CORE 3: Modeling Core

核心3：建模核心

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.