(This is an updated version of a news report first posted on July 19, 2016.)
Life as we know it depends on the existence of microbial communities (microbiomes), in which multiple species of microbes function collaboratively with each other to extract nutrients and energy from their environment. A team of scientists from Argonne National Laboratory (ANL) and Pacific Northwest National Laboratory (PNNL), developed a novel way to model and predict these cross-species metabolic interactions using KBase tools and datasets. Their work is described in the cover article of the November 2016 issue of the Journal of Cellular Physiology.
In the article, “Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction,” KBase scientist Christopher Henry and his collaborators demonstrate various strategies for constructing genome-scale metabolic networks that simulate two species in a microbial consortium exchanging metabolites to sustain life. The analysis workflow described in the article can be viewed as a Narrative (an interactive, reproducible computational experiment in KBase) here. You can copy the Narrative to your KBase account and try rerunning it, or even inserting your own data. (You will need to register first for a free KBase account.)
This case study shows the approach’s potential for discerning complex microbial community metabolic interactions and behavior. Experimental validation of metabolic simulations that show how one organism can support the growth of others is a powerful technique that can serve energy and environmental missions for DOE and beyond.
Image: Confocal micrograph depicting a metabolically coupled microbial consortium composed of cyanobacterium Thermosynechococcus elongatus (red) supporting heterotrophic bacterium, Meiothermus ruber (green). Scientists at PNNL and ANL proposed a new approach for microbial community metabolic modeling using this phototroph-heterotroph co-culture as a model system to study how microorganisms living in communities coordinate their metabolisms in response to partnership. Imaged by PNNL scientist William B. Chrisler.
Henry, C. S., Bernstein, H. C., Weisenhorn, P., Taylor, R. C., Lee, J.-Y., Zucker, J. and Song, H.-S. (2016), Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction. J. Cell. Physiol. doi: 10.1002/jcp.25428