# Modeling Core

> **NIH NIH U19** · SEATTLE CHILDREN'S HOSPITAL · 2021 · $1,035,428

## Abstract

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.

## Key facts

- **NIH application ID:** 10102183
- **Project number:** 5U19AI135976-05
- **Recipient organization:** SEATTLE CHILDREN'S HOSPITAL
- **Principal Investigator:** Nitin S Baliga
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,035,428
- **Award type:** 5
- **Project period:** 2018-02-12 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10102183

## Citation

> US National Institutes of Health, RePORTER application 10102183, Modeling Core (5U19AI135976-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10102183. Licensed CC0.

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