Computational Approaches to Simulate Microbial Ecosystems

Interactions between microbial species are sometimes mediated by the exchange of small molecules, secreted by one species and metabolized by another.

The Science

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.


Microbial metabolism affects biogeochemical cycles and human health. In most natural environments, multiple microbial species interact with each other, forming complex ecosystems whose properties are poorly understood. In an effort to understand inter-microbial interactions, and to explore new metabolic engineering avenues, researchers have started building artificial microbial ecosystems, e.g. pairs of genetically engineered strains that require each other for survival. Here researchers computationally explore the possibility of creating artificial microbial ecosystems without re-engineering the microbes themselves, but rather by manipulating the environment in which they grow. Specifically, using the framework of flux balance analysis, researchers predict environments in which either one or both microbes in a pair would not be able to grow without the other, inducing commensal (one-way) or mutualistic (two-way) interactions, respectively. These algorithms can successfully recapitulate known inter-microbial interactions, and predict millions of new ones across any pair amongst different microbial species. Surprisingly, the researchers find that it is always possible to identify conditions that induce mutualistic or commensal interactions between any two species. Hence, this method should help in mapping naturally occurring microbe-microbe interactions, and in engineering new ones through a novel, environment-driven branch of synthetic ecology.


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