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C4 plants, such as maize, concentrate carbon dioxide in a specializedcompartment surrounding the veins of their leaves to improve the efficiency ofcarbon dioxide assimilation. Nonlinear relationships between carbon dioxide andoxygen levels and reaction rates are key to their physiology but cannot behandled with standard techniques of constraint-based metabolic modeling. Wedemonstrate that incorporating these relationships as constraints on reactionrates and solving the resulting nonlinear optimization problem yields realisticpredictions of the response of C4 systems to environmental and biochemicalperturbations. Using a new genome-scale reconstruction of maize metabolism, webuild an 18000-reaction, nonlinearly constrained model describing mesophyll andbundle sheath cells in 15 segments of the developing maize leaf, interactingvia metabolite exchange, and use RNA-seq and enzyme activity measurements topredict spatial variation in metabolic state by a novel method that optimizescorrelation between fluxes and expression data. Though such correlations areknown to be weak in general, here the predicted fluxes achieve high correlationwith the data, successfully capture the experimentally observed base-to-tiptransition between carbon-importing tissue and carbon-exporting tissue, andinclude a nonzero growth rate, in contrast to prior results from similarmethods in other systems. We suggest that developmental gradients may beparticularly suited to the inference of metabolic fluxes from expression data.

Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves