Starting again

Starting in a new lab is a special time.  You have a space to choose what you’re going to be working on for the year or two.  It’s a nice position to be in, but it also creates its own kind of stress—I find myself thinking I’ve wasted the opportunity if I don’t come up with something OMG genius. Sometimes I think my theoretical ideas are more creative than experimental ones.  I think I do good empirical work, but it seems more… ordinary.

At first I thought about projects that would fit with the scope of the lab’s current grants and would dovetail nicely with my previous work.  My advisors, though, said they believed the grant applications should only be seen as general guidelines for direction.

Instead they asked, “What’s the coolest result you can get?”  They said they wanted me to publish a couple high-profile papers so I could get a good faculty job when I’m done.  I hadn’t really thought of it like that.  Not just the careerism aspect, but also explicitly going after the coolest, most compelling results possible.

There’s a lot to be said for aiming high.  What are the most important problems that need to be solved?  What are the biggest outstanding issues?  What important pieces of the story have been neglected?  I’ve looked back at my “open questions” post a bunch.

I also thought about the tools and techniques I want to learn here.  What do I want to do when I can finally work on the questions closest to my heart?

In the end, the projects I’ve settled on seem pretty solid, even if they’re not exactly revolutionary.  There’s a good spread of general-principle issues that transcend specific systems, old-school animal behavior, neat modern techniques, experiment/theory interaction, and safety versus risk.  Now to the bench!

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    Even bad reviewers can be useful

    I’ve learnt my lesson for the day: even if a reviewer is just plain wrong, what you do in response can still improve the paper. And maybe even show you something new about your own data.

    I’m revising a paper about my older work with plasmids. One of the reviewers, a theoretician (they wrote their review in TeX), thinks the paper “is really lacking a serious mathematical and statistical modeling effort”. And here I was happy to finally have a paper with no math in it! They don’t think it’s clear that my data support the conclusions I make, though it seems obvious enough to me. Plus, they want me to use a specific modelling method I think is seriously questionable.

    At first I was upset at having to spend a bunch of time and effort responding to reviewer comments that were wrong and weren’t going to improve the paper. But then, in the process of doing some math to address a question from the other (more sensible) reviewer, I realized I could extend the math and show, quantitatively, how competing evolutionary hypotheses make different predictions about what should happen in my experiments. In the end, not only am I able to show that my data reject one hypothesis but are consistent with another, but I’m also able to explain the specific shape of my data—something I’d never even attempted to do. I’m actually surprised it fits so well.

    So there you go. Score another one for peer review. It’s even better than the peers doing the reviewing.

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      A generalization of Hamilton’s rule for the evolution of microbial cooperation

      jeff smith, J. David Van Dyken, & Peter C. Zee (2010) “A generalization of Hamilton’s rule for the evolution of microbial cooperation” Science 328: 1700-1703. [Link]

      Abstract: Hamilton’s rule states that cooperation will evolve if the fitness cost to actors is less than the benefit to recipients multiplied by their genetic relatedness. This rule makes many simplifying assumptions, however, and does not accurately describe social evolution in organisms such as microbes where selection is both strong and nonadditive. We derived a generalization of Hamilton’s rule and measured its parameters in Myxococcus xanthus bacteria. Nonadditivity made cooperative sporulation remarkably resistant to exploitation by cheater strains. Selection was driven by higher-order moments of population structure, not relatedness. These results provide an empirically testable cooperation principle applicable to both microbes and multicellular organisms and show how nonlinear interactions among cells insulate bacteria against cheaters.

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        Journal clubs

        Many labs have semi-regular “journal clubs” where they read and discuss scientific papers relevant to their research. Journal clubs provide an opportunity to discuss, in detail, research going on in other labs. A large part of a scientist’s job is to think critically (but fairly) about research in their field. Journal clubs also provide an opportunity for students to learn and hone those skills. My experience has been that assigning people to “present” papers in shortened, verbal form usually isn’t that helpful or interesting. Instead, it’s best for participants to approach papers as an interested but skeptical outsider. As a reviewer, basically.

        Here are some questions I ask myself when reading a paper:

        • Why should anyone read this paper? What is the paper’s main point, the thing that everybody should take away from it? Is this point important?
        • Is the paper right? How do the results support the main point? What are some alternative hypotheses for the results? Did the authors do the right controls? Did they do the right statistical tests?
        • How is this paper wrong? All papers are wrong somehow. If you don’t find something wrong with a paper, you haven’t read closely enough. Some papers are more wrong than others. How do the errors in this one affect its main point? Are they fatal flaws or only minor quibbles?
        • Is the paper well written? Is the data presented clearly? How could the writing be improved?
        • What does the paper do right? What about this paper is worth emulating in your own work?
        • If you were a reviewer, what would you recommend to an editor: accept as is, accept with minor revisions, send back for major revisions and re-review, or reject outright?
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          Being aware of your own blinders

          Scientists have opinions, and they are most interesting when they are controversial.  I have little patience for the pretense of a “fair and balanced view,” when we all know that balance comes out of discussions and disagreements among peers, not from the point of view of a single individual. (Pigliucci 2007 Science 31:317)

          I more or less agree with this. But the same time, I think it’s important to try to recognize your own biases and try to look past them, to the extent that’s possible. I’ve always been impressed by scientists that actively seek out alternative explanations for their own data and then test those hypotheses with more data. Curt Lively is really good at this.

          Why does this come up? Recently I’ve become interested in nonadaptive processes in evolution: the parts of evolution that aren’t natural selection. No, I shouldn’t say interested. More like: I’m mainly interested in selection, but if I’m going to spend all my time studying some particular phenotype of microbes, I want to know if that phenotype is a result of selection, and how.

          Thinking about nonadaptive processes doesn’t come natural to me. I first got into evolution through behavioral ecology, a field that’s almost entirely about selection. But not considering the alternatives can lead you astray. The classic work here is “The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme” by Gould and Lewontin. The basic idea is that not all phenotypes are adaptations; they might just be side effects of other things that are. More recently, Mike Lynch has shown that that many aspects of genome evolution have less to do with selection and more to do with mutation, recombination, and genetic drift.

          So I may know that there’s more to evolution than selection, but it’s hard to just decide to think differently. And now that I come to think of it, my experience pretty much illustrate’s Pigliucci’s point. I only started thinking seriously about nonadaptive processes after talking with colleagues who have different backgrounds and different biases.

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            Prominent journals 3: peer review

            Well, it worked.  Science officially accepted “A generalization of Hamilton’s rule for the evolution of microbial cooperation” by jeff smith, J. David Van Dyken, and Peter Zee.  First and senior author on a paper in one of the most prominent journals in all of science…  You can’t see it, but I’m doing a little victory dance right now.  It’s especially sweet because I really think this paper deserves a high profile — it’s not just spin and luck.

            But we did have decent luck with reviewers.  Their questions, comments, and suggestions helped improve the paper, even if we didn’t always agree with them.  Two of the three were totally on board with what we were trying to do and how we were doing it.  The third mainly took issue with some of our stronger statements knocking Hamilton’s rule.  Worse would’ve been a reviewer antagonistic to our research program or a reviewer that just didn’t get the point, for whatever reason.  In the end, we clarified the problems we had with Hamilton’s rule, toned down our rhetoric somewhat, and that was enough.  I get the impression that many papers in these journals go through a similar cycle — they start out boldly stated to make it to full review (“An accurate replacement for…”), then get toned down on revision (“A generalization of…”).

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              What DNA sequences tell us about the real world of bacteria

              So far, most of my research with bacteria has been experimental—experimental in the sense that I manipulate the genes or environment of bacteria in the laboratory and look at how those manipulations affect fitness, population dynamics, and evolution. One of the great strengths of experimental science is that it lets you change one variable at a time and keep everything else constant. That way, you can be sure that your results are caused by that variable and not something else. And because microbes evolve so quickly, you can use experiments to directly test the predictions of evolutionary theory. All these things are great.

              One of the big disadvantages of the experimental approach, though, is that it tells you how evolution can happen—not necessarily how it does happen in the natural world. The best we can do in the lab is often still very different than an organism’s natural habitat. Experimental approaches also don’t tell you how general a result is. When you get a result, you hope that it holds for other bacteria in other environments, but there’s no guarantee that it should be so. Only by repeating those experiments in other systems do you get an idea about generality—and no one wants to perform, publish, or fund work that’s just repeating what other people did and getting the same results.

              For these reasons, I’ve been becoming more interested in molecular evolution. The information in DNA and protein sequences reflects the actual evolutionary history of organisms in their real-world environment. It’s hard to observe or experiment on microbes in their natural habitat, but it’s not that hard to look at their DNA. And there’s lots of sequence data already available. Of course, sequence analysis has its disadvantages, too. Correllation is not causation, so if you see that two things are associated with each other it’s always possible that the real cause is some unknown third thing. It can also be difficult to exclude alternative explanations for results. Some people in my field feel these problems to be large and dismiss sequence-based studies as “retrospective evidence” and thus inferior to “prospective” experimental studies. But the way I see it, we have so much of this data these days—why not use it? Anything we can use to better understand out how the natural world works is a good thing, in my book. Why can’t experimental and sequence-based approaches complement each other?

              This has been on my mind recently after reading an interesting paper by Fidelma Boyd, Salvador Almagro-Morenoand, and Michelle Parent.

              Bacterial genomes are fluid things. Something like 30% of the genes in an E. coli cell may not even be present in the E. coli cell next to it. Often these differences in gene content are viruses laying dormant in the genome, waiting for the right trigger to emerge and find a new host. In other cases, they are clusters of genes called genomic islands that kind of look like viruses—they have a few genes with similar sequences—but don’t seem to have all the pieces necessary to make viruses on their own. What are they doing there? Microbiologists are interested in genomic islands because, aside from containing virus-like genes, they often also have genes that make bacteria more harmful or resistant to antibiotics.

              Phage P2 (right) and its freeloader P4 (left). © Institute for Molecular Virology, U. Wisconson-Madison.

              There are at least two possible answers. One is that genomic islands are degraded phage (viruses of bacteria). They were once infectious, but at some point mutation inactivated one or more genes necessary for that lifestyle. Now, as the mutations continue to accumulate, they’re sliding toward evolutionary oblivion and their own inevitable deletion. Another possibility is that genomic islands are mobilizeable. This means that they can’t make phage particles on their own, but they can use the proteins made by other phage in the same cell. They’re a kind of freeloader. Phage P4 is a well-known example.

              How do we tell? Boyd and coauthors addressed this question using the tools of molecular evolution. If genomic islands were degraded phage, phylogenetic trees made from their protein and DNA sequences would show genomic islands scattered among the other phage. Because they’re degraded and nonfunctional, they’d be recent derivatives and wouldn’t persist long over evolutionary time. All the branches leading to genomic islands would be near the tips of the tree. If, on the other hand, genomic islands are mobilizeable and have a long evolutionary history of freeloading on self-sufficient phage, then phylogentic trees would show them clustering together on their own branch.

              Boyd and coauthors made phylogenetic trees using the sequences of integrase (Int) genes from many different genomic islands and phage. They found that virtually all the genomic islands clustered together in their own branch that included P4. This evidence is consistent with the mobilizeable hypothesis and inconsistent with the degradation hypothesis. Boom—we’ve managed to exclude one hypothesis, the other one survived an empirical test, and we’ve made a tiny step forward in understanding the natural world. Science in action.

              I wish Boyd and company tested another prediction of the degradation hypothesis: that degraded phage should show evidence of relaxed selection. Once phage get inactivated, natural selection no longer weeds out harmful mutations in their sequences. One kind of evidence for relaxed selection is a larger fraction of pseudogenes—sequences of DNA that once used to be genes but are now prematurely truncated or shifted so that they no longer make functional proteins. Another is that more of the DNA sequence changes should cause differences in the protein sequence (dN/dS, for those who know such things). Not finding these things, or at least putting lower limits on how much they occur, would be another strike against the degradation hypothesis and more support for the mobilizeable hypothesis. The data’s already there—the analysis just needs to be done.

              It’s also wierd that this paper is published as a review article rather than a peer-reviewed results paper in a molecular evolution journal. Because it’s not, and because the paper glosses over many of the details of the phylogenetic analysis, I find myself taking the results with a grain of salt. Hopefully this work can at some point be redone or extended at some point so I can be more confident in the results.

              In any case, this is an example of how sequence analysis lets us get at an evolutionary question—how does natural selection act on genomic islands?—that can’t be answered by experiments alone. We need both types of data. The experiments show us that mobilization can happen and the sequences show us that these elements have been persisting and evolving just fine without their own phage-producing genes.

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                Form and content in scientific writing

                I’ve been helping a friend who’s writing their first scientific papers. Scientists almost always read papers for the content and pay little attention to the craft of what they’re reading. So when it comes to writing your own for the first time, it’s not always obvious how to proceed.

                It helps to know that most scientific writing is pretty formulaic. Journal articles and grant applications have a pretty set structure that journals and funding agencies expect you to follow. The abstract/introduction/methods/results/discussion format is pretty ingrained these days. Even within those sections there are standard ways of doing things. Nature, for example, gives authors a sentence-by-sentence template to follow in their abstracts. Only in review articles or perspective pieces do you have much leeway in terms of large-scale organization.

                In a way, scientific writing is like Bebop. Bebop song structures are pretty rigid and predictible. It’s always head/solos/head. The creativity is all in the melody, the chord changes, and the solos. For scientists, the interesting part of a paper isn’t the writing or organization—it’s the experiments, the results, and what they say about the natural world. Everything else is secondary.

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                  Prominent Journals 2: The cover letter

                  Normally when you submit a paper to a journal, you include a cover letter as a kind of formality. It’s the equivalent of a handshake and an exchange of basic information: number of words in the paper, suggestions for reviewers, contact information, stuff like that. With prominent journals, though, these cover letters are a Big Deal. Most submissions to Science and Nature don’t even get sent out for review, and the cover letter is where you make the case that your paper is interesting and important enough to pass the first cut. The fate of your paper depends on less than a page of text that only a couple people may ever read. It’s crazy.

                  In preparation for our imminent submission, I read whatever I could find about these letters. Pamela Hines, senior editor of Science, gives a talk on how to publish in Science that I found useful. Some things these editors ask themselves when they get a submission are: How is this novel? Is it a big enough scientific advance? Is it widely interesting? They look for work that solves a long-standing problem, overturns conventional wisdom, or has wide implications. It’s your job to figure out what’s most interesting about your work to the largest number of people and put that front and center. It’s a kind of self-promotion that a lot of people find difficult.

                  Nature asks authors for a 100-word summary of their paper for nonscientists. I found a forum post by a Nature staffer claiming that these summaries aren’t actually used by the journal—they’re to help authors think about what makes their findings interesting to a wide audience. It’s funny, a little bit devious, and I think it works.

                  I’m of a mixed mind about the whole process. I can see how high non-review rates can lead to spin being valued more than content. In my field these journals have published several papers that really didn’t deserve such high visibility. But at the same time, I can see how revising my current manuscript with these journals in mind has made it a stronger, clearer work. I guess we’ll soon see if the editors agree.

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                    Open questions in microbial cooperation

                    While putting my Myxo work together into a talk, and trying to present it all with some semblance of coherence, I had the opportunity to think about where the evolution of microbial cooperation is as a field and where I think it should go.  The way I see it, the most important open are these:

                    Is shared genes the primary evolutionary mechanism maintaining cooperation in microbes? By shared genes I mean that the benefits of cooperation are preferentialy experienced by individuals who also share the alleles for expressing the cooperative trait.  This process can be described as kin selection or group selection.  Shared genes is widely thought to be the primary mechanism for the evolution of cooperation in animals—is it true for microbes, too?  And what role do other mechanisms like enforcement, direct benefits, or pleiotropic constraint play?

                    What is the primary cause of genetic correlations among individuals? Limited dispersal, kin recognition, green-beard genes, infectious gene transfer, or something else?  Do cooperative traits themselves create genetic correlations through their effect on migration and motility?  The important part of these questions is getting at what IS happening, not just what CAN happen.

                    How does social evolution shape microbial traits? Many traits seem to involve interactions between individuals (quorum sensing, biofilms, and so on), but are these traits cooperative in the evolutionary sense of increasing the fitness of other individuals?  How does the magnitude, regulation, or form of these traits differ from that what they would evolve to be if they did not have social effects?  To what extent do microbes actively alter their behavior in response to social conditions?  Which traits are adaptations and which are only side-effects of some other function?

                    How do social traits change over evolutionary time? Are social traits under stabilizing selection or do they evolve in evolutionary arms races?  How often are they lost?  If cheaters occur in natural habitats, do they persist because of selection or recurrent mutation?

                    What are the origins of microbial cooperation? What traits were co-opted into becoming the building blocks of cooperation?  Worker behavior in social insects, for example, is a modified form of maternal behavior.  Are the benefits of cooperative traits the same now as they were originally?  How many times have similar cooperative traits originated?  Are cooperative traits usually acquired by horizontal gene transfer or invented de novo?

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                      Prominent journals

                      We’ve been considering submitting one of our recent projects to one of the prominent journals (Science, Nature, PLoS Biology and so forth). The process is somewhat different than normal. Not just the formatting, but also the focus on how interesting the work is to people outside the field and to a non-science audience. I’m a bit averse to the press release, spin-heavy mentality that can go along with these things sometimes, but it has helped me focus better on the bigger picture.

                      Since this is my first time doing this sort of thing, it’s also been a little bit intimidating. I’ve found it helps to mentally compare our work with other related papers that have been published in these journals, rather than some imaginary standard. That helps.

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                        Discovery vs. hypothesis testing

                        Today at lab meeting we discussed the recent Nature paper “Experimental evolution of bet hedging” by Beaumont and colleagues. Nature has an short summary of it, and my colleague Will Ratcliffe at the University of Minnesota has blogged about it.

                        It’s a cool result. The authors passaged bacteria through a set of alternating environments and at each step selected out the first kind that produced a different colony type than the rest of the population. In effect they were selecting for bacteria that looked different. In a couple of their evolving populations, bacteria evolved that could reversibly switch between two colony types, a trait that contributes to virulence in some bacterial pathogens. They went on to characterize what mutations occured in the evolving population, determine which one caused the switching phenotype, and show that it only increased fitness if some of the earlier mutations were already present in the genome.

                        So that’s cool and all, but after reading the paper I still found myself wondering what we know about how evolution works that we didn’t know already. The authors basically did an evolution experiment with the expectation that this phenotype might evolve, and then found that it sometimes did. It’s more like a proof of principle. But it doesn’t really tell us much about when we should expect organisms to adapt to environmental variation through these kinds of bet hedging strategies instead of directly sensing the environment or just evolving rapidly to each one in turn.

                        I guess what I’m reacting to is that this paper is about discovery and not about testing hypotheses. It’s motivated by theory, I guess, but only in a general sense. It’s not trying to distinguish between alternative explanations for some evolutionary phenomenon. It’s more saying: this can happen. Don’t get me wrong—I agree that discovery is a very important part of science. It’s just not something that tends to figure much into my own thinking about the practice of science. Maybe I should do something about that.

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                          Not long after grad school, I realized that from here on out there will always be a manuscript or two I’m working on. There will likely never be a time when there is not something that needs to be written. Which meant that I needed to somehow find a way to balance writing with lab work, data analysis, math, and plain old thinking.

                          Since then, I’ve asked quite a few scientists about when they write and when they think. There’s lots of variation. One of my younger colleagues says he spends an hour or two every morning sitting on his office couch staring at his whiteboard, planning experiments. Another reserves his mornings for writing. Another, a father of two, cherishes the rare opportunities he gets to sit in a coffee shop and just think. Another works out his project ideas during boring talks.

                          I used to work a lot in the evenings at coffee shops and diners, but recently that hasn’t worked as well as it used to. Maybe it’s the coffee shops here, or maybe it’s that I drink more coffee in the morning than I ever have. In any case, these days I’m most most focused in the morning, right after the coffee kicks in. I’ve been trying to reserve that time for writing and statistics—things that require the most mental effort from me—in my home office, where there’s natural light, college radio, and a DIY whiteboard made from storm windows. Afternoons and evenings are for things like lab work and making figures, which I can pretty much do on mental autopilot.

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                            Standards of “solved”

                            While talking with a theorist grad student here, I realized that people can have very different standards by which they judge whether a scientific question is solved.

                            It started with the evolution of sex: I find pretty unsatisfactory the current situation where there are a variety of hypotheses, each with some support in few systems, but nothing that approaches a general explanation across all eukaryotes. To me, the lack of coherence suggests that we really don’t have a handle on what’s going on. The grad student, on the other hand, had no problems with there being a variety of ad-hoc explanations. He pointed out that the natural world is under no obligation to conform to my desire for there to be a single general principle for any particular evolutionary phenomenon.

                            Then we discussed why so many bacterial genes are carried by phages and plasmids. He may have been half-joking, but it seemed like he thought a problem basically solved if there was a theoretical model he liked that seemed to fit the anecdotal evidence. I tend to think a problem isn’t solved until there’s enough direct testing to make a reasonable person who doesn’t want to believe the explanation go “well… I guess so.”

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                              Taking stock

                              It’s not unusual for conferences to open with somebody talking about the state of the field, but seldom has it seemed so clear and useful as when Patrick Philips opened last night for the EvoDevo conference going on here.

                              I was especially struck by the couple slides in which he outlined, in the most general terms possible, the big questions he saw the field addressing, and his assessment of its progress. While he felt that researchers had made progress on the question “How do developmental systems evolve?”, he felt that basically no progress had been made on the question “How does development influence evolution?”. I found the candor refreshing. I was also struck by his claim that there is no theory behind EvoDevo—no conceptual or mathematical framework that allows us to make predictions about patterns of variation or change over time. He contrasted it with population genetics.

                              These days I’m wrapping up my postdoc with Myxococcus and thinking about the future. I’ve been invited to visit ETH Zürich, so perhaps as part of my talk I should do a little taking stock, myself. The idea that I’m even in a position to talk about how our understanding of microbial cooperation has changed over the last 10 years is a little bit wierd.

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