contributing the most to the future generations would be more correct. "Traits such as longer lifespan do not typically affect fecundity/fertility" is a bit wrong. Typically traits such as longer lifespan affect fecundity.

For a better understanding of the evolution of aging, have a look at this post

Thank you for correcting these inaccuracies. While I am familiar with classical arguments regarding post-reproductive fitness, for example in social species who may teach their progeny things that aren't hard-wired, I was not aware that post-reproductive life-span was positively correlated with brood-size. Hmmm, maybe I am not using fecund correctly.


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Very introductory online source of information in evolutionary biology
We receive quite a lot of questions from layman in evolutionary biology in this site that are sometimes difficult to answer just because there is way too much to say. Why don't human keep evolving? is a perfect example. I often want to give some pieces of information and link to a good source of information for them to get introductory knowledge in evolutionary biology.
Obviously nobody that ask a question on a Stack website want to read an answer of the kind You should read that book!. It is often not really pleasant to read a book, it takes time and the OP that get this answer will likely just forget about their question.
What are the best online resources that offer a very introductory understanding in evolutionary biology?
Either a written course of videos such as Khan Academy videos would do (I haven't watched the evolutionary biology section of Khan Academy though and can't really judge of how good it is).

At least online, I think the single best introductory evolution resource is the Evolution 101 tutorial at UC Berkeley's Understanding Evolution project. The site has been designed by some of the top evolutionary biologists and evolution educators in the country, and does a very good job presenting a basic overview of how evolution works.

Thank you +1 I updated my answer there following your recommendations.

Perhaps an even bigger problem is that evolution questions provoke lots of answers from people who think they understand evolution but do not. Worse still, these answers sometimes get upvoted by people who share similar misperceptions.  In a perfect world we might be able to move toward the norm established on other SE sites: people don't simply make things up if they don't know enough to answer authoritatively. In the meantime, I'm not sure quite what to do beyond posting educational links in response to answers as well as to questions.

I like this resource very much. Frankly, I'm not knowledgeable about evolution (although it seems many questioners here have an even foggier image than I do) so I'm reading it for myself and finding it valuable. Now that the site has graduated, I think we should make it a community ad.

@rumtscho What a great idea! Do you want to start a meta thread about it?

@Corvus there is a thread for community ads, but I don't have the time right now to design one. Will see if I can do it when I come back from vacation.

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


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Would it be more genetically efficient to have shorter lifespans?
If, for example, humans had an average lifespan of, say, 30 years, and we reached sexual maturity at age 10, then we could "evolve" more quickly and theoretically "weed-out" more genetic diseases because we would live for shorter times, hence more natural selection.

Why don't we continually evolve for shorter lifespans (or do we)?

possible duplicate of Why does evolution not make our life longer?

@Remi.b, The linked 'duplicate' asks for the opposite. This is not a duplicate from my point of view.

This question is conceptually similar: Why are the fertility rates of large predators kept low? (~why isn't trait X evolving since it should have a positive effect), and the issue of life history tradeoffs found in the answers is relevant here as well.

Extremely briefly, we do not evolve shorter lifespans because natural selection does not act for the good of the species.
As an interesting historical aside, August Weismann proposed essentially the idea you are suggesting in 1889, in his Essays upon heredity and kindred biological problems. Within a few years, however, he backed away from this hypothesis.


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Are upstream activating factor (UAF) and upstream binding factor (UBF) the same thing?
During ribosome pre-40S and pre-60S synthesis, many sources state the importance of UAF or UBF in initiation complex of ribosome DNA transcription. None of the sources I've seen mentions the other when mentioning UAF or UBF, so I want to know if they are actually the same thing.
Another source of confusion is that Wikipedia has article for both:
https://en.wikipedia.org/wiki/Upstream_activating_factor
https://en.wikipedia.org/wiki/UBTF
Thanks in advance.

Many sources: http://www.ncbi.nlm.nih.gov/pubmed/7982918, http://jcs.biologists.org/content/126/21/4815.long

Another source: http://biochemical-pathways.com/#/map/2
If you look at nucleolus part of chart, there's only UBF mentioned.


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Why is the brain dependent on glucose?
The strict dependence of the (human) brain on glucose has always been puzzling to me. While ketones can substitute for a portion of the brain's energy needs, it cannot substitute completely: blood glucose levels below 2--3mM somewhere causes serious neurological problems and can lead to unconsciousness.
Other body tissues are not strictly dependent on glucose, but can oxidize amino acids as well, which is a good backup solution since there is always lots of protein around. But not the brain.
In terms of evolution, this strict glucose dependence must be a major drawback --- falling unconscious just because you don't get enough sugar is probably a bad thing out in the wilderness, when the lions are after you ... So there must be a very important reason.
So why is the brain so dependent on glucose?

Everything is bad when the lions are after you ;)

Actually you can get sugar everywhere where there are plants.

I don't have a clue, but just plausible ideas: (1) amino acid or lipid processing is more dangerous or hinders function. Neurons need to survive for a life time. (2) the rate at which neurons need to regulate their energy level requires fast modulation, and only glucose can do it.

@Memming, those sound like good starting points! For (1), amino acid oxidation does yield ammonia, which perhaps could cause a problems; I'm not aware of toxicity / risks with fatty acid oxidation. (2) sounds like an interesting angle. The brain oxidizes glucose completely though, so it's not related to rapid glycolysis I think.

This is a great question. Most answers one can find are shallow, along the lines neurons rely on glucose because they don't use protein/fat. The equivalent superficial answer for red blood cells is because they don't have mitochondria to do oxidative phosphorylation. But neither explains why neurons specialized in this way. This might well be an unexplored question!

@SeanJ — I don't see why the explanation for red cells is superficial. They are very specialized cells of limited lifespan. Their dependence on glucose is not a problem as the pyruvate is all converted to lactate and recycled via the liver. This situation with neurons is quite different.

Very interesting discussion. i studied biochem and eventually got an MS, but it was over 30 years ago and i don't remember all the metabolic paths and whatnot. And of course the field has advanced a lot in 30+ years. CRISPR, for God's sake! what i have actually been looking for is answer to one question: can the brain get by w/o glucose? Because cancer cells usually can't. They almost always rely on fermention of glucose to pyruvic acid. And they can ferment a heck of lot of glucose, sometimes 200X what a normal cell would use. Which can lead to metabolic drain and acidemia. i was thinking tha

My opinion on this matter is that this is actually quite simply to free up fats for the rest of the body to burn for energy. If I recall correctly in certain research it has been noted that neurons are not strongly dependent on insulin levels for glucose uptake -- i.e., in other words they have "vip/preferential access" to almost however much blood sugar they'd want to uptake regardless of where the blood insulin level is at. As a result they (1) don't have a strong need for fat energy, and (2) it would probably be useful to in fact save that energy for the rest (comment continued ahead...)

(comment continued from before) ... of the body's cells that, unlike neurons, are not simpy blithely free to just go ahead and ignore whatever blood insulin levels the organism has decided to set for itself.

I agree very much with ManRow's idea as it is the one I always thought I was the only one to have. Good hint at insuline, as vice versa, just as the brain gets all the sugar when it's rare, muscles are allowed to store it away only when it is plentiful. Specializing to run a very frugal barn yard. Fast and slow, muscles and brain. If there is a metabolic block that forbids fatty acids being turned into glucose, and considering the blood-brain barrier to fatty acids, that seem the opposite to insuline to me, saving  fatty acids for the muscles that are not dumb at all.

I want to present another (possibly more practical) approach towards this phenomenon. Lets begin with amino acids as alternatives.

Amino acids, apart from being a source of toxic ammonia, are also harmful to the brain in another way. The two (concerned) types of amino acids, aromatic and acidic ones, are a lot more than just energy sources for the brain. Aromatic amino acids: tyrosine, phenylalanine and tryptophan, are precursors for the biosynthesis of neurotransmitters like serotonin, melatonin, norepinephrine, dopamine, etc. The greater the amount of precursors, more the product formed. Talking about acidic amino acids, glutamate and aspartate are themselves neurotransmitters. So, same case could apply to them too. So, it might have been that neurons, in evolution, decided to just get rid of such sensitive and powerful energy sources which can disrupt their basic function. You can see Fernstrom, 1994 for a study on this.
Fatty acids are definitely a nice candidate. But they have some side effects too. For a long time, it was thought that since fatty acids (or lipids in general) are attached to albumin while in blood, they cannot cross the blood-brain barrier (see Stryer for example). But, this is not the case, as now proven. Then why are fatty acids not a preferred energy source for brain? There might be quite a few reasons for this.

First, $\beta$-oxidation of fatty acids demands more oxygen as compared to glucose, making neurons more vulnerable to hypoxia. For example, the reaction for complete oxidation of glucose is (from Molecular Cell Biology):

$\ce{C_6H_{12}O_6 + 6 O_2 + 36 ADP^{3-} + 36 P_i^{2-} + 36 H^+ \rightarrow 6 CO_2 + 36 ATP^{4-} + 42 H_2O}$
On the other hand, the reaction for $\beta$-oxidation of palmitic acid is (from pharmaxchange):
$\ce{C_{37}H_{66}N_7O_{17}P_3S (Palmitoyl-coA) + 23 O_2 + 108 P_i^{2-} + 108 ADP^{3-} \rightarrow C_{21}H_{36}N_7O_{16}P_3S (coenzyme-A) + 108 ATP^{4-} + 16 CO_2 + 23 H_2O}$
where palmitic acid is $\ce{C_{16}H_{32}O_2}$. This clearly shows that glucose oxidation requires less oxygen (1 molecule per carbon) as compared to fatty acids (almost 3 molecules per 2 carbon).

Second, nonesterified fatty acids (NEFA) decrease membrane potential at inner mitochondrial membrane, causing collapse of electrochemical proton gradient.
NEFA also interfere with electron transport chain, stimulating generation of reactive oxygen species like superoxide ions (see Zhang et al, 2006 for example).
Apart from this, the rate of ATP generation from fatty acids is slower as compared to glucose, which makes glucose a better option for neurons (see Stryer).

All these factors might have a played a role in making neurons choosy for glucose. This is also supported by the fact that one of the enzymes of $\beta$-oxidation, 3-ketoacyl coenzyme-A thiolase, has significantly lower activity in neurons as compared to other tissues (Yang et al, 1987). You can see Reiser et al, 2013 for a study on this.

PS: It has been shown that the brain can also use lactate along with ketone bodies as an energy source. See Wyss et al, 2011 to know more about this.

Very good points Homo Sapiens.

Thanks, if you really like it, you can give an upvote :P BTW if you want to reply to somebody, you need to put @ sign and then that person's name, without spaces (if there are spaces in name). For example, @another'Homosapien' to reply to me :)

Thanks for the suggestions. (1) is interesting. Amino acids are already present in brain tissue though, and oxidizing amino acids might not increase their levels. Also, compartments are probably important here. (2) "beta-oxidation demands more oxygen" -- this is true per carbon atom (respiratory quotient), but the relevant parameter is the P/O ratio, which is very similar between the glucose and fat. I'm not sure NEFAs are relevant here since beta-oxidation handles fatty acids coupled to carnitine, but will think about it. Re: glucose oxidation is slower, see comment to @SeanJ

@roland (1) yeah amino acids are already present in brain "tissue" for their usual purpose (not oxidation), using them for oxidation might also create problems (2) I agree with you about P/O ratio. However, I don't understand your point about free fatty acids. That's why I put Reiser et al paper, they talk solely about NEFA. I'll see for glucose oxidation very soon :)

@roland Just to add here, I think you're missing something crucial here: comparison. Glucose oxidation to $\ce{CO_2}$ is slower than glycolysis, fatty acid $\beta$-oxidation is slower than glucose oxidation to $\ce{CO_2}$. Correct me if I'm wrong :)

They are two entirely different comparisons. The chapter in Stryer you cited concerns glucose oxidation vs glycolysis, which is not relevant here; beta-oxidation vs glucose oxidation is the question. If you know of some evidence that beta oxidation is slower (in terms of generated ATP per unit time) than glucose oxidation, I would be very interested to see that.

@roland the only article I found regarding it is this (which also relies on simple logic). I must say that there hasn't been much research on this topic

Also, I don't think the Stryer page is talking about glycolysis vs beta oxidation (it does say glucose oxidation). Also, even if glycolysis is faster than beta oxidation, then since the later steps (krebs cycle) are same, we can still conclude that glucose oxidation is faster than fatty acid oxidation (both need to form acetyl coA first, glycolysis does it faster than beta oxidation).

Glucose is the only fuel normally used by brain cells. Because neurons cannot store glucose, they depend on the bloodstream to deliver a constant supply of this fuel.
Fatty acids do not serve as fuel for the brain, because they are bound to albumin in plasma and so do not traverse the blood-brain barrier. In starvation, ketone bodies generated by the liver partly replace glucose as fuel for the brain. (Biochemistry. 5th edition.Berg JM, Tymoczko JL, Stryer L.)
Also, amino acid catabolism is the process of using amino acids as an energy source. Turning amino acids into molecules that can be used in the Krebs cycle takes energy, which means that burning protein for fuel is not as efficient as burning carbohydrates. In addition, your body needs amino acids to make new proteins. When amino acids are used as an energy source, it reduces the reserves of amino acids that are available for protein synthesis.

Yes, the fact that glucose is the main fuel of the brain is well established -- the question is why. There seems to be plenty of evidence that the brain takes up albumin-bound fatty acids (but not lipoproteins), see for example http://www.ncbi.nlm.nih.gov/pubmed/17901540 . There is also evidence for fatty acid oxidation by the brain, for example http://www.ncbi.nlm.nih.gov/pubmed/3368026 . But still glucose is required.

Regarding amino acids, during fasting the glucose supplied to the brain from the liver derived from amino acids anyway, so I don't think the efficiency argument works. It rather seems like the amino acid metabolism is not possible in brain tissue, for some reason, and is "outsourced" to the liver. Maybe the brain has difficulties disposing of ammonia?

I think protein breakdown is the important factor in using amino acids. It seems more logical to use i.e. muscle mass than brain mass, as to not lose structures with a memory function.

@Sleepses, that sounds reasonable. There are plenty of amino acids in blood though (largely derived from muscle) that other tissues can take up and oxidize.

Well considering how import fats are in the construction of the brain, breaking down fats for energy probably would be counter productive. Our course it could all just be an evolutionary hold over, the blood brain barrier probably prevented the use of anything but sugars in the early origins of the central nervous system and we might just be stuck with the results.

This is something I have been researching on and off for many years. I studied nutrition back in the early 1980s and our lecturer always said that "fats burn in the flame of CHOs". He said that the citric acid cycle was dependent on oxaloacetic acid - the predominant source was from glucose (pyruvic acid).
https://www.sciencedirect.com/topics/medicine-and-dentistry/oxaloacetic-acid
In nutrition, for patients with epilepsy, they are given a ketogenic diet as this is known to suppress brain signals. Clearly the brain functions sub-optimally when deprived of glucose. One of my concerns with the rise in dementia is the high protein/low carb diet proposed, and the claims that the body (and brain) can do without CHOs.
The other piece of research was from Professor Robert Horn (retired, Oslo) in his work for students. He compiled a wonderful PDF on glucose metabolism and here I quote:
"The blood-brain-barrier is comprised of glia cells, primarily astrocytes. A small fraction of the glucose
released from capillaries wanders directly to nerves and synapses. However, most is "trapped" in
astrocytes and oxidized to lactate by these cells. Lactate goes further into the brain and nourishes
the brain's neurons. This seems to be especially important for glutamatergic neurons which
comprise much of the brain. The astrocytes also efficiently pick up released glutamate, convert
this to glutamine, and send the product back to glutamatergic neurons where it continues to cycle
as a neurotransmitter. The axons do not contain glycogen and are, therefore, completely
dependent upon the lactate sent from astrocytes to maintain ATP levels. Astrocytes and other glia
cells appear to have some glycogen which can serve as a very short-term source of lactate.So the answer to the preceding question is that much of the brain is dependent upon lactate from
glia cells to provide substrate for aerobic energy production; ketone bodies cannot cover their
substrate requirements. One can reduce glucose consumption and use ketone bodies during
starvation. However, some neurons must have lactate and the brain must continue to use blood
sugar."

The body uses 2 dedicated groups of fuel molecules (fats and sugars) and can break down its own structure molecules in case of emergency (proteins). The brain uses sugars, mostly glucose, and ketone bodies, a soluble form of acetyl derived from fat breakdown. I love this question, Roland, since you've probably hit on an open, poorly investigated question that goes to the fundamentals of why neurons evolved to be this specialized. So why does the neuron rely much less on fats and proteins than more flexible cells like those of the liver?
Protein digestion could indeed be dangerous for neurons because it produces ammonium (NH4+). Neither fats nor sugars contain significant amounts of nitrogen. Their final breakdown products, H2O and CO2, are harmless. Ammonium, on the other hand, is very similar to potassium (K+), an ion central to the neuronal membrane potential and thus its function. It has a similar size and charge (see image below) and is hypothesized to mess with potassium signaling in the brain. It might do this by interfering with K+ transporters (see this schematic by Eid).

This leaves us with fats. I have 2 hypotheses here. The final steps of fat breakdown in the mitochondrion involve the transfer of electrons onto oxygen to produce water. An unavoidable side reaction here is the creation of a small amounts of reactive oxygen species that have the potential to damage close-by molecules via radical reactions. Since neurons are so long lived, this is very dangerous for them as they accumulate damage little by little. Not so for liver cells which are readily replaced if damaged beyond recovery. Second, fat breakdown is a much slower way to energy/ATP than glucose breakdown, since it involves the sequential shortening of the fatty acid chain (beta oxidation), the generation of reduced intermediates (mostly NADH in the citrate cycle), and finally oxidative phosphorylation - several dozen steps in total. Glucose metabolism yields ATP quickly in just a few steps (glycolysis). Ketone bodies also fit, since they are already very short and thus much more quickly metabolized than long chain fatty acids. A combination of these 2 reasons might be why neurons prefer glucose to fats, but I believe this is a poorly investigated question that merits more research. PhD anybody?

So if you are offering a PhD, what is your proposal to test your hypotheses and solve a problem that people have been aware of for at least 50 years? It's one thing in science identifying a problem, the trick is to realize when either new concepts or new methods appear to make it possible to solve it.

Thanks for the suggestions. Some comments: (1) toxicity ammonia production is an interesting hypothesis (and it has been suggested before) but I haven't seen any evidence that the brain would be particularly sensitive to ammonia. (2) Regarding reactive oxygen species, consider that neurons already oxidize glucose carbon in mitochondria. (3) I don't think it's true that fat oxidation necessarily is slower (in terms of ATP synthesis rate) than glucose oxidation; and generally, the number of enzymatic steps has little to do with flux through a pathway.  Just some critical feedback!

Hey David, one could try to slow or block flow through glycolysis and see how the shifted energy metabolism affects neurons. I don't find that it's a well identified problem. Most people (but not Roland), textbooks, and articles are perfectly happy with the shallow explanation that neurons have downregulated some enzymes and are thus more glucose dependent than other cells.

Hi Roland, 1) have you had a look at the paper by Eid. They present evidence as to why neurons might be more sensitive. 2) Correct. They might also derive some energy from glycolysis to pyruvate/lactate without oxygen. Does anybody know of data quantifying which energy pathway is used the most in neurons? 3) If it were as fast, why do we use glycolysis almost exclusively for bursts of muscle energy and not oxidative phosphorylation? I'd love to read more about steps versus flux if you could recommend a paper or a book. All the best

(1) Sorry, I missed the Eid et al paper link-- it's quite interesting, thanks!  (3) Fast-twitch muscle relies on glycolysis to lactate, that's quite different; here the choice is between glucose oxidation or fat oxidation. Re: flux through pathways, this depends on a number of factors, such as metabolite concentrations, enzyme amounts and turnover rates, Gibb's energies of reactions, compartmentalization, and what not. Suffice it to say that there is no easy relation between the number of steps in a pathway and flux :)  We might want to move this discussion to chat.


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Mathematical modelling of omics data
I'm interested in modelling host-pathogen interactions using mathematics. I know there are good resources in Pubmed but seems I'm looking for a book or introductory reviews.
My background in maths is limited, knowing the basics about calculus and algebra, and more recently some machine learning.
The paper inspiring this question is this one:
Front Microbiol. 2015; 6: 235.
A review on computational systems biology of pathogen–host interactionsA review on computational systems biology of pathogen–host interactions

Why negative vote?

I'm a bit confused because your title and your question description don't seem to align perfectly (-omics data versus host-pathogen interactions). If you are interested in host-pathogen dynamics, in my opinion, the best general introductory text in this area is Keeling and Rohani Modeling infectious diseases and humans and animals.

The paper I posted in the question reflects what I'm trying to ask. I'm interested in studying host-pathogen interactions from an omics perspective.

As @Corvus said before me, the core of your post talks about mathematical modelling of host-pathogen interactions. The title talks about analyzing omics data. What are you really interested in? I will assume you are interested in modelling of host-pathogen interactions (as @Corvus did).
I agree with @Corvus (+1) that Modeling infectious diseases and humans and animals is a good book to understand host-pathogen dynamics.
If you feel a bit uneasy about mathematical modelling, I strongly recommend A guide to mathematical modelling in ecology and evolution.
Aside those recommendations, Evolutionary dynamics might interest you to understand evolution of viruses including evolution of virulence. Finally you might appreciate why we get sick although it is not heavy on math and a bit outdated.
In case you are interested, you will find on this post book recommendations for theoretical population genetics.


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Downstream analysis after in vivo pathogen RNAseq
We performed RNA-Seq of in vivo bacterial samples and identified some key up- and down-regulated pathways.
We compared bacteria during infection with conventional agar plate.
Which could be the next steps to validate our findings? We are interested in antibiotic and vaccine developemnt.

You're going to need to be a lot more specific. To start with, when you say a pathway is up or down-regulated, what comparison are you making? Is there a treatment and you're comparing treated versus control? Are there  different populations and you're comparing to each other?

Updated. We compared bacteria during infection with conventional agar plate.

Okay, first, I would say to everyone planning a high-throughput experiment: Hypothesis first, then experiment. Otherwise you're setting up an expensive and time-consuming test that's not going to answer the questions you want answered.
In this case, the question your experiment is designed to answer is "What genes are crucial to growth in vivo that are not crucial to growth on a plate?", which is probably not the clearest model for vaccine/antibiotic design.
I guess you could say that a difference between the plate sample and the in vivo sample is the presence of a host, and therefore it's possible that upregulated pathways represent the bacteria's host adaptation, and maybe you can attack the bacteria by attacking its ability to colonize the host. A gene involved in immune evasion could make a good target.
(That's a very messy model, as there are tons of other differences going on, but since you've already run the experiment it's probably the best you've got.)
So then, use your data for a new round of hypothesis generation. Take your pathways and come up with a way that they might be allowing the bacteria to adapt to the host-- maybe a secretory pathway that could be interfering with the target immune system, or some membrane signalling molecules that get upregulated, or whatever.
Now you have a hypothesis, so design an experiment. For instance, take a key upregulated member of a pathway. The hypothesis is that it's crucial to growth in a host but not to growth in general. So, knock it down, either with chemical inhibitors or with genomic editing, and see if it's less effective at host colonization.
(Although host colonization effectiveness is pretty difficult to measure ethically if your target organism is humans. But you have some sort of proxy or model system to measure that, right?)

Thanks for your helpful answer @Transcriptase. We work with a pig model of infection, since is a swine pathogen.

Why do you think this is not the clearest model for vaccine/antibiotic design? Do you know better alternatives?

Okay, that does make experimental design much easier.

The reason I say it's not the clearest model, especially for antibiotic design, is that you're investigating the difference between bacteria in the host and bacteria on the plate. An antibiotic in particular probably isn't going to care about that-- it should kill in both situations, right?
And for a vaccine, there are so many differences between culture and in vivo that I'm not sure that you can attribute differences you see to immune evasion. Are there immune-compromised pig models? In vivo samples from those would be a much cleaner control for immune evasion.

Your comment contains some assertions that I agree or I had not think about it. Others are more objectionable. First, antibiotic resistance would be lower in vivo, due to reduced metabolic rate and that  common antibiotics rely on this assumption.

Pointing out that an immunocompromised model would be interesting makes me think why. Tell me if I am right. Immunocompromised models would make bacteria display a different transcriptome than with standard animal model since bacteria will only use certain genes related to immune evasion if animal has a working immune system. Right?

Right. The main thing is that you want your groups in an RNA-Seq experiment to focus as cleanly as possible on the difference you want to investigate. If you're interested in antibiotic resistance, use a resistant and a non-resistant strain. If you're interested in immune evasion, use samples in the presence and absence of an immune response. You expect many differences between cultured and in vivo samples-- you've already mentioned metabolism, antibiotic resistance, and immune evasion, and I'm sure there are lots more. So it's hard to relate any given gene expression change to a phenotype.

Sorry for the off topic but seems there is a mathematical method behind our RNA-Seq experimental designs with bacteria, host and antibiotics. We are using some kind of combinatorics and all these complex experimental variations may be as easier as we think.