Modeling Core

建模核心

• 批准号: 10339372
• 负责人: Nitin S Baliga
• 金额: $ 54.87万
• 依托单位: SEATTLE CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-12 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10339372
• 关键词: Algorithms; Automobile Driving; Biochemical Pathway; Bioinformatics; Blood; Blood specimen; Cells; ChIP-seq; Characteristics; Clinical; DNA; Data; Disease; Disease Progression; Drug Controls; Drug Modelings; Drug Tolerance; Eicosanoids; Environment; Etiology; Exhibits; Gene Expression Profile; Genetic; Genetic Transcription; Genetic Variation; Goals; Human; Immune; Immune response; Individual; Infection; Intervention; Joints; Lung; Mediating; Metabolic; Metabolism; Modeling; Multiomic Data; Mus; Mycobacterium tuberculosis; Outcome; Pathway Analysis; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Physiological; Population Heterogeneity; Preventive; Process; Proteomics; Pulmonary Tuberculosis; Regulation; Regulator Genes; System; Technology; Testing; Tissue Model; Tissues; Translating; Treatment outcome; Work; analysis pipeline; base; cell type; chemokine; comparative; cytokine; data management; experimental study; gene network; gene regulatory network; human model; improved; metabolomics; mouse model; multi-scale modeling; network models; outcome prediction; peripheral blood; predictive signature; response; stressor; transcription regulatory network; transcriptome sequencing; transcriptomics; treatment response

Abstract – Modeling Core The Modeling Core will integrate and mine heterogeneous multiomics data generated in Projects 1 and 2 and the Technology Core to construct multi-scale models of regulatory and metabolic networks that are causally and mechanistically associated with disease progression and treatment outcomes. In Project 1, we will use the Systems Genetics Network AnaLysis (SYGNAL) pipeline to conduct joint modeling of innate and adaptive immune cell subpopulations from blood samples of human TB progressors, as well as orthologous cell subpopulations from mouse model of human TB progression. As input for model construction, we will use transcriptional, cytokine, chemokine and eicosanoid profiles collected over the course of the disease from disease-relevant immune cell types and tissues (lung and blood). Tractability of the mouse model will help to dissect gene networks and mechanisms underlying the etiology of the disease in the lung and how it relates to predictive signature in the blood. We will use interactions deciphered using the SYGNAL network to generate tissue-specific probabilistic Boolean network (PBN) models. Actionable predictions from SYGNAL and PBN network models will drive experiments to identify genetic perturbations that push the immune response towards desirable states. Using comparative network analysis we will then translate this mechanistic understanding from mouse to orthologous mechanisms in human to make predictive blood signatures actionable in terms of guiding preventive or treatment interventions. The goal of the Modeling Core in Project 2 is to decipher how genetic differences across different strains of Mycobacterium tuberculosis (MTB) alter regulatory and metabolic network responses to generate vastly difference treatment outcomes. The input data for modeling will include transcriptomics (RNA-seq), P-P and P-DNA interactions (ChIP-seq, MS-proteomics), TRIP screens, and metabolomics from bulk and sorted drug-tolerant and persister sub-populations of the four MTB strains, subjected to different drugs and stressors. Using a diverse suite of algorithms, we will mine these multi-omic data to generate Environment and Gene Regulatory Influence Network (EGRIN) models and IntegrateD models for REgulation And Metabolism (IDREAM). We will use these network models to drive experimentation and dissect how genetic variation across MTB strains alters their regulatory and metabolic networks to manifest in vastly different clinical outcomes. Finally, the Modeling Core will work with the Data Management and Bioinformatics Core to make data and models available for exploration, allowing biologists to formulate testable hypotheses.

摘要 - 建模核心 建模核心将集成和地雷在项目1和2中生成的异质多解词数据和 构建有因果的监管和代谢网络的多尺度模型的技术核心 并机械与疾病进展和治疗结果相关。在项目1中，我们将使用 系统遗传网络分析（SYGNAL）管道以进行先天和适应性的关节建模 人类结核病进展者的血液样本以及直系同源细胞的免疫细胞亚群 人类TB进展的小鼠模型的亚群。作为模型构建的输入，我们将使用 转录，细胞因子，趋化因子和类花生素谱，从疾病的过程中收集 与疾病相关的免疫细胞类型和组织（肺和血液）。鼠标模型的障碍性将有助于 剖析基因网络和肺部病因的基础机制及其与之相关的 血液中的预测签名。我们将使用使用Sygnal网络确定的互动来生成 组织特异性问题布尔网络（PBN）模型。 Sygnal和PBN的可行预测 网络模型将推动实验以识别遗传扰动，以将免疫反应推向 理想的国家。然后使用比较网络分析，我们将转换这种机械理解 从人类的小鼠到源源机制 指导预防或治疗干预措施。项目2中建模核心的目的是破译 分枝杆菌（MTB）不同菌株之间的遗传差异改变了调节和代谢 网络响应以产生截然不同的治疗结果。用于建模的输入数据将包括 转录组学（RNA-SEQ），P-P和P-DNA相互作用（CHIP-SEQ，MS-POTOTEOMICS），TRIP SCEENS和 来自四个MTB菌株的批量和耐药和持久亚群的代谢组学， 受到不同的药物和压力源。使用潜水算法的算法，我们将挖掘这些多MOMIC 数据以生成环境和基因调节影响网络（Egrin）模型并集成 调节和代谢的模型（IDREAM）。我们将使用这些网络模型来驱动实验 并剖析MTB菌株之间的遗传变异如何改变其调节性和代谢网络 表现为截然不同的临床结果。最后，建模核心将与数据管理一起使用 和生物信息学核心以使数据和模型可用于探索，从而使生物学家能够制定 可检验的假设。