# CORE 3: Modeling Core

> **NIH NIH U19** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $53,909

## Abstract

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.

## Key facts

- **NIH application ID:** 10224016
- **Project number:** 5U19AI135990-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** ANDREJ SALI
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $53,909
- **Award type:** 5
- **Project period:** 2018-08-17 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10224016

## Citation

> US National Institutes of Health, RePORTER application 10224016, CORE 3: Modeling Core (5U19AI135990-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10224016. Licensed CC0.

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