Systems Biology Core

系统生物学核心

• 批准号: 10705640
• 负责人: HARINDER SINGH
• 金额: $ 19.88万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705640
• 关键词: Address; Biological; Biological Markers; Blood Vessels; Cells; Chromatin; Chromatin Remodeling Factor; Complex; Data; Data Set; Decision Trees; Dimensions; Disease Outcome; Elasticity; Epigenetic Process; Fibroblasts; Functional disorder; Gene set enrichment analysis; Genes; Genetic Transcription; Genomics; Goals; Individual; Logic; Lung; Machine Learning; Modeling; Molecular; Molecular Profiling; Multiomic Data; Myeloid Cells; Outcome; Pathway interactions; Population; Process; Proteins; Proteomics; Regulator Genes; Research Project Grants; Signal Pathway; Signal Transduction; Skin; Structure; Systemic Scleroderma; Systems Biology; Testing; Tissue-Specific Gene Expression; Tissues; Validation; Visualization; Work; cell type; clinical translation; combinatorial; complex data; computational suite; computerized tools; gene regulatory network; innovation; insight; machine learning model; multidimensional data; multiple omics; novel; predictive marker; predictive modeling; programs; protein protein interaction; random forest; single-cell RNA sequencing; tool; transcription factor; translational study

The overall goal of the Systems Biology Core (Core C) is to provide computational genomics and machine learning (ML) modeling expertise to the 3 projects in the P50. This expertise will utilize high dimensional datasets generated by the research projects including scRNA-seq, scATAC-seq and proteomics. These datasets will be leveraged using a suite of computational tools, either individually or in an integrated manner to address major biological questions posed by the research projects. Thus, the Core C team will function as an essential collaborative group that will help to test hypotheses and to generate predictions as well as causal inferences underlying the pathophysiology of distinct manifestations of SSc (Project 1) including SSc-ILD (Project 2) and SSc-PAH (Project 3). The aims of Core C include the inference of cell-intrinsic TF-directed genomic programs and cell-extrinsic, intercellular signaling pathways underlying SSc cellular states as well as the identification of gene/protein and module-centric predictive biomarkers of SSc outcomes. These will be enabled via a wide range of state-of-the- art computational genomics and machine learning approaches. Overall, Core C is strongly integrated with the key biological objectives of all 3 projects.

系统生物学核心（Core C）的总体目标是提供计算基因组学和机器 学习（ML）建模专业知识的3个项目在P50。该专业知识将利用高维数据集 scRNA-seq、scATAC-seq和蛋白质组学等研究项目产生的。这些数据集将 利用一套计算工具，单独或以集成方式解决主要问题， 研究项目提出的生物学问题。因此，核心C团队将发挥重要作用， 一个合作小组，将帮助测试假设，并产生预测以及因果关系的推论 SSc（项目1）不同表现的病理生理学基础，包括SSc-ILD（项目2）和 SSc-PAH（项目3）。 核心C的目标包括推断细胞内在TF指导的基因组程序和细胞外在， SSc细胞状态下的细胞间信号传导途径以及基因/蛋白质的鉴定， SSc结果的模块中心预测生物标志物。这些将通过广泛的国家， ART计算基因组学和机器学习方法。总体而言，Core C与 所有3个项目的关键生物学目标。