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Show RESEARCH POSTERS ON THE HILL SPRING 2007 113 Microscopic Computed Tomographic Analysis of Phenotypic Defects in Mice with Targeted Disruption of the Hox-d11 Gene Zachary Warnock and Ross Whitaker Bioengineering, Scientific Computing & Imaging and School of Computing Advances in imaging and image analysis software have increased the efficiency of performing quantitative studies of human and animal anatomy. These new techniques provide powerful tools for rapid diagnosis and acquisition of research data for studying disease, development, and genetics. This study applies digital imaging techniques to mouse mutations in order to study the developmental implications for certain genes. In particular we expect to show that a quantification of the phenotypic effects through digital analysis of three-dimensional, microscopic computed tomography images (micro-CT) allows us to describe the effects of disruptions of the hoxd-11 gene in knock-out mice. The strategy is to build 3D models through micro-CT of wild-type mice and mice homozy-gous for the hoxd-11 mutation, and compare the sizes and shapes of those models in order to understand the effects of the mutation. Our findings will be compared with a previous study of hoxd-11 that used conventional (light microscope) methods to quantify the effects of the mutation. We hope to provide quantitative evidence for effectiveness of micro-CT and digital image analysis in phenotyping small animal models. The use of these tools will provide geneticists with better mechanisms for studying and screening mutations and increase the rate at which we acquire new knowledge of animal genetics. A greater understanding of animal models of human genetic mutations will provide a basis for human genetics research and for development of new prevention and treatment methods for debilitating genetic diseases. We would like to recognize the following participants: Lance Burrell, Joshua Cates, and of the Howard Hughes Medical Institute Mario Capecchi Laboratory: Anne Boulet Ph.D., Mario Capecchi Ph.D. This research is supported with funding from The University of Utah, Scientific Computing and Imaging Institute and The National Institute of Health. |
