As part of our ongoing Meet the NLM Investigators series, it’s my pleasure to introduce you to Nash Rochman, PhD, an Independent Research Scholar in NLM’s National Center for Biotechnology Information (NCBI) Computational Biology Branch and member of the NLM Intramural Research Program.
He recently shared a quote that is a fitting sentiment for the research he does with us: “This present moment used to be the unimaginable future.”
Dr. Rochman’s work is grounded in building a deeper, generalizable understanding of the biological problems we face today so we may be better prepared for what lies ahead. His research focuses on the evolutionary process of host adaptation where a pathogen, such as a virus or bacteria, evolves over time to better fit its present environment while remaining adaptable for when that environment changes. Adaptable pathogens are able to “jump” between host species and evade host immune defenses, including vaccines.
While the human species has faced many past pandemics—and the genome evolution of several pandemics has been studied in great detail—COVID-19 demonstrated that there’s still a lot to learn about how and when these pathogens evolve.
Just look at the many variant forms COVID-19 has taken, from the Alpha variant (B.1.1.7) to the dominant Omicron variant (XBB.1.5) as of late February 2023. Researchers can’t help but wonder where and when the next dominant variant will emerge and what its epidemiological impact will be.
By investigating how viruses adapt using genomic data science and epidemiological modeling, Dr. Rochman and his team are working to bring the unimaginable future, where we can predict and prevent epidemics, a little closer to the present.
Now let’s turn to Dr. Rochman: Learn more about the person behind the research and see what he has to say about his team and their work!
What makes your team unique? Tell us more about the people working in your lab.
I’m currently working with two postbaccalaureate fellows and one high school student. We work on all kinds of science, partly motivated by my interests, partly by theirs. We’re a bit unusual because our breadth is large—from studying optimal soccer strategies to Orthopoxvirus recombination—for a small group of fairly junior students.
What is your advice for young scientists or people interested in pursuing a career in research?
Find something that makes you excited and continue to be focused on your own learning no matter how much of a subject matter expert you become. As long as you are happy with your own work, truly confident that what you do has meaning to you, everything else will come. I’m pretty sure this is what Curly from City Slickers was talking about right?
What do you enjoy about working at NLM?
The NLM Intramural Research Program (IRP) is both a dazzling collection of highly successful labs and a supportive community of kind, encouraging, patient mentors—a rare combination in science today.
What’s your hometown, and where is your favorite place to travel?
I was born in Brooklyn, NYC, but grew up in Ashland, WI, on Lake Superior. It’s a beautiful small town with a vibrant arts community. I frequently travel to western Massachusetts to hike and visit family.
What is a book that you will always recommend?
I’ve most often previously recommended The Most Human Human by Brian Christian. It follows the author as he prepares to participate in a Turing test where he’ll be competing against a chat bot to convince a judge that he is, in fact, the human. The book challenges the reader, in a world that is rapidly adopting tools facilitated by artificial intelligence, to pay attention to the many facets of our lives, however mundane, that are poorly replaced by computers, and in doing so, perhaps we can learn to become more human.
When you’re not in the lab, what do you enjoy doing? I like to compose music, play jazz trumpet, hike, and bake. I’m working on a fusion quintet right now and I’ve been wanting to try Lan Lam’s cranberry curd tart.
You’ve read his words, and now you can hear him for yourself! Follow our NLM YouTube page for more exciting content. If you’d like to learn more about our Intramural Research Program (IRP), view job opportunities, and explore research highlights, I invite you to explore our NLM IRP webpage.
Transcript: [Rochman]* There’s a very old saying in physics that “it’s great to be able to explain, but it’s better to predict.” But it’s also true that it’s impossible to predict without an understanding, without an explanation.
If we’re ever going to have a deeper understanding of any biological system—for example, a relationship between a novel virus and a human host—we’re going to need to have a deeper understanding of host–pathogen relationships with the information we have today.
So I study host-pathogen interactions, and these could be human hosts and viral pathogens like SARS-CoV-2. Host–pathogen relationships involve cancer in the host, or viruses in the host, or bacterial systems in the host. They could be animal hosts and bacterial pathogens. They could even be bacterial hosts and viral pathogens.
And I want to find unifying forces that shape the interactions between hosts and pathogens to predict how the biology of the host can shape the evolution of the pathogen and vice versa. This is important for understanding basic problems in biology. But also, I think this is an interesting area because it provides immediate practical application as well.
If you can find some forces that are shaping viral evolution, you might be able to use that information to help design better vaccines. If you find forces that are shaping bacterial evolution in response to a virus, you might be able to find ways to better invent new antibiotics.
So with more and more data, you can start predicting when the next pandemic will be or what the next pandemic will be. If you can predict it far enough in advance, you could even prevent the first human infection by mitigating the spread of that virus within the intermediate host population.
I want to help build a deeper understanding of well-conserved biological features—so that you can make longer timescale predictions, so you can better respond to biological rare events.
*Transcript edited for clarity.