We adopt a multi-disciplinary experimental and modeling approach to study mechanobiology: biomedical engineering, molecular biology; cell culture; cell and developmental biology; confocal microscopy; live-cell microscopy; image process/analysis; biophysics; computational mechanics; machine learning; network analysis, data analytics, mathematical and computational modeling; and, spatial statistics.
Cellular Mechanobiology
Strategically located at the interface between blood and the surrounding tissue is a monolayer of around 10 trillion cells of a single type—the endothelium. Although the endothelium was once considered an inert cell layer, it is, instead, a highly complex and metabolically dynamic interface, and primarily involved in mammalian physiology. For example, endothelial cells are involved in: hemostasis; regulation of permeability; control of vasomotor tone; intrinsic and adaptive inflammation; and the production, secretion, and metabolism of biochemical substances. Every square millimeter of the endothelium contains approximately 1000 cells. Each endothelial cell typically has six to eight interconnected neighboring cells. This structural arrangement forms an extensive cellular network. The interconnected network is a complex dynamic signal processing and sensory layer.
We study the mechanisms of endothelial mechanotransduction in the context of health and disease. We seek to identify mechano-sensitive biomarkers of health and disease.
Computational Cell Mechanics
Models are impactful when they are coupled to experiments and help explain nonintuitive and/or emergent properties of biological systems. However, there is often a disconnect between models and experiments: in particular, the cell morphology does not match the conditions of the experiments, or is not measured at all. Morphology and subcellular organization are important determinants of endothelial function. Previous experimental studies have shown that blood flow can alter EC morphology and endothelial gene expression. In addition, ECs cultured into different shapes influence regulation of mechanosensitive transcription factors. Normally either morphology or signaling event is measured. However, both are needed simultaneously to resolve endothelial sensing and signaling. This produces a disconnect between the models, experiments, and the way the endothelium functions. Therefore, we need a new approach. Detailed 3D cellular and subcellular morphology must be included in any modeling and experimental methodologies used to understand how individual endothelial cells in a monolayer convert from one phenotype to another.
We are combining machine learning and finite-element modeling to develop predictive models of cell mechanics that can be coupled with live-cell signaling experiments.
Communication-Signaling Networks
We are studying how endothelial cells sense mechanical input, how the cells set up signaling networks, how the cells communicate with other endothelial cells to decipher complex mechanical signals, and how this signaling/networks breaks down (change) during disease. Since calcium is an important secondary signaling molecule, we are imaging and analyzing intracellular calcium to better understand how the endothelium interprets complex mechanical fluid shear stress input signals.
Funding Sources
