Computational biology, a branch of biology involving the application of computers and computer science to the understanding and modeling of the structures and processes of life. It entails the use of computational methods (e.g., algorithms) for the representation and simulation of biological systems, as well as for the interpretation of experimental data, often on a very large scale. We surveyed the iron nutrition-responsive transcriptome of Chlamydomonas reinhardtii using RNA-Seq methodology. Presumed primary targets were identified in comparisons between visually asymptomatic iron-deficient versus iron-replete cells. This includes the known components of high-affinity iron uptake as well as candidates for distributive iron transport in C. reinhardtii. Comparison of growth-inhibited iron-limited versus iron-replete cells revealed changes in the expression of genes in chloroplastic oxidative stress response pathways, among hundreds of other genes. The output from the transcriptome was validated at multiple levels: by quantitative RT-PCR for assessing the data analysis pipeline, by quantitative proteomics for assessing the impact of changes in RNA abundance on the proteome, and by cross-species comparison for identifying conserved or universal response pathways. In addition, we assessed the functional importance of three target genes, VITAMIN C, MONODEHYDROASCORBATE REDUCTASE 1 (MDAR1), and CONSERVED IN THE GREEN LINEAGE AND DIATOMS 27 (CGLD27), by biochemistry or reverse genetics. VTC2 and MDAR1, which are key enzymes in de novo ascorbate synthesis and ascorbate recycling, respectively, are likely responsible for the 10-fold increase in ascorbate content of iron-limited cells. CGLD27/At5g67370 is a highly conserved, presumed chloroplast-localized pioneer protein and is important for growth of Arabidopsis thaliana in low iron.