2026 National Institute for Theory and Mathematics in Biology Annual Meeting

The third NITMB annual meeting at the Simons Foundation was held on April 2–3, 2026. It was a wonderful opportunity for mathematicians and biologists to convene and learn about important new advances in mathematical biology. Sponsored by a partnership between the Simons Foundation and the National Science Foundation, NITMB was founded in 2023 as the hub for scientists working at the interface between mathematics and biology. The institute promotes bold science at the intersection of these two fields and aims to discover new mathematics emerging from biology. It supports a broad variety of convening programs and research to advance our understanding of living systems.

Nearly 100 people attended the meeting in person. The gathering brought together pure and applied mathematicians, computer scientists, theoretical physicists, and empirical biologists. Attendees represented over 40 different institutions. The meeting featured eight talks highlighting the research sponsored by the institute, all enthusiastically received with questions and engagement. The agenda also included multiple poster sessions shared by trainees.

Alasdair Hastewell (National Institute for Theory and Mathematics in Biology), an NITMB fellow, kicked off the meeting in great style by presenting his work on biological dynamics using data-driven modeling. Dr. Hastewell introduced a framework combining geometry-aware spectral mode representations with wavelet analysis and model selection. He demonstrated the flexibility of this approach by applying it to tracking data on cilia dynamics in single-celled algae and also on the jump takeoff kinematics in Amazonian jumping spiders. He used differential algebraic techniques to quantify indistinguishable models. This has significant implications for making model selection algorithms more reliable for real biological data.

Sergei Maslov (University of Illinois at Urbana-Champaign) spoke about crossfeeding dynamics in energy-limited and auxotrophic microbial communities. Microorganisms often survive by sharing nutrients. Dr. Maslov detailed two distinct modeling approaches to understand this process. First, he shared a thermodynamic consumer-resource model for slow-growing communities in energy-limited environments. This model successfully predicts functional convergence in anaerobic digesters and shows how crossfeeding networks stabilize. Second, he presented a higher-order interaction model for auxotrophic communities that depend on essential resource exchange. Using graphical and algebraic methods, his team revealed how amino acid crossfeeding creates networks that enhance community resilience. Applied to experimental data, this model accurately predicted the survival of specific strains in a synthetic community of Escherichia coli.

Jasmine Nirody (University of Chicago) discussed adaptive bacterial motility across species and scales. Bacteria navigate complex environments. They swim through fluids of varying viscosity and interact with surfaces and other bacteria. Dr. Nirody explained how bacteria utilize adaptive dynamics at the level of whole-cell swimming behavior and the molecular machine underlying swimming. Using microfluidic experiments and mathematical modeling, she demonstrated different bacterial species that tune their motility strategies to move through their environments. She also characterized the mechanosensitive remodeling in bacterial flagella that facilitates movement through dynamic mechanical spaces. Using magnetic tweezers to manipulate external torque, her work illustrates how this nanomachine allows bacteria to adapt to changes in their surroundings. Her results reveal conserved principles of adaptive motility across bacterial species.

Danny Abrams (Northwestern University) shared his research on the modeling and analysis of synchronous signaling in biological systems. Dr. Abrams focused on two species where synchronous signaling emerges during mating. These are fireflies that synchronize their flashes and fiddler crabs that synchronize the waving of their major claws. The individuals remain predominantly immobile during synchronization, making their behavior trackable and tractable. This allows researchers to pose mathematical models and test them against observations. The work yields insight into the mechanisms of biological synchrony and provides important clues about its adaptive value.

Andrea Liu (University of Pennsylvania) presented insights into global epistasis in proteins from tunable matter. Proteins have long been modeled as mechanical networks of nodes connected by springs. Dr. Liu explained her work on the inverse problem by starting with mechanical networks and tuning their properties to introduce protein function. The focus of her talk was on allostery, where the binding of a small molecule to a protein triggers a conformational change that enables the binding or unbinding of another molecule. Her work establishes a relationship between structure and function and gains insight into global epistasis.

The second day of the meeting began with Stefano Allesina (University of Chicago) celebrating one hundred years of Lotka–Volterra models. Dr. Allesina provided a gorgeous overview of the history of this model and traced how it has shaped our understanding of ecological dynamics. He also highlighted how Lotka–Volterra models serve as a common framework linking ecology, evolutionary biology, and infectious disease dynamics. These features have attracted scientists for a century, and even though the models are well-understood, new mathematical problems have recently been discovered.

Mercedes Pascual (New York University and the Santa Fe Institute) presented her work toward a theory of strain hyper-diversity in host-pathogen systems. Growing genomic evidence reveals large strain diversity within populations of microbes encoded by multigene families and accessory genomes. Dr. Pascual contrasted the biology of pathogens whose vast strain diversity is defined in large combinatorial spaces of antigenic variation. Relying on the malaria parasite as an example, she presented results of a stochastic computational model on the role of negative frequency-dependent selection in strain diversity. Using a simplified analytical model, she argued that the feedback of ecology and evolution generating a large trait space matters significantly to system resilience.

The meeting was concluded by Lior Pachter (California Institute of Technology) who spoke on the systems biology of a single cell. Dr. Pachter discussed recent computational and technological advancements that allow researchers to investigate the complex biological processes occurring at the individual cell level. His presentation highlighted the importance of rigorous single-cell analysis for understanding broader biological systems and the mathematics behind it.

Danny Abrams
Northwestern University

Modeling and Analysis of Synchronous Signaling in Biological Systems

Self-organization is a hallmark of biological systems. It is characterized by the spontaneous appearance of collective order that arises merely from local interaction among individuals—no leader is needed. In this talk, I will focus on two particular species where synchronous signaling emerges in the context of mating: Pteroptyx malaccae fireflies, which synchronize their flashes, and Austruca perplexa fiddler crabs, which synchronize the waving of their major claws. In these two systems, the individuals remain predominantly immobile while synchronizing, making their behavior both trackable and tractable. This allows us to pose mathematical models and test them against observations, yielding insight into the mechanisms by which biological synchrony can occur and important clues about its adaptive value.
 

Stefano Allesina
University of Chicago

One Hundred Years of Lotka-Volterra Models
View Slides (PDF)

Over the past century, the Generalized Lotka–Volterra (GLV) model has grown from a mathematical curiosity to a cornerstone of theoretical ecology. Here we celebrate the enduring legacy of Lotka and Volterra by tracing how their model has progressively shaped our understanding of ecological dynamics, even as fundamental questions remain open. Along this trajectory, the GLV model has emerged as a common framework linking ecology, evolutionary biology, and infectious disease dynamics, and as a bridge between theory and experiment. Its distinctive balance of tractability and realism has made it a natural arena for studying complex phenomena such as ecological assembly and the behavior of large communities. The same features have attracted researchers from mathematics and physics, sparking advances such as the analysis of GLV models with random parameters. By weaving together these developments and highlighting future directions, we aim to guide the GLV model into its next century.
 

Alasdair Hastewell
National Institute for Theory and Mathematics in Biology

Discovering Insights Into Biological Dynamics Using Data-Driven Modeling
View Slides (PDF)

Living organisms are complex, multi-scale, spatiotemporal dynamical systems. Recent advances in quantitative live imaging enable tracking biological dynamics at unprecedented resolution, yet challenges remain, as important variables are often unmeasured or noisy. To translate high-dimensional, dynamic biological imaging data into interpretable low-dimensional representations and mechanistic models, we introduce a framework that combines geometry-aware spectral mode representations with wavelet analysis and model selection. I will demonstrate the approach’s flexibility by applying it to tracking data on cilia dynamics in single-celled algae and to jump takeoff kinematics in Amazonian jumping spiders to characterize the behavioral complexity and mechanisms of locomotion. When not all important dynamical variables are observed, the applicability of these model-discovery methods is challenged by model non-uniqueness. Using differential algebraic techniques, we quantify the set of indistinguishable models, which has implications for mechanistic insights from data and for strategies to make model selection algorithms robust to real biological data.
 

Andrea Liu
University of Pennsylvania

Insights Into Global Epistasis in Proteins From Tunable Matter

Proteins have long been modeled as mechanical networks of nodes connected by springs. We have studied the inverse problem, of starting with mechanical networks and then tuning their properties to introduce protein function. We focus on allostery, where binding of a small molecule to the protein triggers a conformational change that enables binding or unbinding of another small molecule. Here I will describe what our approach brings to protein allostery, including a relation between structure and function and insight into the phenomenon of global epistasis, where the non-additivity of mutations can have a global contribution, arising from the cost landscape in which the mutation occurs.
 

Sergei Maslov
University of Illinois at Urbana-Champaign

Crossfeeding Dynamics in Energy-Limited and Auxotrophic Microbial Communities

Microorganisms in many environments survive by sharing nutrients with each other in a process called crossfeeding. Some microbes are extremely slow-growing (taking weeks or years to divide instead of hours), while others are auxotrophs that have lost the ability to make essential nutrients and must obtain them from neighboring species. I will describe two distinct modeling approaches for understanding crossfeeding: (1) a thermodynamic consumer-resource model for slow-growing communities in energy-limited environments (George, Wang & Maslov, ISME J, 2023) and (2) a higher-order interaction model for auxotrophic communities dependent on essential resource exchange (Wang & Maslov, Cell Systems, in press).

The thermodynamic model is applicable to slow-growing communities in which metabolic byproducts from reactions close to thermodynamic equilibrium create crossfeeding networks. We derive the principle of maximum free energy dissipation in this model and demonstrate how functional convergence emerges despite taxonomic differences in crossfeeding partnerships. The model successfully predicts functional convergence in anaerobic digesters, showing how crossfeeding networks stabilize at low dilution rates.

The auxotroph model examines communities where organisms are genetically unable to synthesize essential nutrients (e.g. amino acids) and must rely on crossfeeding from other species. Using graphical and algebraic methods, our approach reveals how amino acid crossfeeding creates higher-order interaction networks that enhance community resilience to environmental fluctuations through metabolic complementarity. Applied to experimental data from synthetic E. coli communities, the model accurately predicted the survival of 3 out of 4 strains in a 14-member auxotroph community, correctly identifying key mutualistic crossfeeding partnerships.
 

Jasmine Nirody
University of Chicago

Adaptive Bacterial Motility Across Species and Scales

Bacteria live complicated lives: they swim through fluids of varying viscosity as well as interact with surfaces and other bacteria. We explore how bacteria make use of adaptive dynamics at both the level of whole-cell swimming behavior and at the level of the molecular machine underlying swimming, the bacterial flagellar motor. (1) Observed bacterial motility patterns are a convolution of the underlying control program and constraints from the environment. Using microfluidic experiments and mathematical modeling, we demonstrate how various bacterial species have tuned their motility strategies facilitate movement through their environments, from ‘run-and-tumble’ in the gut-dwelling E. coli to ‘push-pull-wrap’ in the squid symbiont Vibrio fischeri. (2) We characterize mechanosensitive remodeling in bacterial flagella that facilitates movement through complex, dynamic mechanical environments. We use magnetic tweezers to manipulate external torque on the bacterial flagellar motor, and present a model for the dynamics of load-dependent assembly in the flagellar motor. We illustrate how this nanomachine allows bacteria to adapt to changes in their surroundings. Together, our results reveal conserved principles of adaptive motility across bacterial species and length scales.
 

Lior Pachter
California Institute of Technology

The Systems Biology of a Single Cell
 

Mercedes Pascual
New York University and the Santa Fe Institute

Toward a Theory of Strain Hyper-Diversity in Host-Pathogen Systems
View Slides (PDF)

Growing genomic evidence is revealing large strain diversity within populations of microbes encoded by multigene families and accessory genomes. One process of interest in explaining diversity patterns is competition for hosts via cross-immunity, which sets the stage for negative frequency-dependent selection (NFDS, an advantage of the rare and a disadvantage of the common) in transmission dynamics. Starting with literature results on the emergence of niches in mathematical models of competition for fixed and typically low trait spaces, I contrast the biology of pathogens whose vast strain diversity is defined in large combinatorial spaces of antigenic variation. I rely on the malaria parasite Plasmodium falciparum under high transmission as an example for this purpose, and present results of a stochastic computational (agent-based) model on the role of NFDS in strain diversity and its structure. With a simplified, more analytical tractable PDE model, I argue that the feedback of ecology and evolution which generates a large trait space matters to system resilience. I end with some potential implications and generalizations of these ideas to other eco-evolutionary systems.

Watch a playlist of all presentations from this meeting here.