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Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists
Fabrice Jean-Pierre1, Michael A Henson2, George A O'Toole1
1Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States.
Frontiers in Molecular Biosciences
|March 8, 2021
Summary
Metabolic modeling helps understand complex microbial interactions crucial for human health and disease. This computational approach aids in developing new anti-infective drugs by predicting microbial metabolic functions and interactions.
Area of Science:
- Microbiology
- Computational Biology
- Systems Biology
Background:
- Microbiome research has identified microbes linked to health and disease, but understanding their functional interactions remains challenging.
- Current methods like 16S rRNA sequencing, metagenomics, and metabolomics struggle to resolve complex metabolic interactions within polymicrobial communities.
- Metabolite exchange in polymicrobial communities significantly impacts bacterial traits, including virulence and antibiotic resistance, highlighting the need for advanced analytical approaches.
Purpose of the Study:
- To review advances in metabolic modeling as a computational tool for predicting microbial metabolic capabilities and interactions.
- To illustrate how metabolic modeling, combined with experimental approaches, can elucidate microbial community function.
- To propose the integration of metabolic modeling with experimental work for advancing laboratory research and drug discovery.
Main Methods:
- Review of the latest advances in metabolic modeling techniques.
- Analysis of literature examples demonstrating the application of metabolic modeling in microbial ecology.
- Integration of computational predictions with experimental validation.
Main Results:
- Metabolic modeling offers a powerful approach to predict metabolic functions and interactions in microbial systems, from single species to complex communities.
- Case studies demonstrate that combining metabolic modeling with experimental data enhances the understanding of microbial community dynamics.
- This integrated approach can pinpoint microbial determinants influencing community function and host health.
Conclusions:
- Metabolic modeling is essential for deciphering complex microbial metabolic interactions and community functions.
- Cross-disciplinary efforts combining metabolic modeling and experimental work are crucial for advancing microbiome research.
- This integrated strategy holds significant promise for the development of novel anti-infective and antimicrobial therapies.
