When we interviewed Christina Payne on the afternoon of January 9, she had just finished teaching her first class; not her first class of the day—her first class as a faculty member of the University of Kentucky College of Engineering. The assistant professor of chemical engineering arrived this fall after serving as a research scientist at the National Renewable Energy Laboratory in Golden, Colo., and is settling into a rhythm of managing research projects and teaching thermodynamics. Fresh off her teaching debut, she sat down with us to talk about her research.
Q: Tell us about your primary areas of research.
C.P.: I’m a computational scientist primarily working with molecular modeling. It’s all about understanding how atoms and molecules move around and interact with each other and understanding how these interactions might affect molecular structure. I usually work on enzymes, which are proteins that break down substrates by cleaving bonds to make products. Understanding how the protein recognizes its substrate is a big part of my research.
Q: Can your research be applied to larger engineering problems?
C.P. One of the big projects I have going has been carried over from my time at the National Renewable Energy Laboratories, involving enzymes for biofuels. Specifically, we are looking at a model system called chitinase—enzymes quite similar to those used to degrade switchgrass, corn stover and other non-food based feedstocks. The biofuels industry is greatly interested in those enzymes, but they are inherently slow, working on the order of hours or days. That’s too slow for an efficient process so we want to modify the enzymes to increase the rate at which they act on their substrates. There is more to it, but the big picture is that we are looking at making enzymes faster for biofuels.
Q: How does your work utilize the new $2.6 million supercomputer cluster installed in December?
C.P.: Everything I do requires the use of supercomputers. I don’t do wet lab experiments anymore, although I did have a chance to get my hands dirty again as a process engineer for URS Energy and Construction. From a computational and experimental perspective, we examine the molecular level mechanisms occurring and predict mutations that might actually make them faster, or at the very least understand them. Then, experimental collaborators produce them and test them, feeding back information to help us understand how to make the predictions better. The supercomputer’s hardware, unlike anything available for home use, enables us to produce results in hours instead of months or years.
What I am doing is still a very new field. It doesn’t sound new in that we’ve been doing molecular modeling of protein systems since the 80s, but then you could model only hundreds of atoms at one time. Now, we’re up to hundreds of thousands and some researchers examine million atom systems. Even so, a million atom system is still very small relative to the length scales we interact with daily; we’re also limited by time scale, currently around hundreds of nanoseconds. As a field, atomistic molecular modeling is in its infancy. We’re still learning and, at the same time, trying to produce fundamental insights that are valuable for experimentalists.
Q: You mentioned that you were a process engineer for URS Energy and Construction for three years. What made you decide to leave industry for a career in academia?
Well, as engineers, we’re trained to build things and design solutions. After receiving my Ph.D., I thought I wanted to do something more hands-on. Because my background was in computational modeling, I had never really had the wet lab experience. So as a process engineer, I tried the opposite end of the modeling scale and pursued designing full-scale industrial facilities. After a while, I found that there were limited opportunities for me to express creativity. As a process engineer, you build things the way you’ve always built them—according to tight, specific guidelines. Of course, that is the safe way to design processes. I felt I needed to get back to something where I had an opportunity to focus on what I was interested in, follow my curiosity, namely computational modeling.
Q: How do you like Lexington?
It’s a great city. It’s a great environment and there are a lot of sights. Because I’ve been busy getting started as an assistant professor, I have only been able to see Red River Gorge. But since I have arrived here, everyone has been friendly and the food is very good!