The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforcedattention on understanding C4 photosynthesis. Systems biology studies of C4 model plants may reveal important features of C4metabolism. Here we chose foxtail millet (Setaria italica), as a C4 model plant and developed protocols to perform systems biologystudies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combinationwith the use of multi-omics technologies to gain more insights into the metabolism of S.italica. mRNA, protein and metaboliteabundances, were measured in mature and immature stem/leaf phytomers and the multi-omics data were integrated into themetabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seqreads were mapped to the S. italica resulting for 83% coverage of the protein coding genes of S. italica. Besides revealingsimilarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significantgene expression of two malic enzyme isoforms (NADP- ME and NAD-ME). Although much greater expression levels of NADP-ME genesare observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combinedto the significant abundance of metabolites that participates in C4 metabolism of NAD-ME and NADP-ME subtypes suggest that S.italica may use mixed decarboxylation modes of C4 photosynthetic pathways under different plant developmental stages. Theoverall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle,amino acids, fatty acids, lignin and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities andtheir interrelation and regulation over plant development. With this study, we demonstrated that this systems approach ispowerful enough to complement the functional metabolic annotation of bioenergy grasses.