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Title: Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models

Abstract

Microbiomes are vital to human health, agriculture, and protecting the environment. Predicting behavior of self-assembled or synthetic microbiomes, however, remains a challenge. In this work, we used unicellular and guild-based metabolic models to investigate production of medium-chain fatty acids by a mixed microbial community that is fed multiple organic substrates. Modeling results provided insights into metabolic pathways of three medium-chain fatty acid-producing guilds and identified potential strategies to increase production of medium-chain fatty acids. This work demonstrates the role of metabolic models in augmenting multi-omic studies to gain greater insights into microbiome behavior.

Authors:
ORCiD logo [1];  [2];  [3]; ORCiD logo [4]; ORCiD logo [5];
  1. The Great Lakes Bioenergy Research Center, UW-Madison, Madison, Wisconsin, USA, Department of Civil and Environmental Engineering, UW-Madison, Madison, Wisconsin, USA, Department of Civil and Environmental Engineering, University of Vermont, Burlington, Vermont, USA
  2. Department of Biochemistry, UW-Madison, Madison, Wisconsin, USA
  3. Department of Chemical and Biological Engineering, UW-Madison, Madison, Wisconsin, USA
  4. The Great Lakes Bioenergy Research Center, UW-Madison, Madison, Wisconsin, USA, Department of Bacteriology, UW-Madison, Madison, Wisconsin, USA
  5. The Great Lakes Bioenergy Research Center, UW-Madison, Madison, Wisconsin, USA, Department of Civil and Environmental Engineering, UW-Madison, Madison, Wisconsin, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1668109
Grant/Contract Number:  
SC0018409
Resource Type:
Published Article
Journal Name:
mSystems
Additional Journal Information:
Journal Name: mSystems Journal Volume: 5 Journal Issue: 5; Journal ID: ISSN 2379-5077
Publisher:
American Society for Microbiology
Country of Publication:
United States
Language:
English

Citation Formats

Scarborough, Matthew J., Hamilton, Joshua J., Erb, Elizabeth A., Donohue, Timothy J., Noguera, Daniel R., and Rust, ed., Michael. Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models. United States: N. p., 2020. Web. doi:10.1128/mSystems.00755-20.
Scarborough, Matthew J., Hamilton, Joshua J., Erb, Elizabeth A., Donohue, Timothy J., Noguera, Daniel R., & Rust, ed., Michael. Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models. United States. doi:10.1128/mSystems.00755-20.
Scarborough, Matthew J., Hamilton, Joshua J., Erb, Elizabeth A., Donohue, Timothy J., Noguera, Daniel R., and Rust, ed., Michael. Tue . "Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models". United States. doi:10.1128/mSystems.00755-20.
@article{osti_1668109,
title = {Diagnosing and Predicting Mixed-Culture Fermentations with Unicellular and Guild-Based Metabolic Models},
author = {Scarborough, Matthew J. and Hamilton, Joshua J. and Erb, Elizabeth A. and Donohue, Timothy J. and Noguera, Daniel R. and Rust, ed., Michael},
abstractNote = {Microbiomes are vital to human health, agriculture, and protecting the environment. Predicting behavior of self-assembled or synthetic microbiomes, however, remains a challenge. In this work, we used unicellular and guild-based metabolic models to investigate production of medium-chain fatty acids by a mixed microbial community that is fed multiple organic substrates. Modeling results provided insights into metabolic pathways of three medium-chain fatty acid-producing guilds and identified potential strategies to increase production of medium-chain fatty acids. This work demonstrates the role of metabolic models in augmenting multi-omic studies to gain greater insights into microbiome behavior.},
doi = {10.1128/mSystems.00755-20},
journal = {mSystems},
number = 5,
volume = 5,
place = {United States},
year = {2020},
month = {9}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1128/mSystems.00755-20

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