CORE 3: Modeling Core

核心3：建模核心 总结 生物学知识通常以分子网络的形式建模，即由基因- 基因或蛋白质-蛋白质成对相互作用。然而生物系统并不是简单的一个大的成对的 网络，但包括一个深刻的和动态的生物子系统的层次结构， 鳞片在这里，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是由一个名为网络提取的计算框架实现的 Ontologies（NeXO），我们最近已经证明，能够捕获并大大扩展已知的 由途径数据库（如基因本体）记录的细胞组分和过程的层次结构 （GO）.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，代表对分子复合体和途径的全面的、层次化的描述 对宿主对病原体的反应很重要。这种层次结构将使用蛋白质开发- 在项目1中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 通过系统地识别其蛋白质模块及其相互关系来定义细胞。通过比较 这个数据派生的层次到文献策划的基因本体论（目标2），我们可以识别新的子系统 对病原体有反应。接下来，我们将使用这种描述性层次结构来播种预测性全细胞模型。 使用深度神经网络的工具，遗传逻辑将嵌入到细胞中的每个复合体/通路中 层次结构，以模拟这种结构的扰动如何引起宿主表型（目的3）。的 神经网络结构将被精确地设置为Aim 1中组装的宿主-病原体基因本体的结构; 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。这种层次结构不仅是描述性的，而且是预测性的， 细胞通路的基本知识，以一个框架，使用这种知识治疗。最后，在Aim 4，我们将使用Cores生成的各种结构，生物化学，遗传和蛋白质组数据， 用于宿主-病原体蛋白质复合物的结构测定的综合建模方法。通过 为了实现这些目标，我们希望能够大大提高我们对结构和功能的认识。 宿主对病原体作出反应并提供最佳靶点的分子途径的层次结构 治疗干预。

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.