jeff smith

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.

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.

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]

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.”

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.