The scope and breadth of genome-scale metabolic reconstructions has continued to expand over the last decade. However, only a limited number of efforts exist on photosynthetic metabolism reconstruction. Cyanobacteria are an important group of photoautotrophic organisms that can synthesize valuable bio-products by harnessing solar energy. They are endowed with high photosynthetic efficiencies and diverse metabolic capabilities that confer the ability to convert solar energy into a variety of biofuels and their precursors. However, less well studied are the similarities and differences in metabolism of different species of cyanobacteria as they pertain to their suitability as microbial production chassis. Here we assemble, update and compare genome-scale models (iCyt773 and iSyn731) for two phylogenetically related cyanobacterial species, namely Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803. Comparisons of model predictions against gene essentiality data reveal a specificity of 0.94 (94/100) and a sensitivity of 1 (19/19) for the Synechocystis iSyn731 model. The diurnal rhythm of Cyanothece 51142 metabolism is modeled by constructing separate (light/dark) biomass equations and introducing regulatory restrictions over light and dark phases. Specific metabolic pathway differences between the two cyanobacteria alluding to different bioproduction potentials are reflected in both models. In addition to these cyanobacterial species we also develop a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). The metabolic model Zea mays i1563 contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct evidence for the participation of the reaction in maize was found. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). Recently, we develop a second-generation genome-scale metabolic model for the maize leaf to capture C4 carbon fixation by modeling the interactions between the bundle sheath and mesophyll cells. Condition-specific biomass descriptions are introduced that account for amino acids, fatty acids, soluble sugars, proteins, chlorophyll, lingo-cellulose, and nucleic acids as experimentally measured biomass constituents. Compartmentalization of the model is based on proteomic/transcriptomic data and literature evidence. With the incorporation of the information from MetaCrop and MaizeCyc databases, this updated model spans 5824 genes, 8484 reactions, and 8918 metabolites, an increase of approximately five times the size of the earlier i RS1563 model. Transcriptomic and proteomic data is also used to introduce regulatory constraints in the model to simulate the limited nitrogen condition and glutamine synthetase gln1-3 and gln1-4 mutants. In silico results have achieved over 62% accuracy in predicting the direction of change in the metabolite pool under each of the mutant conditions compared to the wild-type condition with 82% accuracy determined in the limited nitrogen condition. The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for any plant tissue-type.
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