Data Science and Non-Traditional Models

Before the emergence of next-generation sequencing technologies, genomic tools were limited to a handful of popular model species (e.g., drosophila, human, mouse,  etc.) Now that the cost of sequencing a genome has dropped significantly, the number of species with assembled genomes is proliferating rapidly. This shift means that cross-species analyses are becoming ever more feasible. These types of studies can provide two types of insights: first, shared patterns across species that speak to the fundamental biology of a trait, and second, cases where a single species or taxon diverges from the rest, which can provide insight into its unique evolutionary history.

With the explosion of data available in computational biology, it is becoming increasingly feasible to conduct large-scale, interspecies analyses using data science methods. Additionally, expansion beyond just genomics to epigenomics, metablomics, proteomics, etc., offers opportunities for complex comparisons across and within species. For this reason, I am excited about data science research in biology and in teaching data science and coding to biologists.

What Can Foxes Tell Us?

I am interested in understanding the biology that influences variation in human psychology and neurodevelopment, especially in terms of interaction with the environment. This topic is something I began thinking about as a high school student volunteering at a summer camp from children who had witnessed domestic violence. Even within a family, some children exhibited more symptoms associated with long-term stress and trauma than others. Once I began studying psychology and biology in college, I began to see that any number of factors could be related to how a child copes with chronic stress, ranging from genetics to onset to intervention and more.

Methodologically, I love thinking about and working with large data sets, but initially there seemed to be limited overlap between my love of big data sets and my interest in the psychobiology of stress. The red fox is an unusual model, but it is a perfect unification of these two interests. The Russian Farm Fox Experiment famously bred two lines of foxes, one of which shows extreme defensive aggression towards human (aggressive line) and one of which shows enthusiastic, dog-like behavior towards humans (tame line). Working with Dr. Anna Kukekova, I have been involved in developing the genomic resources available for the red fox from the ground up. These resources are now allowing us to ask questions about the genetic loci influencing variation in the behavior these foxes exhibit towards humans, including their ability to tolerate the stress of living in close proximity towards humans.

My hope is that my PhD research, along with subsequent research in Dr. Kukekova’s lab that will be conducted using the genomic tools I developed, will provide insight into the biology of social behavior and stress tolerance. Because of the design of the Russian Farm Fox Experiment, methods can be used in fox that are not available in other behaviorally complex models (e.g., QTL mapping). My hope is that this work will have downstream benefits to our understanding of diversity in social behavior and stress tolerance in mammals, including humans,  that may also benefit military and working dog.

Before I began working on foxes, all of my degrees had the word “human” in the title, and I am excited to gain experience working with human datasets again. Carnivores, especially dogs and foxes, offer an excellent complement to research in humans and rodents.