What I read on my winter vacation

It’s been a while since I posted anything serious — I can’t help but feel that I should apologize, although I’m sure that very few of you sit by your computers waiting breathlessly for the next update. Anyway, my excuse, if I need one, is that much of my spare time has been taken up with graduate program applications. And I thought some of you, especially potential future graduate school applicants, might be interested in a few thoughts I have about what goes into an application, and how it’s read (or at least, how I read it).

There are  four main pieces of information in an application for an American graduate program in biological sciences: GREs, college grades (supplemented by an academic transcript), the personal statement and the letters of recommendation. From my perspective the GREs and grades essentially act as thresholds. If your GREs and grades are above a certain level, I don’t much care how good they are. Academic achievement has some correlation with likely success as an independent scientist, but the correlation is not a straightforward or linear one. So, if your GREs are below a certain level (and you’re a native English speaker), it may raise a suspicion in me that your brain is not especially agile, and I may start looking for other evidence to support this hypothesis in the rest of the package; if your grades are poor, especially in subjects that are very relevant to your proposed area of study (or, especially at a school with rampant grade inflation), I may worry about your ability to assimilate facts and reasoning strategies; but I’m probably not going to draw much of a distinction between very good grades and perfect grades. And if one of your letter-writers explains that your very-good-but-less-than-perfect grades are due to spending too much time in the lab, I may even like you better than I would have if you had a 4.0 GPA.

After the GRE/GPA threshold is passed, I have two pieces of information left: your statement and the letters written on your behalf by people who know something about your abilities. I don’t have much to say about the statement — it should be personal, after all — except this: don’t feel that you have to treat it as an exercise in creative writing. I suspect that there’s a book out there, or more likely a website, that encourages applicants to begin their statements with an eloquently written personal anecdote that conveys their love of and excitement about science. I suspect this because so many of the statements I read start with something along these lines: “Staring into the dusky red depths of the Grand Canyon, I had a vision of someday creating cybernetic wings that would allow human-powered flight. My passionate dedication to a life in science dates from that day.”

If you really did have some kind of emotional revelation when you were 10 years old, fine. I don’t mind if you tell me about it, though please try not to be too dramatic. (I’m English, after all.) But from the perspective of a specialized interdisciplinary program, I’m much more interested in your mature thoughts about why you want to focus on systems biology in particular. It’s not an easy path, and I want to know you’ve thought about it. It’s fine to try and paint a picture of who you are as a person, but the more you can explain why you’re interested in working in this field in particular, with concrete reasons from lab or theory work you have done or courses you have taken, the better.  What you say will give me some sense of how much you know about what you’re getting into, as well as whether you can write a clear English sentence and put two ideas together in a logical order.

What about the letters? IMHO these are the most important part of the package, and what’s hard about that is that the quality of the letter is almost completely out of the applicant’s control. What I look for in letters is the following: evidence that the applicant has taken the initiative to seek out interesting research experiences, and an opinion from the person writing the letter about whether the applicant has the qualities needed to make a go of independent research. Unfortunately, what I often get is a letter that says that the applicant got very good grades in class, which I already know from the transcript. Letters of this kind aren’t helpful; nor are the letters from your swimming coach or saxophone teacher, or the personal friend who’s known you since childhood. These people may all have theoretically useful things to say about your strength of character or your work ethic, but they’re not scientists and I don’t know what “hard-working” means to them. The ideal application package has at least two letters from people who know what it takes to be a good scientist, who can talk about a substantial project that you did in their group. These letters ideally go into significant detail about how well you performed in the context of a real research environment — for most of the faculty on the committee, nothing speaks louder than a statement from an advisor about the research you personally did while working with him or her.

Fold your favorite protein in under an hour

The protein folding problem, as it’s called, has been confounding biologists for decades.  Unlike a strand of RNA or DNA, which can be relied upon to follow a few rather simple rules dominated by base pairing, a string of amino acids seems to have so many possible ways to interact with itself as to defy analysis.   But the “problem” isn’t a problem for the protein — proteins fold, for the most part, rather efficiently, even in vitro where they have no help from the rest of the contents of the cell.  So the information on what the fold should look like is sitting there in the sequence, and the question is how to read the code.

The protein folding field has tried many many routes to translate the information in protein sequence into three-dimensional structure, ranging from head-on attempts to use physics plus supercomputers to work out a protein fold from first principles, to efforts to harness the vast problem-solving potential of on-line gamers by translating the rules of folding into a game, to attempts to “cheat” by using evolutionary conservation to get an idea of which parts of the sequence are essential for the shape of the fold.  There’s been progress on all fronts, and it’s now possible to use computational methods of various kinds, often informed by existing structural information, to fold small proteins.  Although you need a fair amount of computer power to be successful, and better models for the complicated forces affecting a biomolecule in water are clearly needed, there’s a feeling that if we keep chewing away at the problem we will eventually be able to solve it.

A new paper (Marks et al. 2011 Protein 3D structure computed from evolutionary sequence variation.  PLoS One doi:10.1371/journal.pone.0028766) now provides a rather startling step forward that dramatically reduces the need for major computational resources.  You can now fold a ~250 amino acid protein on your ordinary laptop.  The one apparent catch is that you can’t do this with just any sequence: you need a fairly large family of homologous sequences, of around 1,000 family members.  Information derived from this family of sequences about changes in one part of the protein that correlate with changes in another part of the protein — covariance — is used to infer how close the two parts of the protein are to each other.  This reduces the “conformational search space”, the number of three-dimensional folds you have to evaluate before settling on the best one, and that in turn not only speeds up the process of sorting through the possibilities. but also increases the chance that you will find the right answer.

                       Identifying a 3D contact reduces the conformational search space.  From Marks et al.

Now, this is far from a new idea.  It has been tried and tried and tried for years and has always failed.  In fact one of the authors (Chris Sander) made one of the earliest attempts, about 15 years ago.  Two things are different this time: the implementation of the idea, and the number of sequences available.

Listen to your gut — bumblebee edition

I like bees, and for a brief and mostly happy period was the host of the Stern/Rudner Swarm — a pair of hives maintained by Bodo Stern and David Rudner that resided in my garden.   My job was to grow the flowers and take a share of the resulting honey.  Sadly they failed to make it through last winter.  I suspect that the problem for these particular hives was the lack of a convenient water source rather than one of the many diseases that seem to be decimating bees worldwide, but it’s no secret that bees are in trouble— and therefore, perhaps, so are we.  Bees are not only responsible for making honey; they also

My worries are different from Hannelore's. Hannelore is from the cartoon Questionable Content: http://questionablecontent.net/view.php?comic=1855.

pollinate agricultural crops, most types of fruit, and many wild plants.  In my list of apocalyptic worries, the collapse of the bee population is not at the top — climate change and the risk of sterilizing the oceans are both up there — but it’s not at the bottom either.  And so I was interested to see a recent paper (Koch and Schmid-Hempel, 2011: Socially transmitted gut microbiota protect bumble bees against an intestinal parasite.  PNAS doi/10.1073/pnas.1110474108) that suggests that the gut microbiota of social bees may give them some protection against certain parasites.

The parasite studied in this paper is a nasty little item called Crithidia bombi, which — if it infects a young queen bee — leads to a ~50% loss in the queen’s ability to found her own colony.  In previous work, the authors found that treating bees with antibiotics led to a very high C. bombi infection rate; but these experiments were hard to interpret since bees apparently really don’t like antibiotics.  About half of the bees in the antibiotic-treated group died just from the antibiotic treatment, before they ever saw the parasite.  So, was the higher infection rate simply caused by the fact that the bees were not very healthy?  Or was there some specific interaction between the microbiota associated with the bees and the parasite?

Communication as a network problem

I recently gave a short talk to a group of post-docs who had organized their own mini-symposium and workshop as a way of bringing the Harvard post-doc community in systems biology together.  Those of you who haven’t worked in the Boston area may be surprised that we need special events to bring together a community that is separated by only  ~4 miles, but in fact the trip from Harvard’s main campus in Cambridge to Harvard Medical School in Boston is a frustrating and lengthy one.  Much of the apparent distance is added by the need to cross the Charles River on one of its few narrow, highly trafficked bridges.  I have often wished for a personal helicopter as I sit in the traffic jam at the BU Bridge.  Hence the title of the occasion: “Across the River”.

Since this was an event focused on communicating and bridge-building (or at least, bridge-crossing) I talked about communication.  Here’s my thesis: communication plays a much larger role in the progress of science than most people (and most scientists) are conscious of.  So, one way to become a better scientist is to think more about how you communicate and try to do it better.

This is the point at which it becomes difficult to be talking to an audience you can’t see: some of you will be saying “sure, of course”, while others will be looking puzzled — science is about doing experiments, not about communication!  If I could see you, I could adapt to your reactions.  Since I can’t, I’ll say this to the “sure, of course” group: I think many of us have been persuaded to think about scientists as natural non-communicators, who live in an ivory tower and think deep thoughts and publish inscrutable, dense, leather-bound tomes that are then read by a tiny group of other eggheads.  Actually the most prevalent image in the popular consciousness may be that of the gentleman scientist from the 18th or 19th century, dressed in tweed knickerbockers and stout shoes, scouring the rocks of a cliff face for fossils in proud isolation while smoking a pipe.

A gentleman scientist relaxing at home. As you can see, a pipe is essential.

If scientists ever lived like this, we don’t now.  Even the lonely fossil collector went home to write long letters to like-minded pipe-smokers across the world about the fascinating fossils he — or she, but that’s another, possibly pipe-free, story — had found.  But this is not the way the world thinks about scientists, and so it is often not the way we think about ourselves.  On top of that, we confuse communicating with the public — which, as a group, we are indeed very bad at — with communicating with ourselves, which we do constantly. The need for communication creeps up on you throughout your career: perhaps you don’t need to be talking and reading constantly as a graduate student, but by the time you make it to your first real job the need to be a good communicator pervades your daily life.

Windows on the cellular soul

One of the things we wonder about a lot in biology is what is going on inside a cell.  We have many ways to get at partial answers — Western blots, GFP fusions, transcriptional profiling, various proteomic techniques — and the number and power of these approaches is increasing. Here’s a new window on the internal state of a cell that makes use of a fundamental process of biology: the presentation of peptides by the class I MHC complex (Caron et al. 2011.  The MHC I immunopeptidome conveys to the cell surface an integrative view of cellular regulation.  Mol. Syst. Biol. 7 533-547, doi:10.1038msb.2011.68).

MHC class I transports peptides to the surface of cells. From Wikipedia, http://en.wikipedia.org/wiki/File:MHC_Class_I_processing.svg

To understand what’s going on here you need to know a little about the amazing mechanisms that underlie an immune response.  One of the problems the immune system has to solve is that viruses live inside cells, so in the early stages of a viral infection there may be little to see, and little for the immune system to respond to, in the extracellular environment.  And so, conveniently, we have evolved a system to allow the immune system to look inside the cell.  For historical reasons it’s called the Major Histocompatibility Complex class I, or MHC class I — it was discovered as a genetic locus that controlled the rejection of skin grafts in mice (hence histocompatibility), by George Snell.  Basically what MHC class I molecules do is to go fishing inside the cell for peptides of a certain length, which are required to be from proteins that are made within the cell.  These peptides are then captured in a “bear trap”-like structure at the top of the MHC molecule (which I have sketched here), transported to the cell surface, and offered up for recognition by T lymphocytes.

You don’t really need to know about the other parts of this system for the purposes of discussing this paper, but thanks to a mechanism called “tolerance”, briefly touched on here,  T lymphocytes generally manage not to respond to peptides that come from proteins made by the host — that’s you.  Instead, they focus on the foreign peptides, which are presumed to originate from viral proteins.  The point to remember is this: the MHC itself isn’t selective for viral peptides, but brings a broad sampling of what’s inside the cell to the cell surface.  It’s not an unbiased sample; peptides from some proteins are over-represented, others under-represented, and specific arrangements of amino acids are preferred for binding.  But it offers a view of what’s going on inside the cell that is hard to get any other way. The question is, what is this view telling us?  Caron et al. set out to answer this question by using a drug to manipulate the internal state of the cell, and looking with mass spec to see what happens to the peptides presented on MHC as a result.

High-probability successes, by design

Boy, it’s hard to get back into the rhythm of blogging once you stop.  It’s been a busy few weeks — if you read the Initiative in Systems Pharmacology post you know a little bit about why, but also there have been a number of grant and fellowship deadlines, and on top of that we’re recruiting this year.  In short, the day job has been taking up (even) more of the evening than it usually does.  I like to be busy, but there is such a thing as going too far. However, somewhat to my surprise, I find myself missing blogging — the rest of my job doesn’t require me to read papers and think about them, so thinking about science can fall by the wayside if I’m not careful.  In some ways it’s like missing the pain from a nagging tooth, but any kind of absence can make the heart grow fonder.  (Have you missed me?)

So, to get back into the swing of things, here’s a paper that I read a while ago but never finished writing about (Barnes et al. 2011. Bayesian design of synthetic biological systems, PNAS doi.10.1073/pnas.101792108).  It deals with ways to do a better job of designing biological systems.  A dominant argument in synthetic biology has been that the job of synthetic biologists is to make biology more modular, to take inspiration from the standardization of engineering parts such as screws and nuts and bolts (which were once wildly varied, but now have standard sizes and screw threads) and attempt to develop similar standardizations of biological parts.  This direction has had some successes, but it’s clear that there are challenges; and the challenges loom ever larger as the system one is trying to design becomes more complex.   Biology is implemented in probabilistic chemical reactions, not cold steel, and the analogy of mechanical engineering can only take us so far.  And so Barnes et al. argue that we should pay more attention to tools from statistics, specifically to Bayesian analysis.

Here’s their argument, which personally I find quite compelling.  Bayesian analysis is used in biology to try to pull network information out of large, noisy data sets.  The general idea is that, given observed data,  Bayesian analysis allows you to infer a range of possible network structures that are consistent with the data.  More importantly, it gives you a rigorous way of ranking how likely it is that a given network produced the data you observed.  (For a slightly less general idea, see this nice Primer by Sean Eddy — you can also find it here).  Tellingly, we call this “reverse engineering”.  The process of designing a system to produce the output you want could be viewed as the reverse of this (“forward engineering” or just “engineering”, perhaps?).  You can define the output you want to see in response to a signal, pretend that you just collected a dataset with the desired characteristics, and ask what kind of network might be able to produce those data.  You could even add experimental error to your pretend data, though that might seem perverse.  What you would get out of this exercise would be a rank-ordered list of network designs that could have produced the data you “observed”, in order of probability: in other words, a list of designs that can give you the output you desire, ranked according to how easy it should be to get the desired result.  Seems useful, no? Barnes et al. comment that “[t]he ability to model how a natural or synthetic system will perform under controlled conditions must in fact be seen as one of the hallmarks of success of the integrated approaches taken by systems or synthetic biology”.

Initiative in Systems Pharmacology

Here is a letter from HMS Dean Jeffrey Flier that came out today.  Many of us have been working on this for months, so it’s exciting to see it go public!  The Boston Globe also has a nice article, here.

Dear Members of the Harvard Medical School Community:

I am excited to announce that Harvard Medical School is launching an Initiative in Systems Pharmacology, a comprehensive strategy to transform drug discovery by convening researchers from an unprecedented range of disciplines to explore together how drugs work in complex systems.

The initiative will be led by Marc Kirschner, the John Franklin Enders University Professor of Systems Biology and chairman of the HMS Department of Systems Biology; Peter Sorger, professor of systems biology; and Tim Mitchison, Hasib Sabbagh Professor of Systems Biology and deputy chairman of the Department of Systems Biology. It will comprise of faculty from a broad array of disciplines, including systems biology, cell biology, genetics, immunology, neurobiology, pharmacology, medicine, physics, computer science and mathematics, drawing on expertise from the Quad and our distinguished affiliated hospitals and research institutions. The initiative will be fueled by a strong and diverse group of existing faculty and new recruits who will be based in several departments, and will be supported by an ambitious fundraising effort.

The Initiative in Systems Pharmacology is a signature component of an HMS Program in Translational Science and Therapeutics. Led by William Chin, the Bertarelli Professor of Translational Medical Science and executive dean for research at HMS, Translational Science and Therapeutics is being created with two broad goals: first, to increase significantly our knowledge of human disease mechanisms, the nature of heterogeneity of disease expression in different individuals, and how therapeutics act in the human system; and second—based on this knowledge—to provide more effective translation of ideas to our patients by improving the quality of drug candidates as they enter the clinical testing and regulatory approval process, aiming to increase the number of efficacious diagnostics and therapies reaching patients.

With this Initiative in Systems Pharmacology, Harvard Medical School is reframing classical pharmacology and marshaling its unparalleled intellectual resources to take a novel approach to an urgent problem: The alarming slowdown in development of new and lifesaving drugs.

A better understanding of the whole system of biological molecules that controls medically important biological behavior, and the effects of drugs on that system, will help to identify the best drug targets and biomarkers. This will help to select earlier the most promising drug candidates, ultimately making drug discovery and development faster, cheaper and more effective. A deeper understanding will also help clinicians personalize drug therapies, making better use of medicine we already have.

The initiative will support both new approaches in translational science, such as failure analysis on unsuccessful drugs and use of chemical biology to develop probes of biological pathways. It will also include a new educational program, one that develops a new generation of students, postdoctoral fellows and physician-scientists, the future leaders in academic and industrial efforts in systems pharmacology and therapeutic discovery.

Transcending disciplines, departments and institutions, the systems pharmacology initiative will present new opportunities for collaboration throughout the HMS community. To learn more about this important initiative and its potential to transform drug discovery and patient care, please visit isp.hms.harvard.edu. You can also see a video on the initiative at http://www.youtube.com/watch?v=l1p_uFI4BCE.

I hope you share our excitement about the potential of this promising initiative, and I welcome your ideas as we move forward.

Sincerely,
Jeffrey S. Flier
Dean, Faculty of Medicine

Designed by iGEM: implemented by nature

I’ve been thinking recently about this year’s iGEM Jamboree, which is coming up soon. For those of you who don’t know, iGEM, the international Genetically Engineered Machines competition, challenges undergraduate students and high school students to make useful machines out of biological parts and implement them in living cells. The ideas are always interesting — usually somewhere between creative and wild, actually — and the Jamboree is where the different teams (165 of them this year) share their results, celebrate the new parts they’ve characterized, and generally have a good time. iGEM has turned out to be a major way for students from engineering and the quantitative sciences to get their first taste of biology.

iGEMmers are always on the lookout for biological modules that can be re-used for other purposes, and quorum sensing is something of a favorite. The system that produces bacterial gas vesicles that allow bacteria to float also seems to be ripe for re-engineering. And so a recent paper that identifies a gas vesicle system controlled by quorum sensing caught my eye (Ramsay et al. 2011. A quorum-sensing molecule acts as a morphogen controlling gas vesicle organelle biogenesis and adaptive flotation in an enterobacterium. PNAS doi:10.1073/pnas.1109169108). Ooh, I thought — that looks interesting. You could target bacteria to something you want to float up — the Titanic, say — and turn on the gas vesicles when you have enough bacteria. And indeed, it’s a natural iGEM project; so much so, that the 2008 Kyoto team already tried to do it. They did not, in fact, raise the Titanic, but they did show [pdf] that their engineered bacteria could move a ~10µm bead. One must start somewhere.

Many bacteria produce gas vesicles to regulate their buoyancy, but we don’t know all that much about how the production of these vesicles is regulated.  Ramsay et al.’s paper is the first to show that quorum sensing can control the production of these vesicles in nature.  Changes in the availability of light or oxygen have also been shown to increase vesicle production in  some cases. Thus, it’s thought that the vesicle-producing cells may turn up the gas when they find themselves drifting too far away from the air-water interface. It’s an alternative to turning on flagellum formation (which would allow swimming towards the surface), and under some circumstances appears to be a more energetically favorable option.

Clumping is good; controlled clumping is better.

When I pouted last week about the fact that other writers had beaten me to the punch in discussing an interesting recent paper on the fitness benefits of clumping in yeast, I had somehow failed to notice that another, similarly fascinating, paper on a related topic had just come out from the Bassler lab (Nadell and Bassler 2011. A fitness trade-off between local competition and dispersal in Vibrio cholerae biofilms.  PNAS doi:10.1073/pnas.1111147108).  This paper is looking at the formation of biofilms in the bacterium Vibrio cholerae, a nasty little bug that has been a major evolutionary force in the development of modern sewage systems.  One of the factors that makes V. cholerae hard to get rid of is the fact that it can, when it chooses, grow in biofilms; it can produce a structural matrix called extracellular polysaccharide (EPS) in which the bacterial cells are embedded. EPS production has a number of benefits, including offering bacteria from many species the opportunity to collaborate and behave as a community.  The puzzling thing, though, is that these community benefits are available to everyone, not just the bacteria who do the work of producing the EPS.  This is a classic set-up for “cheating”; in theory, if some bacteria can gain the benefits of EPS production without paying the price for it, then those “cheating” bacteria would be expected to grow faster than the poor exploited EPS producers.  At some point, the EPS producers (still struggling to build community, no doubt) would die out, and the whole system would collapse.  The theoretical arguments seem very persuasive, but actually EPS-producing bacteria show no signs of going away.  So clearly we need a new theory.

Kevin Foster and colleagues (including Carey Nadell, the first author of this paper) have been working for some time now on the possibility that EPS production, in addition to its benefits for the community, offers direct benefits to the cells that produce it.  If you simulate the growth of EPS-producing microbes in three dimensions, including the way that nutrients and oxygen diffuse and are consumed, you can see that producing EPS can help a lineage of cells to push itself above the masses and get access to better conditions, incidentally suffocating non-EPS producing cells.  This line of argument suggests that, far from being a happy “all for one, one for all” type commune, microbial biofilms are a balancing act between cooperation and competition — much like some other societies you might be aware of.  It also suggests that, though there are some conditions in which “cheaters” (non-EPS producing cells, though now they look lazy and stupid rather than cunning) can win, especially when a group of cells is colonizing a new area, if a biofilm persists for a long time the EPS-producing cells have a strong advantage.  And a particularly clear prediction from the 3D modeling is that, in a mixture of EPS-producers and non-producers, the EPS-producing lines should end up in skyscraper-like towers (reaching towards better oxygen conditions), suffocating the cheaters.

The advantages of clumpiness

A nice paper that emerged from a collaboration between the Murray lab and Kevin Foster (ex-Bauer Fellow, now at the Zoology Department at Oxford University and the Oxford Center for Integrative Systems Biology) just came out in PLoS Biology (Koschwanez et al. Sucrose utilization in budding yeast as a model for the origin of undifferentiated multicellularity. PLoS Biology 9(8): e1001122).  The paper makes two interesting points that are potentially relevant to the evolution of multicellular organisms.   The first is that yeast strains that grow in clumps of cells should, and do, have an advantage over strains that grow as single cells when both strains are grown in low levels of sucrose.  The theoretical argument the authors make goes like this: to metabolize sucrose, cells need to secrete invertase, which chops up sucrose to give fructose and glucose; each cell then needs to absorb as much as possible of the monosaccharides produced before they diffuse away.  A cell can only capture the fructose and glucose that happens to bump into the transporters on its cell wall.  So, even though sucrose has the same food value as glucose + fructose, single cells should grow less effectively on sucrose than on glucose + fructose; and there should be a threshold sucrose concentration below which a single cell cannot grow at all.  In contrast, cells living in a clump should benefit from the fact that all of their neighbors are producing monosaccharides too; even though an individual cell might not be able to take up all the glucose and fructose it produces using its invertase enzymes, it will capture some of the monosaccharides that escape from its neighbors.  Thus, clumping cells should grow better than single cells in low concentrations of sucrose.  When Koschwanez et al. did the experiments all of these predictions were confirmed.

The second point is even more interesting.  The invertase system is a classic set-up for cheating; the cells secreting invertase are effectively producing a “public good” — the monosaccharides produced by the action of the invertase on sucrose are available to anyone nearby.  At the same time, producing invertase has a cost, which the authors measure as a fitness disadvantage of 0.35% in exponentially growing cells.  Under some conditions, therefore, cells that don’t secrete invertase grow better than those that do, because they are able to take up the monosaccharides produced by the cells around them without producing the costly invertase enzyme.  Whether this works depends on factors like cell density (high is good for cheating) and how much the culture is agitated (shaking helps spread sugars around, which is also good for cheating).  But Koschwanez et al. show that the clumping strain does better than single-cell strains when competed with matched clumping or non-clumping cheaters, under a variety of conditions.  Clumping allows the cells that are playing fair to stick together and share resources; conversely, if you’re a cheater growing in a clump it’s harder to escape your (similarly cheating) sisters and daughters.

When an interesting paper like this comes out from not one, but two, of our friends, I would normally make every effort to write a properly detailed post about it.  But this one has been written up all over, at New Scientist, the Harvard Gazette, MSNBC and a PLoS Biology minireview, among others; and most intimidatingly of all by Ed Yong. It’s good to see so much interest, and since I can’t think of a single point that hasn’t already been made I’ll content myself with pointing you to the rest of the coverage.  Enjoy!