How did you come to work at ISB, given your background as a physics major?
I mean, obviously my first love was physics. I decided I wanted to be a physicist in high school. I read Brian Green’s The Elegant Universe and Stephen Hawking’s A Brief History of Time, which sparked my interest. To be quite frank, I didn’t have any interest in biology. I was always just kind of drawn to the simplicity of physics, you know, the ease with which you can model stuff and the interplay between the math and the physical world.
So in college, I got a dual major in physics and math, and then went to graduate school. I wanted to stay in Seattle, so I talked to my advisor, who told me his first ever student at UW got a job at ISB. I started working with a theoretical biophysicist at UW, and then reached out to Nitin Baliga at ISB about switching fields to biophysics. I was immediately fascinated and a little bit overwhelmed by the number of interesting open questions. Then, I met Jenn Hadlock at a job fair, and started working at ISB.
Do you ever have to collaborate with people that have very different backgrounds? How do you find common ground?
In my sphere of research at ISB, I’m pretty much the only physicist, and I’m very hesitant to give up my physics approach. Something physicists do all the time is take a complex system, and simplify it down using basic principles. I think this is something biology needs a lot of because it’s just so complicated, and this is something I butt heads with with a lot of collaborators. I’m all about simple, easy to solve models, but that’s not how a lot of biologists do things. I feel like I’ve gained some support among my colleagues for this type of approach. I’m also very eager to use my math background, although sometimes it’s hard to communicate what I’m doing and why it’s biologically meaningful.
What are the biggest challenges in applying theoretical concepts to biological problems and ensuring they still have practical applications?
The best example I can think of was working on a project for single cell microglia profiling for Alzheimer’s disease. I tried to come up with this approach that was based on the biophysical dynamics of the system. My collaborator took charge of the biology portion, and I handled the physics. We worked well together, but it was definitely challenging to explain it to people other than him. In general, trying to communicate how I’m modeling things and why my models are attempting to be more biologically realistic is certainly a challenge.
Tell us more about single-cell biology. What do you think it needs in the future?
Single cell biology is hard because the data is annoying. It’s very sparse and noisy, which poses a challenge – for example, is the sparsity due to the sparsity of genes being turned on in a cell? Or because we are missing things? What it needs is a good physical model. The methods that are out there are really nice. They’re very high throughput, but they’re definitely missing something. There was a paper recently that kind of formalized a non-identifiability problem – how it’s impossible to distinguish between technical and biological. I think more methods aimed at addressing that would be nice.
What are some of the big questions in the intersection of biology and physics that you want to tackle in the future?
The co-abundance of different biomolecules in a cell. There haven’t been any techniques that can simultaneously sequence RNA, proteins, and DNA in the same cell. I would love to analyze that data. Another big one is protein folding, which is a huge intersection of physics and biology.
How do you verify your work when working with people who have different educational backgrounds?
I usually read a lot of papers and I have a lot of textbooks. I flip through those and make sure I’m doing things correctly. There’s also the classic things, like building models, avoiding overfitting, those kinds of technical things. When you do enough research, you kind of gain this intuition for how correct your math is, so that’s another thing – double, triple, quadruple, quintuple checking every single line.
What has been the most exciting project you’ve worked on?
Dimensionality reduction for single cell data is still really exciting. It’s a nice combination of different areas of mathematics, and it’s exciting to see how all these different abstract areas of math can come together and give you something that’s biologically meaningful.
What is challenging about pursuing a career in science?
There is definitely a huge pressure on scientists to public in the United States, and pressure to secure funding. It’s also very hard to break into scientific communities. A lot of scientists who are successful today would have been rejected in past decades because of a lack of prerequisite education. For young people, I’d say it’s important to gain research experience as an undergrad to set yourselves up for graduate school.