The power of metabolism for predicting microbial community dynamics

Research output: Contribution to journalReview articlepeer-review

Abstract

Quantitative understanding and prediction of microbial community dynamics are an outstanding challenge. We test the hypothesis that metabolic mechanisms provide a foundation for accurate prediction of dynamics in microbial systems. In our research, metabolic models have been able to accurately predict species interactions, evolutionary trajectories, and response to perturbation in simple synthetic consortia. However, metabolic models have many constraints and often serve best as null models to identify additional processes at play. We anticipate that major advances in metabolic systems biology will involve scaling bottom-up approaches to complex communities and expanding the processes that are incorporated in a metabolic perspective. Ultimately, cellular metabolism will inform predictive ecology that enables precision management of microbial systems.

Original languageEnglish (US)
Article numbere00146-19
JournalmSystems
Volume4
Issue number3
DOIs
StatePublished - May 2019

Bibliographical note

Funding Information:
Citation Chacón JM, Harcombe WR. 2019. The power of metabolism for predicting microbial community dynamics. mSystems 4:e00146-19. https://doi.org/10.1128/mSystems.00146-19. Copyright © 2019 Chacón and Harcombe. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. Address correspondence to William R. Harcombe, harcombe@umn.edu. Conflict of Interest Disclosures: J.M.C. has nothing to disclose. W.R.H. reports grant GM121498-01A1 from the National Institute of General Medical Sciences, NIH, during the conduct of the study. mSystems® vol. 4, no. 3, is a special issue sponsored by Illumina.

Funding Information:
We thank Beth Adamowicz, Lisa Fazzino, and Sarah Hammarlund for helpful feedback. This work was supported by NIH grant GM121498.

Keywords

  • Antibiotics
  • Bacteriophage
  • Ecology
  • Evolution
  • Genome-scale modeling
  • Metabolism
  • Systems biology

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