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Computational and Systems Biology Core

计算和系统生物学核心

基本信息

批准号：
10017873
负责人：
Rima F Kaddurah-Daouk
金额：
$ 41.31万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10017873
关键词：
Address Algorithmic Analysis Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease pathology Animal Model Animals Atlases Biochemical Pathway Bioinformatics Biological Biological Markers Blood Brain Complex Computational Biology Computer Models Computing Methodologies Data Data Analyses Data Set Databases Detection Development Diabetes Mellitus Diet Dietary Intervention Disease Disseminated Malignant Neoplasm Elements Environment Ethnic Origin Gender General Population Genes Genetic Genotype Germany Human Human Genome Image International Knowledge Life Link Luxembourg Malignant Neoplasms Medicine Metabolic Metabolic Pathway Metabolism Metabolite Interaction Methods Microbe Modeling Molecular Multiomic Data Network-based New York Nutritional Organ Outcome Pathway Analysis Pathway interactions Patients Phenotype Physiological Plants Play Process Proteomics Publications Publishing Qatar Reaction Research Research Personnel Resources Risk Factors Role Source Standardization Statistical Data Interpretation Statistical Methods Subgroup System Systems Biology Techniques Technology Transcript Universities Work analysis pipeline base biological systems cohort data analysis pipeline data integration data mining endophenotype experience experimental study fecal metabolome genome wide association study genomic data gut microbiome gut-brain axis heterogenous data host microbiome human model in silico innovation knowledge base metabolic phenotype metabolome metabolomics microbial microbiome microbiome analysis microbiome research microbiota molecular phenotype mouse model multidimensional data multiple omics novel novel therapeutics phenotypic data precision medicine protein metabolite reconstruction simulation transcriptomics virtual

项目摘要

ABSTRACT – Computational and Systems Biology Core The Computational and Systems Biology Core will provide access to advanced data analysis algorithms and pipelines for the entire project. High-quality preprocessed data will be seamlessly integrated from the Omics and Technology Core. Our analysis pipelines will perform all major steps of data analysis, including outlier detection, differential analysis, pathway analysis, and advanced network methods. We will develop methods specifically tailored for multi-compartment omics data in this project, e.g. from blood, gut, and brain. Such novel methods for integrated multi-omics, multi-compartment data will provide a unique readout of AD pathology and allow us to unlock the full potential behind these heterogeneous datasets. Moreover, we will work on computational models for human-microbe co-metabolism, which will allow in silico simulations of external influences, such as diet, at physiological scale. The research questions addressed by the core will mainly be driven by the three projects. To this end, we will focus on the blood-gut-brain axis in human omics datasets (project 1), in animal model datasets (project 3), and the effects of environment and diet on molecular phenotypes (project 2). A second, major focus of the core will be on the development and application of a microbiome-centric bioinformatic knowledge base (an “atlas”). To this end, will construct a Neoj4-based network database integrating various heterogenous information, including results from metabolomics GWAS, eQTL studies, Alzheimer-phenotype related association studies (e.g. metabolomics biomarkers of AD endophenotypes), microbiome-metabolome associations etc. The atlas will allow us to answer complex research questions, such as finding the connections between a given set of metabolites, genes, metabolic pathways, GWAS hits, and AD endophenotypes in one single query. In the final part of this project, we will develop advanced network data mining algorithms on the atlas, to extract novel information beyond that of simple associations. This will lead to integrated molecular modules associated with AD, providing a multi-omics view on AD pathobiology. The core will be led by an experienced, international group of PIs with over a decade of experience in the field. The team has a track record in major fields of metabolic research, including diabetes, cancer, Alzheimer’s disease, microbiome analysis, and metabolic GWAS. In summary, the Computational and Systems Biology Core will be central element for computational approaches within the consortium, providing both data analysis and advanced data integration and data mining techniques.

摘要：计算和系统生物学核心