Core D: Computational Genomics Core

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The overall objective of the Computational Genomics Core is to use the state-of-the-art mathematical and computational approaches needed to better understand the complex systems biology presented by injury and critical illness. In addition, built on our developments and experiences in computational biology in the Inflammation and the Host Response to Injury Glue Grant, the Core is also designed to tackle several current computational challenges in translational research of injury. The major functions of the core are: (1) Investigate via computational analyses the genomic mechanism of the adaptive and maladaptive physiological responses to thermo injury in studies of the Research Projects, (la) Develop computational tools for using new exon-junction arrays in detection of gene expression and alternative splicing in animal models (mouse and Rhesus monkeys); (1b) Analyze the genomic effect of activation and/or inhibition of genes important to insulin resistance and mitochondrial dysfunction in animal models; and (1c) Compare cross species the genomic changes between animal models and burn patients, and between LPS and burns. Our goal in this program is using computational analysis to comprehensively identify and catalog the similarities and differences between these inflammatory sources and between patients and model systems. (2) Integrate the genomic, protein activity and metabolic data of the Center for rational target identification of gene candidates for intervention. (2a) Establish a disease specific knowledge-base of molecular derangements in skeletal muscle following thermal injury by integrating findings of the Center Projects with the information systematically harvested from the literature as well as the human transcriptome data of burn patients; (2b) Conduct computational analysis to identify key gene regulators as candidates for intervention studies. (3) Establish web-based portal of the data and knowledgebase as central community resource. Data warehousing and providing web-accessible sharing of the data and results with the research community. Importantly, the successful accomplishments will be achieved by the multidisciplinary group effort of close collaborations between bioinformaticians and statisticians, and the other investigators in the Center.