The physiological and biomedical implications of chaos in fluid flow: fundamental theoretical, computational, andexperimental investigations

The National Institute of General Medical Sciences, part of the National Institutes of Health under the Department of Health and Human Services, awarded The Regents of the University of Colorado $410,012 on September 1, 2026, under the Biomedical Research and Research Training program (CFDA 93.859) to investigate the physiological and biomedical implications of chaos in fluid flow.

The research combines computational and experimental methodologies to address two fundamental questions: how chaotic advection organizes and enhances mixing in physiological and biomedical flows, and how cells and proteins sense, integrate, and respond to chaotic stresses and deformations. Computational work develops fast, high-resolution Lagrangian particle-based analysis frameworks leveraging artificial intelligence and machine learning to quantify mixing and stress-strain in chaotic fluid dynamics. Experimental work establishes flow loops with three-dimensional printed phantoms integrated with multi-modal quantitative dye-visualization to generate and quantify controllable chaotic flow states and probe mechanobiological responses of endothelial and circulating cells. The research applies to blood clot formation, endothelial cell function, drug transport, microfluidic and lab-on-chip technologies, and pathogen transmission.

Performance occurs at the Boulder, Colorado campus. The project runs from September 1, 2026, through May 31, 2031. The assistance type is a Project Grant, a category the source identifies as part of NIGMS's prioritized MIRA (Maximizing Investigators' Research Award) program.

Abstract

Chaotic non-linear fluid flows underlie critical processes in human health, including blood clot formation, endothelial cell function, drug transport, microfluidic and lab-on-chip technologies, and pathogen transmission. Despite their broad relevance, physiological and biomedical implications of chaos remain poorly understood. This is because chaotic flow information is challenging to obtain using current imaging techniques, there are presently no widely accepted metrics relating chaotic flows to physiological processes, and abstract mathematical theories of chaos remains difficult to translate into analysis tools for physiological systems. Consequently, we currently lack interpretable quantitative frameworks to unravel the role of chaotic flows in physiological and biomedical systems. The proposed mira research program will address this GAP, by establishing innovative and interpretable descriptors that can quantify and predict how chaotic flow features shape physiological processes. We will specifically investigate two fundamental questions: (a) how does chaotic advection organize and enhance mixing in physiological and biomedical flows? And (b) how do cells and proteins in these flows sense, integrate, and respond to chaotic stresses and deformations? Our approach will combine complementary in silico and in vitro methodologies. For in silico methods, we will develop fast high-resolution lagrangian particle-based analysis frameworks leveraging AI/machine learning to quantify mixing and stress/strain in chaotic fluid dynamics. For in vitro methods, we will devise flow loops with 3D printed phantoms integrated with multi-modal quantitative DYE-visualization, to generate and quantify controllable chaotic flow states, and probe mechanobiological responses of endothelial and circulating cells. Together these activities will generate impactful findings that enable: discerning the role of rheology in chaotic mixing and loading on cells/proteins; developing tools to inform better mixing properties with applications in drug delivery and micro/milli-fluidics; and characterizing the role of chaos in pathological scenarios involving blood damage, platelet activation, and clot formation. Collectively, these efforts will establish fundamental knowledge that will transform our understanding of many pathophysiological processes and inform new biomedical technologies and therapeutic innovations.