Core metabolic models representing 48 major phylogenetic microbial groups were constructed based on a core model template consisting of a highly curated set of biochemical reactions derived from a diverse set of model organisms. Researchers selected nearly 200 unique reactions comprising 12 key energy biosynthesis pathways linked to central metabolism and variations of bacterial electron transport chains. The models produced by the new pipeline had minimal need for gapfilling, demonstrating their value as functional models that align as closely as possible with raw annotation output, minimizing the number of model-driven conjectures. This study demonstrates that core metabolic models can be used to quickly and accurately (1) determine and predict microbial respiration types and energy (ATP) yields; (2) identify electron acceptors that can be reduced during anaerobic respiration; (3) determine the presence or absence of functional pathways in central metabolism and the phylogenetic distribution of these key pathways; (4) evaluate a microbe’s fermentation capabilities; and (5) assess its ability to produce key pathway intermediates in central metabolism that are precursors of essential biomass compounds.
Edirisinghe, J. N., et al. “Modeling central metabolism and energy biosynthesis across microbial life.” BMC Genomics 17, 568 (2016). [DOI:10.1186/s12864-016-2887-8].
The authors’ core metabolic model construction pipeline and supporting commentary can be accessed through KBase’s Narrative Interface at narrative.kbase.us/narrative/ws.15253.obj.1.
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. They demonstrated various strategies for constructing genome-scale metabolic networks that simulate two species in a microbial consortium exchanging metabolites to sustain life. Their work is described in the cover article of the November 2016 issue of the Journal of Cellular Physiology.
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
The authors’ analysis workflow and supporting commentary can be accessed through KBase’s Narrative Interface at https://narrative.kbase.us/narrative/ws.13807.obj.1.