What is your field and why does it inspire you?
As Dobzhansky, a well-known evolutionary biologist, said, "Nothing in biology makes sense except in the light of evolution." Therefore, understanding how evolutionary processes shape traits and the genome will help inform all other aspects of their biology, including their behaviors and ecology. I am inspired by how pervasive the effects of evolution are.
Describe your current research.
Evolutionary processes generally fall into two categories: selective and neutral. Selective processes include the common examples of natural, sexual and artificial selection that Darwin outlined in his works. Neutral processes are the result of demographics (e.g., population size, birth rates), mutation rates, and other random processes. Both types of evolutionary processes can cause changes in traits and in the genome. My research focuses on trying to understand the interactions between these two types of processes by combining two field of mathematical evolutionary biology (population genetics and quantitative genetics) at a genome-wide scale.
What is the primary aim of your research?
The primary aim of my research at NIMBioS is to use simulation modeling and computational biology to improve predictions for and interpretations of the genome-wide genomic and transcriptomic datasets being generated by biologists.
What is the biggest obstacle to achieving your objective(s)?
There are two major obstacles to my research. First, existing data are piecemeal and of varying quality. My previous research has revealed significant bias in datasets due to the methods used to prepare samples for sequencing, and determining the differences between bias and actual evolutionary signal is incredibly challenging. Second, the systems I am trying to model are incredibly complex, so it can be difficult to build realistic models that can yield insight into evolutionary processes.
How does your work benefit society?
I hope that my work will help allow researchers to take a more hypothesis-driven approach to studying evolutionary genomics in natural populations. This in turn will allow better insights to be made regarding problems facing society, such as how species will fare with changing climates and habitats and understanding the genetic basis of complex diseases.
What do you like best about your work?
My favorite thing about my work is the problem-solving aspects of it. I solve problems daily when debugging code or figuring out a way to model a problem, but the best feeling of solving the puzzle comes from making sense of a confusing and convoluted dataset at the end of a complex study.
What would your elevator speech say?
I grew up with two dogs, both Shetland sheepdogs. One of them was friendly and calm, the other was high-strung and distrustful of non-family members. What made the two dogs so different in personality? Some of it was due to genetics, but what components of their genetic makeup made them so different and why would different animal personality traits evolve? My research answers these sorts of questions in a general framework—I am interested in trying to understand what type of genetic makeup leads to different traits and whether the evolutionary forces are due to random chance and demographics or selective pressures.
Which professional accomplishment are you most proud of?
I am most proud of all of the skills that I acquired during my PhD. I started as a field ecologist and animal behaviorist and learned molecular biology techniques, the bioinformatics and computational skills required to analyze genomic datasets, and computer-programming skills.
On the other hand, what has been your most discouraging professional moment and how did you recover? What did you learn?
My most discouraging moment came when I realized that a dataset that took me 4 years to generate contained a large amount of bias due to methodology. I recovered by doing an incredibly detailed analysis of the dataset and ended up writing two papers, one describing the bias in the dataset and one describing the intended study on a subset of the data that contained a limited amount of bias. I learned that even when things seem bleak, there is usually a way to still get something out of it.
What is the best professional advice you ever received?
Create and maintain a work-life balance as best as you can, but remember that balancing often requires wobbling a bit. It's ok if sometimes work takes over or life takes over, but try to maintain a good balance—and ensure that a big component of the 'life' part of the work-life balance includes staying healthy.
What exciting developments lie in the future for your field?
Soon it will be possible for just about any species to have a decent genome assembly. I think this will lead to comparative studies that will yield insights into common evolutionary patterns and trajectories. I think that evolutionary biology will become increasingly integrative, as enzymatic pathways, developmental biology, and gene expression are required to explain patterns that can't be explained solely by the genome.
Who is your #1 hero and why?
One of my primary heroes is my grandmother, Betty Flanagan. She earned her degree in mathematics at Simmons College in the 1940s and worked as a biostatistician at Harvard and Johns Hopkins for nearly 10 years and even co-authored a scientific paper. She was a woman in STEM in an era when women rarely worked. When she did stop working to start a family, she maintained her curiosity and became an avid birder and she loved to travel. She was independent, intelligent, curious, and loving. Although I hope to have a very different career path than she took, I admire the tenacity and dedication she put into everything she set her mind to do.
What do you do when you're not in the lab or out in the field?
Most of my spare time is dedicated to reading novels (mostly science fiction or fantasy), watching good TV shows, and spending time with friends and family. I also really enjoy hiking, kayaking, travelling, and craft beer.