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ABSTRACT: Imaging data of live cells and tissues encode tremendous spatiotemporal information on molecular transport, interactions, and activities, which are of fundamental importance in biology and medicine. To quantify subcellular diffusion maps based on bio-imaging data, we develop and characterize a general optimization framework with diffusion equation constraints (OPT- PDE). We demonstrate the usability of the solver in recovering spatially heterogeneous and anisotropic diffusion maps with computer-simulated bio-images. The results indicate that the solver can accurately recover piecewise-constant isotropic and anisotropic diffusion coefficients, with efficient and robust convergence. Meanwhile, investigation of dynamic molecular activities in live cells often requires the visualization and quantitation of fluorescence ratio image sequences with high computational efficiency. Hence, we developed an open-source software package, Fluocell, to visualize pixel-wise ratiometric images and calculate ratio time courses with subcellular resolutions in high throughput. The power of Fluocell is demonstrated by the ratiometric analysis of images from fluorescent biosensors based on Förster resonance energy transfer (FRET), allowing efficient quantification of dynamic molecular activities in a heterogeneous population of single live cells.Fluocell was further integrated with the diffusion analysis solver to quantify the relationship between the biochemical and biophysical properties of Lck kinase in single T-cells. The results revealed a crucial amino acid residue Y397 in Lck, which regulates the enzymatic activities of Lck via its physical clustering during T-cell activation. Taken together, our work highlights the power of model-driven analysis in quantitatively inferring biochemical and biophysical signals in live-cell images, with important applications in biomedical sciences.
BIO: Lu is currently a Project Scientist of Bioengineering and Center of Computational Mathematics at UC San Diego. Dr. Lu received a Ph. D. in computational mathematics from UC San Diego (2004). She was a postdoctoral fellow working in the Departments of Bioengineering and Mathematics. She was a research assistant professor (2005-2013) at University of Illinois Urbana-Champaign. She is a recipient of DMS/NIHGMS initiative award at the interface of mathematics and biology. Lu works in the areas of image-driven mathematical and systems biology. She develops computational algorithms and machine learning methods for the high throughput analysis of biomedical imaging data, with applications in T cell immunology and diagnosis of infectious diseases.