To build this systems-level understanding, several genome-scale metabolic reconstructions have recently been published for plant species (Poolman et al., 2009; de Oliveira Dal’molin et al., 2010a,b; Saha et al., 2011; Poolman et al., 2013). Each reconstruction consists of all reactions known to be catalyzed by one or more of the gene products in the plant genome. The methods employed to study these metabolic models, such as flux balance analysis (FBA), consider all reactions in the model when attempting to predict a biological phenotype, such as plant growth. Metabolic reconstructions are built from many data sources, notably public databases and individual publications. Reconstructions are validated by comparing the activity of well-characterized pathways in silico with biochemical evidence in the literature. Poolman et al. (2009) built the first genome-scale plant metabolic reconstruction, which could respire on heterotrophic media in silico and produce biomass components in proportions that matched in vivo observations. de Oliveira Dal’molin et al. (2010a) investigated autotrophic biosynthesis of plant biomass, showing that the model correctly predicted the reactions used for both photosynthesis and photorespiration. de Oliveira Dal’Molin et al. also developed a metabolic reconstruction of a C4 plant (de Oliveira Dal’molin et al., 2010b) containing plastidial reactions for photosynthesis. This reconstruction was shown to be capable of performing three known subtypes of C4 photosynthesis. In other work, Saha et al. (2011) show that genetic perturbations in the phenylpropanoid biosynthesis pathway could be simulated in silico, producing an impact on cell wall composition that compared favorably with experimental data from known maize mutants.