The present work describes genome-scale reconstructions for Panicum virgatum (switchgrass), a perennial plant with increasing importance for biomass production with bioenergy applications. The metabolic model for Panicum virgatum accounts for 3,564 genes and 3,392 reactions. Although there are differences between switchgrass and maize, both are members of the Panicoideae subfamily and employ C4 carbon fixation. Therefore, we used an earlier maize leaf model developed by our group as a reference and performed BLAST using the recently updated genome alignment and annotation for P. virgatum AP13 (Alamo, a lowland cultivar) to search for homology genes in the source model. SwitchgrassCyc was used to incorporate additional reactions and gene associations. We used transcript data to assist in localizing reactions in mesophyll and bundle sheath cells. A constraint-based method was developed which predicts localization scores for unannotated reactions by maximizing the flux through known reactions while parsimoniously filling-in network gaps to ensure the production of biomass precursors. We describe how the model will be expanded into the root, stalk, Inflorescence, and vascular system in addition to the leaf cells, and describe ongoing field experiments that are being performed to collect data for organ biomass composition, biomass growth, and various -omics analysis, and extended to VS16 (Summer, an upland cultivar). The resulting models will be used to determine the metabolic changes that occur between various growth conditions, pinpoint tissue-specific bottlenecks, and to identify key genes responsible for traits such as yield, feedstock composition, and metabolic trade-offs between target phenotypes.
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