Quantitative models and methods for genetic organization

Fred Hutchinson Cancer Research Center, Seattle WA

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Abstract

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.