How to think about research much different from your own.

As a follow up to my post last week about the value in learning about a breadth of topics, I thought it apropos to briefly describe some of the most profound ways in which my scientific thinking has been altered because of talking about disparate research.

Here's a bit of context. I was trained as an undergrad in molecular systematics of plants. I knew a lot about plant evolution and a little about molecular genetics. What did I learn when I started grad school and had to attend seminars about cellular pathways in mice, or behavior in insects? Here are a few examples.

1. Researchers trained in particular fields approach the narratives of science from different perspectives. The way we ask scientific questions, design experiments, and convey our results differs widely depending on the biological scale and phenomena we're addressing. For example, my tendency towards thinking about organismal evolution is strikingly different from a reductionist view of molecular developmental pathways. These ways of thinking are not mutually exclusive, but sometimes it seems we get stuck in thinking about science the same way. One of my current officemates was trained as a physicist, which results in some pretty eye-opening revelations about biological complexity and uncertainty.
2. You can do really cool science by applying methods from one theoretical background to a novel question from another field. There are some obvious examples of the success of these mash-ups. The modern synthesis, evolutionary development, and systems biology are all examples of uniting previously disparate fields of research. My personal favorite is the application of ecological principles to genomics (some examples are here, here and here).
3. Cross-talk assists in uniting themes in biology that are exclusive of model system. A great example of this point comes from journal club last week. Metagenomic methods borrow largely from those developed by ecologists to evaluate how diversity and abundance of organisms differs between ecosystems. It's pretty obvious to ecologists who work on macro-organisms that the average size of species can factor heavily into their influence on an ecosystem. The same argument can apply for microbes that differ widely in average size, yet biomass is rarely considered in microbial studies. Talking about vastly different study systems helps remove model-system specific bias.

Of course, those are just a few of my favorite vignettes to validate the time I spend thinking about research that isn't directly related to my own. Dare I also say that such thought experiments are also simply fun? Basically, I refuse to let myself be impatient about attending seminars or meeting with visiting scientists if their work is very different from my own. I had a great one-on-one meeting with Darwin historian Alistair Sponsel  a few weeks back when he visited NESCent. We only spoke for half an hour, but the time was constructively spent talking about visualization of different types of data as conveyed across a time scale: certainly important insight for both historians and biologists.

My last point is that understanding a breadth of research helps make your own research deliverables more appealing to a broader audience. Some practical applications are obvious: how to communicate in a seminar to a broad audience, how to convince a panel of experts your grant is worth funding. I'll continue this thought in a few days, focusing on one particular part of our job: peer review.

Catalyzing landscape genomics.

Earlier this month, I participated in a catalysis meeting at NESCent on landscape genomics. There is a common theme for each catalysis meeting. Sometimes that uniting principle is a historic area of research with multitudes of literature that could benefit from synthesis and identification of uniting principles. Sometimes the theme is an emerging area of science for which new questions are being elucidated. The landscape genomics catalysis meeting was interesting in that it addressed several major themes in evolutionary biology (population genetics, signatures of selection, comparative genomics) and was looking for ways to unite them theoretically and statistically into predictive methods. I was initially curious why the organizers chose to invite me, but quickly came realize how aligned the themes of the meeting are with my own research interests. Here are a few impressions from the time I spent sharing my thoughts with other scientists.

First, catalysis meetings are an interesting conglomeration of scientists. The goal of such meetings are to throw 25-30 scientists from different research foci and at various stages in their careers into a room together.  No one person was an expert on landscape genomics. There were experts in landscape ecology, comparative genomics and statistics, and we worked together to devise common projects and goals. The beautiful part of synthesis is that contributions from various participants are required to achieve the goals of the meeting. There are many people contributing, and everyone learns something. As a recent article from NESCentians points out, this is a fertile ground for incubating new collaborations, projects, and ideas.

Second, an aggregate of such brains allows new insight into how science works. Well, that's certainly a vague statement. At this meeting in particular, several of us were struck by the differences in how genomics and ecology view data. The former tends to throw out data left and right, paring down sequences and levels of variation so the remaining data can be described more easily. Ecologists, on the other hand, attempt to describe the overall variation in a given system, and try to model the nature of that variation. I personally believe genomicists have a great deal yet to learn from ecologists in this respect.

All in all, I believe this meeting was particularly fruitful. Sometimes tempers and egos flare at these meetings, to the point that I'm urged to start chanting "Fight, fight, fight!" and hope for fisticuffs. As exciting as it is to see other scientists so passionate about their research, I was pleased that members of this meeting were more focused on bridging gaps between disparate disciplines. There are a number of incipient projects jumpstarted from it, including one manuscript I volunteered to lead.

Synthesis FTW!

Impostors in synthetic science

An interesting article about "impostor syndrome" popped up in my twitter timeline this afternoon. It took a few moments to realize the article wasn't talking about some sort of animal mimicry. Rather, it refers to a prevalent phenomenon in the sociology of academic scientists, where we hesitate to offer information or opinions for topics outside our highly specialized area of expertise.

This topic is of particular relevance to me, as my brain has always functioned at the level of "big picture." I recall an assignment from high school English for which my classmates were selecting research topics related to a specific book, while my topic encompassed "feminist literature from the twentieth century." Yup...broad patterns.

In my current position, I think often about the push for specialization in methods, techniques, theory, and research programs. NESCent focuses on synthetic science, which involves fusing sometimes seemingly disparate research areas to achieve novel conclusions or tackle innovative questions. Synthetic science, therefore, is full of "imposters," since we constantly broach new theoretical areas and incorporate methods/results in non-traditional ways.

I'm totally okay with this label. It was nice to read this article and think about other types of scientists that regularly face this issue of being "PhD generalists," not just once during a career (i.e., changing from animal to plant research), but on a daily basis. The thoughts from other self-described impostors at the bottom are also enlightening, highlighting not only the characteristics of such researchers but their value as scientists as well.