Combining Smart Material Platforms and New Computational Tools to Investigate Cell Motility Behavior and Control

Cell-extracellular matrix (ECM) interactions play a critical role in regulating important biological phenomena, including morphogenesis, tissue repair, and disease states. In vivo, cells are subjected to various mechanical, chemical, and electrical cues to collectively guide their functionality within a specific microenvironment. To better understand the mechanisms regulating cell adhesive, differentiation, and motility dynamics, researchers have developed in vitro platforms to synthetically mimic native tissue responses. While important information about cell-ECM interactions have been revealed using these systems, a knowledge gap currently exists regarding how cell responses in static environments relate to the dynamic cell-ECM interaction behaviors observed in vivo. Advances at the intersection of materials science, biophysics, and cell biology have recently enabled the production of dynamic ECM mimics where cells can be exposed to controlled mechanical, electrical or chemical cues to directly decouple cell-ECM related behaviors from cell-cell or cell-environmental factors. Utilization of these dynamic synthetic biomaterials will enable discovery of novel mechanisms fundamental in tissue development, homeostasis, repair, and disease. In this dissertation, the primary goal was to evaluate how mechanical changes in the ECM regulate cell motility and polarization responses. This was accomplished through two major aims: 1) by developing a modular image processing tool that could be applied in complex synthetic in vitro microenvironments to asses cell motility dynamics, and 2) to utilize that tool to advance understanding of mechanobiology and mechanotransduction processes associated with development, wound healing, and disease progression. Therefore, the first portion of this thesis (Chapters 2 and 3) dealt with proof of concept for our newly developed automated cell tracking system, termed ACTIVE (automated contour-based tracking for in vitro environments), while the second portion of this thesis (Chapter 4-7) addressed applying this system in multiple experimental designs to synthesize new knowledge regarding cell-ECM or cell-cell interactions. In Chapter 1, we introduced why cell-ECM interactions are essential for in vivo processes and highlighted the current state of the literature. In Chapter 2, we demonstrated that ACTIVE could achieve greater than 95% segmentation accuracy at multiple cell densities, while improving two-body cell-cell interaction error by up to 43%. In Chapter 3 we showed that ACTIVE could be applied to reveal subtle differences in fibroblast motility atop static wrinkled or static non-wrinkled surfaces at multiple cell densities. In Chapters 4 and 5, we characterized fibroblast motility and intracellular reorganization atop a dynamic shape memory polymer biomaterial, focusing on the role of the Rho-mediated pathway in the observed responses. We then utilized ACTIVE to identify differences in subpopulation dynamics of monoculture versus co-culture endothelial and smooth muscle cells (Chapter 6). In Chapter 7, we applied ACTIVE to investigate E. coli biofilm formation atop poly(dimethylsiloxane) surfaces with varying stiffness and line patterns. Finally, we presented a summary and future work in Chapter 8. Collectively, this work highlights the capabilities of the newly developed ACTIVE tracking system and demonstrates how to synthesize new information about mechanobiology and mechanotransduction processes using dynamic biomaterial platforms

Image-based Material Editing

Photo editing software allows digital images to be blurred, warped or re-colored at the touch of a button. However, it is not currently possible to change the material appearance of an object except by painstakingly painting over the appropriate pixels. Here we present a set of methods for automatically replacing one material with another, completely different material, starting with only a single high dynamic range image, and an alpha matte specifying the object. Our approach exploits the fact that human vision is surprisingly tolerant of certain (sometimes enormous) physical inaccuracies. Thus, it may be possible to produce a visually compelling illusion of material transformations, without fully reconstructing the lighting or geometry. We employ a range of algorithms depending on the target material. First, an approximate depth map is derived from the image intensities using bilateral filters. The resulting surface normals are then used to map data onto the surface of the object to specify its material appearance. To create transparent or translucent materials, the mapped data are derived from the object\u27s background. To create textured materials, the mapped data are a texture map. The surface normals can also be used to apply arbitrary bidirectional reflectance distribution functions to the surface, allowing us to simulate a wide range of materials. To facilitate the process of material editing, we generate the HDR image with a novel algorithm, that is robust against noise in individual exposures. This ensures that any noise, which would possibly have affected the shape recovery of the objects adversely, will be removed. We also present an algorithm to automatically generate alpha mattes. This algorithm requires as input two images--one where the object is in focus, and one where the background is in focus--and then automatically produces an approximate matte, indicating which pixels belong to the object. The result is then improved by a second algorithm to generate an accurate alpha matte, which can be given as input to our material editing techniques

Computational Morphodynamics: A Modeling Framework to Understand Plant Growth

Computational morphodynamics utilizes computer modeling to understand the development of living organisms over space and time. Results from biological experiments are used to construct accurate and predictive models of growth. These models are then used to make novel predictions that provide further insight into the processes involved, which can be tested experimentally to either confirm or rule out the validity of the computational models. This review highlights two fundamental challenges: (a) to understand the feedback between mechanics of growth and chemical or molecular signaling, and (b) to design models that span and integrate single cell behavior with tissue development. We review different approaches to model plant growth and discuss a variety of model types that can be implemented to demonstrate how the interplay between computational modeling and experimentation can be used to explore the morphodynamics of plant development

A real-time neural system for color constancy

A neural network approach to the problem of color constancy is presented. Various algorithms based on Land's retinex theory are discussed with respect to neurobiological parallels, computational efficiency, and suitability for VLSI implementation. The efficiency of one algorithm is improved by the application of resistive grids and is tested in computer simulations; the simulations make clear the strengths and weaknesses of the algorithm. A novel extension to the algorithm is developed to address its weaknesses. An electronic system that is based on the original algorithm and that operates at video rates was built using subthreshold analog CMOS VLSI resistive grids. The system displays color constancy abilities and qualitatively mimics aspects of human color perception