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Quantitative models and methods for genetic organization

遗传组织的定量模型和方法

基本信息

批准号：
8508251
负责人：
Indika Rajapakse
金额：
$ 16.69万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-08-01 至 2014-09-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8508251
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 Profiling 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

项目摘要

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.

描述（由申请人提供）：数学博士 Indika Rajapakse 博士将参加 Fred Hutchinson 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中遗传结构的信息。培训目标包括获得对生物学方法的基本了解，并提高应用定量方法分析基因表达和基因组数据的能力。主要导师和共同导师提供高维数据分析、细胞和分子生物学以及临床研究方面的专业知识。导师包括生物统计学和生物数学 (FHCRC) 联合项目负责人、生物统计学 (UW) 副教授 C. Kooperberg 博士、FHCRC 副主任兼执行副院长、放射肿瘤学和病理学 (UW) 系教授 M. Groudine 博士，以及临床研究部 (FHCRC) 和肿瘤科教授 J. Hansen 博士（华盛顿大学）。定量方法学培训将在 Kooperberg 博士的指导下进行，并通过在华盛顿大学的课程学习、在 Groudine 博士的实验室接触细胞和分子生物学技术以及与 Hansen 博士一起进行临床研究来获得更多经验。基因表达和染色体空间数据（SKY）将从 Groudine 博士的实验室获得，Hansen 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使我们更全面地了解造血过程中协调的基因调控和基因组的组织。将开发动力学模型来描述这一过程。定量方法将包括多元统计技术以及网络、矩阵和动力系统理论。在本次研究过程中获得的经验将使 Rajapakse 博士获得作为定量生物医学领域的独立研究员的独特而卓越的资格。