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.
Krešimir Josić is a John and Rebecca Moores Professor in the Department of Mathematics at the University of Houston with adjunct appointments in Biology and Biochemistry at the University of Houston, and BioSciences at Rice University. Josić is a contributor to the NITMB-supported research project, ‘Impact of higher order structure on the dynamics of neural networks.’
We spoke with Krešimir Josić to discover more about his NITMB-supported research project, and the impact Josić’s exploration of interactions can have on our understanding of organisms from bacteria to humans.
What is a big question you’ve been asking throughout your research?
“I work in several different areas. I work in synthetic biology, computational neuroscience, and in some aspects of cognitive science, collective behavior in particular. The main question that brings these disciplines together is how groups of similar or dissimilar units interact and collectively bring about behavior that is not apparent just from their individual behaviors. If you have multiple agents interacting, you can get emergent behaviors that are very different from the ones that individuals exhibit. One of the central questions that we have is how to develop equations that capture collective behavior. For example, when you have a collection of interacting neurons, when you have a collection of interacting bacteria that communicate with each other via signalling molecules, as well as more recently, models of human societies where people can observe each other’s decisions, thereby influencing their beliefs and future decisions.”
What disciplines does your research integrate?
“I’m a mathematician by training, and my background is in dynamical systems. Along the way I’ve had to learn about many other things. The tools I use most are probability theory and stochastic processes. And when I work with experimentalists, my group and I also use different data analysis methods and statistics to try to understand data that is often noisy. We use those tools to answer questions in biology, often in collaboration with experimentalists who have particular questions about the observed behaviors of these large groups of agents or units. This is mostly true for synthetic biology and neuroscience. We haven’t done this as much on the collective human behavior side, although I’ve done some work on experimental research in the cognitive sciences where people make decisions on their own without interactions.”
Where do you find inspiration?
“In different places. Certainly as all scientists, we read the works of other scientists and find what is interesting. These different fields tend to move in certain directions and what is really exciting is that since I’ve become faculty, the tools that experimentalists have to observe biological phenomena and manipulate living organisms from molecules to whole organisms have advanced dramatically. The inspiration often comes from an observation or a question from experimentalists.”
What aspects of your research could be interesting to mathematicians or applied to biology?
“I think there are several aspects. One I believe is that we so far have a very incomplete picture of human interactions. The models that we have for collective decision making are very simplified, and they’re simplified for a reason. It is very difficult to understand the nuances that humans take into account when they interact with each other to make decisions. The way we interact in-person versus online, or with friends versus strangers, and so on. All of these are really difficult to capture in models. I think an important question that has not been addressed much is how do we validate these models and how do they need to be extended? What other features do they have to capture about human interactions so that they qualitatively describe what really matters in how humans individually and collectively make decisions as part of different social networks. Similarly, we also talk about bacteria interacting and making decisions. I think that with the current experimental approaches, we will start to understand such interactions better. These are questions that are really important to understand how bacterial communities and consortia interact, grow, and collectively behave in ways that individual strains may not.”
What about the NITMB do you find exciting?
“One of my main areas of research is neuroscience. The research funded by NITMB involves neuroscience. What is important there is that the structure of interactions in a network is very important in determining the behaviors that the network exhibits. The question we’re asking is how these structures determine the dynamics and ultimately computations, and how these computations then translate information that arrives from the periphery into the behavior and responses of these organisms. These questions are going to require a large scale effort, and it’s going to include mathematicians who will provide the theoretical foundation for the work, and experimental collaborators who will pose the questions and explain what the data means. A big institute like the NITMB can play a central role in these large scale questions by bringing together people with different backgrounds and expertise, but similar and overlapping interests. I think NITMB is a really unique place right now in the United States, offering a place where these groups can be brought together, ideas exchanged, and new collaborations started.”
What career achievement are you most proud of?
“I’m most proud of having trained a good number of postdocs and graduate students. All of them have gone on to successful careers in industry and academia. I’ve enjoyed working with them and it’s sad to see them go, but I’m proud of the fact they are going on to successful careers. Many of them are continuing in academia doing related work or branching off into new directions of their own. It’s great to see them develop their own research ideas. Others have gone on to successful careers in industry, some in high positions at different companies. It’s great to see how applicable mathematics is by the breadth of career paths in industry they were able to embark on.”
Outside of your research, what other interests do you have?
“It’s a little strange in Texas since there are no mountains here, but I do like mountain biking. I really like music. I play guitar. I read a lot. I think reading both fiction and nonfiction is a good way to get ideas, even if there might not be a direct connection between what you’re reading and your current research.”
What are you hoping to work on in the future?
“I’m really excited about the NITMB-funded project that we have relating to structure and the dynamics in neural networks. There are new tools in statistical physics and in the theory of random matrices that we hope will be applicable to questions in neuroscience. People have been working on this, but I think there’s a lot more that can be done here. Another question I’m excited about is looking at trying to understand how small groups of humans collectively make decisions. This is difficult to do in an unconstrained environment, but we’re thinking about doing this in virtual reality where we’re able to monitor all the communication that people have between each other. It’s going to be difficult to fine tune to a point where we can get useful answers out of this, but I think it’s an exciting possibility to try to understand collective decision making in humans.”
More information on Josić’s work is available on Google Scholar and on GitHub. Josić is also open to discussing his work further over email.