# Systems biology and the evolutionary dynamics in a synthetic microbial community

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2020 · $308,509

## Abstract

Project Summary
The ability to engineer the microbiome could transform treatment and prevention of diseases from obesity to
cancer. The promise of designer microbiomes is largely constrained by lack of understanding of how
community composition and function are encoded in the genomes present in a system. The long-term goal is
to develop systems-level, metabolically-based approaches to connect genomic data to microbial community
function and dynamics. Metabolic mechanisms provide a broadly applicable foundation for understanding and
managing microbial systems as metabolic enzymes can be identified from sequence data, and intracellular
metabolism drives many of the microbial interactions that generate community behavior. The proposed
research will computationally predict and experimentally test the quantitative connection between genome
sequence, metabolic mechanisms, and community properties in a microbial community. A model microbial
community has been engineered in the laboratory with defined metabolic interactions between Escherichia coli,
Salmonella enterica, and Methylobacterium extorquens. Further a computational platform has been developed
that uses genome-scale metabolic models to simulate growth and metabolic interactions and community
function. These cutting-edge tools will be combined to achieve the following specific aims:
Aim 1 – Identify all metabolic and genetic elements that contribute to growth in a defined community.
 Genome-scale knockout libraries will be evaluated computationally and empirically.
Aim 2 – Determine how evolution changes community composition and function.
 High-throughput phenotypic assays and genome sequencing will be used to identify the changes that
 have evolved in eight replicate communities over 400 generations. Metabolic constraints on evolution
 will be computationally investigated.
Aim 3 – Test the prevalence of genetic interactions in a microbial community.
 Genetic interactions within and between genomes will be determined by the frequency with which the
 effect of a mutation changes in the presence of other mutations.
The proposed work will generate the first systems-level data on the genomic basis of microbial community
function. It will provide valuable insights into the metabolic and genetic mechanisms underlying dynamics in
multi-species systems and the extent to which the effects of genetic changes are context dependent. Finally,
the work will enable quantitative prediction of evolutionary trajectories from genome-scale metabolic models.
As we strive to engineer microbiomes it is critical to characterize how genomic changes translate to changes in
the community. Quantitatively connecting genome sequence to community function is a vital step in the
ultimate goal of understanding and rationally managing microbial communities.

## Key facts

- **NIH application ID:** 9994946
- **Project number:** 5R01GM121498-04
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** William Harcombe
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $308,509
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9994946

## Citation

> US National Institutes of Health, RePORTER application 9994946, Systems biology and the evolutionary dynamics in a synthetic microbial community (5R01GM121498-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9994946. Licensed CC0.

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