The Power of Metabolism for Predicting Microbial Community Dynamics

• Main Authors: Jeremy M. Chacón, William R. Harcombe
• Format: Article
• Language: English
• Published: American Society for Microbiology 2019-06-01
• Series: mSystems
• Subjects: metabolism; antibiotics; bacteriophage; ecology; evolution; genome-scale modeling; systems biology
• Online Access: https://doi.org/10.1128/mSystems.00146-19

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.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.