Metabolic models build a characteristic description of the cell’s phenotypic state and give insights into systems’ emergent properties with respect to metabolic functions, adaptability, robustness, and optimality. Moreover, a metabolic model serves as a basis to investigate questions of major biotechnological importance, such as the effects of directed modifications of enzymatic activities to improve a desired property of cellular systems (Alper et al. 2005). Reconstruction of genome — scale metabolic models has led to a better systems level understanding of microbial metabolism, bridging the genotype-phenotype gap. The steady increase in the number of new genome-scale metabolic models over the past decade is clear evi­dence of their utility in investigating biological systems for many applications, including those with applicability for pharmaceutical, chemical, and environmental industries (Feist and Palsson 2008).

Soon after the release of Chlamydomonas’s genome sequence in 2007, a number of groups began the reconstruction of metabolic network models for this alga, resulting in the reconstruction of its central metabolic network in 2009 (Boyle and Morgan 2009; Manichaikul et al. 2009). Two years later, genome-scale recon­structed networks of Chlamydomonas were released by two groups independently. Chang et al. (2011) published a genome-scale metabolic network model for C. reinhardtii, iRC1080, describing and accounting for *2000 reactions, *1000 metabolites and over 1000 associated gene products. Dal’Molin et al. (2011) described a slightly smaller genome-scale reconstruction (AlgaGEM), which encompassed *1700 reactions, *900 genes, and *1900 metabolites. Both are constraint-based models that can predict genome-scale reaction fluxes under steady state growth conditions, as well as a wide range of other metabolic outcomes (see Sects. 10.4 and 10.5 for more details on steady state models).

The iRC1080 model allows for quantitative growth prediction for a given light source. This was accomplished by setting up new reactions that treat light as metabolites. More precisely, reactions for the absorption of light by photosystem I and II (as well as other light driven reactions such as vitamin D3 synthesis and photoisomerase) were defined with their wavelength specificities and stoichiome­tries. The introduced light reactions can accept different values corresponding to different light intensities the cell is exposed to. In summary, the absorption of photons drives photosynthesis and other reactions according to specified absorption coefficient, stoichiometry of the absorbed photon, and wavelengths. The described “light reactions” of the model were experimentally validated by photobioreactor growth studies under different light sources and intensities, i. e., photon fluxes, demonstrating the general agreement of actual biomass and oxygen yields with those predicted by the model (Chang et al. 2011).

The aforementioned network models (i. e., iRC1080 and AlgaGEM) can greatly facilitate future developments of network reconstructions for other species of green algae by providing a framework that can be modified according to the alga’s species-specific metabolic properties. We note that nongreen algal groups, such as diatoms which are evolutionarily distant to green algae, are likely to have distinct metabolic processes relative to green algae, differing in metabolic wiring and presence or absence of various subsystems in the network. The reconstruction of metabolic networks for these organisms is likely to require a significant adjustment of the existing green algae models, if these were to be used as the framework.