# Modeling Core

> **NIH NIH U19** · NORTHWESTERN UNIVERSITY · 2022 · $347,048

## Abstract

Modeling Core Project Summary:
Many physical, biological, and social systems display sudden transitions between qualitatively different states.
An example is the role of nutrient pollution on aquatic ecosystems: when phosphorus and nitrogen levels
increase above a threshold value, a stream, river, or lake can transition from a low biomass/high diversity state
to a high biomass/low diversity state. Systems that are history-dependent demonstrate hysteresis and follow so-
called S-shaped bifurcation curves. We will approach the modeling of the development of pneumonia and its
resolution in response to treatment using the conceptual framework of bifurcation theory. In the simplest case,
with only two states, the model would distinguish a state with low bacterial load and high lung function from one
with a high bacterial load and low lung function. The major challenge in this framework is to determine how to
express the control parameter in terms of biological variables pertinent to pneumonia pathogenesis. We will
pursue an agnostic modeling approach to the challenge of obtaining insight from these high-dimensional data.
Because of the complexity of the problem, we will iteratively apply a variety of cutting-edge methods from
systems biology, data science, dynamical systems, and ecology. By overcoming this challenge, we will achieve
two aims. Aim 1. To identify biological variables (both host and pathogen) that will enable us to predict clinical
outcomes in patients with Pseudomonas aeruginosa or Acinetobacter baumannii and other spp. pneumonia.
Aim 2. To develop a set of hypotheses on the causal drivers of clinical outcomes that will be validated in
subsequent human samples and tested using humanized mouse models. We will use systems biology methods
to define low-dimensionality variables from the high-throughput, high-dimensionality data collected. We will then
use machine learning, and dynamical systems methods — with a focus on methods that have demonstrated
their mettle in ecological applications — to identify biomarkers for specific host/microbiome phenotypes and to
predict the probability of different clinical outcomes for each phenotype. We will determine which biological
variables most contribute to determining the classification by probing the sensitivities of different phenotypes to
specific biological variables in order to generate mechanistic hypotheses that will then be tested experimentally
with humanized mouse models and validated with human samples.

## Key facts

- **NIH application ID:** 10326813
- **Project number:** 5U19AI135964-05
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** LUIS A. Nunes AMARAL
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $347,048
- **Award type:** 5
- **Project period:** 2018-01-17 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10326813

## Citation

> US National Institutes of Health, RePORTER application 10326813, Modeling Core (5U19AI135964-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10326813. Licensed CC0.

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