Mechanical Information Processing from Sub-cellular to Tissue Scales
- Institution
- University of Chicago, IL
- Principal investigator
- Margaret Lise Gardel
- Funding
- $542K
- Duration
- 2025–2029
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Abstract
Project Summary Mechanical Information Processing from Sub-cellular to Tissue Scales The mechanical behaviors of cells control diverse physiological processes including cell proliferation, migration, fate and apoptosis; these behaviors are also key contributors to pathologies such as cancer and cardiovascular diseases. Within multicellular tissues, cell mechanics are largely determined by feedback mechanisms, which coordinately regulate the actin cytoskeleton, cell-matrix adhesions, and cell-cell adhesions. It has become evident that cytoskeletal assemblies are complex mechanochemical systems that employ force-sensitive biochemical regulation to maintain and modulate mechanical response. While the basic architectures of these systems have been suggested, approaches are needed to understand their force-sensing and responses. This will elucidate the mechanical design principles of the cytoskeleton, enabling novel methods for the engineering of a cell or tissue’s shape and dynamics. To address this challenge, my research program leverages an innovative combination of analytical methods: cell biophysics, molecular cell biology, live cell imaging, mathematical modeling, and optogenetics, allowing us to investigate the machines and materials constructed within the cytoskeleton of adherent cells. Our goals over the next five years are to expand the understanding of adherent cell mechanics and mechanobiology. We will explore the underpins of the force-sensitive remodeling of cell-cell adhesions and the role it plays in contact inhibition of proliferation. We will also investigate mechanisms of mechanosensing via LIM domain proteins, a new class of force-sensors within the actin cytoskeleton. We will develop new experimental approaches with which to probe the mechanochemical circuitry within cells, including establishing a new field of data-driven biophysics modeling. Thus, our work will broadly impact the field of cellular biophysics and mechanobiology.
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