Engineering and Modeling Microbiomes
Predicting the composition and metabolism of the gut microbiome
Dr. Joshua Chan developed an algorithm to predict the composition and metabolism of the gut microbiome using genome-scale metabolic models (GEMs) that capture the metabolic capabilities of representative microbes in the microbiome. By implementing a mathematical framework that optimized the average steady-state growth of the gut microbiome under given dietary conditions, a composition profile resembling experimental distributions were predicted, suggesting that the underlying metabolic interactions (e.g., cross feeding of carbohydrates, acetate, hydrogen sulfide, etc.) form an important force shaping the composition. The algorithm also allowed for simulating how changes in diet alter the microbiome in which the Bacteroidetes-Firmicutes interactions were found to be essential for short-chain fatty acid production. The algorithm can be potentially used to identify strategies for altering the gut microbiome through dietary changes. For more details, please go to https://doi.org/10.1371/journal.pcbi.1005539.