New 2025 External Project Grants Awarded

The NSF-Simons National Institute for Theory and Mathematics in Biology is proud to announce the Institute has awarded funding to support six new external research projects. These projects are developing mathematical frameworks that illuminate emergent capabilities of biological systems. Additional details, including project descriptions, are available on the Supported Research page.

Non-equilibrium nucleation in antagonistic microbial communities

Principal Investigators: Andrea Giometto (Assistant Professor, School of Civil and Environmental Engineering at Cornell University) & Giulio Biroli Full (Professor of Theoretical Physics at École Normale Supérieure)

Abstract: Nucleation, the emergence of a new phase within a surrounding medium, is a fundamental process across the physical sciences, but its role in shaping biological systems remains underexplored. This project investigates nucleation in microbial communities where antagonistic interactions, such as toxin production, create ecological analogs of surface tension and energy barriers. These interactions can give rise to critical inoculum sizes below which invading strains fail to establish, a clear signature of nucleation dynamics. By combining mathematical modeling with synthetic microbial ecosystems, the project will study how nucleation barriers and metastable states emerge from microbial interactions. We will examine how the critical inoculum size depends on interaction strength, diffusion, and growth rates, and explore nucleation behavior in communities with more than two antagonistic strains. The work will also incorporate engineered systems that combine antagonism with cross-feeding, enabling controlled exploration of richer, non-classical nucleation dynamics. This project aims to develop microbial nucleation as a tractable model system for studying non-equilibrium phenomena in biology, and to advance mathematical understanding of out-of-equilibrium systems where no underlying free energy function governs the dynamics. The results have the potential to deepen our understanding of ecological pattern formation, stability, and control.

Representing mitochondrial dynamics with abstract algebra

Principal Investigators: Wallace Marshall (Professor of Biochemistry and Biophysics at the University of California, San Francisco) & Moumita Das (Professor, School of Physics and Astronomy, Rochester Institute of Technology)

Abstract: Our cells get their power from mitochondria, tiny structures inside cells that produce chemical energy and control many other cellular processes like cell death. Although often depicted as bean-shaped objects, in most cell types, mitochondria form complex, branching networks. This network structure is thought to help to transport energy-carrying chemicals throughout the cell, and also to help ensure that the mitochondria can be transmitted to daughter cells when the cell divides. Perhaps the most exciting feature of mitochondrial networks is that they are constantly changing. Protein machines in the cell can cut branches of the mitochondria, cause existing branches to resorb, form new branches, and glue existing branches together. It is usually assumed that these processes, working together, give rise to the networks structures that are observed, but we lack a clear understanding of how this happens. For example, we would lik eto be able to predict how the network structures would change if the rate of one or more of these basic processes became faster or slower. A further question is whether the different processes occur randomly, or are they coordinated in some way. Answering this type of question falls into the domain of graph theory, the branch of math that deals with the structure of networks. Each of the processes like breakage or outgrowth of a branch can be represented as making changes to a graph. The problem is that for a complex network, there can be many different ways of applying the same process at different positions on the graph, which create different results. It becomes extremely difficult to keep track of all the possible outcomes. To get around this problem, we are developing a simpler way to represent the network structures of mitochondria, using methods of abstract algebra, a branch of math that has played an important role in theoretical physics but has seen very little application in cell biology. We expect that by developing this simplified representation, we will be able to make inroads into the question of how mitochondria networks are formed. One outcome we hope to achieve is to explain why in some cells, such as the popular model organism budding yeast, the mitochondrion tends to consist of a single large network together with a few much smaller networks. A second outcome is to develop a new way to compare how similar two mitochondrial networks are to each other, which can be the basis for a new way of classifying cells into different cell types based on their mitochondrial networks. But the biggest outcome, we hope, will be a new way to ask about the statistical distribution of networks that should exist in a cell, given the rates of the different processes that alter network structure. This work will be carried out by two professors: Wallace Marshall at the University of California San Francisco (UCSF), and Moumita Das at Rochester Institute of Technology (RIT), working together with a postdoctoral fellow Ximena Garcia Arceo at UCSF and two undergraduate students, one at UCSF and one at RIT.

Mathematical Frameworks for Stability, Evolution, and Control in Microbial Communities

Principal Investigators: Sergei Maslov (Professor of Bioengineering and Physics at the University of Illinois Urbana-Champaign) & Seppe Kuehn (Associate Professor of Ecology and Evolution at the University of Chicago)

Abstract: This project explores how microbial communities evolve, respond to environmental changes, and how they can be effectively manipulated and controlled—questions that are central to understanding and engineering ecosystems, improving health, and advancing biotechnology. Our team will develop novel mathematical frameworks to address these challenges, drawing on tools from stable matching theory, control theory, and topology. These methods will help predict when microbial communities remain stable or shift to new states, and how they adapt their metabolic strategies over time. The ultimate goal is to design interventions that steer these communities toward desired outcomes, such as preventing ecological collapse or optimizing microbiomes for health or agriculture. The research is led by Prof. Sergei Maslov (University of Illinois Urbana-Champaign) and Prof. Seppe Kuehn (University of Chicago), with UIUC Physics Ph.D. student Zihan Wang contributing mathematical modeling and co-authoring several foundational studies on microbial ecosystems.

Learning the Waddington Seascape of Immune T-cell Development and Response

Principal Investigator: Armita Nourmohammad (Associate Professor of Physics at the University of Washington)

Abstract: An individual’s immune response emerges from an interplay of signaling molecules and antigen-recognizing cells to efficiently target and eliminate pathogens. Antigens and inflammatory cues guide immune cell activation, proliferation, memory formation, and death––yet despite our ability to track the phenotypic trajectories of  immune cells in high dimensions, we lack predictive models for how these interactions collectively shape immune outcomes. The classic Waddington "landscape," which portrays cell fate as a downhill slide toward fixed developmental attractors, cannot capture how the dynamic cell-to-cell communications continuously reshape the terrain during immune decision-making. We therefore propose a "Waddington seascape," in which the developmental landscape itself is fluid, reorganizing as immune cells interact with one another and respond to pathogens or inflammatory signals. This project will develop a theoretical framework, grounded in experimental data, to map out this dynamic seascape for immune T-cell development and response. Central to this effort is the new mathematical synthesis that bridges non-equilibrium statistical physics with optimal transport theory, enabling us to model and interpret the forces that drive such seascape, and reveal transient decision points governing T-cell responses. This predictive approach will expose levers for steering immune trajectories, opening new paths for rational design of targeted interventions in a wide range of immune-based therapies. This project will be led by Darin Momayezi, a graduate student working with Prof. Nourmohammad in the Department of Physics at the University of Washington, in close collaboration with Prof. Altan-Bonnet at the NIH.

Mathematical models for studying stochastic systems biology of the cell with single-cell genomics data

Principal Investigator: Lior S. Pachter (Bren Professor of Computational Biology and Computing and Mathematical Sciences at the California Institute of Technology)

Abstract: Systems biology can be characterized as the study of how biological systems regulate and control randomness to drive cellular growth and function. Recently developed high-throughput single-cell genomics technologies have enabled the quantification of molecular variability at single-cell resolution, providing a powerful platform for modeling the transcriptional and translational programs within cells. This project will focus on the biological question of how stochasticity in the transcription process, whereby DNA is copied into RNA, compares to stochasticity in translation, wherein RNA is converted into protein. Answers to this biological question will provide valuable insights that can guide the development of approaches to control the state of cells. The biology will be addressed via developments in mathematics that center on the chemical master equation. New mathematics that allows for coupling the bursty transcription model to translation models will be developed and implemented. This project will therefore drive forward theory and practice in mathematical modeling of transcription and translation processes in the cell, along with answering fundamental questions about stochasticity using single-cell resolution RNA and protein data. The project will be worked on by Prof. Lior Pachter and PhD student Catherine Felce, both at the California Institute of Technology.

Advancing time scales theory to assess Pd infections in little brown bats

Principal Investigators: Sabrina Streipert (Assistant Professor of Mathematics at the University of Pittsburgh) & David Swigon (Professor of Mathematics at the University of Pittsburgh)

Abstract: White-nose syndrome (WNS) is a devastating disease that has caused major declines in North American bat populations since its discovery in 2006. To support efforts in controlling its spread, we propose developing "Seasonal Transition Models'' (STMs) using time scales theory, which can capture the seasonal patterns of bat reproduction and disease dynamics. Our work exploits a new concept of periodicity on time scales to advance analytical tools for studying these models. The theoretical advancement of time scales theory for STMs is led by PIs S. Streipert and D. Swigon and a dedicated Pitt graduate student. In collaboration with WNS experts, particularly Dr. Kate Langwig (Virginia Tech), and using real-world data on little brown bats, our team - with the assistance of three undergraduate researchers at Pitt - will calibrate and validate the models to assess control management strategies for little brown bats affected by WNS.