# Automated, model-guided phenotyping to identify metabolite/gene/microbe interactions

> **NIH NIH R21** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2020 · $178,949

## Abstract

Project Summary/Abstract
DNA sequencing has spawned the “microbiome revolution” -- thousands of microbes and a dizzying number of
microbial interactions that are associated with human health and disease. Unfortunately, most species in the
microbiome are known only by a (partial) genome. The limited phenotypic data on newly discovered bacteria
reveal species that behave unlike any of our model organisms. While genome-scale modeling plays an
important role in understanding the microbiome, the paucity of phenotypic data for most species prevents
detailed simulation of the microbial communities that affect our health.
This project will develop an automated system for profiling, synthesizing, and modeling microbial communities.
The center of our approach is Deep Phenotyping, an automated robotic platform that performs complex growth
experiments on demand. Data from Deep Phenotyping will be used to train metabolic and statistical models of
the oral pathogens Streptococcus mutans and Candida albicans to predict conditions that keep both microbes
in a nonpathogenic state.

## Key facts

- **NIH application ID:** 9852330
- **Project number:** 5R21EB027396-02
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** Paul Anthony Jensen
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $178,949
- **Award type:** 5
- **Project period:** 2019-02-01 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9852330

## Citation

> US National Institutes of Health, RePORTER application 9852330, Automated, model-guided phenotyping to identify metabolite/gene/microbe interactions (5R21EB027396-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9852330. Licensed CC0.

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