Quantitative models and methods for genetic organization

遗传组织的定量模型和方法

• 批准号: 8102977
• 负责人: Indika Rajapakse
• 金额: $ 12.71万
• 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 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8102977
• 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; Health; Hematopoiesis; Hematopoietic; Hematopoietic Neoplasms; Hematopoietic stem cells; In Vitro; Individual; 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

DESCRIPTION (provided by applicant): Dr. Indika Rajapakse, Ph.D Mathematics, will participate in mentored research and training at the Fred Hutchinson Cancer Research Center (FHCRC) and the 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. PUBLIC HEALTH RELEVANCE: 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.

描述（由申请人提供）： 博士数学博士Indika Rajapakse将参加Fred哈钦森癌症研究中心（FHCRC）和华盛顿大学（UW）的指导研究和培训。拟议的研究将应用数学技术从基础科学（细胞）水平和临床（疾病结果）水平分析生物数据，目的是提取有关造血谱系遗传结构的信息。培训目标包括获得生物学方法的基本理解，并熟练应用定量方法分析基因表达和基因组数据。主要和共同导师在高维数据分析，细胞和分子生物学以及临床研究方面提供专业知识。导师包括C博士。Kooperberg，联合项目负责人，生物统计学和生物数学（FHCRC）和附属教授，生物统计学（UW），博士M。Groudine，FHCRC副主任兼执行副总裁，放射肿瘤学和病理学系（UW）教授，以及临床研究部（FHCRC）和肿瘤学系（UW）教授J.汉森博士。定量方法学的培训将由Kooperberg博士指导，并将通过在UW的课程学习、在Groudine博士实验室接触细胞和分子生物学技术以及与汉森博士接触临床研究来获得进一步的经验。基因表达和染色体空间数据（SKY）将从Groudine博士的实验室获得，汉森博士将提供单核苷酸多态性（SNP）标记数据。SNP数据将用于识别有助于疾病结果的重要遗传特征。分析整个基因组的基因表达模式和基因的空间特征的目的将给我们一个更完整的图片协调基因调控和组织的基因组在造血过程中。将开发动态模型来描述这一过程。定量方法将包括多元统计技术和网络，矩阵和动力系统理论。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独立研究人员的独特和特殊资格。 公共卫生相关性：发现协调基因调控的模式将使我们能够更全面地了解造血谱系内的细胞分化过程，这对血癌及其治疗具有重要意义。发现血癌患者中存在的相互作用基因将使我们能够更好地评估遗传风险并确定潜在的治疗靶点。