Computational Approaches to Simulate Microbial Ecosystems


A basic challenge in microbial ecology is to understand and to predict the growth and behavior of complex microbial communities, in fact most isolated microbes cannot be readily grown in culture. These communities are important for biogeochemical processes such as nitrification, hydrogen production, and methanogensis. They also show promise for the degradation of complex oligosaccharides in biomass to fermentable sugars for biofuel production. A new method for genome-scale metabolic simulation has been developed by DOE scientists Niels Klitgord and Daniel Segrè of Boston University that will predict the optimal media for promoting the growth of microbes in a community. The method has been successfully tested on a community consisting of hydrogen producing and methane producing microbes as well as the model co-culture Escherichia coli and Saccharomyces cerevisiae. Research is now underway to extend this method to simulating microbial community growth involving more than two species. The new method has just been published in PLoS Computational Biology. This new predictive capability may expand our ability to take advantage of the vast and diverse capabilities found in the microbial world.


Klitgord, N., and D. Segrè. November 2010. “Environments that Induce Synthetic Microbial Ecosystems,” PLoS Computational Biology 6. DOI:10.1371/journal.pcbi.1001002.