William F Heinz

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The spatial and temporal changes of the mechanical properties of living cells reflect complex underlying physiological processes. Following these changes should provide valuable insight into the biological importance of cellular mechanics and their regulation. The tip of an atomic force microscope (AFM) can be used to indent soft samples, and the force(More)
An experimental approach for producing relative charge density maps of biological surfaces using the atomic force microscope is presented. This approach, called D minus D (D-D) mapping, uses isoforce surfaces collected at different salt concentrations to remove topography and isolate electrostatic contributions to the tip-sample interaction force. This(More)
During development inherited information directs growth and specifies the complex spatial organization of cells and molecules. Here we show that a new information metric, the k-space information (kSI), captures the growth and emergence of spatial organization in a developing embryo. Using zebrafish as a model, we quantify the rate of development over the(More)
During bacterial chemotaxis, a cell acquires information about its environment by sampling changes in the local concentration of a chemoattractant, and then uses that information to bias its motion relative to the source of the chemoattractant. The trajectory of a chemotaxing bacteria is thus a spatial manifestation of the information gathered by the cell.(More)
The spatial relationships between molecules can be quantified in terms of information. In the case of membranes, the spatial organization of molecules in a bilayer is closely related to biophysically and biologically important properties. Here, we present an approach to computing spatial information based on Fourier coefficient distributions. The Fourier(More)
One powerful approach to understanding how cells process spatially variant signals is based on using micropatterned substrates to control the distribution of signaling molecules. However, quantifying spatially complex signals requires an appropriate metric. Here we propose that the Shannon information theory formalism provides a robust and useful way to(More)
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