Systems Biology Core

系统生物学核心(核心C)的总体目标是提供计算基因组学和机器 向P50中的3个项目学习(ML)建模专业知识。该专业知识将利用高维数据集 由包括scRNA-seq、scatac-seq和蛋白质组学在内的研究项目产生。这些数据集将是 使用一套单独或集成的计算工具来解决主要问题 研究项目提出的生物学问题。因此，核心C团队将作为一个必要的 协作小组，将帮助测试假设并生成预测和因果推理 SSc不同表现的病理生理学基础(项目1)，包括SSc-ILD(项目2)和 SSC-PAH(项目3)。 核心C的目的包括推断细胞内的转铁蛋白基因程序和细胞外源的基因组程序， 支持SSC细胞状态的细胞间信号通路以及基因/蛋白和 以模块为中心的预测SSC结果的生物标志物。这些将通过广泛的最新状态- 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。

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.