Jun 17, 2016

MMinte: An application for predicting metabolic interactions among the microbial species in a community

BioRxiv : the Preprint Server for Biology
Helena Mendes-SoaresNicholas Chia


Background. The explosive growth of microbiome research has yielded great quantities of data. These data provide us with many answers, but raise just as many questions. 16S rDNA - the backbone of microbiome analyses - allows us to assess alpha-diversity, beta-diversity, and microbe-microbe associations, which characterize the overall properties of an ecosystem. However, we are still unable to use 16S rDNA data to directly assess the microbe-microbe and microbe-environment interactions that determine that system's broader ecology. Thus, properties such as competition, cooperation, and nutrient conditions remain insufficiently analyzed. Here, we apply predictive community metabolic models of microbes identified with 16S rDNA data to probe the ecology of microbial communities. Results. We developed a methodology for the large-scale assessment of microbial metabolic interactions (MMinte) from 16S rDNA data. MMinte assesses the relative growth rates of interacting pairs of organisms within a community metabolic network and whether that interaction has a positive or negative effect. Moreover, MMinte's simulations take into account the nutritional environment, which play a strong role in determining the metabolism of individual microb...Continue Reading

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Mentioned in this Paper

Computer Software
Metabolic Process, Cellular
Metabolic Networks
Desulfomonas pigra
DNA, Ribosomal

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