How do cells process information? A conversation with Jeremy Gunawardena
- Mar 28
- 6 min read
Researchers from institutions across the United States are contributing to innovative projects at the NSF-Simons National Institute for Theory and Mathematics in Biology. These research projects focus on developing mathematical frameworks that illuminate emergent capabilities of biological systems. The NSF-Simons NITMB is developing the theory and mathematics needed to highlight the fundamental roles of physical, chemical, and biological constraints as organizing principles for understanding biological mechanisms. In order to highlight the diversity of experts developing NITMB Supported Research and the significance of their contributions, we will share insight into our growing community of researchers as part of the NITMB Spotlight series.
Jeremy Gunawardena is an associate professor in the Department of Systems Biology at Harvard Medical School. The Gunawardena Lab explores information processing in mammalian cells. Gunawardena is also a principal investigator for the NITMB-supported research project, ‘Thermodynamic complexity of biological Markov processes.’
We spoke with Jeremy Gunawardena to learn more about his work with cell information processing, and his hopes for the future of mathematics and biology.
What is a big question you’ve been asking throughout your research?
“My lab has always been focused on the question of how cells process information, but that rather broad question has turned into a different question driving the things we think about in the lab. It goes back to a comment made by Albert Einstein. The comment by Einstein was that theory decides what we can observe. Einstein was commenting on a transition that physics went through in the early part of the 20th century. The question for us is whether biology is going through, or will go through, a similar transition. My view is that it will. In fact, some areas of biology already have. The question is, what kinds of mathematical theory do we need in order to make this transition possible? How do we use theory to 'get behind the data?' This has become the central focus of a lot of the work in my lab.”
What disciplines does your research integrate?
“I’m a pure mathematician by training. Although we don’t use a great deal of pure mathematics in our current research, we use a little. For instance, graph theory. Being a pure mathematician has greatly influenced the questions I’ve been drawn to in thinking about biology. So for me, that remains a very important part of how we think. At the same time, my lab has always been an experimental lab. We have used a number of different experimental techniques in biology, primarily around cells, cultures, fluorescence microscopy, biochemistry, and genetics. In recent years, the work has become more in collaboration with people who generally do it better than we can, and so the lab has become more theory focused. But it’s really an integration of mathematics and experimental biology.”
Where do you find inspiration?
“Two sources. One has been teaching. I find there’s nothing better than getting in front of a class and trying to explain something to them to realize that you don’t actually understand it yourself. I think that’s been a source of considerable stimulation. But also, one of the things I’ve found as a mathematician who’s come into biology is that it’s actually quite hard to understand why biologists do what they do because they very rarely talk about their underlying assumptions. Those are often taken for granted and learned through a process of osmosis. I’ve found it extremely useful to go back into the historical literature of biology and try to unearth why it is that biologists in the current time are thinking the way they think.”
What aspects of your research could be interesting to mathematicians or applied to biology?
“The questions we think about are very much driven by biology. They’re not always framed in a way that biologists would find comfortable, but I think they’re important. I also think that in trying to get behind the data, what we encounter are new kinds of mathematical theorems in many cases. If not theorems, then conjectures about theorems, often conjectures we can be very confident about because we have a lot of numerical evidence for them, but they seem to require new ways of thinking in order to be proven mathematically. One of the things I’m very hopeful about in terms of the creation of NITMB is that there will be more of a community of people who might be interested in addressing some of those mathematical questions.”
What about the NITMB do you find exciting?
“I was very excited by the creation of the NITMB because I think I’ve always felt that bringing together mathematics and biology is important for the future of both disciplines. The thing I hope the NITMB will allow us to do is get beyond where we currently are in biology. Modern biology is essentially driven by data. It generates data at ever increasing amounts, and it’s very convenient for biologists to have mathematicians, physicists, engineers, or computer scientists to analyze the data. There are many interesting intellectual challenges in confronting very complex data in biology, and new tools are needed. But if that’s all there is to it, I would be profoundly disappointed because, going back to Einstein’s comment, we really should be thinking about getting to a point where theory drives the kinds of experiments we do and the kinds of interpretations we place on data. My hope is that NITMB will be a locus for helping that transition take place.”
What career achievement are you most proud of?
“My career has been a bit nonstandard. I spent a lot of time in industry before coming back to academic life. I set up industrial academic collaborations like the Basic Research Institute in the Mathematical Sciences, which I think was quite unusual for its time. But if you come to my more current trajectory in biology, I think the achievements are primarily the papers we publish. On the theory front, I feel the development of the graph-theory based 'linear framework' has been on of our most influential contributions. On the experimental front, we published a paper in Current Biology a few years ago which resurrected some experiments that were done 120 years ago on very complex behaviors in single-celled eukaryotes. Those experiments had basically been forgotten about or thought to not be reproducible. What we did was show that they were, in fact correct, and that this is one of the most complex forms of behavior known in a single-celled organism. I think this paper had the most profound impact on the way we think subsequently. It started a lot of new work in my lab thinking about the question of how cells learn.”
Outside of your research, what other interests do you have?
“I read a lot. I suppose the thing I spend a lot of time doing is playing tennis. After not playing for many years, I now try to do that every day, so that occupies quite a lot of time.”
What are you hoping to work on in the future?
“The question of cellular learning that I mentioned earlier has become a very interesting theme in the lab. It’s part of this question of how cells process information. It’s also addressing the question of how we get behind the data. Part of that is we have to rethink what it is that cells are actually doing, and part of what they do is learn from their environments. That’s become a very interesting collaboration with people in cognitive science and psychology, taking ideas from those fields and working on them in the cell. We’ve just recently been awarded a European Research Council Synergy grant to work on this question of cell learning. That’s the exciting thing that’s on the horizon.”
Is there anything else you would like the NITMB community to know about you?
“I’m very interested in talking to pure mathematicians, particularly those who are drawn to biology because I think there’s a very interesting space that needs to be explored. I’m hoping that might emerge from interactions with NITMB.”
More information on Jeremy Gunawardena’s work is available on the Gunawardena Lab website. Gunawardena is also available to discuss his work and answer questions over email.
