Quantitative models and methods for genetic organization

遗传组织的定量模型和方法

• 批准号: 8287687
• 负责人: Indika Rajapakse
• 金额: $ 4.69万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8287687
• 关键词: Affect; Architecture; Basic Science; Behavior; Biological; Biometry; Cancer Patient; Cell physiology; Cellular biology; Chromosomal translocation; Chromosome Structures; Chromosomes; Clinical; Clinical Research; Data; Disease Outcome; Doctor of Philosophy; Entropy; Exposure to; Fred Hutchinson Cancer Research Center; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Gene Structure; Genes; Genetic; Genetic Risk; Genome; Genomics; Goals; Head; Hematopoiesis; Hematopoietic; Hematopoietic Neoplasms; Hematopoietic stem cells; In Vitro; Individual; Instruction; Investigation; Linkage Disequilibrium; Maps; Mathematics; Measures; Mentors; Methodology; Methods; Modeling; Molecular Biology; Molecular Biology Techniques; Nature; Nuclear; Pathology; Pathway interactions; Patients; Pattern; Play; Principal Investigator; Process; Radiation Oncology; Regulation; Relative (related person); Research; Research Personnel; Research Training; Role; Single Nucleotide Polymorphism; Spectral Karyotyping; Statistical Methods; System; Systems Theory; Techniques; Testing; Time; Training; Transcriptional Regulation; Universities; Washington; base; biomathematics; design; disease phenotype; experience; gene interaction; genome wide association study; genome-wide; graft vs host disease; interest; mathematical model; method development; novel; oncology; professor; programs; self organization; theories; therapeutic target

Dr. Indika Rajapakse, Ph.D Mathematics, will participate in mentored research and training at the Fred Hutchinson Cancer Research Center (FHCRC) and ttie University of Washington (UW). The proposed research will apply mathematical techniques to the analysis of biological data from the basic science (cellular) level and the clinical (disease outcome) level, with the goal of extracting information about genetic architecture in the hematopoietic lineage. Training goals include obtaining a basic understanding of biological approaches and developing proficiency in applying quantitative methods to analysis of gene expression and genomic data. The primary and co-mentors lend expertise in analysis of high dimensional data, cell and molecular biology, and clinical research. Mentors include Dr. C. Kooperberg, co-program head, Biostatistics and Biomathematics (FHCRC) and affiliate professor, Biostatistics (UW), Dr. M. Groudine, deputy director and executive vice president of the FHCRC and professor. Department of Radiation Oncology and Pathology (UW), and Dr. J. Hansen, Clinical Research Division (FHCRC) and professor, Division of Oncology (UW). Training in quantitative methodologies will be guided by Dr. Kooperberg, and further experience will be gained through coursework at the UW, exposure to cell and molecular biology techniques in Dr. Groudine's lab, and exposure to clinical research with Dr. Hansen. Gene expression and chromosome spatial data (SKY) will be obtained from Dr. Groudine's lab, and Dr. Hansen will provide single nucleotide polymorphism (SNP) marker data. SNP data will be used to identify significant genetic signatures that contribute to disease outcome. The aim of analyzing gene expression patterns across the genome and spatial characterization of genes will give us a more complete picture of coordinated gene regulation and organization of the genome during hematopoiesis. Dynamical models will be developed to describe this process. Quantitative methods will include multivariate statistical techniques and network, matrix, and dynamical systems theories. Experience gained during this course of research will endow Dr. Rajapakse with unique and exceptional qualifications as an independent researcher in the quantitative biomedical field. RELEVANCE (See instructions): Finding patterns of coordinated gene regulation will allow us more fully understand the process of cellular differentiation within the hematopoietic lineage, which has vital implications for blood cancers and their treatments. Finding interacting genes present in blood cancer patients will enable us to better assess genetic risk and identify potential therapeutic targets.

数学博士Indika Rajapakse博士将参加Fred的指导研究和培训 哈钦森癌症研究中心（FHCRC）和华盛顿大学（UW）。拟议 研究将应用数学技术来分析基础科学的生物数据， （细胞）水平和临床（疾病结果）水平，目的是提取有关遗传的信息， 在造血谱系中的结构。培训目标包括： 生物学方法和发展应用定量方法分析基因的熟练程度 表达和基因组数据。主导师和共同导师在高维分析方面提供专业知识， 数据、细胞和分子生物学以及临床研究。导师包括C博士。库伯伯格，联合项目负责人， 生物统计学和生物数学（FHCRC）和副教授，生物统计学（UW），博士M。格鲁丁， 副主任、常务副院长、教授。放射科 肿瘤学和病理学（UW）和临床研究部（FHCRC）兼教授J.汉森博士 肿瘤学分部（UW）。定量方法的培训将由Kooperberg博士指导， 通过在华盛顿大学的课程学习，接触细胞和分子生物学， Groudine博士实验室的技术，以及接触汉森博士的临床研究。基因表达和 染色体空间数据（SKY）将从Groudine博士的实验室获得，汉森博士将提供单个 核苷酸多态性（SNP）标记数据。SNP数据将用于识别重要的遗传标记 导致疾病结果。目的是分析基因组中的基因表达模式， 基因的空间表征将使我们更全面地了解协调的基因调控， 造血过程中基因组的组织。将开发动态模型来描述这一点 过程定量方法将包括多元统计技术和网络，矩阵， 动力系统理论在这一研究过程中获得的经验将赋予Rajapakse博士 作为定量生物医学领域的独立研究人员，具有独特和卓越的资格。 相关性（参见说明）： 发现协调基因调控的模式将使我们能够更全面地了解细胞凋亡的过程。 造血谱系内的分化，这对血液癌症及其 治疗。在血癌患者中发现相互作用的基因将使我们能够更好地评估 遗传风险和确定潜在的治疗靶点。