Core D: Computational Genomics Core

该中心的计算基因组学核心将进行生物信息学，统计学和途径分析 多个动物模型和人类患者的全基因组表达测定，并开发 全面的知识库的基因组，蛋白质组学，和代谢反应烧伤， 随后的疾病建模和目标预测。核心的总体目标是使用最先进的 数学和计算方法需要更好地理解复杂的系统生物学 如核心的主要职能： 1.通过计算分析研究适应性和适应不良的基因组机制 生理反应热损伤的研究项目，包括1 a.发展 在研究动物模型（小鼠和恒河猴）中使用新的外显子连接阵列的计算工具 猴），lb.分析激活和/或抑制胰岛素重要基因的基因组效应 动物模型中的抗性和线粒体功能障碍，和1c.比较跨物种的基因组 动物模型与烧伤患者之间的变化以及LPS与烧伤之间的变化。 2.整合程序生成的基因组、蛋白质活性和代谢数据， 鉴定用于干预的候选基因，包括2a.建立分子生物学知识库 热损伤后骨骼肌的紊乱，综合研究项目的结果 与分子信息系统地收获的文献以及全面的人类 烧伤患者的转录组数据，和2b.进行计算分析，以确定关键基因调控因子 对于后续干预研究，候选丝氨酸-苏氨酸激酶用于后续抑制。 3.建立数据和知识库的网络门户，作为社区的中心资源。

The Computational Genomics Core in this Center will perform bioinformatic, statistical and pathway analyses of genome-wide expression assays of multiple animal models and human patients, and develop comprehensive knowledgebase of the genomic, proteomic, and metabolic response to burn injury for subsequent disease modeling and target prediction. The overall objective of Core is to use state-of-the-art mathematical and computational approaches needed to better understand the complex systems biology presented by injury and critical illness, as outlined in the Major Functions ofthe Core: 1. Investigate via computational analyses the genomic mechanism ofthe adaptive and maladaptive physiological responses to thermo injury in studies of the Research Projects, including 1 a. Develop computational tools for using new exon-junction arrays in studying animal models (mouse and Rhesus monkeys), lb. 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. 2. Integrate the genomic, protein activity and metabolic data generated by the Program for rational target identification of gene candidates for intervention, including 2a. Develop a knowledgebase of molecular derangements in skeletal muscle following thermal injury by integrating findings from the Research Projects with molecular information systematically harvested from the literature as well as comprehensive human transcriptome data of burn patients, and 2b. Conduct computational analysis to identify key gene regulators for follow up intervention studies candidate serine-threonine kinases for follow-up inhibition. 3. Establish a web-based portal ofthe data and knowledgebase as central community resource.