Arizona Research Labs - Interdisciplinary

Dr. Sprissler is an Associate Research Professor with a PhD in Genetics and a founding member of the Center for Applied Genetics and Genomic Medicine at the University of Arizona. As Director and Lead Scientist of the University of Arizona Genetics Core Dr. Sprissler has led innovative genetic research in a number of disease states including neurodegenerative disorders, hypertension, SARS-CoV-2 and oncology. With over 10 years experience running the University of Arizona's only CAP/CLIA laboratory certified for clinical human genetic testing, Dr. Sprissler has a particular expertise in the development and validation of clinical assays with the intent of expanding access to this cutting edge testing to the State of Arizona and beyond.

Timothy Secomb, PhD, studies the microcirculation, a network of extremely small blood vessels that supply oxygen and nutrients to all parts of our tissues. The focus of work in his research group is the use of mathematical and computational approaches to study blood flow and mass transport in the microcirculation. Working in collaboration with experimentalists, the aim is to understand quantitatively the processes involved. Dr. Secomb examines the relationship between red blood cell mechanics and flow resistance in microvessels. Theoretical predictions agree well with observations in glass tubes, but resistance is higher living tissue. The major cause is the presence of a relatively thick macromolecular lining (endothelial surface layer) on the walls of microvessels. He also simulates oxygen exchange between networks of microvessels and surrounding tissues in skeletal muscle and tumors. In skeletal muscle, oxygen can be exchanged diffusively between arterioles and capillaries, and Dr. Secomb’s lab is studying the determinants of maximal oxygen consumption. In tumors, the relationship between network structure and occurrence of local hypoxic (radiation-resistant) regions is a source of curiosity. They are analyzing the delivery of chemotherapeutic drugs in tumor tissues, and developing improved models to describe the responses of tumor cells to chemotherapy and radiation. Models for the structural responses of microvessels to functional demands are being developed. Maintenance of a stable, functionally adequate distribution of vessel diameters can be achieved if each vessel responds to changes in wall shear stress, intravascular pressure and local metabolic conditions, and if mechanisms exist for information transfer upstream and downstream along flow pathways. Models for the active regulation of blood flow by changes in vascular tone are also being developed, taking into account vascular responses to wall shear stress, pressure and local metabolic state, and including effects of conducted responses along vessel walls. Another project in the group is the development of computer simulations for the dynamics of the left ventricle that can be run in real time and provide a tool for analysis of data derived from ultrasound echocardiography images.

I do neuroimaging, specifically fMRI and DTI. I am especially interested in brain networks and developments in neuroimaging software. We use independent component analysis to identify separate networks in the brain related to processing and learning language. My colleagues and I worked to improve fMRI analysis, display and data sharing options. Beginning with a web-based workbench designed for the dynamic exploration of map-based data, we worked to develop brain maps that could be similarly explored and demonstrated that this approach yielded results similar to those achieved by much more laborious and manual exploration techniques. This has improved our ability to streamline analyses, extract insights from our data and share data online. I have also worked on DWI analysis of the language system for the past 8 years. This has resulted in contributions to tract analyses (Wilson et al., 2011) and to the development of a novel technique (Patterson et al., 2015) to extract not only information about the properties of each tract but also information about the size and location of connected grey matter regions. We continue to explore the implications of these new measures. Keywords: fMRI, DWI, Language, Neuroimaging, MRI

Over the last two decades my work has focused on developing computational platforms and enabling technologies, primarily directed towards improving research productivity and collaboration for interdisciplinary teams and virtual organizations. The key thrust areas for my work encompass life cycle management for: 1. High throughput and automated bio sample processing systems 2. Highly scalable data and metadata management systems 3. High throughput and performance computing systemsMy recent work has been directed towards supporting pervasive computing needs for mHealth (mobile health) initiatives and health interventions, with focus on developing study management platforms that leverage cloud based telephony, messaging and video in conjunction with wearable’s and sensors.Platforms and tools developed by team are utilized in: 1. Managing samples and data for Clinically certified (CAP/CLIA) NGS pipelines 2. Large scale genotyping (million+ samples) with robotic automation 3. National Cyberinfrastructure iPlant; facilitates researchers to effectively manage their data, computation and collaborations using a cohesive computational platform 4. Health interventions and patient monitoring I firmly believe that measured adoption of emerging computational technologies and methods are essential for life scientist to successfully operate at the scale and complexity of data they are constantly encountering. This can only happen if there is continuing education and practical training focused around the use of Cyberinfrastructure and computational thinking. I have developed and taught workshops, graduate and undergraduate project based learning courses with emphasis on these topicsMy team (Bio Computing Facility) engages with the campus community at various levels ranging from multi- institutional collaborative projects, graduate and undergraduate courses for credit and special topic seminars and workshops. With emphasis on enabling digital discoveries for the life sciences.

Michael Hammer is a Research Scientist in the Division of Biotechnology at the University of Arizona with appointments in the Department of Neurology, Ecology and Evolutionary Biology, Bio5, the School of Anthropology, the University of Arizona Cancer Center, and the Steele Children's Research Center. Currently Dr. Hammer is interested in the use of the latest DNA sequencing technology to infer the underlying genetic architecture of neurodevelopmental diseases. Since 1991 Dr. Hammer has directed of the University of Arizona Genetics Core (UAGC), a facility that provides training and molecular biology services to University and biotechnology communities at large. After receiving his Ph.D. in Genetics at the University of California at Berkeley in 1984, he performed post-doctoral research at Princeton and Harvard. Over the past two decades, Dr. Hammer has headed a productive research lab in human evolutionary genetics, resulting in over 100 published articles documenting the African origin of human diversity, interbreeding between modern humans and archaic forms of the genus Homo, and genome diversity in the great apes. His lab and the UAGC were early adopters of next generation sequencing (NGS) technology and the application of whole genome analysis in humans, and his lab has been a key player in the Gibbon and Baboon Genome Projects, as well as a consortium that has analyzed the genomes of over 100 Great Apes (GAPE Project). In the past 3 years, Dr. Hammer's research team has succesfully employed NGS methods to identify molecular lesions causing neurodevelopmental disorders in undiagnosed children. This has led to the publication of articles identifying pathogenic variants associated with early onset epileptic encephalopathies. His lab is also currently pursuing studies to identify modifier genes that alter the expression of major genes and how they contribute to phenotypic heterogeneity in Mendelian disorders.