Genetic variation and regulatory networks: Mechanisms and complexity

遗传变异和调控网络：机制和复杂性

基本信息

批准号：
7937675
负责人：
Dana Pe'er
金额：
$ 9万
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
起止时间：
2007-09-30 至 2012-08-31
项目状态：
已结题

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

项目摘要

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.

本研究的重点是了解序列变异对分子网络的功能。我们将开发计算算法整合基因型、基因表达和表型数据来构建模型描述序列变异如何扰乱调节网络，改变信号处理并表现在细胞表型中。我们的方法基于贝叶斯网络，这是我们为从高通量数据重建分子网络。我们最近应用了这个开发 Geronemo 算法的框架，我们将其应用于酵母并发现了序列特异性 RNA 因子 PUF3 和 P-Bodies 之间的新关系作为 MKT1 中调节这种关系的单核苷酸多态性 (SNP)。这两项新发现均在发现后得到了实验验证。我们的方法基于基因序列和基因序列之间的互补二元性功能基因组学。基因型对表型的显着影响是由精细诱导的调整对控制细胞活动的复杂调节网络的扰动。单个基因表达的变异更容易处理，可以用作帮助将遗传因素与更复杂的下游变化联系起来的中介以分层方式表现型。相反，DNA序列多态性是有效的扰动因素，提供丰富的变异来源，帮助发现监管分子网络中的关系并指导它们的因果关系。我们将使用大量高度可变的酵母菌株来开发我们的方法，我们在众多的条件下生成了稳健的定量增长曲线环境条件。在酵母中试验的方法将扩展到基因型和来自肿瘤样本的基因表达数据试图阐明多重驱动其增殖的遗传因素。这些工具将公开提供，包括友好的图形用户界面和可视化。