Allon Wagner is an assistant professor of computer science in the Department of Electrical Engineering and Computer Sciences, an assistant professor of immunology and molecular medicine in the Department of Molecular & Cell Biology at UC Berkeley, and a member of the Center for Computational Biology. His lab develops data-driven algorithms to analyze single-cell molecular atlases—such as single-cell transcriptomics—and uses these tools to investigate how immune cell metabolism is disrupted in immune-related diseases. The group works closely with experimental biologists to study a range of immune disorders, including cancer, autoimmune diseases, neurological conditions, and infections.
QB3-Berkeley: How do you explain your research to someone outside of science? Are there any real-world problems it aims to solve?
Allon Wagner: Absolutely. When I introduce computational biology to undergrads, I explain that biology used to be a data-poor discipline. Researchers could spend five years on a PhD and end up with just one table of results. But around 2000, omic technologies emerged—methods that allow us to comprehensively profile molecules in cells, like all RNA molecules. These technologies keep improving: Now we can analyze multiple molecule types at single-cell resolution and across large cohorts.
This explosion of data created a need for computational biology and AI in molecular medicine research. At the same time, cellular metabolism—once thought to be a “solved” problem—was rediscovered around 2010 as a vital factor in diseases like cancer and immune disorders. This gave rise to a field called immunometabolism, the study of how immune cell metabolism affects health and disease.
In our lab, we combine computational analysis with single-cell omics to study the metabolism of immune cells in human diseases. And yes, it has very real-world implications. For example, immunotherapy—harnessing the immune system to attack tumors—has become one of the greatest promises for major breakthroughs in cancer treatment. We also study autoimmunity, infectious diseases, and even neuropsychiatric disorders. Since the immune system is involved in almost every health condition, understanding and modulating it could unlock cures—even for conditions once considered incurable.
QB3: How did your academic journey lead you to specialize in this field?
AW: I actually started with a pure computer science background. At the time, I wasn’t thinking about biology at all. But I started feeling disconnected from work that lacked real-world impact. By sheer coincidence, I attended a seminar by Professor Eytan Ruppin—at the time, he was using math to understand bacterial evolution. That talk completely changed my perspective. I spoke with him the next day, switched tracks, and he became my master’s advisor. That was the turning point.
QB3: You mentioned that you transitioned from computer science. What would you say to students thinking about doing the same?
AW: I tell them they absolutely can. I had no background in biology either, and I made the switch. If you’re motivated, it’s very doable and, ultimately, very rewarding.
QB3: What are some of the biggest challenges or most exciting discoveries in your field right now?
AW: The most exciting thing is that everything feels new. The pace of technological development—especially in biotechnology—is incredible. It’s like going from Galileo’s telescope to the James Webb Space Telescope in just four years. We can now measure and observe biological systems in ways we never imagined.
One of the most exciting developments in our field is the emergence of spatial single-cell assays. If before—just a couple of years ago—we could measure all the RNA molecules, for example, in tens of thousands of single cells, now we can do so in a tissue slice. That is, we get the same data, but now we also know where the cells are located with respect to each other. This is crucial because immune cells operate in complex tissues, like cancer, which have clear microanatomical structures that create distinct molecular niches and present distinct immune stimuli.
As for challenges, the main one is that we live in two worlds: computer science and immunology. That means we have to be fluent in both and explain our work to two very different audiences. Staying current with advances in both fields—and making them understandable to collaborators on either side—is a constant challenge.
QB3: What role do collaboration and interdisciplinary work play in your research?
AW: It’s everything. There’s no part of our work that isn’t collaborative or interdisciplinary. I always tell my students: developing a computational method is only half the job. The other half is applying it to new biological data, usually from collaborators in experimental labs. That’s where our work meets the real world—like finding something new about cancer. It’s team science all the way. The days of the lone scientist working in isolation are long gone.
QB3: What advice would you give to someone considering a career in your field or in science more broadly?
AW: That’s a great question. One piece of advice I give students—especially those wondering how to choose a specific path or subfield in computational biology—is this: It almost doesn’t matter which topic you choose. The field is so broad and full of important questions that you’ll do meaningful work no matter what.
What really matters is finding the right people. Collaborators, mentors, and peers—the people you work with will shape your experience and happiness more than the specific project. Focus on finding teams and mentors you enjoy working with. That’s what will make your journey sustainable and fulfilling.