For the first Molecular Ecologist Q&A feature of 2014, I’m excited to present Dr. Pleuni Pennings. Dr. Pennings is a postdoc with Dmitri Petrov at Standord University, where she’s studying the evolution of drug resistance in HIV. In addition to her research, Dr. Pennings make’s fantastic science videos, writes on topics other than HIV, and can be found on twitter. In the questions below, Dr. Pennings comments on ‘broader impacts’, why you probably shouldn’t care about Tajima’s D as much as you do, and explains why you don’t have to grow up collecting beetles to become a biologist:
1) First, can you tell us a bit about what got you interested in biology?
In school, I didn’t like biology much, because I felt it was too much memorizing and not enough understanding. It just didn’t make sense to me. I was also not the outdoorsy type when I was young, and never cared much about dinosaurs.
My opinion changed when I was an undergraduate student in Aberdeen, Scotland. It was my first year in university and I was taking classes in math, physics, art history and spanish. I became friends with three biology students, who lived in the same dorm as I did. And they talked about evolution! I was intrigued! At some point they had to write an essay on “Why there are so many species of insects” and I listened to their discussions with keen interest.*
The next year I transferred to the University of Amsterdam as a biology student.
- One of them is still in evolutionary biology: Tom Pizzari
** Later, one of my first attempts to a scientific paper was entitled: “Why are there so many species of arthropods”
2) Before your PhD, you worked in science education. What role do you think scientists should take in education?
I think it is very important that scientists communicate about their work and collaborate with journalists, teachers, museums and others* who have more contact with non-scientists and who may know more about education and communication than many of us.
I think there are several reasons why we should spend time on education and communication:
- I think a scientist can tell a different kind of story than the kind of stories journalists and teachers tell. In a way, we can be more authentic and direct, although that is no guarantee that the audience always likes or “gets” our story. Just like journalists and teachers though, our stories (in talks, movies, blogs, books, lectures) can inspire people to ask questions, be curious, discover, learn, think.
- I think we owe it to society to make an effort to explain what we do with tax money. If we don’t take time to explain what we do, we shouldn’t be surprised if tax payers are not willing to pay for it.
- I think it is good for our science if we take time to think about what we are doing and why. Whenever you have the chance to explain to a non-scientist what you do, it is a an excellent opportunity to remind yourself what is exciting and cool about your work (and what isn’t!).
Note: I don’t think that every scientist needs to be an education expert. Not everybody can/should do everything. Others may be good at translating results from the bench into profit-making companies, or at editing journals, or at managing a department. For me personally, one reason I spend time on communication is because I enjoy it. Plus, with the tools of the internet, it is something we can do without much money or a large team, next to our daily work.
*like the company I was part of before my PhD: De Praktijk
3) You’ve produced some wonderful videos about evolution and your research. Can you tell us a bit about the inspiration for these videos and what you hope they accomplish?
I started thinking about science movies when Yannick Mahé contacted me. Yannick is a brilliant filmmaker from France and she needed a partner to be able to apply for a German grant. We applied together and got a grant to make 6 short movies for the Darwin year (three each). At that time I needed to learn quickly how to make a movie!
There was one thing I wanted to do differently than most science movies I had seen: I wanted to show the audience the scientists and explain how science was done, as opposed to focussing solely on the resulting discoveries. I wanted people to understand that modern evolutionary research is exciting, not just that evolution is true or important. For me, the traditional Attenborough movies are beautiful, but they usually completely leave out the story of who made certain discoveries and how such discoveries were made.
For the movies about my own research, I make them because I want to tell people about my work. I know that only few people will actually read my papers, so through the movies I can reach more and different people.
4) The scope of your research is impressive, including theoretical work on soft sweeps, slavemaking ants, and HIV. How do these projects relate to each other? Any advice on how to choose projects?
The soft sweeps work and the HIV work is very much related. For example, in the first soft sweeps paper (Genetics 2005), we presented a formula to calculate the probability that adaptation to a new environment occurs due to a pre-existing beneficial mutation (standing genetic variation). This result is not the main reason the paper gets cited (the citations are mostly for what we say about the signature of a sweep when the mutant was pre-existing), but I always liked the result a lot (it’s equation 8). When I started working on HIV, I realized that I could estimate the probability of adaptation (i.e., evolution of drug resistance) due to pre-existing mutations, directly from data from patients (PLoS Comp Bio 2012). This is possible for HIV because the evolutionary process occurs in parallel in many different populations (each patient has one independent HIV population). In some way, studying HIV evolution is like having thousands of somewhat messy “Lenski-lines” with a very high mutation rate that have evolved for a few hundred generations.
The question of pre-existing mutations vs. new mutations in HIV is also clinically relevant, because resistance due to pre-existing mutations needs to be fought in a different way than resistance due to new mutations.
The work on slavemaking ants is very different. In these ants we cannot observe evolution in real time, because their generation time is quite long, so we use more traditional eco-evo methods. The lab I worked in is mostly a behavior lab, so some of the work is squarely in behavioral ecology and relatively far from evolutionary biology.
My advice in the current job market would probably be to not switch topics as often as I have done! But other than that, I think a fruitful strategy to find good questions is to read in different fields, talk to people outside your field, maybe watch David Attenborough movies, and think about general questions you have about the world. If you spend too much time within a very specialized subfield, you slowly start believing that the intermediate questions, that were once suggested as a step towards answering a big question, are actually the big questions. For example, we may start to think that the observed Tajima’s D or Fst values are actually of interest. A short conversation with someone outside your field should remind you that Tajima’s D values in itself are really uninteresting. We use them because we lack a better, more direct way to study evolution.
5) On the more sciencey-side, what is your take on the state of the field to detect loci under selection?
I think we’re in a very exciting time for the search for loci under selection. The data have become so much better (e.g., entire genomes, many individuals, many populations) over recent years and they are still getting better. With better data, we can apply new methods and I expect that we’ll find a lot of loci under selection. Already, the things we are finding in humans (e.g., adaptation to high altitude in Tibet), in Malaria (Artemisinin resistance) and in Drosophila (e.g., Garud et al) are very interesting and still only the tip of the iceberg, I think. Whereas just a few years ago, I thought it looked pretty bleak and it felt like we were not finding much.
My colleagues here at Stanford in the Petrov lab have a paper on Arxiv in which they identify 50 loci in D. melanogaster where a soft sweep likely has occurred recently. This is an exciting result that would have been impossible just a few years ago because the ideas, the methods and the data were not available. Until now, most progress has been made in humans, Drosophila and a few other species, but in the near future, I think we will find loci under selection in many other species. And once we can make comparisons between species, things will get even more interesting.
6) Who are your scientific heroes?
I’ve been inspired by many people. My advisors, people I met at conferences or whose papers I read. I absolutely love Hopi Hoekstra‘s work, such as the work she’s done on coat color evolution and her recent work on burrowing behavior. Also her talks are great. They are clear, informative, funny and inspiring, and afterwards I just want to run back to the lab and get to work to make scientific discoveries.
I’ve also been inspired by many books. I love reading books about science. One book I read multiple times and found very inspiring was “Freakonomics“. In this book, two economists write about all kinds of things they learn by analyzing data in a slightly different way than others did before them. For example, there is a story about the role of money in local elections, where they explain that when the authors were entering data about these elections by hand, they realized that the same two candidates often competed with each other again and again in elections. They then realized that these data were perfect to study the importance of money in these elections. When the two candidates meet each other multiple times in an election, one may have more money in one election and the other may have more in another election, but in both elections the candidates are still the same, so these data provide a clean way to test the effect of money on the outcome of the election. It turned out that money was not very important. I’ve learned from this to look out for unexpected opportunities in data.