# DMS/NIGMS 1: Modeling Microbial Community Response to Invasion: A Multi-Omics and Multifacton

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $170,649

## Abstract

Most microorganisms live in communities containing hundreds or thousands of species, each engaging 
in a rich web of interactions. The complexity of these interactions makes quantitative predictions about 
community dynamics difficult. To overcome this, simple proxies for natural communities, designated 
"model microbial communities," have been designed to support laboratory study. These models are 
complex enough to exhibit community-specific phenomena but simple enough to reveal governing 
principles of community interaction. Invasions are among the most destabilizing events that a microbial 
community can experience, often resulting in community dysfunction or host disease. We propose to use 
THOR, a model community that we developed, to characterize the response to invasion by Pseudomonas 
aeruginosa. We will track population dynamics functionally profile molecular interactions. 
The multi-omics and multifactorial nature of this study present multifaceted opportunities for statistical 
innovation. A truly integrative analysis cannot simply perform parallel hypothesis tests across assays, and 
there is a need for a differential testing framework that blends data sources into a unified molecular 
interaction network. We will draw from advances in selective inference and multi-omics network analysis to 
develop methods that illuminate the molecular interactions driving community response. This will allow us 
to tailor interventions that shape dynamics in the THOR model microbial community. We propose: 
1. Aim 1: Functionally profile THOR's community response to P. aeruginosa using metabolomics, 
metatranscriptomics, and 16S rRNA sequencing and establish associated data curation workflows. These 
are the core data-generating experiments and quality control steps that provide accurate and 
complementary views of THOR under invasion. 
2. Aim 2: Develop differential testing methods that are sensitive to interaction effects and that control 
module-level false discovery rates. We will introduce methods for selective inference of differential 
interactome modules, like activated biosynthetic pathways. 
3. Aim 3: Consolidate software for differential interactome analysis and experimentally validate knockout 
targets. These experiments will illustrate a data-driven approach to control invasion dynamics, and our 
software will make such analysis easily accessible.

## Key facts

- **NIH application ID:** 10935979
- **Project number:** 5R01GM152744-02
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** JO E. HANDELSMAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $170,649
- **Award type:** 5
- **Project period:** 2023-09-26 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10935979

## Citation

> US National Institutes of Health, RePORTER application 10935979, DMS/NIGMS 1: Modeling Microbial Community Response to Invasion: A Multi-Omics and Multifacton (5R01GM152744-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10935979. Licensed CC0.

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