CAREER: A Dynamical Systems Framework for Systemic Inflammatory Response

About This Grant

Systemic inflammatory responses are dramatic immune flares that can be triggered by causes such as infection and surgical trauma. These inflammatory responses can be lethal. The goal of this project is to develop a mathematical framework to describe these responses, using a combination of experiments in a model biological system (zebrafish) and novel Artificial Intelligence (AI) and Machine Learning (ML) tools. This project will use methods from genomics to trace the organism’s immune response and develop computational tools to simulate these responses and design interventions to alter their course. This work contributes to biotechnology via development of high-throughput quantitative genomics methods. The project will also develop new computational tools that contribute to the integration of AI with traditional analytical methods in physics and biology. The project has broader impacts on researchers in adjacent fields, including the biomedical research community. The investigators will also engage in educational initiatives that strengthen the future scientific workforce, especially at the expanding interfaces of physics, computational sciences, and biology. Systemic inflammatory responses are dramatic, organism-wide immune flares, characterized by high levels of immune signaling proteins (“cytokine storms”) and infiltration of immune cells into tissues and organs. A unifying feature of these responses is that they are driven by interactions within the immune system itself, as opposed to external stimuli. It remains a major challenge to understand how these apparent runaway responses emerge from interacting components. While immunologists have long discussed immune states, we lack concrete characterizations of the phase space. Thus, many fundamental questions remain open: to what extent are responses canalized onto predictable paths? What variables control key tipping points to runaway responses? The project will combine theory with experiments in larval zebrafish to develop a dynamical systems framework for systemic inflammatory response in this tractable model system. In Aim 1, the investigators will perform high-throughput RNA sequencing-based measurements to map phase portraits of systemic inflammatory responses. In Aim 2, the investigators will use machine learning to model the responses and test interventions in silico. In Aim 3, the investigators will use live imaging zebrafish to trace response dynamics and test model predictions. This project will yield the first measurements of the basins and attractors governing the behavior of systemic inflammatory responses and provide a predictive mathematical framework for altering the course of these responses. This project integrates broader impacts in both research and education. The project will provide proof-of-principle for a dynamical systems framework that enables prediction and manipulation of systemic inflammatory responses. This framework can be applied by researchers in adjacent fields, including the biomedical research community, to human disease, ultimately improving societal well-being. The investigators will also broaden and strengthen participation in the physics major at the collegiate level via two initiatives: developing collaborative learning sections for the University of Chicago Honors Introductory Physics sequence and integrating physics of living systems problems into our first-year curriculum. These initiatives will strengthen retention and training for physics students in our physics major and introduce students to the power of physics for biological systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.