CORE 3: Modeling Core

核心 3：建模核心

• 批准号: 10224016
• 负责人: ANDREJ SALI
• 金额: $ 5.39万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-17 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10224016
• 关键词: Binding; Biochemical Genetics; Biological; Cell model; Cells; Clinical; Complex; Cryoelectron Microscopy; DNA Sequence Alteration; Data; Databases; Development; Gene Combinations; Gene Proteins; Genes; Genetic; Genotype; Immune response; Infection; Knowledge; Literature; Logic; Maps; Measures; Modeling; Molecular; Names; Ontology; Pathway interactions; Performance; Phenotype; Process; Proteins; Proteomics; Roentgen Rays; Seeds; Structure; System; Technology; Therapeutic; Therapeutic Intervention; Training; Translating; Validation; X-Ray Crystallography; biological systems; cell growth; combinatorial; computer framework; deep neural network; molecular modeling; neural network; novel; pathogen; protein complex; protein protein interaction; response; targeted treatment; tool

CORE 3: MODELING CORE SUMMARY Biological knowledge is often modeled in the form of molecular networks, interaction maps consisting of gene- gene or protein-protein pairwise interactions. Biological systems though are not simply one large pairwise network, but consist of a deep and dynamic hierarchy of biological subsystems ranging across biological scales. Here, we move beyond basic interaction maps to instead use molecular interaction data to directly infer hierarchical subsystems. These plans are enabled by a computational framework called Network-Extracted Ontologies (NeXO), which we have recently shown is able to capture and substantially extend the known hierarchy of cellular components and processes recorded by pathway databases such as the Gene Ontology (GO). First (Aim 1), we will analyze the growing data on molecular networks to infer a Host-Pathogen Gene Ontology, representing a comprehensive, hierarchical description of the molecular complexes and pathways important for the host’s response to pathogens. This hierarchical structure will be developed using the protein- protein interaction data generated in Project 1, backstopped by public network data; it will provide an objective definition of a cell by systematically identifying its protein modules and their interrelationships. By comparing this data-derived hierarchy to the literature-curated Gene Ontology (Aim 2), we can identify new subsystems that respond to pathogens. We will next use this descriptive hierarchy to seed predictive whole-cell models. Using the tools of deep neural networks, genetic logic will be embedded onto each complex/pathway in the cell hierarchical structure to model how perturbations to this structure give rise to host phenotypes (Aim 3). The neural network structure will be set exactly to that of the Host-Pathogen Gene Ontology assembled in Aim 1; we will then train this neural network to translate the combinatorial genetic perturbations from Project 2 into predictions of host cell responses. This hierarchy will be not only descriptive but also predictive, connecting basic knowledge of cellular pathways to a framework for using this knowledge therapeutically. Finally, in Aim 4, we will use various structural, biochemical, genetic and proteomic data generated by Cores using an integrative modeling approach for the structure determination of host-pathogen protein complexes. Through execution of these aims, we hope to substantially advance our knowledge of the structural and functional hierarchy of molecular pathways that host responses to pathogens and provide optimal targets for therapeutical intervention.

核心 3：建模核心 概括 生物知识通常以分子网络、由基因组成的相互作用图的形式建模。 基因或蛋白质-蛋白质成对相互作用。然而，生物系统不仅仅是一个大的成对的系统 网络，但由生物子系统的深度和动态层次结构组成，范围涵盖生物 秤。在这里，我们超越了基本的相互作用图，而是使用分子相互作用数据来直接推断 分层子系统。这些计划由称为 Network-Extracted 的计算框架启用 我们最近展示的本体（NeXO）能够捕获并大幅扩展已知的知识 由基因本体等通路数据库记录的细胞成分和过程的层次结构 （去）。首先（目标 1），我们将分析分子网络上不断增长的数据以推断宿主病原体基因 本体论，代表分子复合物和途径的全面、分层描述 对于宿主对病原体的反应很重要。这种分层结构将使用蛋白质来开发 项目1生成的蛋白质相互作用数据，由公共网络数据支持；它将提供一个目标 通过系统地识别细胞的蛋白质模块及其相互关系来定义细胞。通过比较 将这种数据派生的层次结构引入文献策划的基因本体论（目标 2），我们可以识别新的子系统 对病原体做出反应。接下来我们将使用这个描述性层次结构来构建预测全细胞模型。 利用深度神经网络的工具，遗传逻辑将被嵌入到细胞中的每个复合体/通路中 层次结构来模拟该结构的扰动如何产生宿主表型（目标 3）。这 神经网络结构将完全按照目标1中组装的宿主-病原体基因本体的结构设置； 然后，我们将训练这个神经网络，将项目 2 中的组合遗传扰动转化为 宿主细胞反应的预测。这种层次结构不仅具有描述性，而且具有预测性、连接性 细胞途径的基本知识，以及在治疗上使用这些知识的框架。最后，在目标 4、我们将使用 Cores 生成的各种结构、生化、遗传和蛋白质组数据 用于确定宿主-病原体蛋白复合物结构的综合建模方法。通过 为了实现这些目标，我们希望大幅提高我们对结构和功能的了解 分子途径的层次结构，承载对病原体的反应并提供最佳靶标 治疗干预。