遗传变异和调控网络：机制和复杂性-猫眼课题宝

遗传变异和调控网络：机制和复杂性

批准号：

7937675

负责人：

Dana Pe'er

金额：

$9.0万

依托单位：

COLUMBIA UNIV NEW YORK MORNINGSIDE

依托单位国家：

美国

项目类别：

财政年份：

2007

资助国家：

美国

项目状态：

已结题

起止时间：

2007-09-30 至 2012-08-31

来源：

https://reporter.nih.gov/project-details/7937675

关键词：

Active Learning Address Affect Algorithms Amino Acid Sequence Animal Model Apoptosis Area Arginine Arts Atlases Automobile Driving B-Lymphocytes Behavior Binding Biochemical Process Biochemistry Bioinformatics Biological Biological Assay Biological Models Biological Process Biological Sciences Biology Budgets CD4 Positive T Lymphocytes Cancer Center Cancer Etiology Carbon Categories Cell physiology Cells Cessation of life Chromosome abnormality Chromosomes Classification Clinical Code Collaborations Collection Communities Complex Computational Biology Computational Technique Computational algorithm Computer Analysis Computing Methodologies Cues Cyclic AMP-Dependent Protein Kinases Cytoplasm DNA Sequence DNA Sequence Analysis DNA-Protein Interaction Data Data Analyses Data Set Data Sources Decision Trees Dependency Detection Development Diabetes Mellitus Diagnostic Dimensions Disease Distant Doctor of Philosophy Double Effect Drops ERBB2 gene Ensure Environment Environmental Risk Factor Enzymes Ethanol Etiology Event Evolution Exhibits Experimental Designs Experimental Genetics Face Faculty Feedback Figs - dietary Flow Cytometry Fluorescence Microscopy Foundations Fungal Genome Future Gene Deletion Gene Dosage Gene Expression Gene Expression Regulation Gene Proteins Genes Genetic Genetic Epistasis Genetic Markers Genetic Models Genetic Polymorphism Genetic Predisposition to Disease Genetic Transcription Genetic Translation Genetic Variation Genets Genome Genomic Instability Genomics Genotype Glean Glioblastoma Glucose Goals Grant Growth Heart Heating Heritability Human Humidity Hybridization Array Image Imagery Immunity Individual Institutes Investigation Knowledge Label Language Lead Learning Left Letters Life Link Logic Lung MAPK14 gene MYCN gene Machine Learning Malignant - descriptor Malignant Neoplasms Malignant neoplasm of ovary Mammals Maps Measurement Measures Mediating Membrane Memorial Sloan-Kettering Cancer Center Messenger RNA Metabolic Metabolic Pathway Metabolism Methionine Metabolism Pathway Methodology Methods Methylation Mission Mitochondria Mitogen-Activated Protein Kinase Kinases Modeling Molecular Molecular Biology Molecular Profiling Mus Mutation Nature Needles Neuroblastoma Nitrogen Noise Normal Cell Nuclear Nutrient Optics Oranges Organism Output Paper Parents Pathway Analysis Pathway interactions Pattern Peptide Sequence Determination Performance Pharmaceutical Preparations Phase Phenotype Phosphatidylinositol 4,5-Diphosphate Phosphorylation Physical condensation Physiology Plants Play Point Mutation Population Population Genetics Positioning Attribute Post-Transcriptional Regulation Postdoctoral Fellow Probability Procedures Process Proliferating Protein Binding Proteins Protocols documentation Proxy Publications Published Comment Publishing Quantitative Trait Loci RNA RNA-Binding Proteins Raffinose Recruitment Activity Regulation Regulator Genes Regulatory Pathway Relative (related person)Repression Reproducibility Research Research Design Research Personnel Risk Role Saccharomyces cerevisiae Sampling Scanning Science Seminal Series Signal Pathway Signal Transduction Signaling Molecule Signaling Protein Single Nucleotide Polymorphism Sirolimus Site Small Interfering RNA Sodium Chloride Solutions Sorting - Cell Movement Source Speed Spottings Starvation Statistical Models Stimulus Stress Structure Sucrose Sum Suspension substance Suspensions System T-Lymphocyte TOP2A gene Techniques Technology Temperature Testing Therapeutic Time Training Transcript Translation Initiation Treatment Protocols Tumor-Derived Universities Untranslated Regions Up-Regulation Upper arm Validation Variant Visual Work Yeasts base biological systems cancer cell cancer genome cancer genomics chromatin modification combinatorial comparative comparative genomic hybridization computer based statistical methods computer framework computerized data processing computerized tools cost data integration density design disease phenotype empowered evaporation fitness flexibility functional genomics gene function genetic analysis genetic linkage analysis genetic variant genome wide association study genome-wide graphical user interface improved innovation inorganic phosphate insight interdisciplinary approach interest leukemia loss of function mRNA Expression mRNA Transcript Degradation mRNA decapping malignant breast neoplasm medical schools member method development molecular domain mutant novel novel strategies outcome forecast pressure prognostic programs promoter protein B protein expression prototype reconstruction research study response segregation skills statistics success sugar symposium tool trait transcription factor tumor tumor growth tumor progression tumorigenesis user-friendly yeast genetics

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The focus of the proposed research is to understand the effect of sequence variation on the function of molecular networks. We will develop computational algorithms that integrate genotype, gene expression and phenotype data to construct models that describe how sequence variation perturbs the regulatory network, alters signal processing and is manifested in cellular phenotypes. Our approach is based on Bayesian networks, a framework we pioneered for the reconstruction of molecular networks from high-throughput data. We recently applied this framework to develop the Geronemo algorithm which we applied to yeast and uncovered a novel relationship between the sequence specific RNA factor PUF3 and P-Bodies, as well as a Single Nucleotide Polymorphism (SNP) in MKT1 that modulates this relationship. Both novel findings were experimentally validated subsequent to their discovery. Our approach is based on the complementary duality between genetic sequence and functional genomics. A significant influence of genotype on phenotype is induced by fine tuned perturbations to the complex regulatory network that governs a cell's activity. Variation in the expression of a single gene is more tractable and can be used as an intermediary to help associate genetic factors to the more complex downstream changes in phenotype in a hierarchical fashion. Conversely, DNA sequence polymorphisms are effective perturb-agens which provide a rich source of variation to help uncover regulatory relations in the molecular network as well as direct their causality. We will develop our methods using a large collection of highly variable yeast strains, for which we have generated robust quantitative growth curves under numerous environmental conditions. The methodologies piloted in yeast will be extended to genotype and gene expression data derived from tumor samples to attempt to elucidate the multiple genetic factors that drive their proliferation. These tools will be made publicly available, including a friendly graphical user interface and visualization.

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